system

Abstract

We provide the system. [Solution] Means for collecting learner learning data, A means of identifying common abstract skills across multiple learning domains using generative models, A means of visualizing and displaying identified abstract skills, A means of generating a learning plan based on visualized skills, A means of monitoring learners' progress and dynamically adjusting learning plans, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 a conventional educational system, since learners study each subject individually, the relevance of skills common to different subjects is often overlooked. As a result, it is difficult to comprehensively strengthen abstract skills, and learning efficiency decreases. Also, it is difficult to reflect the progress information of learning in real time and dynamically adjust individual learning plans, and maintaining the motivation of learners is also an issue.

Means for Solving the Problems

[0005] This system collects learner learning data and provides a means to identify common abstract skills across multiple learning areas using generative models. It then visualizes and displays these identified abstract skills to support learners in understanding the relationships between them. Furthermore, it optimizes the individual learning experience by using algorithms that monitor learner progress and dynamically adjust learning plans based on progress information and feedback. This enables learners to efficiently strengthen their skills and maintain motivation while learning.

[0006] "Learning data" refers to information about the learning activities undertaken by learners, including learning time, performance, progress, and self-assessment.

[0007] A "generative model" is an algorithmic method used to identify new patterns and skills based on collected and analyzed data.

[0008] "Abstract skills" refer to general abilities that are not dependent on specific subject matter, such as problem-solving ability, logical thinking ability, and interpretive ability.

[0009] "Visualization" refers to displaying data and skills in the form of graphs, charts, and other diagrams to make the relationships between them easier to understand intuitively.

[0010] A "learning plan" refers to a combination of schedules and assignments that indicate what specific learning activities should be undertaken with the aim of improving the learner's progress and skills.

[0011] "Progress information" refers to data that shows the progress of a learner's learning activities and serves as the basis for evaluating how close they are to achieving their learning goals.

[0012] An "algorithm" is a set of computational procedures and processing methods for problem solving and data analysis, and in particular, dynamic adjustments enable real-time optimization.

[0013] "Dynamic adjustment" refers to a process that aims for optimal learning effectiveness by changing learning plans and methods in real time based on progress information and feedback. [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

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 processor with a reference number (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), etc.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number 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 storage with a reference number 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 is a system for optimizing learners' learning experiences. This system collects individual learning data from learners, identifies abstract skills using a generative model, and maps those skills across different learning domains.

[0036] First, the user engages in learning activities and inputs the activity data into the device. This includes learning time, tasks completed, number of correct answers, and self-assessment. The device then transmits this data to the server in real time.

[0037] The server analyzes the received data and identifies abstract skills common to different learning areas through a generative model. Based on the analysis results, the server visualizes the skills and sends them to the terminal. The terminal provides the user with the visualized skill mapping, making it easy for learners to understand which skills are used in which subjects.

[0038] Furthermore, the server generates an optimized learning plan based on the visualized information. This learning plan includes the tasks and learning activities necessary to enhance skills. For example, to strengthen logical thinking, it may include tackling mathematical proof problems as well as reading comprehension problems in Japanese.

[0039] This plan is sent to the user via their device and reflected in their daily learning activities. Learning progress is constantly monitored and fed back to the server. The server dynamically adjusts the learning plan based on the progress information, providing an efficient learning experience.

[0040] For example, if a learner quickly masters a specific concept in science, the server uses that information to quickly suggest the next steps and add challenging tasks to support continuous learning growth. This system provides a customizable learning environment tailored to the individual learner's needs, enabling efficient skill acquisition.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user begins a learning activity and inputs data such as learning time, answer status, and self-assessment into the device. This records specific learning outcomes and progress.

[0044] Step 2:

[0045] The device formats the collected training data and sends it to the server in real time. The data is then prepared in a format that can be used for statistical analysis and machine learning models.

[0046] Step 3:

[0047] The server feeds the received data into a generative model to identify common abstract skills across multiple learning domains. Here, the user's learning history and past patterns are also considered to extract skills such as problem-solving ability and logical thinking.

[0048] Step 4:

[0049] The server maps identified abstract skills across different learning areas and outputs the relationships between those skills to the terminal as visualized information. This visually shows the areas of focus and relationships in learning.

[0050] Step 5:

[0051] The device provides users with visualized information, facilitating understanding. This allows learners to intuitively grasp which skills they need to strengthen.

[0052] Step 6:

[0053] The server generates individually optimized learning plans based on the visualized information. Specifically, it combines necessary learning materials and assignments to create a schedule tailored to the user's learning style.

[0054] Step 7:

[0055] The device notifies the user of the generated learning plan and displays its contents. The user then proceeds with their daily learning according to this plan.

[0056] Step 8:

[0057] The server monitors progress information periodically sent from the terminal and dynamically adjusts the learning plan using a reinforcement learning algorithm. Depending on the level of understanding and speed of progress of the task, changes such as adding more challenging tasks are made.

[0058] Step 9:

[0059] The user receives a dynamically adjusted learning plan and continues learning towards new goals. This process continues in a loop, always providing the optimal learning experience.

[0060] (Example 1)

[0061] 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."

[0062] The current education system makes it difficult to provide learning plans tailored to the individual needs of learners and to appropriately connect and utilize skills acquired from different educational fields. It is necessary to dynamically adjust plans based on learners' progress and provide an efficient learning experience.

[0063] 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.

[0064] In this invention, the server includes means for collecting learner learning utilization information, means for identifying common abstract abilities across multiple educational fields using a generative AI model, and means for visualizing the identified abstract abilities and presenting them on a human-controlled device. This makes it possible to dynamically optimize educational plans based on the individual needs of learners and to associate the abilities acquired in each educational field.

[0065] "Learner" refers to an individual participating in educational activities, and the target of these activities is the individual's learning progress and ability improvement.

[0066] "Learning utilization information" refers to data accumulated by learners through educational activities, and includes specific elements such as time spent, assignment content, number of correct answers, and self-assessment.

[0067] A "generative AI model" refers to a computational system that uses artificial intelligence technology to perform patterns and inferences from data, generating new insights or information for specific purposes.

[0068] The term "educational field" refers to a specific category of academic discipline or ability, which can be divided into subjects such as mathematics, Japanese language, and science, or into areas of skills.

[0069] "Abstract comprehension" refers to a collection of generalized skills and knowledge applicable to multiple educational fields beyond specific content, including logical thinking and problem-solving abilities.

[0070] "Visualization" refers to the process of representing data and information in concrete forms such as graphs and charts to make them easier to understand.

[0071] A "human-controlled device" is a device that learners interact with or operate directly, and is a device that provides a user interface, such as a computer or smartphone.

[0072] "Dynamic adaptation" refers to the ability to instantly change plans and methods based on given circumstances and new information, with the goal of keeping the system in an optimal state at all times.

[0073] An "educational plan" is a guideline that systematically outlines the goals and activities that learners should achieve over a specific period, and is designed to promote effective learning.

[0074] This system provides a set of means for collecting and analyzing learner data and optimizing the learning process. First, the user engages in learning activities and inputs the resulting progress information and evaluations into a terminal. This terminal uses information processing devices such as computers and smartphones to record detailed information such as learning time, task completion rate, and number of correct answers.

[0075] The device transmits the collected data to the server in real time. The server has a generative AI model implemented, which analyzes the received data to identify abstract abilities across learning domains. This AI model uses the latest machine learning algorithms to extract learners' skills through pattern recognition and data mining.

[0076] Next, the server visualizes the identified abstract abilities. This visualization is displayed on the terminal as graphs and charts to allow learners to intuitively understand their own abilities. This display is interactive and designed to allow learners to check their progress in detail.

[0077] Furthermore, based on the visualized data, the server generates an optimal learning plan for each learner. The generated plan is customized according to the learner's characteristics and progress, aiming to maximize their individual abilities. For example, as a concrete example of strengthening logical thinking, it may suggest logical reading comprehension problems in Japanese in addition to math problems.

[0078] Throughout this entire process, the program provides feedback to learners via the user interface, which is then reflected in daily educational activities. Based on the learner's progress, the server dynamically adjusts the learning plan, delivering an efficient and personalized learning experience on the digital platform.

[0079] A concrete example of a prompt message would be, "Use the user-inputted learning data to identify common abstract skills and generate a visualized skill mapping." Through this prompt, the generating AI model begins analyzing the user data and provides appropriate educational support.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] After completing a learning activity, the user inputs information such as learning time, task content, number of correct answers, and self-assessment into the terminal. This input data is detailed information about individual learning behaviors and provides basic information necessary for future analysis. Specifically, this involves the user manually recording information in input fields on the interface.

[0083] Step 2:

[0084] The terminal transmits the input training data to the server in real time. By converting the input data into an appropriate format and transmitting it via a communication protocol, analysis on the server side becomes possible. Specifically, data packets are transferred over a secure channel using the network connection.

[0085] Step 3:

[0086] The server stores the received training data in a database and analyzes the data using a generative AI model. This analysis extracts patterns from learner activities and identifies abstract skills common to different educational fields. The input is learner activity data, and the output is a list of identified abstract skills. Statistical methods and machine learning algorithms are applied to the analysis.

[0087] Step 4:

[0088] The server visualizes the identified abstract skills. This visualization generates graphs and dashboards showing the relationships between abstract skills, presenting the information in an easily understandable format. The input is a list of identified abstract skills, and the output is visualized data. Data visualization tools are used for this visualization.

[0089] Step 5:

[0090] The server generates an optimized educational plan based on visualized abstract skills. This plan is customized to enhance learners' skills and includes tasks and activities to be accomplished. A generative AI model analyzes the visualized data and designs tasks based on specific educational objectives.

[0091] Step 6:

[0092] The device presents the user with a created educational plan. The user then proceeds with their daily learning based on this plan. The plan includes specific instructions for skill enhancement and provides criteria for self-managing progress. This information is accessible through an interactive interface on the device.

[0093] Step 7:

[0094] The server monitors learners' progress and dynamically adjusts the lesson plan as needed. It analyzes learning progress data and evaluates whether each task in the plan is being properly completed. Based on the progress evaluation, it automatically modifies the content or adds new tasks. The input is progress data, and the output is the latest lesson plan.

[0095] (Application Example 1)

[0096] 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."

[0097] Traditional learning systems have faced the challenge of failing to provide a learning experience that fully meets the individual needs of each learner. In particular, there is a need to integrate and utilize abstract skills developed in different learning domains to efficiently optimize learning. Furthermore, while personalized support for individual learners within the home is required, achieving this presents challenges in terms of effort and cost.

[0098] 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.

[0099] In this invention, the server includes means for collecting learner learning information, means for identifying common abstract skills across multiple learning domains using a generative model, and means for providing learning support and interactive feedback via consumer-grade machines. This enables the provision of an optimal learning plan tailored to the individual characteristics of each learner and efficient learning support at home.

[0100] "Learning information" refers to data generated by learners during their daily learning activities, and specifically includes learning time, tasks completed, number of correct answers, and self-assessment.

[0101] A "generative model" is an algorithm or statistical model that analyzes training data based on artificial intelligence technology and plays a role in identifying common skills across different learning domains.

[0102] "Abstract skills" refer to conceptual skills such as logical thinking and problem-solving abilities that are required in common across multiple learning fields, going beyond specific learning content.

[0103] "Visualization" is a technique that displays data in a graphical format, enabling users to understand information intuitively and easily.

[0104] "Consumer electronic devices" refer to electronic devices such as robots used in the home, which are used to provide learning support and interactive communication.

[0105] "Interactive feedback" is a feature that enables real-time, two-way communication with learners and provides immediate feedback.

[0106] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server processes the learner's learning information using a generative AI model and transmits visualized insights to the terminal. The terminal takes the form of a consumer device and provides interactive feedback to the user.

[0107] Specifically, users input information about their learning activities into their devices. This includes, for example, the content and time spent on daily studies, the tasks they worked on, and their results. The devices then transmit this data to the server in real time.

[0108] The server operates a generative AI model using TENSORFLOW® or similar technologies, depending on the conditions, to analyze the collected learning information and identify abstract skills in different learning domains. The identified skills are visualized graphically and resent to the terminal.

[0109] The device, specifically the home robot, provides necessary feedback through interaction with the user. The user then uses this feedback to improve their learning plan. For example, the robot might prompt the user with questions like, "What is the optimal time to study to memorize new words?"

[0110] This series of processes enables learners to advance their learning activities more efficiently and effectively.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users input learning information through their devices. This input includes study time, completed tasks, number of correct answers, and self-assessment. As this information is entered, the system collects basic data on the user's learning activities in real time.

[0114] Step 2:

[0115] The terminal sends the collected learning information to the server. The input data received by the server consists of numerical and textual information regarding the user's learning progress and results. The server stores this data in storage as reference data for analysis.

[0116] Step 3:

[0117] The server uses a generative AI model to identify abstract skills from the training data. Based on the input data in this process, data calculations are performed to extract common skills required across different learning domains.

[0118] Step 4:

[0119] The server visualizes identified abstract skills and outputs them as diagrams and charts. This visualized data serves as a resource to provide users with insights that help them understand the current situation.

[0120] Step 5:

[0121] The terminal receives visualized data from the server and presents it to the user. The user then receives assistance in selecting interactive and specific actions based on the information. This presentation step contributes to the user's decision-making process in real time.

[0122] Step 6:

[0123] Users adjust and dynamically change their learning plans via their devices. New learning goals and schedules are entered from the device, and this data is sent back to the server, creating a cycle of steps.

[0124] This series of steps enables users to spontaneously implement optimal learning plans based on their learning data, supporting efficient learning activities.

[0125] 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.

[0126] This invention is an optimization system that incorporates learner emotional information to make the learner's learning experience more effective. In addition to collecting learning data, visualizing skills, and dynamically adjusting the learning plan, this system includes an emotion engine that recognizes the user's emotions and reflects them in the learning plan.

[0127] When a user begins a learning activity, the device senses their facial expressions through voice input or the camera. This sensed data is then converted into emotional information through voice analysis and image processing algorithms. For example, it can detect whether the user is tired or focused.

[0128] Next, the device sends the user's emotional information to the server. The server combines this with training data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and skills. The resulting skill and emotional information is visualized and provided as feedback to the user via the device.

[0129] Furthermore, the server generates an optimized learning plan based on training data that includes emotional information. This learning plan incorporates not only tasks to enhance specific skills, but also options tailored to the user's current emotional state. For example, if the user is feeling stressed, it will include tasks that help them relax or activities that help them unwind.

[0130] This plan is displayed on the device, and the user proceeds with their learning based on it. The server monitors emotional information in real time as the user progresses and dynamically updates the learning plan. Adjustments are made in response to new challenges and emotional states to enhance learning effectiveness. For example, more challenging tasks are provided when the user is calm, maximizing their focused time.

[0131] In this way, by utilizing the emotion engine, a personalized learning environment that takes into account the learner's emotional state can be provided, enabling improved learning effectiveness and maintenance of motivation.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user starts a learning session, and the device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. This data is sent to the emotion engine.

[0135] Step 2:

[0136] The device activates an emotion engine and analyzes recorded facial expressions and voice data. Using facial expression recognition algorithms and voice emotion analysis, it identifies the user's emotional state (e.g., relaxed, focused, stressed).

[0137] Step 3:

[0138] The device sends the analyzed emotion data to the server. The server receives this data along with the user's learning data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and learning performance.

[0139] Step 4:

[0140] The server visualizes identified skills and emotional information and outputs it to the learner's device as a dashboard in a way that makes sense to them. This allows users to see the correlation between their learning style and their emotions.

[0141] Step 5:

[0142] The server generates a learning plan that takes emotional information into account. For example, if the user is fatigued, it will include easier tasks, and if they are relaxed, it will add more challenging tasks.

[0143] Step 6:

[0144] The device provides the user with a generated learning plan, displaying the assignment options and the order in which they should be completed. The user then proceeds with their learning activities according to this plan.

[0145] Step 7:

[0146] As learning progresses, the device monitors emotional data and learning progress in real time and reports it to the server. Based on this, the server determines whether the learning plan is appropriate and makes dynamic adjustments as needed.

[0147] Step 8:

[0148] The adjusted learning plan is again notified to the user via the device, and a new learning approach is presented. This ensures that a learning environment that is always considerate of the user's emotional state is maintained.

[0149] (Example 2)

[0150] 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".

[0151] Traditional learning systems often create learning plans based primarily on learner progress data, without considering learners' emotional states. This results in insufficient individual optimization. Furthermore, dynamic adjustments based on learners' states are difficult, making it challenging to maximize learning efficiency. Solving these problems will improve learner effectiveness and motivation.

[0152] 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.

[0153] In this invention, the server includes means for collecting learner learning data, means for identifying abstract skills using a generative model, and means for sensing and collecting learner emotional information. This makes it possible to accurately grasp the learner's emotional state and analyze it in conjunction with progress data. Based on this, an optimized learning plan can be proposed and the plan can be dynamically adjusted, thereby providing a learning experience tailored to each individual and enhancing the effectiveness of learning.

[0154] "Learning data" refers to all information related to the knowledge and skills acquired by learners, and includes evaluations and progress reports.

[0155] A "generative model" refers to a program or algorithm that automatically extracts specific patterns or abstract concepts based on input data.

[0156] "Abstract skills" refer to general abilities and knowledge that can be applied across multiple fields of study.

[0157] "Visualization" refers to the process of converting abstract information into a form that is easy for humans to understand and display.

[0158] "Emotional information" refers to data that indicates the learner's emotional state, and includes psychological and physiological information obtained from facial expressions and voice.

[0159] An "optimized learning plan" refers to a combination of learning paths and materials that are tailored to maximize learning effectiveness based on the learner's progress and emotional state.

[0160] "Dynamic adjustment" means changing plans and procedures in real time in response to changes in circumstances and conditions.

[0161] The following describes "modes for carrying out the invention."

[0162] This invention is a system for optimizing the learner's learning experience, and is primarily realized through the interaction of a server, a terminal, and a user.

[0163] The server collects and analyzes training data and emotional information, and uses a generative AI model to identify relevant abstract skills. This model extracts specific patterns based on the input data and helps analyze the learner's learning progress and emotional state. Based on these analysis results, the server generates a learning plan tailored to each individual learner and sends a dynamically adjusted plan to the device according to their emotional state.

[0164] When a user begins learning, the device uses its built-in camera and microphone to sense the user's facial expressions and voice. This information is processed in real time by facial recognition software and voice analysis algorithms and acquired as the user's emotional information. The device displays an optimized learning plan sent from the server to the user and provides feedback tailored to their emotional state.

[0165] As users progress through their learning process via their devices, they can naturally reflect their emotional state in the system. This allows for adjustments tailored to their learning style and current mood.

[0166] For example, if a user feels fatigued while solving a math problem, the device will receive suggestions from the server for a relaxing, easy puzzle or a short break. In this process, the server uses prompts to analyze the user and generate appropriate feedback. An example of a prompt throughout this process is, "Based on the learner's mood and progress, suggest the most appropriate learning task to present next."

[0167] This system combines emotions and learning data to provide a personalized learning experience, maintaining learner motivation and improving learning efficiency.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user logs into the device to begin learning activities and enables the use of the camera and microphone devices. This prepares the device to sense the user's facial expressions and voice in real time.

[0171] Step 2:

[0172] The device acquires user facial data via its camera and audio data via its microphone. Using this data as input, facial recognition software performs image processing, and simultaneously, an audio analysis algorithm analyzes the audio data. As a result, the user's emotional state (e.g., joy, concentration, fatigue) is output.

[0173] Step 3:

[0174] The device sends the analyzed emotional information to the server. The server uses this information as input to analyze a generative AI model using prompt statements. An example of a prompt statement is, "How should learning progress be optimized based on the user's emotional state?" This process outputs abstract skills related to the user's emotional state.

[0175] Step 4:

[0176] The server generates a learning plan based on the outputted abstract skill information and emotional information. This includes incorporating tasks to strengthen specific skills based on the analysis results, as well as choices that correspond to emotional states. The plan is optimized by the server and output as the final learning plan.

[0177] Step 5:

[0178] The server sends the generated learning plan to the device. The device visualizes this learning plan and displays it to the user. The user can then proceed with their learning based on the displayed learning plan. The device continuously monitors the user's progress while obtaining feedback.

[0179] Step 6:

[0180] The server analyzes progress and new sentiment information in real time, dynamically adjusting the learning plan as needed. For example, if a user is highly engaged, it adds more complex tasks to maximize learning effectiveness. This iterative adjustment ensures that the user's learning experience is always optimized.

[0181] (Application Example 2)

[0182] 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".

[0183] In modern learning environments, uniform learning plans are often provided without considering the learner's emotional state, which can lead to decreased motivation and concentration. Therefore, there is a need to provide optimized learning plans that take into account the emotional state of each individual learner. To solve this problem, it is necessary to develop a system that can accurately detect a learner's emotional state and provide a dynamic learning plan based on that state.

[0184] 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.

[0185] In this invention, the server includes means for sensing the learner's emotional state, means for transmitting the learner's learning data and emotional information to the server, and means for analyzing the emotional data and learning data using a generative model to identify the relationship between emotions and skills. This makes it possible to provide an individually optimized learning plan that takes the learner's emotional state into consideration.

[0186] A "learner" refers to someone who uses this system to advance their learning.

[0187] "Emotional state" refers to the user's psychological and physiological state, derived from the user's facial expressions and voice, which are analyzed by machine learning algorithms.

[0188] "Learning data" refers to information about the learning materials and problems that learners are working on, as well as their progress.

[0189] A "server" refers to a device with central computing capabilities that receives data sent from learners, performs analysis, and generates learning plans.

[0190] A "generative model" refers to a sophisticated algorithm that identifies learners' emotional states and skill levels from input data and analyzes their relationships.

[0191] "Abstract skills" refer to fundamental knowledge and abilities that are useful across different fields of study.

[0192] "Visualization" means presenting analyzed data in a visual format, such as graphs or charts, to make it easier for learners to understand.

[0193] A "learning plan" refers to a set of learning materials and assignments designed to achieve specific learning objectives.

[0194] "Progress" refers to an indicator that shows how well learners are progressing in their studies according to their learning plan.

[0195] "Dynamic adjustment" means flexibly changing the learning plan in real time according to the learner's status and progress.

[0196] The system implementing this invention incorporates a program designed for emotion analysis and dynamic adjustment of the learning program. The system primarily consists of three main components: a terminal, a server, and a user.

[0197] The device includes hardware that captures the user's facial expressions with a camera and takes voice input via a microphone. This sensory data is immediately processed as a digital signal, and the user's emotional state is identified in real time by an emotion analysis algorithm. OpenCV is used for image processing, and a speech processing library is used for voice analysis.

[0198] The analyzed emotional information is transmitted to a server via the internet. The server integrates the received emotional data with training data and uses a generative AI model to analyze the user's current learning status. For example, it determines whether the user is tired or focused and generates corresponding skill-related information. Furthermore, based on the generated information, it dynamically creates a learning plan optimized for the user.

[0199] This plan is visualized on the device's display and presented to the user. The learning plan is designed to include challenging tasks and relaxation activities, depending on the user's current situation.

[0200] For example, if the server determines that a primary school student user is tired of studying, it will prioritize incorporating learning materials that include simple puzzles or short videos for relaxation into the planning. This makes it easier for the user to maintain their motivation.

[0201] An example of a prompt message would be, "If the user's facial expression indicates fatigue, suggest an optimal plan for evening study. A combination of relaxation activities and simple tasks would be preferable." This allows the system to provide a personalized learning experience for each learner.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device captures the user's face with a camera and collects facial expression data. This image data is used as input for the next step, where facial expression analysis is performed using OpenCV. As a result of the analysis, digital data indicating the user's emotional state is output.

[0205] Step 2:

[0206] Simultaneously, the device records the user's voice using a microphone. The obtained audio data is analyzed using a speech processing library to estimate the user's emotional state from their speech content and tone. This analysis result is integrated with facial expression analysis data to provide input for identifying the overall emotional state.

[0207] Step 3:

[0208] The device sends the collected emotion analysis data to the server. The server receives this data and performs analysis by combining it with training data using a generative AI model. This evaluates the relationship between the user's emotion information and learning status and outputs abstract skill information.

[0209] Step 4:

[0210] Based on the analysis results, the server dynamically generates a learning plan tailored to the user's emotional state. The generated learning plan includes challenging tasks and relaxation activities. This information is appropriately combined based on the learning objectives and output as an optimized list.

[0211] Step 5:

[0212] Finally, the generated learning plan is sent to the device and presented to the user visually. The user begins learning based on the plan and receives feedback as they progress. This feedback is processed in real time on the server, and the processing cycle is restarted as needed.

[0213] 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.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention is a system for optimizing learners' learning experiences. This system collects individual learning data from learners, identifies abstract skills using a generative model, and maps those skills across different learning domains.

[0230] First, the user engages in learning activities and inputs the activity data into the device. This includes learning time, tasks completed, number of correct answers, and self-assessment. The device then transmits this data to the server in real time.

[0231] The server analyzes the received data and identifies abstract skills common to different learning areas through a generative model. Based on the analysis results, the server visualizes the skills and sends them to the terminal. The terminal provides the user with the visualized skill mapping, making it easy for learners to understand which skills are used in which subjects.

[0232] Furthermore, the server generates an optimized learning plan based on the visualized information. This learning plan includes the tasks and learning activities necessary to enhance skills. For example, to strengthen logical thinking, it may include tackling mathematical proof problems as well as reading comprehension problems in Japanese.

[0233] This plan is sent to the user via their device and reflected in their daily learning activities. Learning progress is constantly monitored and fed back to the server. The server dynamically adjusts the learning plan based on the progress information, providing an efficient learning experience.

[0234] For example, if a learner quickly masters a specific concept in science, the server uses that information to quickly suggest the next steps and add challenging tasks to support continuous learning growth. This system provides a customizable learning environment tailored to the individual learner's needs, enabling efficient skill acquisition.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user begins a learning activity and inputs data such as learning time, answer status, and self-assessment into the device. This records specific learning outcomes and progress.

[0238] Step 2:

[0239] The device formats the collected training data and sends it to the server in real time. The data is then prepared in a format that can be used for statistical analysis and machine learning models.

[0240] Step 3:

[0241] The server feeds the received data into a generative model to identify common abstract skills across multiple learning domains. Here, the user's learning history and past patterns are also considered to extract skills such as problem-solving ability and logical thinking.

[0242] Step 4:

[0243] The server maps identified abstract skills across different learning areas and outputs the relationships between those skills to the terminal as visualized information. This visually shows the areas of focus and relationships in learning.

[0244] Step 5:

[0245] The device provides users with visualized information, facilitating understanding. This allows learners to intuitively grasp which skills they need to strengthen.

[0246] Step 6:

[0247] The server generates individually optimized learning plans based on the visualized information. Specifically, it combines necessary learning materials and assignments to create a schedule tailored to the user's learning style.

[0248] Step 7:

[0249] The device notifies the user of the generated learning plan and displays its contents. The user then proceeds with their daily learning according to this plan.

[0250] Step 8:

[0251] The server monitors progress information periodically sent from the terminal and dynamically adjusts the learning plan using a reinforcement learning algorithm. Depending on the level of understanding and speed of progress of the task, changes such as adding more challenging tasks are made.

[0252] Step 9:

[0253] The user receives a dynamically adjusted learning plan and continues learning towards new goals. This process continues in a loop, always providing the optimal learning experience.

[0254] (Example 1)

[0255] 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."

[0256] The current education system makes it difficult to provide learning plans tailored to the individual needs of learners and to appropriately connect and utilize skills acquired from different educational fields. It is necessary to dynamically adjust plans based on learners' progress and provide an efficient learning experience.

[0257] 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.

[0258] In this invention, the server includes means for collecting learner learning utilization information, means for identifying common abstract abilities across multiple educational fields using a generative AI model, and means for visualizing the identified abstract abilities and presenting them on a human-controlled device. This makes it possible to dynamically optimize educational plans based on the individual needs of learners and to associate the abilities acquired in each educational field.

[0259] "Learner" refers to an individual participating in educational activities, and the target of these activities is the individual's learning progress and ability improvement.

[0260] "Learning utilization information" refers to data accumulated by learners through educational activities, and includes specific elements such as time spent, assignment content, number of correct answers, and self-assessment.

[0261] A "generative AI model" refers to a computational system that uses artificial intelligence technology to perform patterns and inferences from data, generating new insights or information for specific purposes.

[0262] The term "educational field" refers to a specific category of academic discipline or ability, which can be divided into subjects such as mathematics, Japanese language, and science, or into areas of skills.

[0263] "Abstract comprehension" refers to a collection of generalized skills and knowledge applicable to multiple educational fields beyond specific content, including logical thinking and problem-solving abilities.

[0264] "Visualization" refers to the process of representing data and information in concrete forms such as graphs and charts to make them easier to understand.

[0265] A "human-controlled device" is a device that learners interact with or operate directly, and is a device that provides a user interface, such as a computer or smartphone.

[0266] "Dynamic adaptation" refers to the ability to instantly change plans and methods based on given circumstances and new information, with the goal of keeping the system in an optimal state at all times.

[0267] An "educational plan" is a guideline that systematically outlines the goals and activities that learners should achieve over a specific period, and is designed to promote effective learning.

[0268] This system provides a set of means for collecting and analyzing learner data and optimizing the learning process. First, the user engages in learning activities and inputs the resulting progress information and evaluations into a terminal. This terminal uses information processing devices such as computers and smartphones to record detailed information such as learning time, task completion rate, and number of correct answers.

[0269] The device transmits the collected data to the server in real time. The server has a generative AI model implemented, which analyzes the received data to identify abstract abilities across learning domains. This AI model uses the latest machine learning algorithms to extract learners' skills through pattern recognition and data mining.

[0270] Next, the server visualizes the identified abstract abilities. This visualization is displayed on the terminal as graphs and charts to allow learners to intuitively understand their own abilities. This display is interactive and designed to allow learners to check their progress in detail.

[0271] Furthermore, based on the visualized data, the server generates an optimal learning plan for each learner. The generated plan is customized according to the learner's characteristics and progress, aiming to maximize their individual abilities. For example, as a concrete example of strengthening logical thinking, it may suggest logical reading comprehension problems in Japanese in addition to math problems.

[0272] Throughout this entire process, the program provides feedback to learners via the user interface, which is then reflected in daily educational activities. Based on the learner's progress, the server dynamically adjusts the learning plan, delivering an efficient and personalized learning experience on the digital platform.

[0273] A concrete example of a prompt message would be, "Use the user-inputted learning data to identify common abstract skills and generate a visualized skill mapping." Through this prompt, the generating AI model begins analyzing the user data and provides appropriate educational support.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] After completing a learning activity, the user inputs information such as learning time, task content, number of correct answers, and self-assessment into the terminal. This input data is detailed information about individual learning behaviors and provides basic information necessary for future analysis. Specifically, this involves the user manually recording information in input fields on the interface.

[0277] Step 2:

[0278] The terminal transmits the input training data to the server in real time. By converting the input data into an appropriate format and transmitting it via a communication protocol, analysis on the server side becomes possible. Specifically, data packets are transferred over a secure channel using the network connection.

[0279] Step 3:

[0280] The server stores the received training data in a database and analyzes the data using a generative AI model. This analysis extracts patterns from learner activities and identifies abstract skills common to different educational fields. The input is learner activity data, and the output is a list of identified abstract skills. Statistical methods and machine learning algorithms are applied to the analysis.

[0281] Step 4:

[0282] The server visualizes the specified abstract skills. In visualization, a graph or dashboard showing the relationships of the abstract skills is generated, and the information is presented in an easy-to-understand form. The input is a list of the specified abstract skills, and the output is the visualized data. A data visualization tool is used for visualization.

[0283] Step 5:

[0284] The server generates an optimized education plan based on the visualized abstract skills. This plan is customized for the learner's skill enhancement and includes tasks and activities to be achieved. A generative AI model analyzes the visualization data and designs tasks based on specific educational goals.

[0285] Step 6:

[0286] The created education plan is presented to the user by the terminal. The user progresses with daily learning based on this plan. The plan includes specific instructions for skill enhancement and also provides criteria for self-managing the progress. This information is operable through an interactive interface on the terminal.

[0287] Step 7:

[0288] The server monitors the learner's continuation status and dynamically adjusts the education plan as needed. It analyzes the learning progress data and evaluates whether each task of the plan is being appropriately implemented. Based on the progress evaluation, it automatically makes changes to the content or adds new tasks. The input is the progress data, and the output is the latest education plan.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] Traditional learning systems have faced the challenge of failing to provide a learning experience that fully meets the individual needs of each learner. In particular, there is a need to integrate and utilize abstract skills developed in different learning domains to efficiently optimize learning. Furthermore, while personalized support for individual learners within the home is required, achieving this presents challenges in terms of effort and cost.

[0292] 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.

[0293] In this invention, the server includes means for collecting learner learning information, means for identifying common abstract skills across multiple learning domains using a generative model, and means for providing learning support and interactive feedback via consumer-grade machines. This enables the provision of an optimal learning plan tailored to the individual characteristics of each learner and efficient learning support at home.

[0294] "Learning information" refers to data generated by learners during their daily learning activities, and specifically includes learning time, tasks completed, number of correct answers, and self-assessment.

[0295] A "generative model" is an algorithm or statistical model that analyzes training data based on artificial intelligence technology and plays a role in identifying common skills across different learning domains.

[0296] "Abstract skills" refer to conceptual skills such as logical thinking and problem-solving abilities that are required in common across multiple learning fields, going beyond specific learning content.

[0297] "Visualization" is a technique that displays data in a graphical format, enabling users to understand information intuitively and easily.

[0298] "Consumer electronic devices" refer to electronic devices such as robots used in the home, which are used to provide learning support and interactive communication.

[0299] "Interactive feedback" is a feature that enables real-time, two-way communication with learners and provides immediate feedback.

[0300] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server processes the learner's learning information using a generative AI model and transmits visualized insights to the terminal. The terminal takes the form of a consumer device and provides interactive feedback to the user.

[0301] Specifically, users input information about their learning activities into their devices. This includes, for example, the content and time spent on daily studies, the tasks they worked on, and their results. The devices then transmit this data to the server in real time.

[0302] The server runs a generative AI model using TensorFlow or similar tools depending on the conditions, analyzes the collected training information, and identifies abstract skills in different learning domains. The identified skills are visualized graphically and resent to the terminal.

[0303] The device, specifically the home robot, provides necessary feedback through interaction with the user. The user then uses this feedback to improve their learning plan. For example, the robot might prompt the user with questions like, "What is the optimal time to study to memorize new words?"

[0304] This series of processes enables learners to advance their learning activities more efficiently and effectively.

[0305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0306] Step 1:

[0307] The user inputs learning information through the terminal. The input includes learning time, tasks completed, number of correct answers, self-evaluation, etc. By inputting this information, the system collects the basic data of the user's learning activities in real time.

[0308] Step 2:

[0309] The terminal sends the collected learning information to the server. The input data received by the server is numerical and text information regarding the user's learning progress and achievements. The server stores this as reference data for analysis in storage.

[0310] Step 3:

[0311] The server uses a generative AI model to identify abstract skills from the learning data. Based on the data input in this process, data operations are performed to extract the common skills required between different learning areas.

[0312] Step 4:

[0313] The server visualizes the identified abstract skills and outputs them as diagrams or charts. This visualized data serves as a material for providing insights useful for the user to understand the current situation.

[0314] Step 5:

[0315] The terminal receives the visualized data from the server and presents it to the user. The user gets help in selecting interactive and specific actions based on the information. This presentation step contributes to the user's decision-making process in real time.

[0316] Step 6:

[0317] The user adjusts and dynamically changes the learning plan via the terminal. New learning goals and schedules are input from the terminal, and when this data is sent to the server again, a cycle of steps is formed.

[0318] This series of steps enables users to spontaneously implement optimal learning plans based on their learning data, supporting efficient learning activities.

[0319] 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.

[0320] This invention is an optimization system that incorporates learner emotional information to make the learner's learning experience more effective. In addition to collecting learning data, visualizing skills, and dynamically adjusting the learning plan, this system includes an emotion engine that recognizes the user's emotions and reflects them in the learning plan.

[0321] When a user begins a learning activity, the device senses their facial expressions through voice input or the camera. This sensed data is then converted into emotional information through voice analysis and image processing algorithms. For example, it can detect whether the user is tired or focused.

[0322] Next, the device sends the user's emotional information to the server. The server combines this with training data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and skills. The resulting skill and emotional information is visualized and provided as feedback to the user via the device.

[0323] Furthermore, the server generates an optimized learning plan based on training data that includes emotional information. This learning plan incorporates not only tasks to enhance specific skills, but also options tailored to the user's current emotional state. For example, if the user is feeling stressed, it will include tasks that help them relax or activities that help them unwind.

[0324] This plan is displayed on the device, and the user proceeds with their learning based on it. The server monitors emotional information in real time as the user progresses and dynamically updates the learning plan. Adjustments are made in response to new challenges and emotional states to enhance learning effectiveness. For example, more challenging tasks are provided when the user is calm, maximizing their focused time.

[0325] In this way, by utilizing the emotion engine, a personalized learning environment that takes into account the learner's emotional state can be provided, enabling improved learning effectiveness and maintenance of motivation.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user starts a learning session, and the device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. This data is sent to the emotion engine.

[0329] Step 2:

[0330] The device activates an emotion engine and analyzes recorded facial expressions and voice data. Using facial expression recognition algorithms and voice emotion analysis, it identifies the user's emotional state (e.g., relaxed, focused, stressed).

[0331] Step 3:

[0332] The device sends the analyzed emotion data to the server. The server receives this data along with the user's learning data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and learning performance.

[0333] Step 4:

[0334] The server visualizes identified skills and emotional information and outputs it to the learner's device as a dashboard in a way that makes sense to them. This allows users to see the correlation between their learning style and their emotions.

[0335] Step 5:

[0336] The server generates a learning plan that takes emotional information into account. For example, if the user is fatigued, it will include easier tasks, and if they are relaxed, it will add more challenging tasks.

[0337] Step 6:

[0338] The device provides the user with a generated learning plan, displaying the assignment options and the order in which they should be completed. The user then proceeds with their learning activities according to this plan.

[0339] Step 7:

[0340] As learning progresses, the device monitors emotional data and learning progress in real time and reports it to the server. Based on this, the server determines whether the learning plan is appropriate and makes dynamic adjustments as needed.

[0341] Step 8:

[0342] The adjusted learning plan is again notified to the user via the device, and a new learning approach is presented. This ensures that a learning environment that is always considerate of the user's emotional state is maintained.

[0343] (Example 2)

[0344] 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".

[0345] Traditional learning systems often create learning plans based primarily on learner progress data, without considering learners' emotional states. This results in insufficient individual optimization. Furthermore, dynamic adjustments based on learners' states are difficult, making it challenging to maximize learning efficiency. Solving these problems will improve learner effectiveness and motivation.

[0346] 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.

[0347] In this invention, the server includes means for collecting learner learning data, means for identifying abstract skills using a generative model, and means for sensing and collecting learner emotional information. This makes it possible to accurately grasp the learner's emotional state and analyze it in conjunction with progress data. Based on this, an optimized learning plan can be proposed and the plan can be dynamically adjusted, thereby providing a learning experience tailored to each individual and enhancing the effectiveness of learning.

[0348] "Learning data" refers to all information related to the knowledge and skills acquired by learners, and includes evaluations and progress reports.

[0349] A "generative model" refers to a program or algorithm that automatically extracts specific patterns or abstract concepts based on input data.

[0350] "Abstract skills" refer to general abilities and knowledge that can be applied across multiple fields of study.

[0351] "Visualization" refers to the process of converting abstract information into a form that is easy for humans to understand and display.

[0352] "Emotional information" refers to data that indicates the learner's emotional state, and includes psychological and physiological information obtained from facial expressions and voice.

[0353] An "optimized learning plan" refers to a combination of learning paths and materials that are tailored to maximize learning effectiveness based on the learner's progress and emotional state.

[0354] "Dynamic adjustment" means changing plans and procedures in real time in response to changes in circumstances and conditions.

[0355] The following describes "modes for carrying out the invention."

[0356] This invention is a system for optimizing the learner's learning experience, and is primarily realized through the interaction of a server, a terminal, and a user.

[0357] The server collects and analyzes training data and emotional information, and uses a generative AI model to identify relevant abstract skills. This model extracts specific patterns based on the input data and helps analyze the learner's learning progress and emotional state. Based on these analysis results, the server generates a learning plan tailored to each individual learner and sends a dynamically adjusted plan to the device according to their emotional state.

[0358] When a user begins learning, the device uses its built-in camera and microphone to sense the user's facial expressions and voice. This information is processed in real time by facial recognition software and voice analysis algorithms and acquired as the user's emotional information. The device displays an optimized learning plan sent from the server to the user and provides feedback tailored to their emotional state.

[0359] As users progress through their learning process via their devices, they can naturally reflect their emotional state in the system. This allows for adjustments tailored to their learning style and current mood.

[0360] For example, if a user feels fatigued while solving a math problem, the device will receive suggestions from the server for a relaxing, easy puzzle or a short break. In this process, the server uses prompts to analyze the user and generate appropriate feedback. An example of a prompt throughout this process is, "Based on the learner's mood and progress, suggest the most appropriate learning task to present next."

[0361] This system combines emotions and learning data to provide a personalized learning experience, maintaining learner motivation and improving learning efficiency.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user logs into the device to begin learning activities and enables the use of the camera and microphone devices. This prepares the device to sense the user's facial expressions and voice in real time.

[0365] Step 2:

[0366] The device acquires user facial data via its camera and audio data via its microphone. Using this data as input, facial recognition software performs image processing, and simultaneously, an audio analysis algorithm analyzes the audio data. As a result, the user's emotional state (e.g., joy, concentration, fatigue) is output.

[0367] Step 3:

[0368] The device sends the analyzed emotional information to the server. The server uses this information as input to analyze a generative AI model using prompt statements. An example of a prompt statement is, "How should learning progress be optimized based on the user's emotional state?" This process outputs abstract skills related to the user's emotional state.

[0369] Step 4:

[0370] The server generates a learning plan based on the outputted abstract skill information and emotional information. This includes incorporating tasks to strengthen specific skills based on the analysis results, as well as choices that correspond to emotional states. The plan is optimized by the server and output as the final learning plan.

[0371] Step 5:

[0372] The server sends the generated learning plan to the device. The device visualizes this learning plan and displays it to the user. The user can then proceed with their learning based on the displayed learning plan. The device continuously monitors the user's progress while obtaining feedback.

[0373] Step 6:

[0374] The server analyzes progress and new sentiment information in real time, dynamically adjusting the learning plan as needed. For example, if a user is highly engaged, it adds more complex tasks to maximize learning effectiveness. This iterative adjustment ensures that the user's learning experience is always optimized.

[0375] (Application Example 2)

[0376] 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."

[0377] In modern learning environments, uniform learning plans are often provided without considering the learner's emotional state, which can lead to decreased motivation and concentration. Therefore, there is a need to provide optimized learning plans that take into account the emotional state of each individual learner. To solve this problem, it is necessary to develop a system that can accurately detect a learner's emotional state and provide a dynamic learning plan based on that state.

[0378] 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.

[0379] In this invention, the server includes means for sensing the learner's emotional state, means for transmitting the learner's learning data and emotional information to the server, and means for analyzing the emotional data and learning data using a generative model to identify the relationship between emotions and skills. This makes it possible to provide an individually optimized learning plan that takes the learner's emotional state into consideration.

[0380] A "learner" refers to someone who uses this system to advance their learning.

[0381] "Emotional state" refers to the user's psychological and physiological state, derived from the user's facial expressions and voice, which are analyzed by machine learning algorithms.

[0382] "Learning data" refers to information about the learning materials and problems that learners are working on, as well as their progress.

[0383] A "server" refers to a device with central computing capabilities that receives data sent from learners, performs analysis, and generates learning plans.

[0384] A "generative model" refers to a sophisticated algorithm that identifies learners' emotional states and skill levels from input data and analyzes their relationships.

[0385] "Abstract skills" refer to fundamental knowledge and abilities that are useful across different fields of study.

[0386] "Visualization" means presenting analyzed data in a visual format, such as graphs or charts, to make it easier for learners to understand.

[0387] A "learning plan" refers to a set of learning materials and assignments designed to achieve specific learning objectives.

[0388] "Progress" refers to an indicator that shows how well learners are progressing in their studies according to their learning plan.

[0389] "Dynamic adjustment" means flexibly changing the learning plan in real time according to the learner's status and progress.

[0390] The system implementing this invention incorporates a program designed for emotion analysis and dynamic adjustment of the learning program. The system primarily consists of three main components: a terminal, a server, and a user.

[0391] The device includes hardware that captures the user's facial expressions with a camera and takes voice input via a microphone. This sensory data is immediately processed as a digital signal, and the user's emotional state is identified in real time by an emotion analysis algorithm. OpenCV is used for image processing, and a speech processing library is used for voice analysis.

[0392] The analyzed emotional information is transmitted to a server via the internet. The server integrates the received emotional data with training data and uses a generative AI model to analyze the user's current learning status. For example, it determines whether the user is tired or focused and generates corresponding skill-related information. Furthermore, based on the generated information, it dynamically creates a learning plan optimized for the user.

[0393] This plan is visualized on the device's display and presented to the user. The learning plan is designed to include challenging tasks and relaxation activities, depending on the user's current situation.

[0394] For example, if the server determines that a primary school student user is tired of studying, it will prioritize incorporating learning materials that include simple puzzles or short videos for relaxation into the planning. This makes it easier for the user to maintain their motivation.

[0395] An example of a prompt message would be, "If the user's facial expression indicates fatigue, suggest an optimal plan for evening study. A combination of relaxation activities and simple tasks would be preferable." This allows the system to provide a personalized learning experience for each learner.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The device captures the user's face with a camera and collects facial expression data. This image data is used as input for the next step, where facial expression analysis is performed using OpenCV. As a result of the analysis, digital data indicating the user's emotional state is output.

[0399] Step 2:

[0400] Simultaneously, the device records the user's voice using a microphone. The obtained audio data is analyzed using a speech processing library to estimate the user's emotional state from their speech content and tone. This analysis result is integrated with facial expression analysis data to provide input for identifying the overall emotional state.

[0401] Step 3:

[0402] The device sends the collected emotion analysis data to the server. The server receives this data and performs analysis by combining it with training data using a generative AI model. This evaluates the relationship between the user's emotion information and learning status and outputs abstract skill information.

[0403] Step 4:

[0404] Based on the analysis results, the server dynamically generates a learning plan tailored to the user's emotional state. The generated learning plan includes challenging tasks and relaxation activities. This information is appropriately combined based on the learning objectives and output as an optimized list.

[0405] Step 5:

[0406] Finally, the generated learning plan is sent to the device and presented to the user visually. The user begins learning based on the plan and receives feedback as they progress. This feedback is processed in real time on the server, and the processing cycle is restarted as needed.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention is a system for optimizing learners' learning experiences. This system collects individual learning data from learners, identifies abstract skills using a generative model, and maps those skills across different learning domains.

[0424] First, the user engages in learning activities and inputs the activity data into the device. This includes learning time, tasks completed, number of correct answers, and self-assessment. The device then transmits this data to the server in real time.

[0425] The server analyzes the received data and identifies abstract skills common to different learning areas through a generative model. Based on the analysis results, the server visualizes the skills and sends them to the terminal. The terminal provides the user with the visualized skill mapping, making it easy for learners to understand which skills are used in which subjects.

[0426] Furthermore, the server generates an optimized learning plan based on the visualized information. This learning plan includes the tasks and learning activities necessary to enhance skills. For example, to strengthen logical thinking, it may include tackling mathematical proof problems as well as reading comprehension problems in Japanese.

[0427] This plan is sent to the user via their device and reflected in their daily learning activities. Learning progress is constantly monitored and fed back to the server. The server dynamically adjusts the learning plan based on the progress information, providing an efficient learning experience.

[0428] For example, if a learner quickly masters a specific concept in science, the server uses that information to quickly suggest the next steps and add challenging tasks to support continuous learning growth. This system provides a customizable learning environment tailored to the individual learner's needs, enabling efficient skill acquisition.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The user begins a learning activity and inputs data such as learning time, answer status, and self-assessment into the device. This records specific learning outcomes and progress.

[0432] Step 2:

[0433] The device formats the collected training data and sends it to the server in real time. The data is then prepared in a format that can be used for statistical analysis and machine learning models.

[0434] Step 3:

[0435] The server feeds the received data into a generative model to identify common abstract skills across multiple learning domains. Here, the user's learning history and past patterns are also considered to extract skills such as problem-solving ability and logical thinking.

[0436] Step 4:

[0437] The server maps identified abstract skills across different learning areas and outputs the relationships between those skills to the terminal as visualized information. This visually shows the areas of focus and relationships in learning.

[0438] Step 5:

[0439] The device provides users with visualized information, facilitating understanding. This allows learners to intuitively grasp which skills they need to strengthen.

[0440] Step 6:

[0441] The server generates individually optimized learning plans based on the visualized information. Specifically, it combines necessary learning materials and assignments to create a schedule tailored to the user's learning style.

[0442] Step 7:

[0443] The device notifies the user of the generated learning plan and displays its contents. The user then proceeds with their daily learning according to this plan.

[0444] Step 8:

[0445] The server monitors progress information periodically sent from the terminal and dynamically adjusts the learning plan using a reinforcement learning algorithm. Depending on the level of understanding and speed of progress of the task, changes such as adding more challenging tasks are made.

[0446] Step 9:

[0447] The user receives a dynamically adjusted learning plan and continues learning towards new goals. This process continues in a loop, always providing the optimal learning experience.

[0448] (Example 1)

[0449] 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."

[0450] The current education system makes it difficult to provide learning plans tailored to the individual needs of learners and to appropriately connect and utilize skills acquired from different educational fields. It is necessary to dynamically adjust plans based on learners' progress and provide an efficient learning experience.

[0451] 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.

[0452] In this invention, the server includes means for collecting learner learning utilization information, means for identifying common abstract abilities across multiple educational fields using a generative AI model, and means for visualizing the identified abstract abilities and presenting them on a human-controlled device. This makes it possible to dynamically optimize educational plans based on the individual needs of learners and to associate the abilities acquired in each educational field.

[0453] "Learner" refers to an individual participating in educational activities, and the target of these activities is the individual's learning progress and ability improvement.

[0454] "Learning utilization information" refers to data accumulated by learners through educational activities, and includes specific elements such as time spent, assignment content, number of correct answers, and self-assessment.

[0455] A "generative AI model" refers to a computational system that uses artificial intelligence technology to perform patterns and inferences from data, generating new insights or information for specific purposes.

[0456] The term "educational field" refers to a specific category of academic discipline or ability, which can be divided into subjects such as mathematics, Japanese language, and science, or into areas of skills.

[0457] "Abstract comprehension" refers to a collection of generalized skills and knowledge applicable to multiple educational fields beyond specific content, including logical thinking and problem-solving abilities.

[0458] "Visualization" refers to the process of representing data and information in concrete forms such as graphs and charts to make them easier to understand.

[0459] A "human-controlled device" is a device that learners interact with or operate directly, and is a device that provides a user interface, such as a computer or smartphone.

[0460] "Dynamic adaptation" refers to the ability to instantly change plans and methods based on given circumstances and new information, with the goal of keeping the system in an optimal state at all times.

[0461] An "educational plan" is a guideline that systematically outlines the goals and activities that learners should achieve over a specific period, and is designed to promote effective learning.

[0462] This system provides a set of means for collecting and analyzing learner data and optimizing the learning process. First, the user engages in learning activities and inputs the resulting progress information and evaluations into a terminal. This terminal uses information processing devices such as computers and smartphones to record detailed information such as learning time, task completion rate, and number of correct answers.

[0463] The device transmits the collected data to the server in real time. The server has a generative AI model implemented, which analyzes the received data to identify abstract abilities across learning domains. This AI model uses the latest machine learning algorithms to extract learners' skills through pattern recognition and data mining.

[0464] Next, the server visualizes the identified abstract abilities. This visualization is displayed on the terminal as graphs and charts to allow learners to intuitively understand their own abilities. This display is interactive and designed to allow learners to check their progress in detail.

[0465] Furthermore, based on the visualized data, the server generates an optimal learning plan for each learner. The generated plan is customized according to the learner's characteristics and progress, aiming to maximize their individual abilities. For example, as a concrete example of strengthening logical thinking, it may suggest logical reading comprehension problems in Japanese in addition to math problems.

[0466] Throughout this entire process, the program provides feedback to learners via the user interface, which is then reflected in daily educational activities. Based on the learner's progress, the server dynamically adjusts the learning plan, delivering an efficient and personalized learning experience on the digital platform.

[0467] A concrete example of a prompt message would be, "Use the user-inputted learning data to identify common abstract skills and generate a visualized skill mapping." Through this prompt, the generating AI model begins analyzing the user data and provides appropriate educational support.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] After completing a learning activity, the user inputs information such as learning time, task content, number of correct answers, and self-assessment into the terminal. This input data is detailed information about individual learning behaviors and provides basic information necessary for future analysis. Specifically, this involves the user manually recording information in input fields on the interface.

[0471] Step 2:

[0472] The terminal transmits the input training data to the server in real time. By converting the input data into an appropriate format and transmitting it via a communication protocol, analysis on the server side becomes possible. Specifically, data packets are transferred over a secure channel using the network connection.

[0473] Step 3:

[0474] The server stores the received training data in a database and analyzes the data using a generative AI model. This analysis extracts patterns from learner activities and identifies abstract skills common to different educational fields. The input is learner activity data, and the output is a list of identified abstract skills. Statistical methods and machine learning algorithms are applied to the analysis.

[0475] Step 4:

[0476] The server visualizes the identified abstract skills. This visualization generates graphs and dashboards showing the relationships between abstract skills, presenting the information in an easily understandable format. The input is a list of identified abstract skills, and the output is visualized data. Data visualization tools are used for this visualization.

[0477] Step 5:

[0478] The server generates an optimized educational plan based on visualized abstract skills. This plan is customized to enhance learners' skills and includes tasks and activities to be accomplished. A generative AI model analyzes the visualized data and designs tasks based on specific educational objectives.

[0479] Step 6:

[0480] The device presents the user with a created educational plan. The user then proceeds with their daily learning based on this plan. The plan includes specific instructions for skill enhancement and provides criteria for self-managing progress. This information is accessible through an interactive interface on the device.

[0481] Step 7:

[0482] The server monitors learners' progress and dynamically adjusts the lesson plan as needed. It analyzes learning progress data and evaluates whether each task in the plan is being properly completed. Based on the progress evaluation, it automatically modifies the content or adds new tasks. The input is progress data, and the output is the latest lesson plan.

[0483] (Application Example 1)

[0484] 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."

[0485] Traditional learning systems have faced the challenge of failing to provide a learning experience that fully meets the individual needs of each learner. In particular, there is a need to integrate and utilize abstract skills developed in different learning domains to efficiently optimize learning. Furthermore, while personalized support for individual learners within the home is required, achieving this presents challenges in terms of effort and cost.

[0486] 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.

[0487] In this invention, the server includes means for collecting learner learning information, means for identifying common abstract skills across multiple learning domains using a generative model, and means for providing learning support and interactive feedback via consumer-grade machines. This enables the provision of an optimal learning plan tailored to the individual characteristics of each learner and efficient learning support at home.

[0488] "Learning information" refers to data generated by learners during their daily learning activities, and specifically includes learning time, tasks completed, number of correct answers, and self-assessment.

[0489] A "generative model" is an algorithm or statistical model that analyzes training data based on artificial intelligence technology and plays a role in identifying common skills across different learning domains.

[0490] "Abstract skills" refer to conceptual skills such as logical thinking and problem-solving abilities that are required in common across multiple learning fields, going beyond specific learning content.

[0491] "Visualization" is a technique that displays data in a graphical format, enabling users to understand information intuitively and easily.

[0492] "Consumer electronic devices" refer to electronic devices such as robots used in the home, which are used to provide learning support and interactive communication.

[0493] "Interactive feedback" is a feature that enables real-time, two-way communication with learners and provides immediate feedback.

[0494] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server processes the learner's learning information using a generative AI model and transmits visualized insights to the terminal. The terminal takes the form of a consumer device and provides interactive feedback to the user.

[0495] Specifically, users input information about their learning activities into their devices. This includes, for example, the content and time spent on daily studies, the tasks they worked on, and their results. The devices then transmit this data to the server in real time.

[0496] The server runs a generative AI model using TensorFlow or similar tools depending on the conditions, analyzes the collected training information, and identifies abstract skills in different learning domains. The identified skills are visualized graphically and resent to the terminal.

[0497] The device, specifically the home robot, provides necessary feedback through interaction with the user. The user then uses this feedback to improve their learning plan. For example, the robot might prompt the user with questions like, "What is the optimal time to study to memorize new words?"

[0498] This series of processes enables learners to advance their learning activities more efficiently and effectively.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] Users input learning information through their devices. This input includes study time, completed tasks, number of correct answers, and self-assessment. As this information is entered, the system collects basic data on the user's learning activities in real time.

[0502] Step 2:

[0503] The terminal sends the collected learning information to the server. The input data received by the server consists of numerical and textual information regarding the user's learning progress and results. The server stores this data in storage as reference data for analysis.

[0504] Step 3:

[0505] The server uses a generative AI model to identify abstract skills from the training data. Based on the input data in this process, data calculations are performed to extract common skills required across different learning domains.

[0506] Step 4:

[0507] The server visualizes identified abstract skills and outputs them as diagrams and charts. This visualized data serves as a resource to provide users with insights that help them understand the current situation.

[0508] Step 5:

[0509] The terminal receives visualized data from the server and presents it to the user. The user then receives assistance in selecting interactive and specific actions based on the information. This presentation step contributes to the user's decision-making process in real time.

[0510] Step 6:

[0511] Users adjust and dynamically change their learning plans via their devices. New learning goals and schedules are entered from the device, and this data is sent back to the server, creating a cycle of steps.

[0512] This series of steps enables users to spontaneously implement optimal learning plans based on their learning data, supporting efficient learning activities.

[0513] 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.

[0514] This invention is an optimization system that incorporates learner emotional information to make the learner's learning experience more effective. In addition to collecting learning data, visualizing skills, and dynamically adjusting the learning plan, this system includes an emotion engine that recognizes the user's emotions and reflects them in the learning plan.

[0515] When a user begins a learning activity, the device senses their facial expressions through voice input or the camera. This sensed data is then converted into emotional information through voice analysis and image processing algorithms. For example, it can detect whether the user is tired or focused.

[0516] Next, the device sends the user's emotional information to the server. The server combines this with training data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and skills. The resulting skill and emotional information is visualized and provided as feedback to the user via the device.

[0517] Furthermore, the server generates an optimized learning plan based on training data that includes emotional information. This learning plan incorporates not only tasks to enhance specific skills, but also options tailored to the user's current emotional state. For example, if the user is feeling stressed, it will include tasks that help them relax or activities that help them unwind.

[0518] This plan is displayed on the device, and the user proceeds with their learning based on it. The server monitors emotional information in real time as the user progresses and dynamically updates the learning plan. Adjustments are made in response to new challenges and emotional states to enhance learning effectiveness. For example, more challenging tasks are provided when the user is calm, maximizing their focused time.

[0519] In this way, by utilizing the emotion engine, a personalized learning environment that takes into account the learner's emotional state can be provided, enabling improved learning effectiveness and maintenance of motivation.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The user starts a learning session, and the device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. This data is sent to the emotion engine.

[0523] Step 2:

[0524] The device activates an emotion engine and analyzes recorded facial expressions and voice data. Using facial expression recognition algorithms and voice emotion analysis, it identifies the user's emotional state (e.g., relaxed, focused, stressed).

[0525] Step 3:

[0526] The device sends the analyzed emotion data to the server. The server receives this data along with the user's learning data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and learning performance.

[0527] Step 4:

[0528] The server visualizes identified skills and emotional information and outputs it to the learner's device as a dashboard in a way that makes sense to them. This allows users to see the correlation between their learning style and their emotions.

[0529] Step 5:

[0530] The server generates a learning plan that takes emotional information into account. For example, if the user is fatigued, it will include easier tasks, and if they are relaxed, it will add more challenging tasks.

[0531] Step 6:

[0532] The device provides the user with a generated learning plan, displaying the assignment options and the order in which they should be completed. The user then proceeds with their learning activities according to this plan.

[0533] Step 7:

[0534] As learning progresses, the device monitors emotional data and learning progress in real time and reports it to the server. Based on this, the server determines whether the learning plan is appropriate and makes dynamic adjustments as needed.

[0535] Step 8:

[0536] The adjusted learning plan is again notified to the user via the device, and a new learning approach is presented. This ensures that a learning environment that is always considerate of the user's emotional state is maintained.

[0537] (Example 2)

[0538] 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."

[0539] Traditional learning systems often create learning plans based primarily on learner progress data, without considering learners' emotional states. This results in insufficient individual optimization. Furthermore, dynamic adjustments based on learners' states are difficult, making it challenging to maximize learning efficiency. Solving these problems will improve learner effectiveness and motivation.

[0540] 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.

[0541] In this invention, the server includes means for collecting learner learning data, means for identifying abstract skills using a generative model, and means for sensing and collecting learner emotional information. This makes it possible to accurately grasp the learner's emotional state and analyze it in conjunction with progress data. Based on this, an optimized learning plan can be proposed and the plan can be dynamically adjusted, thereby providing a learning experience tailored to each individual and enhancing the effectiveness of learning.

[0542] "Learning data" refers to all information related to the knowledge and skills acquired by learners, and includes evaluations and progress reports.

[0543] A "generative model" refers to a program or algorithm that automatically extracts specific patterns or abstract concepts based on input data.

[0544] "Abstract skills" refer to general abilities and knowledge that can be applied across multiple fields of study.

[0545] "Visualization" refers to the process of converting abstract information into a form that is easy for humans to understand and display.

[0546] "Emotional information" refers to data that indicates the learner's emotional state, and includes psychological and physiological information obtained from facial expressions and voice.

[0547] An "optimized learning plan" refers to a combination of learning paths and materials that are tailored to maximize learning effectiveness based on the learner's progress and emotional state.

[0548] "Dynamic adjustment" means changing plans and procedures in real time in response to changes in circumstances and conditions.

[0549] The following describes "modes for carrying out the invention."

[0550] This invention is a system for optimizing the learner's learning experience, and is primarily realized through the interaction of a server, a terminal, and a user.

[0551] The server collects and analyzes training data and emotional information, and uses a generative AI model to identify relevant abstract skills. This model extracts specific patterns based on the input data and helps analyze the learner's learning progress and emotional state. Based on these analysis results, the server generates a learning plan tailored to each individual learner and sends a dynamically adjusted plan to the device according to their emotional state.

[0552] When a user begins learning, the device uses its built-in camera and microphone to sense the user's facial expressions and voice. This information is processed in real time by facial recognition software and voice analysis algorithms and acquired as the user's emotional information. The device displays an optimized learning plan sent from the server to the user and provides feedback tailored to their emotional state.

[0553] As users progress through their learning process via their devices, they can naturally reflect their emotional state in the system. This allows for adjustments tailored to their learning style and current mood.

[0554] For example, if a user feels fatigued while solving a math problem, the device will receive suggestions from the server for a relaxing, easy puzzle or a short break. In this process, the server uses prompts to analyze the user and generate appropriate feedback. An example of a prompt throughout this process is, "Based on the learner's mood and progress, suggest the most appropriate learning task to present next."

[0555] This system combines emotions and learning data to provide a personalized learning experience, maintaining learner motivation and improving learning efficiency.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The user logs into the device to begin learning activities and enables the use of the camera and microphone devices. This prepares the device to sense the user's facial expressions and voice in real time.

[0559] Step 2:

[0560] The device acquires user facial data via its camera and audio data via its microphone. Using this data as input, facial recognition software performs image processing, and simultaneously, an audio analysis algorithm analyzes the audio data. As a result, the user's emotional state (e.g., joy, concentration, fatigue) is output.

[0561] Step 3:

[0562] The device sends the analyzed emotional information to the server. The server uses this information as input to analyze a generative AI model using prompt statements. An example of a prompt statement is, "How should learning progress be optimized based on the user's emotional state?" This process outputs abstract skills related to the user's emotional state.

[0563] Step 4:

[0564] The server generates a learning plan based on the outputted abstract skill information and emotional information. This includes incorporating tasks to strengthen specific skills based on the analysis results, as well as choices that correspond to emotional states. The plan is optimized by the server and output as the final learning plan.

[0565] Step 5:

[0566] The server sends the generated learning plan to the device. The device visualizes this learning plan and displays it to the user. The user can then proceed with their learning based on the displayed learning plan. The device continuously monitors the user's progress while obtaining feedback.

[0567] Step 6:

[0568] The server analyzes progress and new sentiment information in real time, dynamically adjusting the learning plan as needed. For example, if a user is highly engaged, it adds more complex tasks to maximize learning effectiveness. This iterative adjustment ensures that the user's learning experience is always optimized.

[0569] (Application Example 2)

[0570] 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."

[0571] In modern learning environments, uniform learning plans are often provided without considering the learner's emotional state, which can lead to decreased motivation and concentration. Therefore, there is a need to provide optimized learning plans that take into account the emotional state of each individual learner. To solve this problem, it is necessary to develop a system that can accurately detect a learner's emotional state and provide a dynamic learning plan based on that state.

[0572] 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.

[0573] In this invention, the server includes means for sensing the learner's emotional state, means for transmitting the learner's learning data and emotional information to the server, and means for analyzing the emotional data and learning data using a generative model to identify the relationship between emotions and skills. This makes it possible to provide an individually optimized learning plan that takes the learner's emotional state into consideration.

[0574] A "learner" refers to someone who uses this system to advance their learning.

[0575] "Emotional state" refers to the user's psychological and physiological state, derived from the user's facial expressions and voice, which are analyzed by machine learning algorithms.

[0576] "Learning data" refers to information about the learning materials and problems that learners are working on, as well as their progress.

[0577] A "server" refers to a device with central computing capabilities that receives data sent from learners, performs analysis, and generates learning plans.

[0578] A "generative model" refers to a sophisticated algorithm that identifies learners' emotional states and skill levels from input data and analyzes their relationships.

[0579] "Abstract skills" refer to fundamental knowledge and abilities that are useful across different fields of study.

[0580] "Visualization" means presenting analyzed data in a visual format, such as graphs or charts, to make it easier for learners to understand.

[0581] A "learning plan" refers to a set of learning materials and assignments designed to achieve specific learning objectives.

[0582] "Progress" refers to an indicator that shows how well learners are progressing in their studies according to their learning plan.

[0583] "Dynamic adjustment" means flexibly changing the learning plan in real time according to the learner's status and progress.

[0584] The system implementing this invention incorporates a program designed for emotion analysis and dynamic adjustment of the learning program. The system primarily consists of three main components: a terminal, a server, and a user.

[0585] The device includes hardware that captures the user's facial expressions with a camera and takes voice input via a microphone. This sensory data is immediately processed as a digital signal, and the user's emotional state is identified in real time by an emotion analysis algorithm. OpenCV is used for image processing, and a speech processing library is used for voice analysis.

[0586] The analyzed emotional information is transmitted to a server via the internet. The server integrates the received emotional data with training data and uses a generative AI model to analyze the user's current learning status. For example, it determines whether the user is tired or focused and generates corresponding skill-related information. Furthermore, based on the generated information, it dynamically creates a learning plan optimized for the user.

[0587] This plan is visualized on the device's display and presented to the user. The learning plan is designed to include challenging tasks and relaxation activities, depending on the user's current situation.

[0588] For example, if the server determines that a primary school student user is tired of studying, it will prioritize incorporating learning materials that include simple puzzles or short videos for relaxation into the planning. This makes it easier for the user to maintain their motivation.

[0589] An example of a prompt message would be, "If the user's facial expression indicates fatigue, suggest an optimal plan for evening study. A combination of relaxation activities and simple tasks would be preferable." This allows the system to provide a personalized learning experience for each learner.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The device captures the user's face with a camera and collects facial expression data. This image data is used as input for the next step, where facial expression analysis is performed using OpenCV. As a result of the analysis, digital data indicating the user's emotional state is output.

[0593] Step 2:

[0594] Simultaneously, the device records the user's voice using a microphone. The obtained audio data is analyzed using a speech processing library to estimate the user's emotional state from their speech content and tone. This analysis result is integrated with facial expression analysis data to provide input for identifying the overall emotional state.

[0595] Step 3:

[0596] The device sends the collected emotion analysis data to the server. The server receives this data and performs analysis by combining it with training data using a generative AI model. This evaluates the relationship between the user's emotion information and learning status and outputs abstract skill information.

[0597] Step 4:

[0598] Based on the analysis results, the server dynamically generates a learning plan tailored to the user's emotional state. The generated learning plan includes challenging tasks and relaxation activities. This information is appropriately combined based on the learning objectives and output as an optimized list.

[0599] Step 5:

[0600] Finally, the generated learning plan is sent to the device and presented to the user visually. The user begins learning based on the plan and receives feedback as they progress. This feedback is processed in real time on the server, and the processing cycle is restarted as needed.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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".

[0618] This invention is a system for optimizing learners' learning experiences. This system collects individual learning data from learners, identifies abstract skills using a generative model, and maps those skills across different learning domains.

[0619] First, the user engages in learning activities and inputs the activity data into the device. This includes learning time, tasks completed, number of correct answers, and self-assessment. The device then transmits this data to the server in real time.

[0620] The server analyzes the received data and identifies abstract skills common to different learning areas through a generative model. Based on the analysis results, the server visualizes the skills and sends them to the terminal. The terminal provides the user with the visualized skill mapping, making it easy for learners to understand which skills are used in which subjects.

[0621] Furthermore, the server generates an optimized learning plan based on the visualized information. This learning plan includes the tasks and learning activities necessary to enhance skills. For example, to strengthen logical thinking, it may include tackling mathematical proof problems as well as reading comprehension problems in Japanese.

[0622] This plan is sent to the user via their device and reflected in their daily learning activities. Learning progress is constantly monitored and fed back to the server. The server dynamically adjusts the learning plan based on the progress information, providing an efficient learning experience.

[0623] For example, if a learner quickly masters a specific concept in science, the server uses that information to quickly suggest the next steps and add challenging tasks to support continuous learning growth. This system provides a customizable learning environment tailored to the individual learner's needs, enabling efficient skill acquisition.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The user begins a learning activity and inputs data such as learning time, answer status, and self-assessment into the device. This records specific learning outcomes and progress.

[0627] Step 2:

[0628] The device formats the collected training data and sends it to the server in real time. The data is then prepared in a format that can be used for statistical analysis and machine learning models.

[0629] Step 3:

[0630] The server feeds the received data into a generative model to identify common abstract skills across multiple learning domains. Here, the user's learning history and past patterns are also considered to extract skills such as problem-solving ability and logical thinking.

[0631] Step 4:

[0632] The server maps identified abstract skills across different learning areas and outputs the relationships between those skills to the terminal as visualized information. This visually shows the areas of focus and relationships in learning.

[0633] Step 5:

[0634] The device provides users with visualized information, facilitating understanding. This allows learners to intuitively grasp which skills they need to strengthen.

[0635] Step 6:

[0636] The server generates individually optimized learning plans based on the visualized information. Specifically, it combines necessary learning materials and assignments to create a schedule tailored to the user's learning style.

[0637] Step 7:

[0638] The device notifies the user of the generated learning plan and displays its contents. The user then proceeds with their daily learning according to this plan.

[0639] Step 8:

[0640] The server monitors progress information periodically sent from the terminal and dynamically adjusts the learning plan using a reinforcement learning algorithm. Depending on the level of understanding and speed of progress of the task, changes such as adding more challenging tasks are made.

[0641] Step 9:

[0642] The user receives a dynamically adjusted learning plan and continues learning towards new goals. This process continues in a loop, always providing the optimal learning experience.

[0643] (Example 1)

[0644] 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".

[0645] The current education system makes it difficult to provide learning plans tailored to the individual needs of learners and to appropriately connect and utilize skills acquired from different educational fields. It is necessary to dynamically adjust plans based on learners' progress and provide an efficient learning experience.

[0646] 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.

[0647] In this invention, the server includes means for collecting learner learning utilization information, means for identifying common abstract abilities across multiple educational fields using a generative AI model, and means for visualizing the identified abstract abilities and presenting them on a human-controlled device. This makes it possible to dynamically optimize educational plans based on the individual needs of learners and to associate the abilities acquired in each educational field.

[0648] "Learner" refers to an individual participating in educational activities, and the target of these activities is the individual's learning progress and ability improvement.

[0649] "Learning utilization information" refers to data accumulated by learners through educational activities, and includes specific elements such as time spent, assignment content, number of correct answers, and self-assessment.

[0650] A "generative AI model" refers to a computational system that uses artificial intelligence technology to perform patterns and inferences from data, generating new insights or information for specific purposes.

[0651] The term "educational field" refers to a specific category of academic discipline or ability, which can be divided into subjects such as mathematics, Japanese language, and science, or into areas of skills.

[0652] "Abstract comprehension" refers to a collection of generalized skills and knowledge applicable to multiple educational fields beyond specific content, including logical thinking and problem-solving abilities.

[0653] "Visualization" refers to the process of representing data and information in concrete forms such as graphs and charts to make them easier to understand.

[0654] A "human-controlled device" is a device that learners interact with or operate directly, and is a device that provides a user interface, such as a computer or smartphone.

[0655] "Dynamic adaptation" refers to the ability to instantly change plans and methods based on given circumstances and new information, with the goal of keeping the system in an optimal state at all times.

[0656] An "educational plan" is a guideline that systematically outlines the goals and activities that learners should achieve over a specific period, and is designed to promote effective learning.

[0657] This system provides a set of means for collecting and analyzing learner data and optimizing the learning process. First, the user engages in learning activities and inputs the resulting progress information and evaluations into a terminal. This terminal uses information processing devices such as computers and smartphones to record detailed information such as learning time, task completion rate, and number of correct answers.

[0658] The device transmits the collected data to the server in real time. The server has a generative AI model implemented, which analyzes the received data to identify abstract abilities across learning domains. This AI model uses the latest machine learning algorithms to extract learners' skills through pattern recognition and data mining.

[0659] Next, the server visualizes the identified abstract abilities. This visualization is displayed on the terminal as graphs and charts to allow learners to intuitively understand their own abilities. This display is interactive and designed to allow learners to check their progress in detail.

[0660] Furthermore, based on the visualized data, the server generates an optimal learning plan for each learner. The generated plan is customized according to the learner's characteristics and progress, aiming to maximize their individual abilities. For example, as a concrete example of strengthening logical thinking, it may suggest logical reading comprehension problems in Japanese in addition to math problems.

[0661] Throughout this entire process, the program provides feedback to learners via the user interface, which is then reflected in daily educational activities. Based on the learner's progress, the server dynamically adjusts the learning plan, delivering an efficient and personalized learning experience on the digital platform.

[0662] A concrete example of a prompt message would be, "Use the user-inputted learning data to identify common abstract skills and generate a visualized skill mapping." Through this prompt, the generating AI model begins analyzing the user data and provides appropriate educational support.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] After completing a learning activity, the user inputs information such as learning time, task content, number of correct answers, and self-assessment into the terminal. This input data is detailed information about individual learning behaviors and provides basic information necessary for future analysis. Specifically, this involves the user manually recording information in input fields on the interface.

[0666] Step 2:

[0667] The terminal transmits the input training data to the server in real time. By converting the input data into an appropriate format and transmitting it via a communication protocol, analysis on the server side becomes possible. Specifically, data packets are transferred over a secure channel using the network connection.

[0668] Step 3:

[0669] The server stores the received training data in a database and analyzes the data using a generative AI model. This analysis extracts patterns from learner activities and identifies abstract skills common to different educational fields. The input is learner activity data, and the output is a list of identified abstract skills. Statistical methods and machine learning algorithms are applied to the analysis.

[0670] Step 4:

[0671] The server visualizes the identified abstract skills. This visualization generates graphs and dashboards showing the relationships between abstract skills, presenting the information in an easily understandable format. The input is a list of identified abstract skills, and the output is visualized data. Data visualization tools are used for this visualization.

[0672] Step 5:

[0673] The server generates an optimized educational plan based on visualized abstract skills. This plan is customized to enhance learners' skills and includes tasks and activities to be accomplished. A generative AI model analyzes the visualized data and designs tasks based on specific educational objectives.

[0674] Step 6:

[0675] The device presents the user with a created educational plan. The user then proceeds with their daily learning based on this plan. The plan includes specific instructions for skill enhancement and provides criteria for self-managing progress. This information is accessible through an interactive interface on the device.

[0676] Step 7:

[0677] The server monitors learners' progress and dynamically adjusts the lesson plan as needed. It analyzes learning progress data and evaluates whether each task in the plan is being properly completed. Based on the progress evaluation, it automatically modifies the content or adds new tasks. The input is progress data, and the output is the latest lesson plan.

[0678] (Application Example 1)

[0679] 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".

[0680] Traditional learning systems have faced the challenge of failing to provide a learning experience that fully meets the individual needs of each learner. In particular, there is a need to integrate and utilize abstract skills developed in different learning domains to efficiently optimize learning. Furthermore, while personalized support for individual learners within the home is required, achieving this presents challenges in terms of effort and cost.

[0681] 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.

[0682] In this invention, the server includes means for collecting learner learning information, means for identifying common abstract skills across multiple learning domains using a generative model, and means for providing learning support and interactive feedback via consumer-grade machines. This enables the provision of an optimal learning plan tailored to the individual characteristics of each learner and efficient learning support at home.

[0683] "Learning information" refers to data generated by learners during their daily learning activities, and specifically includes learning time, tasks completed, number of correct answers, and self-assessment.

[0684] A "generative model" is an algorithm or statistical model that analyzes training data based on artificial intelligence technology and plays a role in identifying common skills across different learning domains.

[0685] "Abstract skills" refer to conceptual skills such as logical thinking and problem-solving abilities that are required in common across multiple learning fields, going beyond specific learning content.

[0686] "Visualization" is a technique that displays data in a graphical format, enabling users to understand information intuitively and easily.

[0687] "Consumer electronic devices" refer to electronic devices such as robots used in the home, which are used to provide learning support and interactive communication.

[0688] "Interactive feedback" is a feature that enables real-time, two-way communication with learners and provides immediate feedback.

[0689] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server processes the learner's learning information using a generative AI model and transmits visualized insights to the terminal. The terminal takes the form of a consumer device and provides interactive feedback to the user.

[0690] Specifically, users input information about their learning activities into their devices. This includes, for example, the content and time spent on daily studies, the tasks they worked on, and their results. The devices then transmit this data to the server in real time.

[0691] The server runs a generative AI model using TensorFlow or similar tools depending on the conditions, analyzes the collected training information, and identifies abstract skills in different learning domains. The identified skills are visualized graphically and resent to the terminal.

[0692] The device, specifically the home robot, provides necessary feedback through interaction with the user. The user then uses this feedback to improve their learning plan. For example, the robot might prompt the user with questions like, "What is the optimal time to study to memorize new words?"

[0693] This series of processes enables learners to advance their learning activities more efficiently and effectively.

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] Users input learning information through their devices. This input includes study time, completed tasks, number of correct answers, and self-assessment. As this information is entered, the system collects basic data on the user's learning activities in real time.

[0697] Step 2:

[0698] The terminal sends the collected learning information to the server. The input data received by the server consists of numerical and textual information regarding the user's learning progress and results. The server stores this data in storage as reference data for analysis.

[0699] Step 3:

[0700] The server uses a generative AI model to identify abstract skills from the training data. Based on the input data in this process, data calculations are performed to extract common skills required across different learning domains.

[0701] Step 4:

[0702] The server visualizes identified abstract skills and outputs them as diagrams and charts. This visualized data serves as a resource to provide users with insights that help them understand the current situation.

[0703] Step 5:

[0704] The terminal receives visualized data from the server and presents it to the user. The user then receives assistance in selecting interactive and specific actions based on the information. This presentation step contributes to the user's decision-making process in real time.

[0705] Step 6:

[0706] Users adjust and dynamically change their learning plans via their devices. New learning goals and schedules are entered from the device, and this data is sent back to the server, creating a cycle of steps.

[0707] This series of steps enables users to spontaneously implement optimal learning plans based on their learning data, supporting efficient learning activities.

[0708] 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.

[0709] This invention is an optimization system that incorporates learner emotional information to make the learner's learning experience more effective. In addition to collecting learning data, visualizing skills, and dynamically adjusting the learning plan, this system includes an emotion engine that recognizes the user's emotions and reflects them in the learning plan.

[0710] When a user begins a learning activity, the device senses their facial expressions through voice input or the camera. This sensed data is then converted into emotional information through voice analysis and image processing algorithms. For example, it can detect whether the user is tired or focused.

[0711] Next, the device sends the user's emotional information to the server. The server combines this with training data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and skills. The resulting skill and emotional information is visualized and provided as feedback to the user via the device.

[0712] Furthermore, the server generates an optimized learning plan based on training data that includes emotional information. This learning plan incorporates not only tasks to enhance specific skills, but also options tailored to the user's current emotional state. For example, if the user is feeling stressed, it will include tasks that help them relax or activities that help them unwind.

[0713] This plan is displayed on the device, and the user proceeds with their learning based on it. The server monitors emotional information in real time as the user progresses and dynamically updates the learning plan. Adjustments are made in response to new challenges and emotional states to enhance learning effectiveness. For example, more challenging tasks are provided when the user is calm, maximizing their focused time.

[0714] In this way, by utilizing the emotion engine, a personalized learning environment that takes into account the learner's emotional state can be provided, enabling improved learning effectiveness and maintenance of motivation.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user starts a learning session, and the device uses its built-in camera and microphone to record the user's facial expressions and voice in real time. This data is sent to the emotion engine.

[0718] Step 2:

[0719] The device activates an emotion engine and analyzes recorded facial expressions and voice data. Using facial expression recognition algorithms and voice emotion analysis, it identifies the user's emotional state (e.g., relaxed, focused, stressed).

[0720] Step 3:

[0721] The device sends the analyzed emotion data to the server. The server receives this data along with the user's learning data, uses a generative model to identify abstract skills, and analyzes the relationship between emotions and learning performance.

[0722] Step 4:

[0723] The server visualizes identified skills and emotional information and outputs it to the learner's device as a dashboard in a way that makes sense to them. This allows users to see the correlation between their learning style and their emotions.

[0724] Step 5:

[0725] The server generates a learning plan that takes emotional information into account. For example, if the user is fatigued, it will include easier tasks, and if they are relaxed, it will add more challenging tasks.

[0726] Step 6:

[0727] The device provides the user with a generated learning plan, displaying the assignment options and the order in which they should be completed. The user then proceeds with their learning activities according to this plan.

[0728] Step 7:

[0729] As learning progresses, the device monitors emotional data and learning progress in real time and reports it to the server. Based on this, the server determines whether the learning plan is appropriate and makes dynamic adjustments as needed.

[0730] Step 8:

[0731] The adjusted learning plan is again notified to the user via the device, and a new learning approach is presented. This ensures that a learning environment that is always considerate of the user's emotional state is maintained.

[0732] (Example 2)

[0733] 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".

[0734] Traditional learning systems often create learning plans based primarily on learner progress data, without considering learners' emotional states. This results in insufficient individual optimization. Furthermore, dynamic adjustments based on learners' states are difficult, making it challenging to maximize learning efficiency. Solving these problems will improve learner effectiveness and motivation.

[0735] 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.

[0736] In this invention, the server includes means for collecting learner learning data, means for identifying abstract skills using a generative model, and means for sensing and collecting learner emotional information. This makes it possible to accurately grasp the learner's emotional state and analyze it in conjunction with progress data. Based on this, an optimized learning plan can be proposed and the plan can be dynamically adjusted, thereby providing a learning experience tailored to each individual and enhancing the effectiveness of learning.

[0737] "Learning data" refers to all information related to the knowledge and skills acquired by learners, and includes evaluations and progress reports.

[0738] A "generative model" refers to a program or algorithm that automatically extracts specific patterns or abstract concepts based on input data.

[0739] "Abstract skills" refer to general abilities and knowledge that can be applied across multiple fields of study.

[0740] "Visualization" refers to the process of converting abstract information into a form that is easy for humans to understand and display.

[0741] "Emotional information" refers to data that indicates the learner's emotional state, and includes psychological and physiological information obtained from facial expressions and voice.

[0742] An "optimized learning plan" refers to a combination of learning paths and materials that are tailored to maximize learning effectiveness based on the learner's progress and emotional state.

[0743] "Dynamic adjustment" means changing plans and procedures in real time in response to changes in circumstances and conditions.

[0744] The following describes "modes for carrying out the invention."

[0745] This invention is a system for optimizing the learner's learning experience, and is primarily realized through the interaction of a server, a terminal, and a user.

[0746] The server collects and analyzes training data and emotional information, and uses a generative AI model to identify relevant abstract skills. This model extracts specific patterns based on the input data and helps analyze the learner's learning progress and emotional state. Based on these analysis results, the server generates a learning plan tailored to each individual learner and sends a dynamically adjusted plan to the device according to their emotional state.

[0747] When a user begins learning, the device uses its built-in camera and microphone to sense the user's facial expressions and voice. This information is processed in real time by facial recognition software and voice analysis algorithms and acquired as the user's emotional information. The device displays an optimized learning plan sent from the server to the user and provides feedback tailored to their emotional state.

[0748] As users progress through their learning process via their devices, they can naturally reflect their emotional state in the system. This allows for adjustments tailored to their learning style and current mood.

[0749] For example, if a user feels fatigued while solving a math problem, the device will receive suggestions from the server for a relaxing, easy puzzle or a short break. In this process, the server uses prompts to analyze the user and generate appropriate feedback. An example of a prompt throughout this process is, "Based on the learner's mood and progress, suggest the most appropriate learning task to present next."

[0750] This system combines emotions and learning data to provide a personalized learning experience, maintaining learner motivation and improving learning efficiency.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The user logs into the device to begin learning activities and enables the use of the camera and microphone devices. This prepares the device to sense the user's facial expressions and voice in real time.

[0754] Step 2:

[0755] The device acquires user facial data via its camera and audio data via its microphone. Using this data as input, facial recognition software performs image processing, and simultaneously, an audio analysis algorithm analyzes the audio data. As a result, the user's emotional state (e.g., joy, concentration, fatigue) is output.

[0756] Step 3:

[0757] The device sends the analyzed emotional information to the server. The server uses this information as input to analyze a generative AI model using prompt statements. An example of a prompt statement is, "How should learning progress be optimized based on the user's emotional state?" This process outputs abstract skills related to the user's emotional state.

[0758] Step 4:

[0759] The server generates a learning plan based on the outputted abstract skill information and emotional information. This includes incorporating tasks to strengthen specific skills based on the analysis results, as well as choices that correspond to emotional states. The plan is optimized by the server and output as the final learning plan.

[0760] Step 5:

[0761] The server sends the generated learning plan to the device. The device visualizes this learning plan and displays it to the user. The user can then proceed with their learning based on the displayed learning plan. The device continuously monitors the user's progress while obtaining feedback.

[0762] Step 6:

[0763] The server analyzes progress and new sentiment information in real time, dynamically adjusting the learning plan as needed. For example, if a user is highly engaged, it adds more complex tasks to maximize learning effectiveness. This iterative adjustment ensures that the user's learning experience is always optimized.

[0764] (Application Example 2)

[0765] 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".

[0766] In modern learning environments, uniform learning plans are often provided without considering the learner's emotional state, which can lead to decreased motivation and concentration. Therefore, there is a need to provide optimized learning plans that take into account the emotional state of each individual learner. To solve this problem, it is necessary to develop a system that can accurately detect a learner's emotional state and provide a dynamic learning plan based on that state.

[0767] 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.

[0768] In this invention, the server includes means for sensing the learner's emotional state, means for transmitting the learner's learning data and emotional information to the server, and means for analyzing the emotional data and learning data using a generative model to identify the relationship between emotions and skills. This makes it possible to provide an individually optimized learning plan that takes the learner's emotional state into consideration.

[0769] A "learner" refers to someone who uses this system to advance their learning.

[0770] "Emotional state" refers to the user's psychological and physiological state, derived from the user's facial expressions and voice, which are analyzed by machine learning algorithms.

[0771] "Learning data" refers to information about the learning materials and problems that learners are working on, as well as their progress.

[0772] A "server" refers to a device with central computing capabilities that receives data sent from learners, performs analysis, and generates learning plans.

[0773] A "generative model" refers to a sophisticated algorithm that identifies learners' emotional states and skill levels from input data and analyzes their relationships.

[0774] "Abstract skills" refer to fundamental knowledge and abilities that are useful across different fields of study.

[0775] "Visualization" means presenting analyzed data in a visual format, such as graphs or charts, to make it easier for learners to understand.

[0776] A "learning plan" refers to a set of learning materials and assignments designed to achieve specific learning objectives.

[0777] "Progress" refers to an indicator that shows how well learners are progressing in their studies according to their learning plan.

[0778] "Dynamic adjustment" means flexibly changing the learning plan in real time according to the learner's status and progress.

[0779] The system implementing this invention incorporates a program designed for emotion analysis and dynamic adjustment of the learning program. The system primarily consists of three main components: a terminal, a server, and a user.

[0780] The device includes hardware that captures the user's facial expressions with a camera and takes voice input via a microphone. This sensory data is immediately processed as a digital signal, and the user's emotional state is identified in real time by an emotion analysis algorithm. OpenCV is used for image processing, and a speech processing library is used for voice analysis.

[0781] The analyzed emotional information is transmitted to a server via the internet. The server integrates the received emotional data with training data and uses a generative AI model to analyze the user's current learning status. For example, it determines whether the user is tired or focused and generates corresponding skill-related information. Furthermore, based on the generated information, it dynamically creates a learning plan optimized for the user.

[0782] This plan is visualized on the device's display and presented to the user. The learning plan is designed to include challenging tasks and relaxation activities, depending on the user's current situation.

[0783] For example, if the server determines that a primary school student user is tired of studying, it will prioritize incorporating learning materials that include simple puzzles or short videos for relaxation into the planning. This makes it easier for the user to maintain their motivation.

[0784] An example of a prompt message would be, "If the user's facial expression indicates fatigue, suggest an optimal plan for evening study. A combination of relaxation activities and simple tasks would be preferable." This allows the system to provide a personalized learning experience for each learner.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The device captures the user's face with a camera and collects facial expression data. This image data is used as input for the next step, where facial expression analysis is performed using OpenCV. As a result of the analysis, digital data indicating the user's emotional state is output.

[0788] Step 2:

[0789] Simultaneously, the device records the user's voice using a microphone. The obtained audio data is analyzed using a speech processing library to estimate the user's emotional state from their speech content and tone. This analysis result is integrated with facial expression analysis data to provide input for identifying the overall emotional state.

[0790] Step 3:

[0791] The device sends the collected emotion analysis data to the server. The server receives this data and performs analysis by combining it with training data using a generative AI model. This evaluates the relationship between the user's emotion information and learning status and outputs abstract skill information.

[0792] Step 4:

[0793] Based on the analysis results, the server dynamically generates a learning plan tailored to the user's emotional state. The generated learning plan includes challenging tasks and relaxation activities. This information is appropriately combined based on the learning objectives and output as an optimized list.

[0794] Step 5:

[0795] Finally, the generated learning plan is sent to the device and presented to the user visually. The user begins learning based on the plan and receives feedback as they progress. This feedback is processed in real time on the server, and the processing cycle is restarted as needed.

[0796] 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.

[0797] 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.

[0798] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] Means for collecting learner learning data,

[0820] A means of identifying common abstract skills across multiple learning domains using generative models,

[0821] A means of visualizing and displaying identified abstract skills,

[0822] A means of generating a learning plan based on visualized skills,

[0823] A means of monitoring learners' progress and dynamically adjusting learning plans,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, characterized by using an algorithm for improving the learning plan based on progress information and feedback.

[0827] (Claim 3)

[0828] The system according to claim 1, characterized by presenting a dynamically adjusted learning plan to the learner and optimizing the learning experience.

[0829] "Example 1"

[0830] (Claim 1)

[0831] A means of collecting learners' learning application information,

[0832] A means of identifying common abstract abilities across multiple educational fields using generative AI models,

[0833] A means of visualizing identified abstract abilities and presenting them on a human-like device,

[0834] A means of generating an advanced educational plan based on visualized capabilities,

[0835] A means of monitoring learners' progress and dynamically adapting the educational plan,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, characterized by using a calculation method for improving the educational plan based on progress information and feedback.

[0839] (Claim 3)

[0840] The system according to claim 1, characterized by presenting learners with dynamically adapted educational plans and optimizing their learning experience.

[0841] "Application Example 1"

[0842] (Claim 1)

[0843] Means for collecting learners' learning information,

[0844] A means of identifying common abstract skills across multiple learning domains using generative models,

[0845] A means of visualizing and outputting identified abstract skills,

[0846] A means of developing a learning plan based on visualized skills,

[0847] A means of monitoring learners' progress and dynamically modifying learning plans,

[0848] A means of providing learning support and interactive feedback through consumer-grade machines,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, characterized by using a method for improving the learning plan based on progress information and feedback.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized by presenting a dynamically modified learning plan to the learner and optimizing the learning experience.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] Means for collecting learner learning data,

[0857] A means of identifying common abstract skills across multiple learning domains using generative models,

[0858] A means of visualizing and displaying identified abstract skills,

[0859] A means of generating a learning plan based on visualized skills,

[0860] A means of monitoring learners' progress and dynamically adjusting learning plans,

[0861] Means for sensing and collecting learners' emotional information,

[0862] A method for analyzing collected emotional information and training data in combination,

[0863] A means for optimizing the learning plan based on the analysis results,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, characterized by using an algorithm for improving the learning plan based on progress information, feedback, and emotional information.

[0867] (Claim 3)

[0868] The system according to claim 1, characterized by presenting a dynamically adjusted learning plan to the learner and optimizing the learning experience by taking emotional information into consideration.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] Means for sensing the emotional state of learners,

[0872] A means of sending learner's learning data and sentiment information to a server,

[0873] A method for analyzing emotional data and training data using a generative model to identify the relationship between emotions and skills,

[0874] A means of visualizing and displaying extracted emotional and skill information,

[0875] A means for generating a dynamically optimized learning plan that takes emotional information into consideration,

[0876] A means of monitoring learners' progress and adjusting learning plans according to their emotional state,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, characterized by using an algorithm for improving the learning plan based on progress information, feedback, and sentiment information.

[0880] (Claim 3)

[0881] The system according to claim 1, characterized by presenting a dynamically adjusted learning plan to the learner along with emotional information, thereby optimizing the learning experience. [Explanation of Symbols]

[0882] 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. Means for collecting learner learning data, A means of identifying common abstract skills across multiple learning domains using generative models, A means of visualizing and displaying identified abstract skills, A means of generating a learning plan based on visualized skills, A means of monitoring learners' progress and dynamically adjusting learning plans, A system that includes this.

2. The system according to claim 1, characterized by using an algorithm for improving the learning plan based on progress information and feedback.

3. The system according to claim 1, characterized by presenting a dynamically adjusted learning plan to the learner and optimizing the learning experience.

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