system

Abstract

Provide a system. 【Solution means】 An information processing device for inputting user information, Means for setting individual goals based on the user information using a generated artificial intelligence model, Means for generating an education plan customized based on the individual goals, Means for providing information related to the education plan and goals to the information processing device, Means for monitoring the progress of the user and generating feedback based on the progress information, Means for providing the feedback to the information processing device, Means for adjusting the support implemented based on the learning situation of the user, [[ID=?]] A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional mentoring and training systems, it is difficult to provide personalized guidance according to the individual needs of users. Also, existing means have problems in that progress management and feedback provision are insufficient, making it difficult to support the continuous growth of users. Furthermore, there is a demand for providing high-quality mentoring with limited resources and designing cost-effective plans.

Means for Solving the Problems

[0005] This invention provides a system that offers mentoring tailored to the specific needs of users by utilizing a generative artificial intelligence model to set individual goals based on user information and generate customized training plans. Furthermore, it creates an environment where users can learn while confirming their own growth by monitoring progress information in real time and providing feedback based on that information to the terminal. By updating training plans and goals as needed to respond to changing needs and providing comprehensive support, including mental health support, it supports sustainable growth.

[0006] "User information" refers to individual data provided by users, such as their goals, skill levels, and available time.

[0007] A "terminal" refers to an electronic device used by a user to input information and communicate data with a server.

[0008] A "generative artificial intelligence model" refers to artificial intelligence technology that analyzes user information to generate individualized goal setting and training plans.

[0009] "Individual goals" refer to specific achievement objectives that the generative artificial intelligence model sets based on user information and tailored to the user's needs.

[0010] A "training plan" refers to a plan generated by an artificial intelligence model based on individual goals, designed to guide the user's daily activities.

[0011] "Progress" refers to information that shows how much progress a user is currently making toward their individual goals.

[0012] "Feedback" refers to the suggestions for improvement and next steps that a generative artificial intelligence model generates and provides to the user based on the user's progress information.

[0013] "Mental health support" refers to the assistance and resources provided to maintain and improve the mental health of users. [Brief explanation of the drawing]

[0014] [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]A sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled 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.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to a system using generative artificial intelligence technology, aiming to provide users with personalized mentoring and training. In one embodiment of this system, the user first inputs their own information using a terminal. This information includes the user's goals, current skill level, and the amount of time they can dedicate to learning. The terminal formats this information and sends it to the server.

[0036] The server receives this input information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Next, based on these individual goals, it generates a customized training plan. This training plan provides specific guidance for the user's daily learning and activities, including online courses and practical assignments.

[0037] The server sends the generated training plan to the device, which then provides it to the user visually. The user periodically uses the device to report their progress, and this data is compiled by the server. Based on the progress information, the server determines how close the user is to their goal and generates feedback. This feedback includes the next steps to take and areas for improvement, and is provided to the user through the device.

[0038] As a concrete example, let's say the user is an IT engineer who wants to learn a new programming language. This user aims to master the new programming language and complete a project within six months. Based on this goal, the server incorporates the most suitable courses and practice problems into the user's learning plan and provides feedback according to their progress. In this way, the user can efficiently acquire new skills and have a clear path to achieving their goal.

[0039] This system also has the functionality to update training plans and goals as needed to ensure users can continuously grow. Furthermore, it provides resources to support users' mental health. This allows users to receive personalized support over the long term.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses a device to input information such as their goals, skill level, and the amount of time they can dedicate to learning. The device formats the entered information and sends it to the server.

[0043] Step 2:

[0044] The server receives user information sent from the terminal. This information is stored in the database, and a user ID is generated, or the ID is referenced if the user is an existing user.

[0045] Step 3:

[0046] The server activates an artificial intelligence model based on the stored user information. This model is then used to set individual user goals.

[0047] Step 4:

[0048] The server generates a customized training plan based on the individual goals that have been created. This includes specific instructions regarding the user's learning and activities.

[0049] Step 5:

[0050] The server sends the generated training plan and goal information to the device. The device then displays this information to the user.

[0051] Step 6:

[0052] Users periodically report their progress using their devices. This progress includes learning achievement and time taken. The devices send the reported progress information to the server.

[0053] Step 7:

[0054] The server analyzes the received progress information. Using a generative artificial intelligence model, it evaluates how close the user is to their goal and generates feedback based on that evaluation.

[0055] Step 8:

[0056] The server sends the generated feedback to the terminal. The terminal provides the feedback to the user, clearly indicating areas for improvement and what needs to be addressed next.

[0057] Step 9:

[0058] The server updates training plans and goals as needed, based on the user's progress and growth. It also provides continuous support to the user, including mental health-related resources.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, there is a demand for creating and providing appropriate training plans to support individual skill improvement and learning. However, a system that automatically creates plans optimized according to individual goals and skill levels, and provides effective feedback based on those plans, is not yet fully established. Furthermore, it is necessary to simultaneously support the mental health of users. Therefore, the present invention aims to provide a learning support system that meets the individual needs of users.

[0062] 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.

[0063] In this invention, the server includes a terminal for inputting information, means for updating plans and goals according to the user's progress, and means for having resources to provide mental health support to the user. This allows the user to efficiently improve their skills by obtaining a personalized learning plan aligned with their goals and receiving progress and feedback accordingly.

[0064] A "terminal for inputting information" is a device used by users to input information related to their learning, such as their goals and skill levels.

[0065] "Generative artificial intelligence" is a technology that automatically generates user goals and training plans based on the input information.

[0066] "Individualized goals" are objectives that must be achieved according to the user's specific needs and skill level.

[0067] An "individualized plan" is a plan that includes learning and practice steps tailored to the individual needs, based on the goals set by the user.

[0068] "User progress" refers to information that reflects the progress of activities and learning that users are undertaking based on their plans.

[0069] "Feedback" refers to information about future steps and areas for improvement that is generated based on the user's progress.

[0070] "Resources that provide mental health support" is a general term for means and tools that support users' psychological stability and mental health.

[0071] This invention provides a system that supports personalized learning and training for users. This system is primarily composed of a terminal and a server.

[0072] Users can use the device to input personal information and goals they wish to achieve. This input includes the user's current skill level, learning time constraints, and areas of interest. The device converts the information received from the user into a digital format and securely transmits it to the server.

[0073] The server analyzes the received data using artificial intelligence to generate a personalized plan optimized for the user's goals. This plan includes learning resources such as online lectures, practice problems, and hands-on assignments. The server further dynamically adjusts these plans and provides feedback based on the user's daily progress and achievements. This feedback is designed to improve the user's learning efficiency.

[0074] As a concrete example, consider a situation where a user wants to acquire new technical skills in a short period of time. In this case, the user might set a goal of mastering a specific programming language within six months. The server uses generative artificial intelligence to analyze this need and create a plan incorporating the most suitable online courses and practice problems. This plan clearly outlines the milestones to be achieved and the resources to be used.

[0075] An example of a prompt would be, "Create a learning plan for a beginner engineer who has just started learning Python, so that they can acquire basic data analysis skills in three months." Based on this prompt, the generative artificial intelligence model designs a specific learning curriculum and feedback.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users use a device to input personal information and goals. This input includes, for example, the skills they want to achieve, their current skill level, and the amount of time they can dedicate to learning. The device converts this information into a digital format, formats it, and sends it to the server. The input data forms the basis for setting individual goals based on the user's learning needs.

[0079] Step 2:

[0080] The server receives user information sent from the terminal and performs analysis using a generated AI model. Specifically, it performs data analysis to set optimal individual goals tailored to the user's current skill level and objectives. This process outputs data that helps formulate a plan for what the user should work on and how to achieve it.

[0081] Step 3:

[0082] The server uses a generative AI model to generate a robust training plan based on the individual goals set in Step 2. This plan includes specific learning activities such as online lectures, practice problems, and practical assignments. The generated plan is customized to maximize the user's learning efficiency. Detailed training plan data is provided as output.

[0083] Step 4:

[0084] The server sends a training plan to the device, which then visually presents it to the user. The user can then begin learning according to the presented plan. In this step, a timeline or calendar is used as a visual guide to provide a user-friendly interface. The output is the learning plan displayed on the user interface.

[0085] Step 5:

[0086] Users report their learning progress via their devices. The devices send progress data to the server. This data indicates how well the user is progressing according to plan. The server aggregates this information and outputs it as progress evaluation data.

[0087] Step 6:

[0088] The server uses a generative AI model to generate feedback based on progress evaluation data. This feedback includes what to focus on next and key points for improvement. This feedback helps to adjust the user's learning direction. Detailed feedback information is generated as output and sent to the device.

[0089] Step 7:

[0090] The device presents the user with feedback sent from the server. Based on this feedback, the user decides on the next learning step. At this stage, it is important that the feedback is presented in a format that is easy for the user to understand. The output provided to the user is the feedback that was displayed.

[0091] (Application Example 1)

[0092] 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."

[0093] In today's family environment, providing learning support optimized for each individual member is not easy. Furthermore, it is difficult to provide effective educational support while managing learning progress and mental health, taking into account busy family schedules. In such circumstances, there is a need to provide a balanced approach to individualized educational plans and mental health management.

[0094] 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.

[0095] In this invention, the server includes an information processing device for inputting user information, means for setting individual goals based on the user information using a generative artificial intelligence model, and means for generating a customized educational plan based on the individual goals. This enables efficient learning support and mental health management tailored to each individual member, even in a home environment.

[0096] An "information processing device" is a device that receives data and processes it according to a specific purpose.

[0097] A "generative artificial intelligence model" is an artificial intelligence algorithm that, by analyzing and learning from vast amounts of data, can make predictions and generate new data based on certain patterns.

[0098] An "individualized goal" is a unique achievement target set according to the user's specific needs and circumstances.

[0099] An "educational plan" is a detailed plan that outlines customized learning procedures and content based on specific goals, in order to make learning progress more effective.

[0100] "Home environment" refers to the physical and social living environment in which individual users spend their daily lives.

[0101] "Mental health management" refers to activities and support aimed at maintaining and improving an individual's mental health.

[0102] This invention provides a system for providing optimized learning support to individual members within a home environment. The system operates primarily through an information processing device and a server.

[0103] The server uses a generative AI model to set individual learning goals for each user based on information obtained from the user, such as goals, current skill level, and available time. Considering these goals, it generates a customized educational plan that the user should achieve daily. This educational plan may include, for example, online courses and practical assignments.

[0104] The generated educational plan is sent to an information processing device installed in the home and presented to the user through a visual interface. This information processing device is hardware such as a Raspberry Pi, running control software developed in Python.

[0105] Users record their progress on an information processing device while engaging in daily learning activities. This progress information is collected and analyzed by a server to evaluate how close the user is to their individual goals. Based on this evaluation, feedback is generated, providing instructions on the next steps and areas for improvement.

[0106] For example, if a user is learning a new language, it's possible to suggest the next learning methods and materials based on the number of vocabulary words learned that day and the accuracy rate of grammar questions. Furthermore, if the user's learning burden increases, resources to support their mental health management can also be provided.

[0107] An example of a prompt message is: "Provide an example of a home educational support robot that uses an AI system to provide a customized learning plan based on user information and gives feedback according to progress."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users use an information processing device to input personal information such as their goals, current skill level, and the time they can dedicate to learning. This information is formatted in JSON format and sent to the server. The server receives this data and prepares it for analysis.

[0111] Step 2:

[0112] The server uses the received user information to generate individual learning goals using a generative artificial intelligence model. This model operates using machine learning algorithms and sets optimal goals based on past data and similar user data. The output is a list of specific goals tailored to the user.

[0113] Step 3:

[0114] The server creates a customized educational plan based on the individual goals generated. This plan includes links to online courses and instructions for practical assignments. The output is data containing the details of the educational plan.

[0115] Step 4:

[0116] The server sends the completed educational plan to the information processing device. The terminal receives the data and launches an interface to provide the user with the educational plan visually or audibly. The user plans their daily learning through this interface.

[0117] Step 5:

[0118] Users record their daily learning activities through their devices. The devices transmit the entered progress data to the server in real time. This data includes the number of tasks completed and the accuracy of the answers.

[0119] Step 6:

[0120] The server analyzes the received progress data and evaluates how close the user is to achieving their set goal. Statistical methods and predictive models are used to process the data and generate quantitative evaluations related to the user's progress.

[0121] Step 7:

[0122] The server generates feedback based on the evaluation results. This feedback includes the next steps to take and areas that need improvement. A feedback message is prepared as output and sent to the terminal.

[0123] Step 8:

[0124] The device provides the user with feedback received from the server, either visually or audibly. Based on this feedback, the user can readjust their next learning plan.

[0125] Step 9:

[0126] The user then works on new progress based on the feedback, and new progress data is sent to the server via their device. The process returns to step 6, and learning is continuously improved through the PDCA cycle.

[0127] 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.

[0128] The present invention is a system that provides personalized mentoring and training to users, and aims to provide adaptive support that takes the user's emotions into account, particularly by combining it with an emotion engine. In an embodiment of this system, the user first uses a terminal to input their goals, skill level, available learning time, and other information. The terminal then sends this input information to a server.

[0129] The server receives the transmitted user information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Furthermore, it generates a customized training plan and provides it to the device. The user's progress is periodically reported from the device to the server, and the server uses the generative artificial intelligence model to generate progress-based feedback.

[0130] A key feature of this invention is that an emotion engine is integrated into the server. This emotion engine analyzes user input information and behavioral data to recognize the user's emotional state. This emotional information is considered an important element in generating training plans and feedback. For example, if a user is feeling stressed, the server can adjust the learning pace or incorporate activities that promote relaxation into the plan accordingly.

[0131] As a concrete example, consider a situation where a user wants to acquire new technology in a short period of time. The user sets the goal as "acquire the technology in three months and start a new project." Based on this goal, the server generates an appropriate training plan. At the same time, it uses an emotion engine to evaluate the user's stress level and motivation, and adjusts the plan and provides feedback accordingly. As a result, the user can effectively progress towards their goal in a learning environment optimized for their emotional state.

[0132] Thus, this invention, which combines an emotion engine, provides comprehensive support that takes user emotions into account, resulting in a richer learning experience.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user uses the device to enter their goals, skill level, time available for learning, and other personal information. The device formats this information and sends it to the server.

[0136] Step 2:

[0137] The server stores user information received from the terminal in a database. Based on this information, it activates a generated artificial intelligence model and sets individual goals for the user.

[0138] Step 3:

[0139] The server generates a customized training plan based on the individual goals set. This plan includes specific online lessons and practice exercises.

[0140] Step 4:

[0141] The server sends the generated training plan and goal information to the terminal. The terminal then provides this information to the user visually.

[0142] Step 5:

[0143] Users periodically use their devices to report their learning progress and assignment completion status. The devices then send this progress information to the server.

[0144] Step 6:

[0145] The server receives progress information and uses a generative artificial intelligence model to analyze how close the user is to their goal. Furthermore, it uses an emotion engine to recognize the user's emotional state, including stress levels and motivational assessments received from user input.

[0146] Step 7:

[0147] The server generates feedback based on emotional information and progress analysis, and makes adjustments necessary for the user's learning. This feedback includes emotionally responsive actions (e.g., slowing down the learning pace or providing advice to increase motivation).

[0148] Step 8:

[0149] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user, clarifying areas for improvement and next steps.

[0150] Step 9:

[0151] The server updates training plans and goals based on the user's progress and emotional state. Additional mental health support resources are provided as needed. This entire process allows users to continue growing in an emotionally responsive learning environment.

[0152] (Example 2)

[0153] 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".

[0154] Many conventional learning support systems fail to adequately consider users' progress and emotional states, offering uniform educational plans and thus failing to provide effective learning tailored to individual characteristics. Furthermore, a lack of means for users to receive feedback adapted to their emotional state makes it difficult to obtain an optimized learning experience.

[0155] 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.

[0156] In this invention, the server includes means for receiving user information through an information terminal, means for determining individual goals based on the user information using a generative AI model, and means for evaluating the user's emotional state and optimizing the learning experience by incorporating emotion recognition technology. This makes it possible to provide a specialized educational plan and adaptive feedback tailored to the user's individual characteristics and emotional state.

[0157] "User information" refers to data provided by learners, such as their goals, skill level, and available study time.

[0158] An "information terminal" is a computer or smart device used by a user to input or receive data.

[0159] A "generative AI model" is a program that uses artificial intelligence technology to generate optimal learning goals and educational plans for the user.

[0160] "Individual goals" are specific learning achievement items tailored to the user's characteristics and objectives.

[0161] An "educational plan" is a learning plan or curriculum designed according to individual goals.

[0162] "Emotion recognition technology" is a technical means of analyzing and determining a user's emotional state.

[0163] "Progress data" refers to information about the status and results that a user has achieved through learning.

[0164] "Response" refers to advice and feedback provided in accordance with the user's progress and emotional state.

[0165] This invention is a system that provides users with a personalized learning experience. Specific embodiments of this system are described below.

[0166] The terminal serves to receive information entered by the user. Users can input their learning goals, skill levels, and the time required for learning into this information input terminal. The terminal can utilize a variety of user-friendly devices, such as smartphones, tablets, or computers.

[0167] The server is the central system for processing information received from users. The server uses a generative AI model to generate optimal individual goals from user input. This generative AI model is based on Python and utilizes the machine learning framework TENSORFLOW®. This model sets learning goals tailored to each user based on their information. The server also uses emotion recognition technology to evaluate the user's emotional state. Emotion recognition involves sentiment analysis using natural language processing libraries, providing user-specific feedback and adjustments. This emotional information is used to further optimize the user's learning plan.

[0168] Users receive a customized training plan from the server via their device. This training plan is based on individual goals and is tailored to the user's learning style and emotional state. Users report their learning progress to the server via their device. The server then generates a response based on this progress and provides feedback to the user.

[0169] As a concrete example, suppose a user enters a goal into their device: "Learn a new programming language in three months." The server generates a training plan optimized for this goal and monitors the user's stress level using emotion recognition technology. If the user's stress level rises, the server can adjust the plan, such as suggesting activities to promote relaxation.

[0170] Another example of a prompt is, "Generate an optimal training plan based on the learning goals set by the user, and use the emotion engine to adjust it according to the stress level." This is the format of instructions input to the generative AI model and forms the basis for optimizing each user's individual learning process.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user inputs information such as learning goals, skill level, and available time for learning into the device. The device receives this information and formats it in JSON format. Specific data regarding the user's needs is then entered, which becomes the foundational information needed for calculations in the next processing step.

[0174] Step 2:

[0175] The terminal sends formatted user information to the server. Here, the data is securely transferred using the HTTPS protocol. The input data is the user's text information, and error checking is performed to enhance data reliability. The transmitted data is output as a basis for processing on the server.

[0176] Step 3:

[0177] The server inputs the received user information into a generative artificial intelligence model to determine the most suitable individual goals for the user. The generative AI model analyzes the user's input data and outputs the optimal goals using machine learning algorithms. This model uses Python libraries for data normalization and feature extraction. Specific and appropriate goals are set, such as "Learn a programming language in 3 months."

[0178] Step 4:

[0179] The server generates customized learning plans based on individual goals. Based on these goals, it constructs specific steps and tasks to manage learning progress. This is output as a plan that includes a learning schedule and daily tasks. Algorithms are used to optimize time management and task allocation during plan generation.

[0180] Step 5:

[0181] The server uses emotion recognition technology to assess the user's emotional state. Based on the data collected from the user, natural language processing technology is applied to analyze emotions and stress levels. This analysis helps to adjust the user's learning plan and provides specific feedback.

[0182] Step 6:

[0183] The server sends the educational plan and emotion recognition results to the device. Customized feedback and plans are displayed on the device as HTML or a mobile UI. The educational plan is output as specific tasks and schedules, which the user can use to proceed with their learning activities.

[0184] Step 7:

[0185] Users progress through their learning activities based on the learning plan they have used, and input progress information into their device. This progress includes the completion status of updated tasks and newly acquired skills. The progress data collected on the device is prepared as input data for the server.

[0186] Step 8:

[0187] The device sends user progress information to the server. For security reasons, the progress information is again transmitted using the HTTPS protocol and delivered to the server in JSON format.

[0188] Step 9:

[0189] The server uses a generated AI model based on progress information to assess the user's growth and produce adaptive responses. Combining this with emotion recognition technology, it outputs feedback optimized for the user's current learning state. The generated responses include suggestions for determining the next learning stage.

[0190] Step 10:

[0191] The server sends the generated feedback to the device. The feedback is designed to motivate the user, and the user can receive it through the device and use it to guide their next learning steps.

[0192] (Application Example 2)

[0193] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0194] In today's social environment, individual users have diverse learning goals and schedules, and require appropriate support tailored to their emotional state. However, traditional personalized education systems and training tools have struggled to adequately consider users' emotions and adjust learning plans accordingly in real time, preventing users from enjoying an efficient and optimal learning experience.

[0195] 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.

[0196] In this invention, the server includes means for using a device for inputting user information, means for setting individual goals using a generative artificial intelligence model, and means for recognizing the user's emotional state through an emotion engine. This enables the provision and feedback of adaptive learning plans in accordance with the user's emotional state.

[0197] "User information" refers to information that users enter into the system, such as their personal goals, skill level, and available learning time.

[0198] A "device" refers to hardware or software that provides an interface for users to input information and receive feedback.

[0199] A "generative artificial intelligence model" is an AI technology used to create individual learning goals and training plans based on user information.

[0200] "Individual goals" are personalized, specific, and achievable learning or activity goals for the user.

[0201] A "learning plan" is a set of learning activities and training content customized based on the user's individual goals.

[0202] "Progress information" refers to data and evaluation results regarding the user's progress as they engage in learning or training.

[0203] "Feedback" refers to instructions, advice, and evaluations generated based on the user's progress.

[0204] An "emotion engine" is a software component that analyzes user behavior data and input information to recognize the user's emotional state.

[0205] "Emotional state" refers to the psychological and emotional conditions a user exhibits while using the system.

[0206] The system for implementing this invention begins with user information being entered into a terminal. The terminal is responsible for acquiring information such as the user's goals, skill level, and available learning time, and transmitting it to a server. The server receives this user information and sets individual goals using a generative AI model. This generative AI model incorporates machine learning algorithms to formulate personalized learning goals.

[0207] Next, a customized learning plan is generated by an artificial intelligence model. This learning plan can be adjusted in real time based on the user's progress information, and the server receives the progress data and analyzes the emotional state via an emotion engine. This emotion engine operates on a data analysis platform and understands the user's psychological and emotional state.

[0208] The server also generates and provides feedback to the terminal. This feedback is crucial information for appropriately adjusting the learning content and pace based on the user's emotional state and progress. For example, if the emotional analysis indicates that the user is "stressed," the server will suggest learning activities that promote relaxation.

[0209] As a concrete example, consider a scenario where a user aims to learn a new language. In this case, the device sends a message to the server stating that the user's goal is to "master basic conversation in three months." The server uses a generative AI model to select the most suitable learning materials and provide feedback based on the user's progress. It also adjusts the learning speed and provides advice to boost motivation based on analysis results from an emotion engine.

[0210] The system of this invention is practical, and an example of a prompt message would be, "Please tell me what encouraging message to provide when the user's mood is 'motivated'." By using this prompt, a learning experience optimized for each user can be provided.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The terminal acquires user information and sends it to the server. Specifically, it collects data such as the user's goals, skill level, and available learning time, organizes it in a digital format, and then sends it to the server via a secure communication protocol. The input to the server is user information, and the output is organized digital data.

[0214] Step 2:

[0215] The server analyzes the received user information and sets individual learning goals using a generative AI model. This process utilizes machine learning algorithms to calculate personalized goals based on the user's characteristics. The input here is user information, and the output is individual goal setting data.

[0216] Step 3:

[0217] The server generates a customized learning plan based on the individually set goals. Through a generative AI model, it determines the most suitable learning materials and activities for each user and constructs them as a set of plans. The input is the individual goals, and the output is the learning plan data.

[0218] Step 4:

[0219] The server provides the generated learning plan to the terminal, making it accessible to the user. The terminal displays the received learning plan and provides an interface to facilitate user access. The input is the learning plan data, and the output is the plan information viewable by the user.

[0220] Step 5:

[0221] The device monitors the user's progress and periodically sends it to the server. This includes a history of the user's learning activities and outcome data. The input is the user's learning activities, and the output is progress information data.

[0222] Step 6:

[0223] The server uses an emotion engine to analyze the user's emotional state from their progress and input information. The emotion engine analyzes psychological data and employs algorithms to evaluate the user's emotional state. The input is progress information and additional user information, and the output is the user's emotional state.

[0224] Step 7:

[0225] The server generates feedback based on the analyzed emotional state and adjusts the learning plan as needed. The generated feedback includes the adjusted learning pace and additional resource information as required. The inputs are the emotional state and the learning plan, and the output is the feedback data.

[0226] Step 8:

[0227] The server provides feedback to the terminal, which the user receives. This feedback is provided as helpful advice and encouraging messages to the user. The input is feedback data, and the output is a feedback notification to the user.

[0228] 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.

[0229] Data generation model 58 is a type of 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention relates to a system using generative artificial intelligence technology, aiming to provide users with personalized mentoring and training. In one embodiment of this system, the user first inputs their own information using a terminal. This information includes the user's goals, current skill level, and the amount of time they can dedicate to learning. The terminal formats this information and sends it to the server.

[0245] The server receives this input information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Next, based on these individual goals, it generates a customized training plan. This training plan provides specific guidance for the user's daily learning and activities, including online courses and practical assignments.

[0246] The server sends the generated training plan to the device, which then provides it to the user visually. The user periodically uses the device to report their progress, and this data is compiled by the server. Based on the progress information, the server determines how close the user is to their goal and generates feedback. This feedback includes the next steps to take and areas for improvement, and is provided to the user through the device.

[0247] As a concrete example, let's say the user is an IT engineer who wants to learn a new programming language. This user aims to master the new programming language and complete a project within six months. Based on this goal, the server incorporates the most suitable courses and practice problems into the user's learning plan and provides feedback according to their progress. In this way, the user can efficiently acquire new skills and have a clear path to achieving their goal.

[0248] This system also has the functionality to update training plans and goals as needed to ensure users can continuously grow. Furthermore, it provides resources to support users' mental health. This allows users to receive personalized support over the long term.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user uses a device to input information such as their goals, skill level, and the amount of time they can dedicate to learning. The device formats the entered information and sends it to the server.

[0252] Step 2:

[0253] The server receives user information sent from the terminal. This information is stored in the database, and a user ID is generated, or the ID is referenced if the user is an existing user.

[0254] Step 3:

[0255] The server activates an artificial intelligence model based on the stored user information. This model is then used to set individual user goals.

[0256] Step 4:

[0257] The server generates a customized training plan based on the individual goals that have been created. This includes specific instructions regarding the user's learning and activities.

[0258] Step 5:

[0259] The server sends the generated training plan and goal information to the device. The device then displays this information to the user.

[0260] Step 6:

[0261] Users periodically report their progress using their devices. This progress includes learning achievement and time taken. The devices send the reported progress information to the server.

[0262] Step 7:

[0263] The server analyzes the received progress information. Using a generative artificial intelligence model, it evaluates how close the user is to their goal and generates feedback based on that evaluation.

[0264] Step 8:

[0265] The server sends the generated feedback to the terminal. The terminal provides the feedback to the user, clearly indicating areas for improvement and what needs to be addressed next.

[0266] Step 9:

[0267] The server updates training plans and goals as needed, based on the user's progress and growth. It also provides continuous support to the user, including mental health-related resources.

[0268] (Example 1)

[0269] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In modern society, there is a demand for creating and providing appropriate training plans to support individual skill improvement and learning. However, a system that automatically creates plans optimized according to individual goals and skill levels, and provides effective feedback based on those plans, is not yet fully established. Furthermore, it is necessary to simultaneously support the mental health of users. Therefore, the present invention aims to provide a learning support system that meets the individual needs of users.

[0271] 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.

[0272] In this invention, the server includes a terminal for inputting information, means for updating plans and goals according to the user's progress, and means for having resources to provide mental health support to the user. This allows the user to efficiently improve their skills by obtaining a personalized learning plan aligned with their goals and receiving progress and feedback accordingly.

[0273] A "terminal for inputting information" is a device used by users to input information related to their learning, such as their goals and skill levels.

[0274] "Generative artificial intelligence" is a technology that automatically generates user goals and training plans based on the input information.

[0275] "Individualized goals" are objectives that must be achieved according to the user's specific needs and skill level.

[0276] An "individualized plan" is a plan that includes learning and practice steps tailored to the individual needs, based on the goals set by the user.

[0277] "User progress" refers to information that reflects the progress of activities and learning that users are undertaking based on their plans.

[0278] "Feedback" refers to information about future steps and areas for improvement that is generated based on the user's progress.

[0279] "Resources that provide mental health support" is a general term for means and tools that support users' psychological stability and mental health.

[0280] This invention provides a system that supports personalized learning and training for users. This system is primarily composed of a terminal and a server.

[0281] Users can use the terminal to input personal information and goals they want to achieve. This input includes the user's current skill level, time constraints for learning, areas of interest, etc. The terminal converts the information received from the user into digital format and securely transmits it to the server.

[0282] The server analyzes the received data using a generative artificial intelligence and generates an individualized plan optimal for the user's goals. This plan includes learning resources such as online courses, practice questions, and practical tasks. The server further dynamically adjusts these plans based on the user's daily progress and achievements and provides feedback. This feedback is for improving the user's learning efficiency.

[0283] As a specific example, assume a situation where a user wants to acquire new technical skills in a short period. In this case, the user can set a goal of acquiring a specific programming language within six months. The server analyzes this need using a generative artificial intelligence and creates a plan that incorporates the optimal online course and practice questions. This plan specifies the milestones to be achieved and the resources to be used.

[0284] As an example of a prompt sentence, "Please create a learning plan for beginner engineers who have started learning Python to acquire basic data analysis skills in three months" can be cited. Based on this prompt, the generative artificial intelligence model designs a specific learning curriculum and feedback.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] Users use a device to input personal information and goals. This input includes, for example, the skills they want to achieve, their current skill level, and the amount of time they can dedicate to learning. The device converts this information into a digital format, formats it, and sends it to the server. The input data forms the basis for setting individual goals based on the user's learning needs.

[0288] Step 2:

[0289] The server receives user information sent from the terminal and performs analysis using a generated AI model. Specifically, it performs data analysis to set optimal individual goals tailored to the user's current skill level and objectives. This process outputs data that helps formulate a plan for what the user should work on and how to achieve it.

[0290] Step 3:

[0291] The server uses a generative AI model to generate a robust training plan based on the individual goals set in Step 2. This plan includes specific learning activities such as online lectures, practice problems, and practical assignments. The generated plan is customized to maximize the user's learning efficiency. Detailed training plan data is provided as output.

[0292] Step 4:

[0293] The server sends a training plan to the device, which then visually presents it to the user. The user can then begin learning according to the presented plan. In this step, a timeline or calendar is used as a visual guide to provide a user-friendly interface. The output is the learning plan displayed on the user interface.

[0294] Step 5:

[0295] Users report their learning progress via their devices. The devices send progress data to the server. This data indicates how well the user is progressing according to plan. The server aggregates this information and outputs it as progress evaluation data.

[0296] Step 6:

[0297] The server uses a generative AI model to generate feedback based on progress evaluation data. This feedback includes what to focus on next and key points for improvement. This feedback helps to adjust the user's learning direction. Detailed feedback information is generated as output and sent to the device.

[0298] Step 7:

[0299] The device presents the user with feedback sent from the server. Based on this feedback, the user decides on the next learning step. At this stage, it is important that the feedback is presented in a format that is easy for the user to understand. The output provided to the user is the feedback that was displayed.

[0300] (Application Example 1)

[0301] 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."

[0302] In today's family environment, providing learning support optimized for each individual member is not easy. Furthermore, it is difficult to provide effective educational support while managing learning progress and mental health, taking into account busy family schedules. In such circumstances, there is a need to provide a balanced approach to individualized educational plans and mental health management.

[0303] 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.

[0304] In this invention, the server includes an information processing device for inputting user information, means for setting individual goals based on the user information using a generative artificial intelligence model, and means for generating a customized education plan based on the individual goals. As a result, it becomes possible to provide efficient learning support and mental health management tailored to each individual member even in a home environment.

[0305] An "information processing device" is a device that receives data and performs processing according to a specific purpose.

[0306] A "generative artificial intelligence model" is an artificial intelligence algorithm that can perform predictions or generations based on certain patterns for new data by analyzing and learning a vast amount of data.

[0307] An "individual goal" is a unique achievement goal set according to the specific needs and situations of the user.

[0308] An "education plan" is a plan that details the learning procedures and content customized based on specific goals in order to effectively facilitate the progress of learning.

[0309] A "home environment" is the physical and social living environment in which individual users lead their daily lives.

[0310] "Mental health management" is activities and support for maintaining and improving an individual's mental health.

[0311] This invention provides a system for providing optimized learning support for individual members in a home environment. The system mainly operates through an information processing device and a server.

[0312] The server uses a generative AI model to set individual learning goals for each user based on information obtained from the user, such as goals, current skill level, and available time. Considering these goals, it generates a customized educational plan that the user should achieve daily. This educational plan may include, for example, online courses and practical assignments.

[0313] The generated educational plan is sent to an information processing device installed in the home and presented to the user through a visual interface. This information processing device is hardware such as a Raspberry Pi, running control software developed in Python.

[0314] Users record their progress on an information processing device while engaging in daily learning activities. This progress information is collected and analyzed by a server to evaluate how close the user is to their individual goals. Based on this evaluation, feedback is generated, providing instructions on the next steps and areas for improvement.

[0315] For example, if a user is learning a new language, it's possible to suggest the next learning methods and materials based on the number of vocabulary words learned that day and the accuracy rate of grammar questions. Furthermore, if the user's learning burden increases, resources to support their mental health management can also be provided.

[0316] An example of a prompt message is: "Provide an example of a home educational support robot that uses an AI system to provide a customized learning plan based on user information and gives feedback according to progress."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] Users use an information processing device to input personal information such as their goals, current skill level, and the time they can dedicate to learning. This information is formatted in JSON format and sent to the server. The server receives this data and prepares it for analysis.

[0320] Step 2:

[0321] The server uses the received user information to generate individual learning goals using a generative artificial intelligence model. This model operates using machine learning algorithms and sets optimal goals based on past data and similar user data. The output is a list of specific goals tailored to the user.

[0322] Step 3:

[0323] The server creates a customized educational plan based on the individual goals generated. This plan includes links to online courses and instructions for practical assignments. The output is data containing the details of the educational plan.

[0324] Step 4:

[0325] The server sends the completed educational plan to the information processing device. The terminal receives the data and launches an interface to provide the user with the educational plan visually or audibly. The user plans their daily learning through this interface.

[0326] Step 5:

[0327] Users record their daily learning activities through their devices. The devices transmit the entered progress data to the server in real time. This data includes the number of tasks completed and the accuracy of the answers.

[0328] Step 6:

[0329] The server analyzes the received progress data and evaluates how close the user is to achieving their set goal. Statistical methods and predictive models are used to process the data and generate quantitative evaluations related to the user's progress.

[0330] Step 7:

[0331] The server generates feedback based on the evaluation results. This feedback includes the next steps to take and areas that need improvement. A feedback message is prepared as output and sent to the terminal.

[0332] Step 8:

[0333] The device provides the user with feedback received from the server, either visually or audibly. Based on this feedback, the user can readjust their next learning plan.

[0334] Step 9:

[0335] The user then works on new progress based on the feedback, and new progress data is sent to the server via their device. The process returns to step 6, and learning is continuously improved through the PDCA cycle.

[0336] 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.

[0337] The present invention is a system that provides personalized mentoring and training to users, and aims to provide adaptive support that takes the user's emotions into account, particularly by combining it with an emotion engine. In an embodiment of this system, the user first uses a terminal to input their goals, skill level, available learning time, and other information. The terminal then sends this input information to a server.

[0338] The server receives the transmitted user information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Furthermore, it generates a customized training plan and provides it to the device. The user's progress is periodically reported from the device to the server, and the server uses the generative artificial intelligence model to generate progress-based feedback.

[0339] A key feature of this invention is that an emotion engine is integrated into the server. This emotion engine analyzes user input information and behavioral data to recognize the user's emotional state. This emotional information is considered an important element in generating training plans and feedback. For example, if a user is feeling stressed, the server can adjust the learning pace or incorporate activities that promote relaxation into the plan accordingly.

[0340] As a concrete example, consider a situation where a user wants to acquire new technology in a short period of time. The user sets the goal as "acquire the technology in three months and start a new project." Based on this goal, the server generates an appropriate training plan. At the same time, it uses an emotion engine to evaluate the user's stress level and motivation, and adjusts the plan and provides feedback accordingly. As a result, the user can effectively progress towards their goal in a learning environment optimized for their emotional state.

[0341] Thus, this invention, which combines an emotion engine, provides comprehensive support that takes user emotions into account, resulting in a richer learning experience.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user uses the device to enter their goals, skill level, time available for learning, and other personal information. The device formats this information and sends it to the server.

[0345] Step 2:

[0346] The server stores user information received from the terminal in a database. Based on this information, it activates a generated artificial intelligence model and sets individual goals for the user.

[0347] Step 3:

[0348] The server generates a customized training plan based on the individual goals set. This plan includes specific online lessons and practice exercises.

[0349] Step 4:

[0350] The server sends the generated training plan and goal information to the terminal. The terminal then provides this information to the user visually.

[0351] Step 5:

[0352] Users periodically use their devices to report their learning progress and assignment completion status. The devices then send this progress information to the server.

[0353] Step 6:

[0354] The server receives progress information and uses a generative artificial intelligence model to analyze how close the user is to their goal. Furthermore, it uses an emotion engine to recognize the user's emotional state, including stress levels and motivational assessments received from user input.

[0355] Step 7:

[0356] The server generates feedback based on emotional information and progress analysis, and makes adjustments necessary for the user's learning. This feedback includes emotionally responsive actions (e.g., slowing down the learning pace or providing advice to increase motivation).

[0357] Step 8:

[0358] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user, clarifying areas for improvement and next steps.

[0359] Step 9:

[0360] The server updates training plans and goals based on the user's progress and emotional state. Additional mental health support resources are provided as needed. This entire process allows users to continue growing in an emotionally responsive learning environment.

[0361] (Example 2)

[0362] 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".

[0363] Many conventional learning support systems fail to adequately consider users' progress and emotional states, offering uniform educational plans and thus failing to provide effective learning tailored to individual characteristics. Furthermore, a lack of means for users to receive feedback adapted to their emotional state makes it difficult to obtain an optimized learning experience.

[0364] 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.

[0365] In this invention, the server includes means for receiving user information through an information terminal, means for determining individual goals based on the user information using a generative AI model, and means for evaluating the user's emotional state and optimizing the learning experience by incorporating emotion recognition technology. This makes it possible to provide a specialized educational plan and adaptive feedback tailored to the user's individual characteristics and emotional state.

[0366] "User information" refers to data provided by learners, such as their goals, skill level, and available study time.

[0367] An "information terminal" is a computer or smart device used by a user to input or receive data.

[0368] A "generative AI model" is a program that uses artificial intelligence technology to generate optimal learning goals and educational plans for the user.

[0369] "Individual goals" are specific learning achievement items tailored to the user's characteristics and objectives.

[0370] An "educational plan" is a learning plan or curriculum designed according to individual goals.

[0371] "Emotion recognition technology" is a technical means of analyzing and determining a user's emotional state.

[0372] "Progress data" refers to information about the status and results that a user has achieved through learning.

[0373] "Response" refers to advice and feedback provided in accordance with the user's progress and emotional state.

[0374] This invention is a system that provides users with a personalized learning experience. Specific embodiments of this system are described below.

[0375] The terminal serves to receive information entered by the user. Users can input their learning goals, skill levels, and the time required for learning into this information input terminal. The terminal can utilize a variety of user-friendly devices, such as smartphones, tablets, or computers.

[0376] The server is the central system for processing information received from users. The server uses a generative AI model to generate optimal individual goals from the user's input. This generative AI model is based on Python and leverages TensorFlow, a machine learning framework. Based on the user's information, this model sets learning goals tailored to them. The server also uses emotion recognition technology to evaluate the user's emotional state. Emotion recognition involves sentiment analysis using natural language processing libraries, providing user-specific feedback and adjustments. This emotional information is used to further optimize the user's learning plan.

[0377] Users receive a customized training plan from the server via their device. This training plan is based on individual goals and is tailored to the user's learning style and emotional state. Users report their learning progress to the server via their device. The server then generates a response based on this progress and provides feedback to the user.

[0378] As a concrete example, suppose a user enters a goal into their device: "Learn a new programming language in three months." The server generates a training plan optimized for this goal and monitors the user's stress level using emotion recognition technology. If the user's stress level rises, the server can adjust the plan, such as suggesting activities to promote relaxation.

[0379] Another example of a prompt is, "Generate an optimal training plan based on the learning goals set by the user, and use the emotion engine to adjust it according to the stress level." This is the format of instructions input to the generative AI model and forms the basis for optimizing each user's individual learning process.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The user inputs information such as learning goals, skill level, and available time for learning into the device. The device receives this information and formats it in JSON format. Specific data regarding the user's needs is then entered, which becomes the foundational information needed for calculations in the next processing step.

[0383] Step 2:

[0384] The terminal sends formatted user information to the server. Here, the data is securely transferred using the HTTPS protocol. The input data is the user's text information, and error checking is performed to enhance data reliability. The transmitted data is output as a basis for processing on the server.

[0385] Step 3:

[0386] The server inputs the received user information into a generative artificial intelligence model to determine the most suitable individual goals for the user. The generative AI model analyzes the user's input data and outputs the optimal goals using machine learning algorithms. This model uses Python libraries for data normalization and feature extraction. Specific and appropriate goals are set, such as "Learn a programming language in 3 months."

[0387] Step 4:

[0388] The server generates customized learning plans based on individual goals. Based on these goals, it constructs specific steps and tasks to manage learning progress. This is output as a plan that includes a learning schedule and daily tasks. Algorithms are used to optimize time management and task allocation during plan generation.

[0389] Step 5:

[0390] The server uses emotion recognition technology to assess the user's emotional state. Based on the data collected from the user, natural language processing technology is applied to analyze emotions and stress levels. This analysis helps to adjust the user's learning plan and provides specific feedback.

[0391] Step 6:

[0392] The server sends the educational plan and emotion recognition results to the device. Customized feedback and plans are displayed on the device as HTML or a mobile UI. The educational plan is output as specific tasks and schedules, which the user can use to proceed with their learning activities.

[0393] Step 7:

[0394] Users progress through their learning activities based on the learning plan they have used, and input progress information into their device. This progress includes the completion status of updated tasks and newly acquired skills. The progress data collected on the device is prepared as input data for the server.

[0395] Step 8:

[0396] The device sends user progress information to the server. For security reasons, the progress information is again transmitted using the HTTPS protocol and delivered to the server in JSON format.

[0397] Step 9:

[0398] The server uses a generated AI model based on progress information to assess the user's growth and produce adaptive responses. Combining this with emotion recognition technology, it outputs feedback optimized for the user's current learning state. The generated responses include suggestions for determining the next learning stage.

[0399] Step 10:

[0400] The server sends the generated feedback to the device. The feedback is designed to motivate the user, and the user can receive it through the device and use it to guide their next learning steps.

[0401] (Application Example 2)

[0402] 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."

[0403] In today's social environment, individual users have diverse learning goals and schedules, and require appropriate support tailored to their emotional state. However, traditional personalized education systems and training tools have struggled to adequately consider users' emotions and adjust learning plans accordingly in real time, preventing users from enjoying an efficient and optimal learning experience.

[0404] 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.

[0405] In this invention, the server includes means for using a device for inputting user information, means for setting individual goals using a generative artificial intelligence model, and means for recognizing the user's emotional state through an emotion engine. This enables the provision and feedback of adaptive learning plans in accordance with the user's emotional state.

[0406] "User information" refers to information that users enter into the system, such as their personal goals, skill level, and available learning time.

[0407] A "device" refers to hardware or software that provides an interface for users to input information and receive feedback.

[0408] A "generative artificial intelligence model" is an AI technology used to create individual learning goals and training plans based on user information.

[0409] "Individual goals" are personalized, specific, and achievable learning or activity goals for the user.

[0410] A "learning plan" is a set of learning activities and training content customized based on the user's individual goals.

[0411] "Progress information" refers to data and evaluation results regarding the user's progress as they engage in learning or training.

[0412] "Feedback" refers to instructions, advice, and evaluations generated based on the user's progress.

[0413] An "emotion engine" is a software component that analyzes user behavior data and input information to recognize the user's emotional state.

[0414] "Emotional state" refers to the psychological and emotional conditions a user exhibits while using the system.

[0415] The system for implementing this invention begins with user information being entered into a terminal. The terminal is responsible for acquiring information such as the user's goals, skill level, and available learning time, and transmitting it to a server. The server receives this user information and sets individual goals using a generative AI model. This generative AI model incorporates machine learning algorithms to formulate personalized learning goals.

[0416] Next, a customized learning plan is generated by an artificial intelligence model. This learning plan can be adjusted in real time based on the user's progress information, and the server receives the progress data and analyzes the emotional state via an emotion engine. This emotion engine operates on a data analysis platform and understands the user's psychological and emotional state.

[0417] The server also generates and provides feedback to the terminal. This feedback is crucial information for appropriately adjusting the learning content and pace based on the user's emotional state and progress. For example, if the emotional analysis indicates that the user is "stressed," the server will suggest learning activities that promote relaxation.

[0418] As a concrete example, consider a scenario where a user aims to learn a new language. In this case, the device sends a message to the server stating that the user's goal is to "master basic conversation in three months." The server uses a generative AI model to select the most suitable learning materials and provide feedback based on the user's progress. It also adjusts the learning speed and provides advice to boost motivation based on analysis results from an emotion engine.

[0419] The system of this invention is practical, and an example of a prompt message would be, "Please tell me what encouraging message to provide when the user's mood is 'motivated'." By using this prompt, a learning experience optimized for each user can be provided.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The terminal acquires user information and sends it to the server. Specifically, it collects data such as the user's goals, skill level, and available learning time, organizes it in a digital format, and then sends it to the server via a secure communication protocol. The input to the server is user information, and the output is organized digital data.

[0423] Step 2:

[0424] The server analyzes the received user information and sets individual learning goals using a generative AI model. This process utilizes machine learning algorithms to calculate personalized goals based on the user's characteristics. The input here is user information, and the output is individual goal setting data.

[0425] Step 3:

[0426] The server generates a customized learning plan based on the individually set goals. Through a generative AI model, it determines the most suitable learning materials and activities for each user and constructs them as a set of plans. The input is the individual goals, and the output is the learning plan data.

[0427] Step 4:

[0428] The server provides the generated learning plan to the terminal, making it accessible to the user. The terminal displays the received learning plan and provides an interface to facilitate user access. The input is the learning plan data, and the output is the plan information viewable by the user.

[0429] Step 5:

[0430] The device monitors the user's progress and periodically sends it to the server. This includes a history of the user's learning activities and outcome data. The input is the user's learning activities, and the output is progress information data.

[0431] Step 6:

[0432] The server uses an emotion engine to analyze the user's emotional state from their progress and input information. The emotion engine analyzes psychological data and employs algorithms to evaluate the user's emotional state. The input is progress information and additional user information, and the output is the user's emotional state.

[0433] Step 7:

[0434] The server generates feedback based on the analyzed emotional state and adjusts the learning plan as needed. The generated feedback includes the adjusted learning pace and additional resource information as required. The inputs are the emotional state and the learning plan, and the output is the feedback data.

[0435] Step 8:

[0436] The server provides feedback to the terminal, which the user receives. This feedback is provided as helpful advice and encouraging messages to the user. The input is feedback data, and the output is a feedback notification to the user.

[0437] 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.

[0438] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] This invention relates to a system using generative artificial intelligence technology, aiming to provide users with personalized mentoring and training. In one embodiment of this system, the user first inputs their own information using a terminal. This information includes the user's goals, current skill level, and the amount of time they can dedicate to learning. The terminal formats this information and sends it to the server.

[0454] The server receives this input information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Next, based on these individual goals, it generates a customized training plan. This training plan provides specific guidance for the user's daily learning and activities, including online courses and practical assignments.

[0455] The server sends the generated training plan to the device, which then provides it to the user visually. The user periodically uses the device to report their progress, and this data is compiled by the server. Based on the progress information, the server determines how close the user is to their goal and generates feedback. This feedback includes the next steps to take and areas for improvement, and is provided to the user through the device.

[0456] As a concrete example, let's say the user is an IT engineer who wants to learn a new programming language. This user aims to master the new programming language and complete a project within six months. Based on this goal, the server incorporates the most suitable courses and practice problems into the user's learning plan and provides feedback according to their progress. In this way, the user can efficiently acquire new skills and have a clear path to achieving their goal.

[0457] This system also has the functionality to update training plans and goals as needed to ensure users can continuously grow. Furthermore, it provides resources to support users' mental health. This allows users to receive personalized support over the long term.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The user uses a device to input information such as their goals, skill level, and the amount of time they can dedicate to learning. The device formats the entered information and sends it to the server.

[0461] Step 2:

[0462] The server receives user information sent from the terminal. This information is stored in the database, and a user ID is generated, or the ID is referenced if the user is an existing user.

[0463] Step 3:

[0464] The server activates an artificial intelligence model based on the stored user information. This model is then used to set individual user goals.

[0465] Step 4:

[0466] The server generates a customized training plan based on the individual goals that have been created. This includes specific instructions regarding the user's learning and activities.

[0467] Step 5:

[0468] The server sends the generated training plan and goal information to the device. The device then displays this information to the user.

[0469] Step 6:

[0470] Users periodically report their progress using their devices. This progress includes learning achievement and time taken. The devices send the reported progress information to the server.

[0471] Step 7:

[0472] The server analyzes the received progress information. Using a generative artificial intelligence model, it evaluates how close the user is to their goal and generates feedback based on that evaluation.

[0473] Step 8:

[0474] The server sends the generated feedback to the terminal. The terminal provides the feedback to the user, clearly indicating areas for improvement and what needs to be addressed next.

[0475] Step 9:

[0476] The server updates training plans and goals as needed, based on the user's progress and growth. It also provides continuous support to the user, including mental health-related resources.

[0477] (Example 1)

[0478] 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."

[0479] In modern society, there is a demand for creating and providing appropriate training plans to support individual skill improvement and learning. However, a system that automatically creates plans optimized according to individual goals and skill levels, and provides effective feedback based on those plans, is not yet fully established. Furthermore, it is necessary to simultaneously support the mental health of users. Therefore, the present invention aims to provide a learning support system that meets the individual needs of users.

[0480] 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.

[0481] In this invention, the server includes a terminal for inputting information, means for updating plans and goals according to the user's progress, and means for having resources to provide mental health support to the user. This allows the user to efficiently improve their skills by obtaining a personalized learning plan aligned with their goals and receiving progress and feedback accordingly.

[0482] A "terminal for inputting information" is a device used by users to input information related to their learning, such as their goals and skill levels.

[0483] "Generative artificial intelligence" is a technology that automatically generates user goals and training plans based on the input information.

[0484] "Individualized goals" are objectives that must be achieved according to the user's specific needs and skill level.

[0485] An "individualized plan" is a plan that includes learning and practice steps tailored to the individual needs, based on the goals set by the user.

[0486] "User progress" refers to information that reflects the progress of activities and learning that users are undertaking based on their plans.

[0487] "Feedback" refers to information about future steps and areas for improvement that is generated based on the user's progress.

[0488] "Resources that provide mental health support" is a general term for means and tools that support users' psychological stability and mental health.

[0489] This invention provides a system that supports personalized learning and training for users. This system is primarily composed of a terminal and a server.

[0490] Users can use the device to input personal information and goals they wish to achieve. This input includes the user's current skill level, learning time constraints, and areas of interest. The device converts the information received from the user into a digital format and securely transmits it to the server.

[0491] The server analyzes the received data using artificial intelligence to generate a personalized plan optimized for the user's goals. This plan includes learning resources such as online lectures, practice problems, and hands-on assignments. The server further dynamically adjusts these plans and provides feedback based on the user's daily progress and achievements. This feedback is designed to improve the user's learning efficiency.

[0492] As a concrete example, consider a situation where a user wants to acquire new technical skills in a short period of time. In this case, the user might set a goal of mastering a specific programming language within six months. The server uses generative artificial intelligence to analyze this need and create a plan incorporating the most suitable online courses and practice problems. This plan clearly outlines the milestones to be achieved and the resources to be used.

[0493] An example of a prompt would be, "Create a learning plan for a beginner engineer who has just started learning Python, so that they can acquire basic data analysis skills in three months." Based on this prompt, the generative artificial intelligence model designs a specific learning curriculum and feedback.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] Users use a device to input personal information and goals. This input includes, for example, the skills they want to achieve, their current skill level, and the amount of time they can dedicate to learning. The device converts this information into a digital format, formats it, and sends it to the server. The input data forms the basis for setting individual goals based on the user's learning needs.

[0497] Step 2:

[0498] The server receives user information sent from the terminal and performs analysis using a generated AI model. Specifically, it performs data analysis to set optimal individual goals tailored to the user's current skill level and objectives. This process outputs data that helps formulate a plan for what the user should work on and how to achieve it.

[0499] Step 3:

[0500] The server uses a generative AI model to generate a robust training plan based on the individual goals set in Step 2. This plan includes specific learning activities such as online lectures, practice problems, and practical assignments. The generated plan is customized to maximize the user's learning efficiency. Detailed training plan data is provided as output.

[0501] Step 4:

[0502] The server sends a training plan to the device, which then visually presents it to the user. The user can then begin learning according to the presented plan. In this step, a timeline or calendar is used as a visual guide to provide a user-friendly interface. The output is the learning plan displayed on the user interface.

[0503] Step 5:

[0504] Users report their learning progress via their devices. The devices send progress data to the server. This data indicates how well the user is progressing according to plan. The server aggregates this information and outputs it as progress evaluation data.

[0505] Step 6:

[0506] The server uses a generative AI model to generate feedback based on progress evaluation data. This feedback includes what to focus on next and key points for improvement. This feedback helps to adjust the user's learning direction. Detailed feedback information is generated as output and sent to the device.

[0507] Step 7:

[0508] The device presents the user with feedback sent from the server. Based on this feedback, the user decides on the next learning step. At this stage, it is important that the feedback is presented in a format that is easy for the user to understand. The output provided to the user is the feedback that was displayed.

[0509] (Application Example 1)

[0510] 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."

[0511] In today's family environment, providing learning support optimized for each individual member is not easy. Furthermore, it is difficult to provide effective educational support while managing learning progress and mental health, taking into account busy family schedules. In such circumstances, there is a need to provide a balanced approach to individualized educational plans and mental health management.

[0512] 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.

[0513] In this invention, the server includes an information processing device for inputting user information, means for setting individual goals based on the user information using a generative artificial intelligence model, and means for generating a customized educational plan based on the individual goals. This enables efficient learning support and mental health management tailored to each individual member, even in a home environment.

[0514] An "information processing device" is a device that receives data and processes it according to a specific purpose.

[0515] A "generative artificial intelligence model" is an artificial intelligence algorithm that, by analyzing and learning from vast amounts of data, can make predictions and generate new data based on certain patterns.

[0516] An "individualized goal" is a unique achievement target set according to the user's specific needs and circumstances.

[0517] An "educational plan" is a detailed plan that outlines customized learning procedures and content based on specific goals, in order to make learning progress more effective.

[0518] "Home environment" refers to the physical and social living environment in which individual users spend their daily lives.

[0519] "Mental health management" refers to activities and support aimed at maintaining and improving an individual's mental health.

[0520] This invention provides a system for providing optimized learning support to individual members within a home environment. The system operates primarily through an information processing device and a server.

[0521] The server uses a generative AI model to set individual learning goals for each user based on information obtained from the user, such as goals, current skill level, and available time. Considering these goals, it generates a customized educational plan that the user should achieve daily. This educational plan may include, for example, online courses and practical assignments.

[0522] The generated educational plan is sent to an information processing device installed in the home and presented to the user through a visual interface. This information processing device is hardware such as a Raspberry Pi, running control software developed in Python.

[0523] Users record their progress on an information processing device while engaging in daily learning activities. This progress information is collected and analyzed by a server to evaluate how close the user is to their individual goals. Based on this evaluation, feedback is generated, providing instructions on the next steps and areas for improvement.

[0524] For example, if a user is learning a new language, it's possible to suggest the next learning methods and materials based on the number of vocabulary words learned that day and the accuracy rate of grammar questions. Furthermore, if the user's learning burden increases, resources to support their mental health management can also be provided.

[0525] An example of a prompt message is: "Provide an example of a home educational support robot that uses an AI system to provide a customized learning plan based on user information and gives feedback according to progress."

[0526] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0527] Step 1:

[0528] Users use an information processing device to input personal information such as their goals, current skill level, and the time they can dedicate to learning. This information is formatted in JSON format and sent to the server. The server receives this data and prepares it for analysis.

[0529] Step 2:

[0530] The server uses the received user information to generate individual learning goals using a generative artificial intelligence model. This model operates using machine learning algorithms and sets optimal goals based on past data and similar user data. The output is a list of specific goals tailored to the user.

[0531] Step 3:

[0532] The server creates a customized educational plan based on the individual goals generated. This plan includes links to online courses and instructions for practical assignments. The output is data containing the details of the educational plan.

[0533] Step 4:

[0534] The server sends the completed educational plan to the information processing device. The terminal receives the data and launches an interface to provide the user with the educational plan visually or audibly. The user plans their daily learning through this interface.

[0535] Step 5:

[0536] Users record their daily learning activities through their devices. The devices transmit the entered progress data to the server in real time. This data includes the number of tasks completed and the accuracy of the answers.

[0537] Step 6:

[0538] The server analyzes the received progress data and evaluates how close the user is to achieving their set goal. Statistical methods and predictive models are used to process the data and generate quantitative evaluations related to the user's progress.

[0539] Step 7:

[0540] The server generates feedback based on the evaluation results. This feedback includes the next steps to take and areas that need improvement. A feedback message is prepared as output and sent to the terminal.

[0541] Step 8:

[0542] The device provides the user with feedback received from the server, either visually or audibly. Based on this feedback, the user can readjust their next learning plan.

[0543] Step 9:

[0544] The user then works on new progress based on the feedback, and new progress data is sent to the server via their device. The process returns to step 6, and learning is continuously improved through the PDCA cycle.

[0545] 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.

[0546] The present invention is a system that provides personalized mentoring and training to users, and aims to provide adaptive support that takes the user's emotions into account, particularly by combining it with an emotion engine. In an embodiment of this system, the user first uses a terminal to input their goals, skill level, available learning time, and other information. The terminal then sends this input information to a server.

[0547] The server receives the transmitted user information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Furthermore, it generates a customized training plan and provides it to the device. The user's progress is periodically reported from the device to the server, and the server uses the generative artificial intelligence model to generate progress-based feedback.

[0548] A key feature of this invention is that an emotion engine is integrated into the server. This emotion engine analyzes user input information and behavioral data to recognize the user's emotional state. This emotional information is considered an important element in generating training plans and feedback. For example, if a user is feeling stressed, the server can adjust the learning pace or incorporate activities that promote relaxation into the plan accordingly.

[0549] As a concrete example, consider a situation where a user wants to acquire new technology in a short period of time. The user sets the goal as "acquire the technology in three months and start a new project." Based on this goal, the server generates an appropriate training plan. At the same time, it uses an emotion engine to evaluate the user's stress level and motivation, and adjusts the plan and provides feedback accordingly. As a result, the user can effectively progress towards their goal in a learning environment optimized for their emotional state.

[0550] Thus, this invention, which combines an emotion engine, provides comprehensive support that takes user emotions into account, resulting in a richer learning experience.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The user uses the device to enter their goals, skill level, time available for learning, and other personal information. The device formats this information and sends it to the server.

[0554] Step 2:

[0555] The server stores user information received from the terminal in a database. Based on this information, it activates a generated artificial intelligence model and sets individual goals for the user.

[0556] Step 3:

[0557] The server generates a customized training plan based on the individual goals set. This plan includes specific online lessons and practice exercises.

[0558] Step 4:

[0559] The server sends the generated training plan and goal information to the terminal. The terminal then provides this information to the user visually.

[0560] Step 5:

[0561] Users periodically use their devices to report their learning progress and assignment completion status. The devices then send this progress information to the server.

[0562] Step 6:

[0563] The server receives progress information and uses a generative artificial intelligence model to analyze how close the user is to their goal. Furthermore, it uses an emotion engine to recognize the user's emotional state, including stress levels and motivational assessments received from user input.

[0564] Step 7:

[0565] The server generates feedback based on emotional information and progress analysis, and makes adjustments necessary for the user's learning. This feedback includes emotionally responsive actions (e.g., slowing down the learning pace or providing advice to increase motivation).

[0566] Step 8:

[0567] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user, clarifying areas for improvement and next steps.

[0568] Step 9:

[0569] The server updates training plans and goals based on the user's progress and emotional state. Additional mental health support resources are provided as needed. This entire process allows users to continue growing in an emotionally responsive learning environment.

[0570] (Example 2)

[0571] 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."

[0572] Many conventional learning support systems fail to adequately consider users' progress and emotional states, offering uniform educational plans and thus failing to provide effective learning tailored to individual characteristics. Furthermore, a lack of means for users to receive feedback adapted to their emotional state makes it difficult to obtain an optimized learning experience.

[0573] 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.

[0574] In this invention, the server includes means for receiving user information through an information terminal, means for determining individual goals based on the user information using a generative AI model, and means for evaluating the user's emotional state and optimizing the learning experience by incorporating emotion recognition technology. This makes it possible to provide a specialized educational plan and adaptive feedback tailored to the user's individual characteristics and emotional state.

[0575] "User information" refers to data provided by learners, such as their goals, skill level, and available study time.

[0576] An "information terminal" is a computer or smart device used by a user to input or receive data.

[0577] A "generative AI model" is a program that uses artificial intelligence technology to generate optimal learning goals and educational plans for the user.

[0578] "Individual goals" are specific learning achievement items tailored to the user's characteristics and objectives.

[0579] An "educational plan" is a learning plan or curriculum designed according to individual goals.

[0580] "Emotion recognition technology" is a technical means of analyzing and determining a user's emotional state.

[0581] "Progress data" refers to information about the status and results that a user has achieved through learning.

[0582] "Response" refers to advice and feedback provided in accordance with the user's progress and emotional state.

[0583] This invention is a system that provides users with a personalized learning experience. Specific embodiments of this system are described below.

[0584] The terminal serves to receive information entered by the user. Users can input their learning goals, skill levels, and the time required for learning into this information input terminal. The terminal can utilize a variety of user-friendly devices, such as smartphones, tablets, or computers.

[0585] The server is the central system for processing information received from users. The server uses a generative AI model to generate optimal individual goals from the user's input. This generative AI model is based on Python and leverages TensorFlow, a machine learning framework. Based on the user's information, this model sets learning goals tailored to them. The server also uses emotion recognition technology to evaluate the user's emotional state. Emotion recognition involves sentiment analysis using natural language processing libraries, providing user-specific feedback and adjustments. This emotional information is used to further optimize the user's learning plan.

[0586] Users receive a customized training plan from the server via their device. This training plan is based on individual goals and is tailored to the user's learning style and emotional state. Users report their learning progress to the server via their device. The server then generates a response based on this progress and provides feedback to the user.

[0587] As a concrete example, suppose a user enters a goal into their device: "Learn a new programming language in three months." The server generates a training plan optimized for this goal and monitors the user's stress level using emotion recognition technology. If the user's stress level rises, the server can adjust the plan, such as suggesting activities to promote relaxation.

[0588] Another example of a prompt is, "Generate an optimal training plan based on the learning goals set by the user, and use the emotion engine to adjust it according to the stress level." This is the format of instructions input to the generative AI model and forms the basis for optimizing each user's individual learning process.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The user inputs information such as learning goals, skill level, and available time for learning into the device. The device receives this information and formats it in JSON format. Specific data regarding the user's needs is then entered, which becomes the foundational information needed for calculations in the next processing step.

[0592] Step 2:

[0593] The terminal sends formatted user information to the server. Here, the data is securely transferred using the HTTPS protocol. The input data is the user's text information, and error checking is performed to enhance data reliability. The transmitted data is output as a basis for processing on the server.

[0594] Step 3:

[0595] The server inputs the received user information into a generative artificial intelligence model to determine the most suitable individual goals for the user. The generative AI model analyzes the user's input data and outputs the optimal goals using machine learning algorithms. This model uses Python libraries for data normalization and feature extraction. Specific and appropriate goals are set, such as "Learn a programming language in 3 months."

[0596] Step 4:

[0597] The server generates customized learning plans based on individual goals. Based on these goals, it constructs specific steps and tasks to manage learning progress. This is output as a plan that includes a learning schedule and daily tasks. Algorithms are used to optimize time management and task allocation during plan generation.

[0598] Step 5:

[0599] The server uses emotion recognition technology to assess the user's emotional state. Based on the data collected from the user, natural language processing technology is applied to analyze emotions and stress levels. This analysis helps to adjust the user's learning plan and provides specific feedback.

[0600] Step 6:

[0601] The server sends the educational plan and emotion recognition results to the device. Customized feedback and plans are displayed on the device as HTML or a mobile UI. The educational plan is output as specific tasks and schedules, which the user can use to proceed with their learning activities.

[0602] Step 7:

[0603] Users progress through their learning activities based on the learning plan they have used, and input progress information into their device. This progress includes the completion status of updated tasks and newly acquired skills. The progress data collected on the device is prepared as input data for the server.

[0604] Step 8:

[0605] The device sends user progress information to the server. For security reasons, the progress information is again transmitted using the HTTPS protocol and delivered to the server in JSON format.

[0606] Step 9:

[0607] The server uses a generated AI model based on progress information to assess the user's growth and produce adaptive responses. Combining this with emotion recognition technology, it outputs feedback optimized for the user's current learning state. The generated responses include suggestions for determining the next learning stage.

[0608] Step 10:

[0609] The server sends the generated feedback to the device. The feedback is designed to motivate the user, and the user can receive it through the device and use it to guide their next learning steps.

[0610] (Application Example 2)

[0611] 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."

[0612] In today's social environment, individual users have diverse learning goals and schedules, and require appropriate support tailored to their emotional state. However, traditional personalized education systems and training tools have struggled to adequately consider users' emotions and adjust learning plans accordingly in real time, preventing users from enjoying an efficient and optimal learning experience.

[0613] 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.

[0614] In this invention, the server includes means for using a device for inputting user information, means for setting individual goals using a generative artificial intelligence model, and means for recognizing the user's emotional state through an emotion engine. This enables the provision and feedback of adaptive learning plans in accordance with the user's emotional state.

[0615] "User information" refers to information that users enter into the system, such as their personal goals, skill level, and available learning time.

[0616] A "device" refers to hardware or software that provides an interface for users to input information and receive feedback.

[0617] A "generative artificial intelligence model" is an AI technology used to create individual learning goals and training plans based on user information.

[0618] "Individual goals" are personalized, specific, and achievable learning or activity goals for the user.

[0619] A "learning plan" is a set of learning activities and training content customized based on the user's individual goals.

[0620] "Progress information" refers to data and evaluation results regarding the user's progress as they engage in learning or training.

[0621] "Feedback" refers to instructions, advice, and evaluations generated based on the user's progress.

[0622] An "emotion engine" is a software component that analyzes user behavior data and input information to recognize the user's emotional state.

[0623] "Emotional state" refers to the psychological and emotional conditions a user exhibits while using the system.

[0624] The system for implementing this invention begins with user information being entered into a terminal. The terminal is responsible for acquiring information such as the user's goals, skill level, and available learning time, and transmitting it to a server. The server receives this user information and sets individual goals using a generative AI model. This generative AI model incorporates machine learning algorithms to formulate personalized learning goals.

[0625] Next, a customized learning plan is generated by an artificial intelligence model. This learning plan can be adjusted in real time based on the user's progress information, and the server receives the progress data and analyzes the emotional state via an emotion engine. This emotion engine operates on a data analysis platform and understands the user's psychological and emotional state.

[0626] The server also generates and provides feedback to the terminal. This feedback is crucial information for appropriately adjusting the learning content and pace based on the user's emotional state and progress. For example, if the emotional analysis indicates that the user is "stressed," the server will suggest learning activities that promote relaxation.

[0627] As a concrete example, consider a scenario where a user aims to learn a new language. In this case, the device sends a message to the server stating that the user's goal is to "master basic conversation in three months." The server uses a generative AI model to select the most suitable learning materials and provide feedback based on the user's progress. It also adjusts the learning speed and provides advice to boost motivation based on analysis results from an emotion engine.

[0628] The system of this invention is practical, and an example of a prompt message would be, "Please tell me what encouraging message to provide when the user's mood is 'motivated'." By using this prompt, a learning experience optimized for each user can be provided.

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] The terminal acquires user information and sends it to the server. Specifically, it collects data such as the user's goals, skill level, and available learning time, organizes it in a digital format, and then sends it to the server via a secure communication protocol. The input to the server is user information, and the output is organized digital data.

[0632] Step 2:

[0633] The server analyzes the received user information and sets individual learning goals using a generative AI model. This process utilizes machine learning algorithms to calculate personalized goals based on the user's characteristics. The input here is user information, and the output is individual goal setting data.

[0634] Step 3:

[0635] The server generates a customized learning plan based on the individually set goals. Through a generative AI model, it determines the most suitable learning materials and activities for each user and constructs them as a set of plans. The input is the individual goals, and the output is the learning plan data.

[0636] Step 4:

[0637] The server provides the generated learning plan to the terminal, making it accessible to the user. The terminal displays the received learning plan and provides an interface to facilitate user access. The input is the learning plan data, and the output is the plan information viewable by the user.

[0638] Step 5:

[0639] The device monitors the user's progress and periodically sends it to the server. This includes a history of the user's learning activities and outcome data. The input is the user's learning activities, and the output is progress information data.

[0640] Step 6:

[0641] The server uses an emotion engine to analyze the user's emotional state from their progress and input information. The emotion engine analyzes psychological data and employs algorithms to evaluate the user's emotional state. The input is progress information and additional user information, and the output is the user's emotional state.

[0642] Step 7:

[0643] The server generates feedback based on the analyzed emotional state and adjusts the learning plan as needed. The generated feedback includes the adjusted learning pace and additional resource information as required. The inputs are the emotional state and the learning plan, and the output is the feedback data.

[0644] Step 8:

[0645] The server provides feedback to the terminal, which the user receives. This feedback is provided as helpful advice and encouraging messages to the user. The input is feedback data, and the output is a feedback notification to the user.

[0646] 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.

[0647] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0648] 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.

[0649] [Fourth Embodiment]

[0650] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0651] 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.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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".

[0663] This invention relates to a system using generative artificial intelligence technology, aiming to provide users with personalized mentoring and training. In one embodiment of this system, the user first inputs their own information using a terminal. This information includes the user's goals, current skill level, and the amount of time they can dedicate to learning. The terminal formats this information and sends it to the server.

[0664] The server receives this input information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Next, based on these individual goals, it generates a customized training plan. This training plan provides specific guidance for the user's daily learning and activities, including online courses and practical assignments.

[0665] The server sends the generated training plan to the device, which then provides it to the user visually. The user periodically uses the device to report their progress, and this data is compiled by the server. Based on the progress information, the server determines how close the user is to their goal and generates feedback. This feedback includes the next steps to take and areas for improvement, and is provided to the user through the device.

[0666] As a concrete example, let's say the user is an IT engineer who wants to learn a new programming language. This user aims to master the new programming language and complete a project within six months. Based on this goal, the server incorporates the most suitable courses and practice problems into the user's learning plan and provides feedback according to their progress. In this way, the user can efficiently acquire new skills and have a clear path to achieving their goal.

[0667] This system also has the functionality to update training plans and goals as needed to ensure users can continuously grow. Furthermore, it provides resources to support users' mental health. This allows users to receive personalized support over the long term.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The user uses a device to input information such as their goals, skill level, and the amount of time they can dedicate to learning. The device formats the entered information and sends it to the server.

[0671] Step 2:

[0672] The server receives user information sent from the terminal. This information is stored in the database, and a user ID is generated, or the ID is referenced if the user is an existing user.

[0673] Step 3:

[0674] The server activates an artificial intelligence model based on the stored user information. This model is then used to set individual user goals.

[0675] Step 4:

[0676] The server generates a customized training plan based on the individual goals that have been created. This includes specific instructions regarding the user's learning and activities.

[0677] Step 5:

[0678] The server sends the generated training plan and goal information to the device. The device then displays this information to the user.

[0679] Step 6:

[0680] Users periodically report their progress using their devices. This progress includes learning achievement and time taken. The devices send the reported progress information to the server.

[0681] Step 7:

[0682] The server analyzes the received progress information. Using a generative artificial intelligence model, it evaluates how close the user is to their goal and generates feedback based on that evaluation.

[0683] Step 8:

[0684] The server sends the generated feedback to the terminal. The terminal provides the feedback to the user, clearly indicating areas for improvement and what needs to be addressed next.

[0685] Step 9:

[0686] The server updates training plans and goals as needed, based on the user's progress and growth. It also provides continuous support to the user, including mental health-related resources.

[0687] (Example 1)

[0688] 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".

[0689] In modern society, there is a demand for creating and providing appropriate training plans to support individual skill improvement and learning. However, a system that automatically creates plans optimized according to individual goals and skill levels, and provides effective feedback based on those plans, is not yet fully established. Furthermore, it is necessary to simultaneously support the mental health of users. Therefore, the present invention aims to provide a learning support system that meets the individual needs of users.

[0690] 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.

[0691] In this invention, the server includes a terminal for inputting information, means for updating plans and goals according to the user's progress, and means for having resources to provide mental health support to the user. This allows the user to efficiently improve their skills by obtaining a personalized learning plan aligned with their goals and receiving progress and feedback accordingly.

[0692] A "terminal for inputting information" is a device used by users to input information related to their learning, such as their goals and skill levels.

[0693] "Generative artificial intelligence" is a technology that automatically generates user goals and training plans based on the input information.

[0694] "Individualized goals" are objectives that must be achieved according to the user's specific needs and skill level.

[0695] An "individualized plan" is a plan that includes learning and practice steps tailored to the individual needs, based on the goals set by the user.

[0696] "User progress" refers to information that reflects the progress of activities and learning that users are undertaking based on their plans.

[0697] "Feedback" refers to information about future steps and areas for improvement that is generated based on the user's progress.

[0698] "Resources that provide mental health support" is a general term for means and tools that support users' psychological stability and mental health.

[0699] This invention provides a system that supports personalized learning and training for users. This system is primarily composed of a terminal and a server.

[0700] Users can use the device to input personal information and goals they wish to achieve. This input includes the user's current skill level, learning time constraints, and areas of interest. The device converts the information received from the user into a digital format and securely transmits it to the server.

[0701] The server analyzes the received data using artificial intelligence to generate a personalized plan optimized for the user's goals. This plan includes learning resources such as online lectures, practice problems, and hands-on assignments. The server further dynamically adjusts these plans and provides feedback based on the user's daily progress and achievements. This feedback is designed to improve the user's learning efficiency.

[0702] As a concrete example, consider a situation where a user wants to acquire new technical skills in a short period of time. In this case, the user might set a goal of mastering a specific programming language within six months. The server uses generative artificial intelligence to analyze this need and create a plan incorporating the most suitable online courses and practice problems. This plan clearly outlines the milestones to be achieved and the resources to be used.

[0703] An example of a prompt would be, "Create a learning plan for a beginner engineer who has just started learning Python, so that they can acquire basic data analysis skills in three months." Based on this prompt, the generative artificial intelligence model designs a specific learning curriculum and feedback.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0705] Step 1:

[0706] Users use a device to input personal information and goals. This input includes, for example, the skills they want to achieve, their current skill level, and the amount of time they can dedicate to learning. The device converts this information into a digital format, formats it, and sends it to the server. The input data forms the basis for setting individual goals based on the user's learning needs.

[0707] Step 2:

[0708] The server receives user information sent from the terminal and performs analysis using a generated AI model. Specifically, it performs data analysis to set optimal individual goals tailored to the user's current skill level and objectives. This process outputs data that helps formulate a plan for what the user should work on and how to achieve it.

[0709] Step 3:

[0710] The server uses a generative AI model to generate a robust training plan based on the individual goals set in Step 2. This plan includes specific learning activities such as online lectures, practice problems, and practical assignments. The generated plan is customized to maximize the user's learning efficiency. Detailed training plan data is provided as output.

[0711] Step 4:

[0712] The server sends a training plan to the device, which then visually presents it to the user. The user can then begin learning according to the presented plan. In this step, a timeline or calendar is used as a visual guide to provide a user-friendly interface. The output is the learning plan displayed on the user interface.

[0713] Step 5:

[0714] Users report their learning progress via their devices. The devices send progress data to the server. This data indicates how well the user is progressing according to plan. The server aggregates this information and outputs it as progress evaluation data.

[0715] Step 6:

[0716] The server uses a generative AI model to generate feedback based on progress evaluation data. This feedback includes what to focus on next and key points for improvement. This feedback helps to adjust the user's learning direction. Detailed feedback information is generated as output and sent to the device.

[0717] Step 7:

[0718] The device presents the user with feedback sent from the server. Based on this feedback, the user decides on the next learning step. At this stage, it is important that the feedback is presented in a format that is easy for the user to understand. The output provided to the user is the feedback that was displayed.

[0719] (Application Example 1)

[0720] 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".

[0721] In today's family environment, providing learning support optimized for each individual member is not easy. Furthermore, it is difficult to provide effective educational support while managing learning progress and mental health, taking into account busy family schedules. In such circumstances, there is a need to provide a balanced approach to individualized educational plans and mental health management.

[0722] 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.

[0723] In this invention, the server includes an information processing device for inputting user information, means for setting individual goals based on the user information using a generative artificial intelligence model, and means for generating a customized educational plan based on the individual goals. This enables efficient learning support and mental health management tailored to each individual member, even in a home environment.

[0724] An "information processing device" is a device that receives data and processes it according to a specific purpose.

[0725] A "generative artificial intelligence model" is an artificial intelligence algorithm that, by analyzing and learning from vast amounts of data, can make predictions and generate new data based on certain patterns.

[0726] An "individualized goal" is a unique achievement target set according to the user's specific needs and circumstances.

[0727] An "educational plan" is a detailed plan that outlines customized learning procedures and content based on specific goals, in order to make learning progress more effective.

[0728] "Home environment" refers to the physical and social living environment in which individual users spend their daily lives.

[0729] "Mental health management" refers to activities and support aimed at maintaining and improving an individual's mental health.

[0730] This invention provides a system for providing optimized learning support to individual members within a home environment. The system operates primarily through an information processing device and a server.

[0731] The server uses a generative AI model to set individual learning goals for each user based on information obtained from the user, such as goals, current skill level, and available time. Considering these goals, it generates a customized educational plan that the user should achieve daily. This educational plan may include, for example, online courses and practical assignments.

[0732] The generated educational plan is sent to an information processing device installed in the home and presented to the user through a visual interface. This information processing device is hardware such as a Raspberry Pi, running control software developed in Python.

[0733] Users record their progress on an information processing device while engaging in daily learning activities. This progress information is collected and analyzed by a server to evaluate how close the user is to their individual goals. Based on this evaluation, feedback is generated, providing instructions on the next steps and areas for improvement.

[0734] For example, if a user is learning a new language, it's possible to suggest the next learning methods and materials based on the number of vocabulary words learned that day and the accuracy rate of grammar questions. Furthermore, if the user's learning burden increases, resources to support their mental health management can also be provided.

[0735] An example of a prompt message is: "Provide an example of a home educational support robot that uses an AI system to provide a customized learning plan based on user information and gives feedback according to progress."

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] Users use an information processing device to input personal information such as their goals, current skill level, and the time they can dedicate to learning. This information is formatted in JSON format and sent to the server. The server receives this data and prepares it for analysis.

[0739] Step 2:

[0740] The server uses the received user information to generate individual learning goals using a generative artificial intelligence model. This model operates using machine learning algorithms and sets optimal goals based on past data and similar user data. The output is a list of specific goals tailored to the user.

[0741] Step 3:

[0742] The server creates a customized educational plan based on the individual goals generated. This plan includes links to online courses and instructions for practical assignments. The output is data containing the details of the educational plan.

[0743] Step 4:

[0744] The server sends the completed educational plan to the information processing device. The terminal receives the data and launches an interface to provide the user with the educational plan visually or audibly. The user plans their daily learning through this interface.

[0745] Step 5:

[0746] Users record their daily learning activities through their devices. The devices transmit the entered progress data to the server in real time. This data includes the number of tasks completed and the accuracy of the answers.

[0747] Step 6:

[0748] The server analyzes the received progress data and evaluates how close the user is to achieving their set goal. Statistical methods and predictive models are used to process the data and generate quantitative evaluations related to the user's progress.

[0749] Step 7:

[0750] The server generates feedback based on the evaluation results. This feedback includes the next steps to take and areas that need improvement. A feedback message is prepared as output and sent to the terminal.

[0751] Step 8:

[0752] The device provides the user with feedback received from the server, either visually or audibly. Based on this feedback, the user can readjust their next learning plan.

[0753] Step 9:

[0754] The user then works on new progress based on the feedback, and new progress data is sent to the server via their device. The process returns to step 6, and learning is continuously improved through the PDCA cycle.

[0755] 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.

[0756] The present invention is a system that provides personalized mentoring and training to users, and aims to provide adaptive support that takes the user's emotions into account, particularly by combining it with an emotion engine. In an embodiment of this system, the user first uses a terminal to input their goals, skill level, available learning time, and other information. The terminal then sends this input information to a server.

[0757] The server receives the transmitted user information and uses a generative artificial intelligence model to set personalized goals best suited to the user. Furthermore, it generates a customized training plan and provides it to the device. The user's progress is periodically reported from the device to the server, and the server uses the generative artificial intelligence model to generate progress-based feedback.

[0758] A key feature of this invention is that an emotion engine is integrated into the server. This emotion engine analyzes user input information and behavioral data to recognize the user's emotional state. This emotional information is considered an important element in generating training plans and feedback. For example, if a user is feeling stressed, the server can adjust the learning pace or incorporate activities that promote relaxation into the plan accordingly.

[0759] As a concrete example, consider a situation where a user wants to acquire new technology in a short period of time. The user sets the goal as "acquire the technology in three months and start a new project." Based on this goal, the server generates an appropriate training plan. At the same time, it uses an emotion engine to evaluate the user's stress level and motivation, and adjusts the plan and provides feedback accordingly. As a result, the user can effectively progress towards their goal in a learning environment optimized for their emotional state.

[0760] Thus, this invention, which combines an emotion engine, provides comprehensive support that takes user emotions into account, resulting in a richer learning experience.

[0761] The following describes the processing flow.

[0762] Step 1:

[0763] The user uses the device to enter their goals, skill level, time available for learning, and other personal information. The device formats this information and sends it to the server.

[0764] Step 2:

[0765] The server stores user information received from the terminal in a database. Based on this information, it activates a generated artificial intelligence model and sets individual goals for the user.

[0766] Step 3:

[0767] The server generates a customized training plan based on the individual goals set. This plan includes specific online lessons and practice exercises.

[0768] Step 4:

[0769] The server sends the generated training plan and goal information to the terminal. The terminal then provides this information to the user visually.

[0770] Step 5:

[0771] Users periodically use their devices to report their learning progress and assignment completion status. The devices then send this progress information to the server.

[0772] Step 6:

[0773] The server receives progress information and uses a generative artificial intelligence model to analyze how close the user is to their goal. Furthermore, it uses an emotion engine to recognize the user's emotional state, including stress levels and motivational assessments received from user input.

[0774] Step 7:

[0775] The server generates feedback based on emotional information and progress analysis, and makes adjustments necessary for the user's learning. This feedback includes emotionally responsive actions (e.g., slowing down the learning pace or providing advice to increase motivation).

[0776] Step 8:

[0777] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user, clarifying areas for improvement and next steps.

[0778] Step 9:

[0779] The server updates training plans and goals based on the user's progress and emotional state. Additional mental health support resources are provided as needed. This entire process allows users to continue growing in an emotionally responsive learning environment.

[0780] (Example 2)

[0781] 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".

[0782] Many conventional learning support systems fail to adequately consider users' progress and emotional states, offering uniform educational plans and thus failing to provide effective learning tailored to individual characteristics. Furthermore, a lack of means for users to receive feedback adapted to their emotional state makes it difficult to obtain an optimized learning experience.

[0783] 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.

[0784] In this invention, the server includes means for receiving user information through an information terminal, means for determining individual goals based on the user information using a generative AI model, and means for evaluating the user's emotional state and optimizing the learning experience by incorporating emotion recognition technology. This makes it possible to provide a specialized educational plan and adaptive feedback tailored to the user's individual characteristics and emotional state.

[0785] "User information" refers to data provided by learners, such as their goals, skill level, and available study time.

[0786] An "information terminal" is a computer or smart device used by a user to input or receive data.

[0787] A "generative AI model" is a program that uses artificial intelligence technology to generate optimal learning goals and educational plans for the user.

[0788] "Individual goals" are specific learning achievement items tailored to the user's characteristics and objectives.

[0789] An "educational plan" is a learning plan or curriculum designed according to individual goals.

[0790] "Emotion recognition technology" is a technical means of analyzing and determining a user's emotional state.

[0791] "Progress data" refers to information about the status and results that a user has achieved through learning.

[0792] "Response" refers to advice and feedback provided in accordance with the user's progress and emotional state.

[0793] This invention is a system that provides users with a personalized learning experience. Specific embodiments of this system are described below.

[0794] The terminal serves to receive information entered by the user. Users can input their learning goals, skill levels, and the time required for learning into this information input terminal. The terminal can utilize a variety of user-friendly devices, such as smartphones, tablets, or computers.

[0795] The server is the central system for processing information received from users. The server uses a generative AI model to generate optimal individual goals from the user's input. This generative AI model is based on Python and leverages TensorFlow, a machine learning framework. Based on the user's information, this model sets learning goals tailored to them. The server also uses emotion recognition technology to evaluate the user's emotional state. Emotion recognition involves sentiment analysis using natural language processing libraries, providing user-specific feedback and adjustments. This emotional information is used to further optimize the user's learning plan.

[0796] Users receive a customized training plan from the server via their device. This training plan is based on individual goals and is tailored to the user's learning style and emotional state. Users report their learning progress to the server via their device. The server then generates a response based on this progress and provides feedback to the user.

[0797] As a concrete example, suppose a user enters a goal into their device: "Learn a new programming language in three months." The server generates a training plan optimized for this goal and monitors the user's stress level using emotion recognition technology. If the user's stress level rises, the server can adjust the plan, such as suggesting activities to promote relaxation.

[0798] Another example of a prompt is, "Generate an optimal training plan based on the learning goals set by the user, and use the emotion engine to adjust it according to the stress level." This is the format of instructions input to the generative AI model and forms the basis for optimizing each user's individual learning process.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The user inputs information such as learning goals, skill level, and available time for learning into the device. The device receives this information and formats it in JSON format. Specific data regarding the user's needs is then entered, which becomes the foundational information needed for calculations in the next processing step.

[0802] Step 2:

[0803] The terminal sends formatted user information to the server. Here, the data is securely transferred using the HTTPS protocol. The input data is the user's text information, and error checking is performed to enhance data reliability. The transmitted data is output as a basis for processing on the server.

[0804] Step 3:

[0805] The server inputs the received user information into a generative artificial intelligence model to determine the most suitable individual goals for the user. The generative AI model analyzes the user's input data and outputs the optimal goals using machine learning algorithms. This model uses Python libraries for data normalization and feature extraction. Specific and appropriate goals are set, such as "Learn a programming language in 3 months."

[0806] Step 4:

[0807] The server generates customized learning plans based on individual goals. Based on these goals, it constructs specific steps and tasks to manage learning progress. This is output as a plan that includes a learning schedule and daily tasks. Algorithms are used to optimize time management and task allocation during plan generation.

[0808] Step 5:

[0809] The server uses emotion recognition technology to assess the user's emotional state. Based on the data collected from the user, natural language processing technology is applied to analyze emotions and stress levels. This analysis helps to adjust the user's learning plan and provides specific feedback.

[0810] Step 6:

[0811] The server sends the educational plan and emotion recognition results to the device. Customized feedback and plans are displayed on the device as HTML or a mobile UI. The educational plan is output as specific tasks and schedules, which the user can use to proceed with their learning activities.

[0812] Step 7:

[0813] Users progress through their learning activities based on the learning plan they have used, and input progress information into their device. This progress includes the completion status of updated tasks and newly acquired skills. The progress data collected on the device is prepared as input data for the server.

[0814] Step 8:

[0815] The device sends user progress information to the server. For security reasons, the progress information is again transmitted using the HTTPS protocol and delivered to the server in JSON format.

[0816] Step 9:

[0817] The server uses a generated AI model based on progress information to assess the user's growth and produce adaptive responses. Combining this with emotion recognition technology, it outputs feedback optimized for the user's current learning state. The generated responses include suggestions for determining the next learning stage.

[0818] Step 10:

[0819] The server sends the generated feedback to the device. The feedback is designed to motivate the user, and the user can receive it through the device and use it to guide their next learning steps.

[0820] (Application Example 2)

[0821] 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".

[0822] In today's social environment, individual users have diverse learning goals and schedules, and require appropriate support tailored to their emotional state. However, traditional personalized education systems and training tools have struggled to adequately consider users' emotions and adjust learning plans accordingly in real time, preventing users from enjoying an efficient and optimal learning experience.

[0823] 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.

[0824] In this invention, the server includes means for using a device for inputting user information, means for setting individual goals using a generative artificial intelligence model, and means for recognizing the user's emotional state through an emotion engine. This enables the provision and feedback of adaptive learning plans in accordance with the user's emotional state.

[0825] "User information" refers to information that users enter into the system, such as their personal goals, skill level, and available learning time.

[0826] A "device" refers to hardware or software that provides an interface for users to input information and receive feedback.

[0827] A "generative artificial intelligence model" is an AI technology used to create individual learning goals and training plans based on user information.

[0828] "Individual goals" are personalized, specific, and achievable learning or activity goals for the user.

[0829] A "learning plan" is a set of learning activities and training content customized based on the user's individual goals.

[0830] "Progress information" refers to data and evaluation results regarding the user's progress as they engage in learning or training.

[0831] "Feedback" refers to instructions, advice, and evaluations generated based on the user's progress.

[0832] An "emotion engine" is a software component that analyzes user behavior data and input information to recognize the user's emotional state.

[0833] "Emotional state" refers to the psychological and emotional conditions a user exhibits while using the system.

[0834] The system for implementing this invention begins with user information being entered into a terminal. The terminal is responsible for acquiring information such as the user's goals, skill level, and available learning time, and transmitting it to a server. The server receives this user information and sets individual goals using a generative AI model. This generative AI model incorporates machine learning algorithms to formulate personalized learning goals.

[0835] Next, a customized learning plan is generated by an artificial intelligence model. This learning plan can be adjusted in real time based on the user's progress information, and the server receives the progress data and analyzes the emotional state via an emotion engine. This emotion engine operates on a data analysis platform and understands the user's psychological and emotional state.

[0836] The server also generates and provides feedback to the terminal. This feedback is crucial information for appropriately adjusting the learning content and pace based on the user's emotional state and progress. For example, if the emotional analysis indicates that the user is "stressed," the server will suggest learning activities that promote relaxation.

[0837] As a concrete example, consider a scenario where a user aims to learn a new language. In this case, the device sends a message to the server stating that the user's goal is to "master basic conversation in three months." The server uses a generative AI model to select the most suitable learning materials and provide feedback based on the user's progress. It also adjusts the learning speed and provides advice to boost motivation based on analysis results from an emotion engine.

[0838] The system of this invention is practical, and an example of a prompt message would be, "Please tell me what encouraging message to provide when the user's mood is 'motivated'." By using this prompt, a learning experience optimized for each user can be provided.

[0839] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0840] Step 1:

[0841] The terminal acquires user information and sends it to the server. Specifically, it collects data such as the user's goals, skill level, and available learning time, organizes it in a digital format, and then sends it to the server via a secure communication protocol. The input to the server is user information, and the output is organized digital data.

[0842] Step 2:

[0843] The server analyzes the received user information and sets individual learning goals using a generative AI model. This process utilizes machine learning algorithms to calculate personalized goals based on the user's characteristics. The input here is user information, and the output is individual goal setting data.

[0844] Step 3:

[0845] The server generates a customized learning plan based on the individually set goals. Through a generative AI model, it determines the most suitable learning materials and activities for each user and constructs them as a set of plans. The input is the individual goals, and the output is the learning plan data.

[0846] Step 4:

[0847] The server provides the generated learning plan to the terminal, making it accessible to the user. The terminal displays the received learning plan and provides an interface to facilitate user access. The input is the learning plan data, and the output is the plan information viewable by the user.

[0848] Step 5:

[0849] The device monitors the user's progress and periodically sends it to the server. This includes a history of the user's learning activities and outcome data. The input is the user's learning activities, and the output is progress information data.

[0850] Step 6:

[0851] The server uses an emotion engine to analyze the user's emotional state from their progress and input information. The emotion engine analyzes psychological data and employs algorithms to evaluate the user's emotional state. The input is progress information and additional user information, and the output is the user's emotional state.

[0852] Step 7:

[0853] The server generates feedback based on the analyzed emotional state and adjusts the learning plan as needed. The generated feedback includes the adjusted learning pace and additional resource information as required. The inputs are the emotional state and the learning plan, and the output is the feedback data.

[0854] Step 8:

[0855] The server provides feedback to the terminal, which the user receives. This feedback is provided as helpful advice and encouraging messages to the user. The input is feedback data, and the output is a feedback notification to the user.

[0856] 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.

[0857] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0858] 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 robot 414.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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."

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] The following is further disclosed regarding the embodiments described above.

[0878] (Claim 1)

[0879] A terminal for entering user information,

[0880] A means for setting individual goals based on the user information using a generative artificial intelligence model,

[0881] Means for generating a customized training plan based on the aforementioned individual goals,

[0882] Means for providing the terminal with information regarding the training plan and goals,

[0883] Means for monitoring user progress and generating feedback based on said progress information,

[0884] Means for providing the aforementioned feedback to the terminal,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, further comprising means for updating the training plan and goals in accordance with the user's growth.

[0888] (Claim 3)

[0889] The system according to claim 1, comprising resources for providing mental health support to users.

[0890] "Example 1"

[0891] (Claim 1)

[0892] A terminal for entering information,

[0893] A means for setting individual goals based on the aforementioned information using generative artificial intelligence,

[0894] Means for generating an individualized plan based on the aforementioned individual goals,

[0895] Means for presenting information regarding the aforementioned plan and objectives to the terminal,

[0896] A means for monitoring the user's progress and generating feedback based on said progress information,

[0897] means for transmitting the aforementioned feedback to the terminal,

[0898] A means to automatically adjust the order and type of contents within the plan,

[0899] ...

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising means for updating the plan and goals in accordance with the user's growth.

[0903] (Claim 3)

[0904] The system according to claim 1, which has resources to provide mental health support to users.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] An information processing device for inputting user information,

[0908] A means for setting individual goals based on the user information using a generative artificial intelligence model,

[0909] Means for generating a customized educational plan based on the aforementioned individual goals,

[0910] Means for providing information about the aforementioned education plan and objectives to the information processing device,

[0911] Means for monitoring user progress and generating feedback based on said progress information,

[0912] means for providing the aforementioned feedback to the information processing device,

[0913] A means of coordinating the support provided based on the user's learning progress,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising means for updating the educational plan and goals in accordance with the user's growth and for providing individualized learning support in the home environment.

[0917] (Claim 3)

[0918] The system according to claim 1, comprising resources for providing mental health management support to users, and means for supplying learning resources suitable for the home environment.

[0919] "Example 2 of combining an emotion engine"

[0920] (Claim 1)

[0921] An information terminal for receiving user information,

[0922] A means for determining individual goals based on the user information using a generative AI model,

[0923] A means for creating a specialized educational plan based on the aforementioned individual objectives,

[0924] Means for transmitting data relating to the aforementioned educational plan and objectives to the information terminal,

[0925] A means of optimizing the learning experience by incorporating emotion recognition technology to evaluate the user's emotional state,

[0926] Means for tracking user progress and generating responses based on said progress data,

[0927] Means for delivering the response to the information terminal,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, further comprising means for revising the aforementioned educational plan and goals in accordance with the user's evolution.

[0931] (Claim 3)

[0932] The system according to claim 1, which has resources to provide psychological health support to users.

[0933] "Application example 2 when combining with an emotional engine"

[0934] (Claim 1)

[0935] A device for entering user information,

[0936] A means for setting individual goals based on the user information using a generative artificial intelligence model,

[0937] Means for generating a customized learning plan based on the aforementioned individual goals,

[0938] Means for providing the device with information regarding the learning plan and objectives,

[0939] Means for monitoring user progress and generating feedback based on said progress information,

[0940] It includes an emotion engine and means for recognizing the user's emotional state,

[0941] Means for adaptively adjusting the learning plan or feedback based on the aforementioned emotional state,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, further comprising means for updating the learning plan and goals in accordance with the user's growth and emotional state.

[0945] (Claim 3)

[0946] The system according to claim 1, comprising resources that provide psychological health support to users. [Explanation of Symbols]

[0947] 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. An information processing device for inputting user information, A means for setting individual goals based on the user information using a generative artificial intelligence model, Means for generating a customized educational plan based on the aforementioned individual goals, Means for providing information about the aforementioned education plan and objectives to the information processing device, Means for monitoring user progress and generating feedback based on said progress information, means for providing the aforementioned feedback to the information processing device, A means of coordinating the support provided based on the user's learning progress, A system that includes this.

2. The system according to claim 1, further comprising means for updating the educational plan and goals in accordance with the user's growth and for providing individualized learning support in the home environment.

3. The system according to claim 1, comprising resources for providing mental health management support to users, and means for supplying learning resources suitable for the home environment.

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