system

Abstract

We provide the system. [Solution] A means for collecting learner history data and generating individual learning profiles based on that information, A means of generating a skill map that visualizes the relationships between skills and concepts across different educational subjects, A means for proposing an optimal learning sequence based on the aforementioned skill map and learning profile, A means to analyze progress in real time and dynamically adjust the plan, A means of generating individual travel profiles based on historical data and proposing efficient routes, 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 method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional educational systems, it has been difficult to effectively incorporate the relevance of skills and concepts across different educational subjects into education, and isolated learning has been carried out in each subject. For this reason, learners cannot fully utilize abstract skills, and there is a problem that academic improvement is hindered due to a lack of learning motivation. Furthermore, it is difficult to provide a learning experience optimized for individual learners, which also contributes to the widening of the educational gap.

Means for Solving the Problems

[0005] This invention provides means for collecting learner history data and generating individual learning profiles, thereby realizing an optimized educational approach for each learner. Furthermore, by using a skill map generation means that visualizes the relationships between skills and concepts across different educational subjects, it connects educational content across domains, deepening learner understanding. In addition, by combining means for proposing an optimal learning sequence based on the skill map and learning profile, and means for analyzing learning progress in real time and dynamically adjusting the learning plan as needed, the invention provides a system that supports efficient and sustainable academic improvement.

[0006] "Learner history data" refers to information such as learning activities, grades, test results, and learning behaviors that learners have experienced in the past.

[0007] A "learning profile" refers to a dataset that comprehensively evaluates and organizes information about individual learners, including their learning style, strengths and weaknesses in different subjects, and skill levels.

[0008] A "skill map" refers to a structure that visualizes the relationships between common skills and concepts across different educational subjects.

[0009] A "learning sequence" refers to a structured flow of learning activities and tasks designed to help learners effectively acquire knowledge.

[0010] "Real-time analysis" refers to the process of immediately analyzing data and information the moment it is acquired and deriving results.

[0011] "A means of dynamic adjustment" refers to a method that has the function of changing or correcting to the optimal state each time the situation or conditions change. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide an optimized educational experience for learners.

[0034] Server Role

[0035] The server first collects learner history data, including information about past assessments, grades, and learning patterns. Using this data, the server generates an individualized learning profile that reflects the learner's strengths and weaknesses, as well as detailed skill levels. Furthermore, the server generates a skills map to visualize the relationships between skills and concepts across different subjects. This skills map is designed to enable learners to apply skills acquired in one subject to others.

[0036] Based on the generated learning profile and skill map, the server proposes a customized learning sequence to the learner. The proposed sequence is optimized for efficient learning. The server also analyzes the learner's progress in real time and dynamically adjusts the learning plan as needed. This ensures that learners are always provided with an optimal learning environment tailored to their progress.

[0037] Terminal role

[0038] The terminal is a device that visually presents learning plans and skill maps received from the server to the user. Specifically, it displays the user's learning progress and next assignments as graphs and charts. Furthermore, the terminal also plays a role in receiving and responding to user input.

[0039] User roles

[0040] Users independently progress through their learning by following the learning plan displayed on their device. They complete assigned tasks by solving problems and viewing learning materials. Learning progress is recorded on the device in real time and sent to the server for use as analysis material.

[0041] Specific example

[0042] For example, consider a high school student who primarily studies mathematics and science. This student excels at logical operations in mathematics but struggles with physics. Using this system, the server presents a customized learning sequence that applies mathematical skills to solving physics problems. Through the terminal, the user can apply the logical thinking skills cultivated in mathematics to physics experiment problems, developing practical application skills. In this way, the learning experience is optimized to individual needs, enabling efficient knowledge acquisition.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects learner history data. It retrieves learner performance on past assignments, test results, and learning activity logs from the database.

[0046] Step 2:

[0047] The server generates individual learner profiles based on the collected data. It identifies the learner's strongest subjects, skill level, and learning style, and saves them as profiles.

[0048] Step 3:

[0049] The server generates a skill map that visualizes skill relationships between different subjects. It references profiles and graphs common abstract skills, such as math and science.

[0050] Step 4:

[0051] The server suggests the optimal learning sequence for the user based on their skill map and learning profile. It determines learning priorities and plans how the learner should progress through each subject.

[0052] Step 5:

[0053] The terminal visually displays the learning sequence and skill map received from the server. Graphs and charts are used to intuitively communicate the next steps to the learner.

[0054] Step 6:

[0055] Users follow the learning plan displayed on their device, solving problems and studying related materials.

[0056] Step 7:

[0057] The device records the user's learning activities. It saves information such as the progress made on each assignment, the accuracy of the answers, and the time spent on learning.

[0058] Step 8:

[0059] The server analyzes progress data sent from the terminal in real time. It evaluates the user's skill improvement and determines the effectiveness of the learning plan.

[0060] Step 9:

[0061] The server dynamically adjusts the learning plan based on the analysis results. It updates the next learning content and resets the optimized plan according to the user's progress.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Conventional educational support systems have struggled to provide uniform learning plans and individually optimize learning progress, making it difficult to deliver effective educational experiences that meet the diverse needs of learners. Furthermore, they lacked real-time adjustments based on learning progress, which prevented improvements in learning efficiency.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for collecting learner history information and generating individual learning profiles based on that information; means for generating skill mappings that visualize the relationships between abilities and concepts across different educational domains; means for proposing an optimal learning process based on the skill mappings and learning profiles; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for visually displaying the progress status to the learner on a terminal and accepting user input. This makes it possible to provide an efficient and appropriate learning environment that meets individual learning needs.

[0067] "History information" refers to all data related to the learning activities that a learner has undertaken to date, including past grades and behavioral history.

[0068] A "learning profile" is a personalized collection of information generated based on collected historical data, reflecting the learner's strengths and skill levels.

[0069] "Skill mapping" refers to diagrams and representations used to visually display the relationships between abilities and concepts across different educational domains.

[0070] The term "learning process" refers to the sequence and order of learning activities that learners should follow in order to acquire optimal knowledge.

[0071] "Progress analysis" is a process that analyzes the progress of learners' learning activities in real time and evaluates their current position and level of achievement.

[0072] "Visual information" refers to information in a visually easy-to-understand format, such as graphs and charts, provided to learners via a device.

[0073] "User input" refers to information that learners provide to the system through their devices, including feedback and progress reports.

[0074] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide learners with an individualized educational experience.

[0075] Server Role

[0076] The server possesses advanced data processing capabilities, collecting learner history information and generating individual learning profiles. This utilizes a database processing system, applying machine learning algorithms and generative AI models to analyze the information. For example, data analysis tools built in Python are used to identify learner trends from past learning data and formulate individual learning routes. The server also generates skill mappings that visualize competency relationships across different educational domains, and uses this to suggest the optimal learning process for each learner. Real-time progress analysis and dynamic plan adjustments are also included as server functions, with cloud services assisting in the processing and synchronization of real-time data.

[0077] Terminal role

[0078] The terminal functions as a display device that provides learners with information transmitted from the server. For example, a tablet or laptop may be used, displaying the learner's progress and skill mapping as charts. The terminal also collects user feedback and sends it to the server to allow for further adjustments to the learning plan. User input is performed via a graphical user interface.

[0079] User roles

[0080] Users progress through their learning activities based on information presented via their devices. For example, they might follow a displayed roadmap to tackle the next task. Users also report feedback and progress via their devices, which is sent to the server, providing a more personalized learning sequence. This allows learners to develop practical application skills and acquire knowledge efficiently.

[0081] Specific example

[0082] For example, if a high school student is studying mathematics and physics, the server analyzes their past performance and current learning content and suggests ways to solve physics problems that utilize their logical thinking skills in mathematics. This information is presented intuitively to the user via a terminal, assisting them in their actual learning process.

[0083] Example of a prompt

[0084] "A high school student is strong in mathematical logic but struggles to understand physics. How can you provide a learning sequence that allows them to apply their mathematical skills to physics?"

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server collects learner history information. Specifically, it retrieves data such as past learning performance, test results, and study time from a database. The input is the learner ID, and the output is the history information associated with that ID. This collected data serves as the basic information necessary for subsequent profile generation.

[0088] Step 2:

[0089] The server generates individual learning profiles based on collected historical information. This process uses a generative AI model to analyze the acquired historical data and identify learner tendencies. The input is the collected historical information, and the output is a learning profile including the learner's strengths and weaknesses in different subjects and their skill level. The algorithm used for analysis utilizes pattern recognition technology.

[0090] Step 3:

[0091] The server generates skill mappings based on learning profiles. Here, we use the power of a generative AI model to visualize the transferability of skills across different educational domains. The input is the learning profile, and the output is the skill mapping. This mapping shows, for example, how mathematical logical thinking can be applied to solving problems in physics.

[0092] Step 4:

[0093] The server proposes an optimal learning process based on skill mapping and learning profiles. The input is skill mapping and learning profiles, and the output is a learning process optimized for the learner. The generative AI model derives an efficient and personalized learning sequence.

[0094] Step 5:

[0095] The terminal visually presents the proposed learning process and skill mapping to the user. Input is the learning process and skill mapping sent from the server, and output is visual information using charts and graphs. The user interface allows learners to understand their learning progress and the next steps.

[0096] Step 6:

[0097] Users perform learning activities according to the learning process displayed on their device. They report their learning progress and assignment completion status as input to the device. The output consists of learning results and feedback information. This information is sent to the server and used to adjust future learning plans.

[0098] Step 7:

[0099] The server receives user progress data and feedback as input and analyzes learning progress in real time. Based on the analysis results, it dynamically adjusts the learning plan. The output is the updated learning process, which is then provided to the user again via the terminal.

[0100] (Application Example 1)

[0101] 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."

[0102] In online learning environments and delivery operations, there is a need for systems that can propose optimal plans based on the learning or work progress of individual users or delivery personnel. However, existing technologies make it difficult to provide real-time, dynamically personalized, and efficient progress plans. In particular, in mobile operations, the challenge is to constantly propose efficient routes in response to changing traffic conditions.

[0103] 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.

[0104] In this invention, the server includes means for collecting learner and delivery person history data and generating individual profiles based on that information; means for generating maps that visualize the relationships between different operations and objectives; and means for proposing an optimal progression sequence or route based on the map and profiles. This enables the plan to be dynamically adjusted according to the user's progress and traffic conditions, resulting in optimized and efficient achievement.

[0105] "Historical data" refers to a collection of information that shows records of actions and operations performed in the past.

[0106] A "profile" is a dataset that compiles information indicating an individual's characteristics and tendencies.

[0107] A "skill map" is a diagram that visually shows the relationships between different activities and abilities.

[0108] A "sequence" is a list that describes a series of steps or operations in the optimal order.

[0109] "Real-time" refers to a state that immediately reflects the ongoing situation.

[0110] "Dynamic adjustment" means making flexible changes in response to changing circumstances.

[0111] A "mobility profile" is a dataset that shows the characteristics and trends of individual travel.

[0112] A "route" refers to the optimal direction or path from a specific point to a destination.

[0113] To implement this system, the server first collects user history data and generates individual profiles based on it. Specifically, it analyzes past behavioral patterns and characteristics using programming languages ​​such as Python and data analysis libraries (e.g., Pandas and Scikit-learn). Based on the generated profiles, it utilizes KMeans clustering and geopy to create skill maps that visualize the relationships between different activities. The server uses this information to propose the optimal sequence and travel path to the user in real time.

[0114] The terminal uses information provided by the server to visually display optimal learning plans and route information to the user. Specifically, it uses devices such as smartphones and tablets to provide a user-friendly display via a graphical user interface (GUI). The terminal also receives input from the user in real time and sends data to the server, updating information on both sides.

[0115] Users improve their skills and perform tasks efficiently based on the learning plans and route information displayed on their devices. For example, in delivery work, safe and fast routes are recommended based on past driving data. This system can use the following as an example of a prompt: "Please suggest the optimal delivery route considering current traffic conditions and past delivery patterns."

[0116] This allows users to obtain optimal guidance tailored to their characteristics and circumstances, and to enjoy a highly customized experience.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server collects user history data. This data includes logs of the user's past actions and operations. Based on this, a data analysis library is used to extract specific trends and patterns, generating individual profiles. These profiles summarize the characteristics and trends of past activities.

[0120] Step 2:

[0121] The server utilizes profile data to generate a map that visualizes the relationships between different activities. The input is the individual profiles obtained in Step 1. From these profiles, KMeans clustering is used to group related points and create a skill map as a graph structure. The output is a visually displayable relationship map.

[0122] Step 3:

[0123] The server proposes the optimal progression sequence and travel route based on the skill map and profile. The input here is the skill map and individual profile generated in step 2. Using this information, prompts are input to the generated AI model, taking into account real-time changing conditions, and the optimal plan is determined. As a specific example, the prompt "Please propose the optimal delivery route considering the current traffic conditions and past delivery patterns" is used.

[0124] Step 4:

[0125] The terminal provides users with visual information based on data provided by the server. The input includes optimal route information and plans. This is displayed via a GUI, making it easily understandable and visualized for the user. The output is a user-operable display screen, which the user uses to guide their activities.

[0126] Step 5:

[0127] The user effectively executes their activities using the information displayed on the device. The input here is the visual information on the device. Based on this, the user makes decisions and performs actions, and the results are fed back to the server via the device. The output is the data of the executed results, which is used for optimization in the next process.

[0128] 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.

[0129] This invention aims to provide learners with an optimal learning experience by incorporating an emotion engine into an educational support system. The system consists of a server, terminals, users, and an emotion engine.

[0130] Server Role

[0131] The server first collects learner history data and generates individual learning profiles. Based on the learner's past performance and learning activities, it clarifies their strengths and weaknesses in different subjects and their skill levels. It then enables cross-domain learning by generating skill maps that visualize the relationships between skills and concepts across different educational subjects. Furthermore, the server analyzes emotional data obtained from the emotion engine and adjusts the learning plan accordingly. The emotion engine monitors the learner's emotional state (e.g., excitement, concentration, stress) and modifies the learning sequence in real time based on this.

[0132] Terminal role

[0133] The terminal is a device that visually presents information sent from the server to the learner. By displaying skill maps, learning sequences, and feedback based on sentiment data on the interface, learners can progress through their learning while understanding their own progress.

[0134] User roles

[0135] Users learn according to personalized learning plans provided via their devices. Emotional data acquired by the emotion engine is fed back to the user, allowing learners to explore the optimal learning method while being aware of their own emotional state.

[0136] The role of the emotional engine

[0137] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the user's emotions in real time. The detected emotion information is sent to a server and used to provide a learning environment optimized for the learner's current emotional state.

[0138] Specific example

[0139] Consider a case of a university student studying physics and biology. This student often experiences stress when studying physics but is highly focused on biology. The emotion engine collects data when the user experiences stress while viewing physics content, and the server adjusts the learning plan based on this information. In the next learning session, it prioritizes biology topics and presents content designed to enhance learning motivation. Through this dynamic adaptation, the user can effectively continue to acquire knowledge.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The server collects the learner's past history data. This data includes test results and performance on past learning assignments.

[0143] Step 2:

[0144] The server generates a personalized learning profile for each learner based on the collected data. This profile meticulously records their strengths and learning style.

[0145] Step 3:

[0146] The server generates a skills map. It identifies the relationships between skills and concepts across different subjects and visualizes them in a map.

[0147] Step 4:

[0148] The server designs an optimized learning sequence, taking into account the generated learning profiles and skill maps.

[0149] Step 5:

[0150] The emotion engine collects emotional data in real time from the user's facial expressions and voice. This data includes emotions such as joy, concentration, and stress.

[0151] Step 6:

[0152] The server dynamically adjusts the learning sequence based on emotional data. It analyzes how the user's emotional state affects learning and modifies the learning content as needed.

[0153] Step 7:

[0154] The device visually presents the user with a learning plan and skill map that have been coordinated by the server. The user can then proceed with their learning according to this plan.

[0155] Step 8:

[0156] Users engage in learning activities according to the presented learning plan. They manage their own learning pace while receiving emotional feedback.

[0157] Step 9:

[0158] The device tracks the user's progress and sends information to the server in real time. This allows the server to make further adjustments and always provide the optimal learning environment.

[0159] (Example 2)

[0160] 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".

[0161] Providing a learning experience optimized for each individual learner is difficult, given that learners' motivation to learn changes depending on the situation. In particular, it is necessary to propose an appropriate learning plan while considering the learner's emotional state, but conventional methods do not adequately perform real-time sentiment analysis, resulting in incomplete optimization of learning.

[0162] 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.

[0163] In this invention, the server includes means for accumulating learners' past history and generating individual educator profiles based on that information, means for generating technology maps that visualize the relationships between technologies and concepts across different teaching areas, and means for analyzing emotional states in real time and dynamically adjusting the teaching plan. This makes it possible to provide an optimized learning environment based on learners' historical data and real-time emotional states.

[0164] "A learner's past history" refers to information about the learning activities and performance that a learner has undertaken to date.

[0165] An "educator profile" refers to an individualized learner profile constructed based on information such as the learner's strengths in subjects and skill level.

[0166] "Teaching area" refers to a specific subject or field in which educational activities are conducted.

[0167] "Relevance of technologies and concepts" refers to how common technologies and concepts are related across different areas of instruction.

[0168] A "technology map" refers to a diagram that visually displays the relationships between technologies and concepts across different areas of instruction.

[0169] "Analyzing emotional states in real time" refers to the process of instantly evaluating and digitizing the learner's emotions on the spot.

[0170] "Dynamically adjusting educational plans" refers to flexibly modifying learning plans and sequences in response to analyzed emotional states and learning progress.

[0171] This invention is an educational support system that provides an optimal learning environment for learners, and consists of a server, terminals, users, and an emotion engine.

[0172] Server role:

[0173] The server uses a database management system to collect learners' past history data. Specifically, it uses database software to manage learners' performance and activity logs, thereby generating individual educator profiles. Machine learning algorithms are used to analyze learners' strengths and skill levels in profile generation. Furthermore, it generates a technology map showing the relationships between techniques and concepts in different teaching areas, and visualizes these relationships using a Python library. Finally, it analyzes sentiment data obtained from an emotion engine and uses a generative AI model to create prompts, dynamically adjusting the teaching plan in real time.

[0174] Terminal role:

[0175] The terminal visually displays learning information transmitted from the server. This involves implementing an interactive user interface using web technologies, allowing learners to intuitively understand their learning progress and feedback. Technical maps, learning sequences, and feedback are displayed on this interface to aid learner comprehension.

[0176] User roles:

[0177] Users progress through their learning based on a learning plan provided via their device. Based on feedback from the emotion engine, users become aware of their own emotional state and, under that awareness, explore the optimal learning style.

[0178] The role of the emotional engine:

[0179] The emotion engine uses sensors such as cameras and microphones to detect learners' emotions in real time. To achieve this, it employs computer vision and natural language processing technologies to analyze learners' facial expressions and voices. The resulting emotion data is sent to a server and used to adjust the educational plan.

[0180] As a concrete example, if a student experiences stress while learning physics, the emotion engine detects this emotion and reports it to the server. Upon receiving this report, the server adjusts its plan to prioritize biology content in the next session. An example of a prompt might be a command given to the generating AI model: "Please provide appropriate learning adjustments for when a user experiences stress while learning physics."

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The server collects learner history data. Specifically, it uses a database management system to extract learners' past grades and activity logs. Inputs include learner IDs and activity records for each semester, and output is an individual learner dataset. This dataset is used for profile generation in the next step.

[0184] Step 2:

[0185] The server generates individual educator profiles based on collected historical data. This process uses machine learning algorithms to analyze subjects of expertise and skill levels using the dataset as input. Specifically, k-means clustering is used to divide learners' characteristics into clusters. As output, individual educator profiles are generated.

[0186] Step 3:

[0187] The server generates a technology map that visualizes the relationships between technologies and concepts across teaching disciplines. The input is the generated educator profile and associated teaching data. A Python library (e.g., NetworkX) is used to build and visualize a network showing the relationships between each subject. The output is the technology map, which is used to propose learning sequences in the next step.

[0188] Step 4:

[0189] The emotion engine collects real-time emotional data of the user using sensors such as cameras and microphones. It receives real-time facial images and audio data as input and performs analysis using facial recognition software (e.g., OpenCV) and voice analysis tools (e.g., NLP technology). The output is data representing the user's current emotional state.

[0190] Step 5:

[0191] The server analyzes emotional data and dynamically adjusts the learning plan. Inputs include emotional state data obtained from the emotion engine and the server-side educator profile. A generative AI model generates prompts and proposes a new learning sequence. The output is a refined learning plan, which is then provided to the user.

[0192] Step 6:

[0193] The terminal displays data received from the server. Using specific web technologies (e.g., HTML, CSS, JavaScript®), it receives a customized learning plan and technology map as input and displays them on an interactive user screen. The output is an easily viewable interface that supports the user's learning.

[0194] Step 7:

[0195] Users engage in learning activities based on a learning plan provided through their device. Specific actions include answering presented questions and viewing related content. Input consists of receiving instructions from the learning plan via the device, while output includes recording learning progress and new history data.

[0196] (Application Example 2)

[0197] 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".

[0198] Current learning support systems have a problem in that they have difficulty dynamically adjusting learning plans to take into account an individual's emotional state. As a result, learners may experience stress and decreased concentration, which hinders effective learning. There is also the challenge of not being able to visualize the relationships between different educational fields and provide a learning environment that responds to emotional states.

[0199] 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.

[0200] In this invention, the server includes means for collecting learner history data and generating individual learning profiles based on that information; means for generating knowledge maps that visualize the relationships between skills and concepts across different educational fields; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for identifying the learner's emotional state using emotion-detecting sensor technology and providing a learning environment that corresponds to that emotional state. By analyzing the learner's emotional state and dynamically adjusting the individual learning plan, more effective learning becomes possible.

[0201] "Historical data" refers to information about a learner's past learning activities and performance, and is used to generate a profile unique to each individual learner.

[0202] A "learning profile" is a collection of information generated based on historical data that indicates a learner's strengths and weaknesses and their skill level.

[0203] A "knowledge map" is a visual representation of the relationships between skills and concepts across different educational fields, helping to foster cross-domain understanding in learning.

[0204] "Emotional state" refers to the learner's current psychological state, and includes, for example, states such as stress, concentration, and relaxation.

[0205] "Sensor technology" refers to technologies used to detect emotional states, utilizing methods such as facial recognition and voice analysis.

[0206] A "learning sequence" refers to a series of learning steps that learners should follow to achieve a specific educational objective.

[0207] "Dynamic adjustment" is a method of optimization that involves analyzing data in real time and making changes to systems and processes based on the results.

[0208] This educational support system includes servers, terminals, and an emotion engine as its main components.

[0209] The server first collects learner history data and uses this information to generate individual learning profiles. These profiles are created based on the learner's past performance and activities and support learning plans aimed at specific educational goals. Furthermore, the server generates knowledge maps that visualize the relationships between skills and concepts across different educational fields, facilitating cross-domain learning. The server monitors learning progress in real time, dynamically adjusts the learning plan as needed, and provides an optimal learning sequence tailored to each individual learner.

[0210] The terminal plays a role in visually presenting information transmitted from the server to the learner. This includes knowledge maps, learning sequences, and feedback based on sentiment information. This interface makes it easier for learners to understand their own state and promotes self-regulated learning.

[0211] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the learner's emotional state in real time. This emotional data is sent to a server and used to provide the learner with the optimal learning environment. For example, if it detects that the learner is feeling stressed, it can dynamically adapt by switching to relaxing content or learning content that encourages a change of pace.

[0212] As a concrete example, consider a scenario where an elementary school student uses the system to learn at home. If the emotion engine detects a stress response while the student is working on a math problem, the system supports the student by providing simple games or relaxing content. Through this system, the student can continue learning effectively while reducing stress.

[0213] An example of a prompt message is, "Please suggest the best learning support method when the user is under stress." In this way, the system utilizes a generative AI model to provide an individually adapted learning experience.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server collects learner history data. It receives past learning activity data and performance data as input to obtain basic information for generating individual learning profiles. This data is analyzed to create learning profiles that include strengths and weaknesses.

[0217] Step 2:

[0218] The server generates knowledge maps that visualize the relationships between skills and concepts across different educational disciplines. It takes learning profile data as input and models those relationships to output visual knowledge maps that enable cross-domain learning.

[0219] Step 3:

[0220] The device uses learning profiles and knowledge maps sent from the server to present the optimal learning sequence to the learner. It displays the received information on the interface, visualizing the learning content and steps available to the learner.

[0221] Step 4:

[0222] As the user progresses through the learning process, the server analyzes the learning progress in real time. It receives the learner's activity status as input, dynamically adjusts the learning plan based on the progress, and outputs the results.

[0223] Step 5:

[0224] The emotion engine detects the learner's emotional state in real time. It acquires emotional data as input using a facial recognition camera and microphone, analyzes this data to identify the current emotional state, and sends this information to a server.

[0225] Step 6:

[0226] The server adjusts the learning environment based on emotional data obtained from the emotion engine. If the emotional state is determined to be stressful, it suggests relaxing content or breaks to the learner. The optimized learning environment settings are then sent to the terminal as output.

[0227] Step 7:

[0228] The device presents the learner with the learning environment settings received from the server. It displays content and activities tailored to the learner on the screen, supporting efficient and appropriate learning.

[0229] This series of processes makes it possible to provide a learning experience tailored to the individual needs of each learner. Generative AI models are also used to generate prompts that further optimize the user experience.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide an optimized educational experience for learners.

[0247] Server Role

[0248] The server first collects learner history data, including information about past assessments, grades, and learning patterns. Using this data, the server generates an individualized learning profile that reflects the learner's strengths and weaknesses, as well as detailed skill levels. Furthermore, the server generates a skills map to visualize the relationships between skills and concepts across different subjects. This skills map is designed to enable learners to apply skills acquired in one subject to others.

[0249] Based on the generated learning profile and skill map, the server proposes a customized learning sequence to the learner. The proposed sequence is optimized for efficient learning. The server also analyzes the learner's progress in real time and dynamically adjusts the learning plan as needed. This ensures that learners are always provided with an optimal learning environment tailored to their progress.

[0250] Terminal role

[0251] The terminal is a device that visually presents learning plans and skill maps received from the server to the user. Specifically, it displays the user's learning progress and next assignments as graphs and charts. Furthermore, the terminal also plays a role in receiving and responding to user input.

[0252] User roles

[0253] Users independently progress through their learning by following the learning plan displayed on their device. They complete assigned tasks by solving problems and viewing learning materials. Learning progress is recorded on the device in real time and sent to the server for use as analysis material.

[0254] Specific example

[0255] For example, consider a high school student who primarily studies mathematics and science. This student excels at logical operations in mathematics but struggles with physics. Using this system, the server presents a customized learning sequence that applies mathematical skills to solving physics problems. Through the terminal, the user can apply the logical thinking skills cultivated in mathematics to physics experiment problems, developing practical application skills. In this way, the learning experience is optimized to individual needs, enabling efficient knowledge acquisition.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The server collects learner history data. It retrieves learner performance on past assignments, test results, and learning activity logs from the database.

[0259] Step 2:

[0260] The server generates individual learner profiles based on the collected data. It identifies the learner's strongest subjects, skill level, and learning style, and saves them as profiles.

[0261] Step 3:

[0262] The server generates a skill map that visualizes skill relationships between different subjects. It references profiles and graphs common abstract skills, such as math and science.

[0263] Step 4:

[0264] The server suggests the optimal learning sequence for the user based on their skill map and learning profile. It determines learning priorities and plans how the learner should progress through each subject.

[0265] Step 5:

[0266] The terminal visually displays the learning sequence and skill map received from the server. Graphs and charts are used to intuitively communicate the next steps to the learner.

[0267] Step 6:

[0268] Users follow the learning plan displayed on their device, solving problems and studying related materials.

[0269] Step 7:

[0270] The device records the user's learning activities. It saves information such as the progress made on each assignment, the accuracy of the answers, and the time spent on learning.

[0271] Step 8:

[0272] The server analyzes progress data sent from the terminal in real time. It evaluates the user's skill improvement and determines the effectiveness of the learning plan.

[0273] Step 9:

[0274] The server dynamically adjusts the learning plan based on the analysis results. It updates the next learning content and resets the optimized plan according to the user's progress.

[0275] (Example 1)

[0276] 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".

[0277] Conventional educational support systems have struggled to provide uniform learning plans and individually optimize learning progress, making it difficult to deliver effective educational experiences that meet the diverse needs of learners. Furthermore, they lacked real-time adjustments based on learning progress, which prevented improvements in learning efficiency.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0279] In this invention, the server includes means for collecting the learner's history information and generating an individual learning profile based on that information, means for generating a skill mapping that visualizes the relevance of abilities and concepts between different educational areas, means for proposing an optimal learning process based on the skill mapping and the learning profile, means for analyzing the progress of learning in real time and dynamically adjusting the learning plan, and means for visually displaying the progress status to the learner on the terminal and receiving user input. Thereby, it becomes possible to provide an efficient and appropriate learning environment corresponding to individual learning needs.

[0280] "History information" refers to all data related to the learning activities that the learner has carried out so far, including past grades and behavioral histories.

[0281] "Learning profile" is an individualized information set generated based on the collected history information, reflecting the learner's strong subjects and skill levels.

[0282] "Skill mapping" is a diagram or representation means for visually displaying the relevance of abilities and concepts between different educational areas.

[0283] "Learning process" refers to the flow and sequence of a series of learning activities that the learner should carry out aiming at optimal knowledge acquisition.

[0284] "Progress analysis" is a process of analyzing the progress of the learner's learning activities in real time and evaluating the current position and degree of achievement.

[0285] "Visual information" is information in a visually easy-to-understand form such as graphs and charts provided to the learner via the terminal.

[0286] "User input" refers to the information provided by the learner to the system through the terminal, including feedback, progress reports, etc.

[0287] This invention is implemented as an educational support system in which the server, terminal, and user cooperate to provide an individualized educational experience for the learner.

[0288] Role of the server

[0289] The server has advanced data processing capabilities, collects the learner's historical information, and generates an individual learning profile. For this, a database processing system is used, and machine learning algorithms and generative AI models are applied for information analysis. For example, a data analysis tool built in Python is used to identify the learner's tendencies from past learning data and formulate an individual learning route. The server also generates a skill mapping that visualizes the ability relationships between different educational areas and proposes an optimal learning process for the learner based on it. Real-time progress analysis and dynamic plan adjustment are also included as functions of the server, and cloud services assist in the processing and synchronization of real-time data.

[0290] Role of the terminal

[0291] The terminal functions as a display device that provides the information sent from the server to the learner. For example, a tablet or a notebook computer is used, and the learner's progress and skill mapping are displayed as charts. The terminal also collects the user's feedback and sends it to the server to enable further adjustment of the learning plan. User input is performed through a graphical user interface.

[0292] Role of the user

[0293] Users progress through their learning activities based on information presented via their devices. For example, they might follow a displayed roadmap to tackle the next task. Users also report feedback and progress via their devices, which is sent to the server, providing a more personalized learning sequence. This allows learners to develop practical application skills and acquire knowledge efficiently.

[0294] Specific example

[0295] For example, if a high school student is studying mathematics and physics, the server analyzes their past performance and current learning content and suggests ways to solve physics problems that utilize their logical thinking skills in mathematics. This information is presented intuitively to the user via a terminal, assisting them in their actual learning process.

[0296] Example of a prompt

[0297] "A high school student is strong in mathematical logic but struggles to understand physics. How can you provide a learning sequence that allows them to apply their mathematical skills to physics?"

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] The server collects learner history information. Specifically, it retrieves data such as past learning performance, test results, and study time from a database. The input is the learner ID, and the output is the history information associated with that ID. This collected data serves as the basic information necessary for subsequent profile generation.

[0301] Step 2:

[0302] The server generates individual learning profiles based on the collected historical information. For this task, a generative AI model is used to analyze the acquired historical information to identify the tendencies of the learners. The input is the collected historical information, and the output is a learning profile that includes the learners' strong and weak subjects and skill levels. The algorithm used for the analysis utilizes pattern recognition technology.

[0303] Step 3:

[0304] The server generates a skill mapping based on the learning profile. Here, leveraging the power of the generative AI model, the transferability of skills between different educational areas is visualized. The input is the learning profile, and the output is the skill mapping. This mapping shows, for example, how the logical thinking in mathematics can be applied to solving physics problems.

[0305] Step 4:

[0306] The server proposes an optimal learning process based on the skill mapping and the learning profile. The input is the skill mapping and the learning profile, and the output is a learning process optimized for the learner. The generative AI model derives an efficient and individualized learning sequence.

[0307] Step 5:

[0308] The terminal visually presents the proposed learning process and skill mapping to the user. The input is the learning process and skill mapping sent from the server, and the output is visual information using charts and graphs. Through the user interface, the learner can grasp the progress of their learning and the next steps.

[0309] Step 6:

[0310] The user conducts learning activities according to the learning process displayed on the terminal. The progress of learning and the degree of task achievement are reported as input to the terminal. The output is the learning result and feedback information. This information is sent to the server and used for adjusting future learning plans.

[0311] Step 7:

[0312] The server receives user progress data and feedback as input and analyzes learning progress in real time. Based on the analysis results, it dynamically adjusts the learning plan. The output is the updated learning process, which is then provided to the user again via the terminal.

[0313] (Application Example 1)

[0314] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0315] In online learning environments and delivery operations, there is a need for systems that can propose optimal plans based on the learning or work progress of individual users or delivery personnel. However, existing technologies make it difficult to provide real-time, dynamically personalized, and efficient progress plans. In particular, in mobile operations, the challenge is to constantly propose efficient routes in response to changing traffic conditions.

[0316] 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.

[0317] In this invention, the server includes means for collecting learner and delivery person history data and generating individual profiles based on that information; means for generating maps that visualize the relationships between different operations and objectives; and means for proposing an optimal progression sequence or route based on the map and profiles. This enables the plan to be dynamically adjusted according to the user's progress and traffic conditions, resulting in optimized and efficient achievement.

[0318] "Historical data" refers to a collection of information that shows records of actions and operations performed in the past.

[0319] A "profile" is a dataset that compiles information indicating an individual's characteristics and tendencies.

[0320] A "skill map" is a diagram that visually shows the relationships between different activities and abilities.

[0321] A "sequence" is a list that describes a series of steps or operations in the optimal order.

[0322] "Real-time" refers to a state that immediately reflects the ongoing situation.

[0323] "Dynamic adjustment" means making flexible changes in response to changing circumstances.

[0324] A "mobility profile" is a dataset that shows the characteristics and trends of individual travel.

[0325] A "route" refers to the optimal direction or path from a specific point to a destination.

[0326] To implement this system, the server first collects user history data and generates individual profiles based on it. Specifically, it analyzes past behavioral patterns and characteristics using programming languages ​​such as Python and data analysis libraries (e.g., Pandas and Scikit-learn). Based on the generated profiles, it utilizes KMeans clustering and geopy to create skill maps that visualize the relationships between different activities. The server uses this information to propose the optimal sequence and travel path to the user in real time.

[0327] The terminal uses information provided by the server to visually display optimal learning plans and route information to the user. Specifically, it uses devices such as smartphones and tablets to provide a user-friendly display via a graphical user interface (GUI). The terminal also receives input from the user in real time and sends data to the server, updating information on both sides.

[0328] Users improve their skills and perform tasks efficiently based on the learning plans and route information displayed on their devices. For example, in delivery work, safe and fast routes are recommended based on past driving data. This system can use the following as an example of a prompt: "Please suggest the optimal delivery route considering current traffic conditions and past delivery patterns."

[0329] This allows users to obtain optimal guidance tailored to their characteristics and circumstances, and to enjoy a highly customized experience.

[0330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0331] Step 1:

[0332] The server collects user history data. This data includes logs of the user's past actions and operations. Based on this, a data analysis library is used to extract specific trends and patterns, generating individual profiles. These profiles summarize the characteristics and trends of past activities.

[0333] Step 2:

[0334] The server utilizes profile data to generate a map that visualizes the relationships between different activities. The input is the individual profiles obtained in Step 1. From these profiles, KMeans clustering is used to group related points and create a skill map as a graph structure. The output is a visually displayable relationship map.

[0335] Step 3:

[0336] The server proposes the optimal progression sequence and travel route based on the skill map and profile. The input here is the skill map and individual profile generated in step 2. Using this information, prompts are input to the generated AI model, taking into account real-time changing conditions, and the optimal plan is determined. As a specific example, the prompt "Please propose the optimal delivery route considering the current traffic conditions and past delivery patterns" is used.

[0337] Step 4:

[0338] The terminal provides users with visual information based on data provided by the server. The input includes optimal route information and plans. This is displayed via a GUI, making it easily understandable and visualized for the user. The output is a user-operable display screen, which the user uses to guide their activities.

[0339] Step 5:

[0340] The user effectively executes their activities using the information displayed on the device. The input here is the visual information on the device. Based on this, the user makes decisions and performs actions, and the results are fed back to the server via the device. The output is the data of the executed results, which is used for optimization in the next process.

[0341] 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.

[0342] This invention aims to provide learners with an optimal learning experience by incorporating an emotion engine into an educational support system. The system consists of a server, terminals, users, and an emotion engine.

[0343] Server Role

[0344] The server first collects learner history data and generates individual learning profiles. Based on the learner's past performance and learning activities, it clarifies their strengths and weaknesses in different subjects and their skill levels. It then enables cross-domain learning by generating skill maps that visualize the relationships between skills and concepts across different educational subjects. Furthermore, the server analyzes emotional data obtained from the emotion engine and adjusts the learning plan accordingly. The emotion engine monitors the learner's emotional state (e.g., excitement, concentration, stress) and modifies the learning sequence in real time based on this.

[0345] Terminal role

[0346] The terminal is a device that visually presents information sent from the server to the learner. By displaying skill maps, learning sequences, and feedback based on sentiment data on the interface, learners can progress through their learning while understanding their own progress.

[0347] User roles

[0348] Users learn according to personalized learning plans provided via their devices. Emotional data acquired by the emotion engine is fed back to the user, allowing learners to explore the optimal learning method while being aware of their own emotional state.

[0349] The role of the emotional engine

[0350] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the user's emotions in real time. The detected emotion information is sent to a server and used to provide a learning environment optimized for the learner's current emotional state.

[0351] Specific example

[0352] Consider a case of a university student studying physics and biology. This student often experiences stress when studying physics but is highly focused on biology. The emotion engine collects data when the user experiences stress while viewing physics content, and the server adjusts the learning plan based on this information. In the next learning session, it prioritizes biology topics and presents content designed to enhance learning motivation. Through this dynamic adaptation, the user can effectively continue to acquire knowledge.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The server collects the learner's past history data. This data includes test results and performance on past learning assignments.

[0356] Step 2:

[0357] The server generates a personalized learning profile for each learner based on the collected data. This profile meticulously records their strengths and learning style.

[0358] Step 3:

[0359] The server generates a skills map. It identifies the relationships between skills and concepts across different subjects and visualizes them in a map.

[0360] Step 4:

[0361] The server designs an optimized learning sequence, taking into account the generated learning profiles and skill maps.

[0362] Step 5:

[0363] The emotion engine collects emotional data in real time from the user's facial expressions and voice. This data includes emotions such as joy, concentration, and stress.

[0364] Step 6:

[0365] The server dynamically adjusts the learning sequence based on emotional data. It analyzes how the user's emotional state affects learning and modifies the learning content as needed.

[0366] Step 7:

[0367] The device visually presents the user with a learning plan and skill map that have been coordinated by the server. The user can then proceed with their learning according to this plan.

[0368] Step 8:

[0369] Users engage in learning activities according to the presented learning plan. They manage their own learning pace while receiving emotional feedback.

[0370] Step 9:

[0371] The device tracks the user's progress and sends information to the server in real time. This allows the server to make further adjustments and always provide the optimal learning environment.

[0372] (Example 2)

[0373] 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".

[0374] Providing a learning experience optimized for each individual learner is difficult, given that learners' motivation to learn changes depending on the situation. In particular, it is necessary to propose an appropriate learning plan while considering the learner's emotional state, but conventional methods do not adequately perform real-time sentiment analysis, resulting in incomplete optimization of learning.

[0375] 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.

[0376] In this invention, the server includes means for accumulating learners' past history and generating individual educator profiles based on that information, means for generating technology maps that visualize the relationships between technologies and concepts across different teaching areas, and means for analyzing emotional states in real time and dynamically adjusting the teaching plan. This makes it possible to provide an optimized learning environment based on learners' historical data and real-time emotional states.

[0377] "A learner's past history" refers to information about the learning activities and performance that a learner has undertaken to date.

[0378] An "educator profile" refers to an individualized learner profile constructed based on information such as the learner's strengths in subjects and skill level.

[0379] "Teaching area" refers to a specific subject or field in which educational activities are conducted.

[0380] "Relevance of technologies and concepts" refers to how common technologies and concepts are related across different areas of instruction.

[0381] A "technology map" refers to a diagram that visually displays the relationships between technologies and concepts across different areas of instruction.

[0382] "Analyzing emotional states in real time" refers to the process of instantly evaluating and digitizing the learner's emotions on the spot.

[0383] "Dynamically adjusting educational plans" refers to flexibly modifying learning plans and sequences in response to analyzed emotional states and learning progress.

[0384] This invention is an educational support system that provides an optimal learning environment for learners, and consists of a server, terminals, users, and an emotion engine.

[0385] Server role:

[0386] The server uses a database management system to collect learners' past history data. Specifically, it uses database software to manage learners' performance and activity logs, thereby generating individual educator profiles. Machine learning algorithms are used to analyze learners' strengths and skill levels in profile generation. Furthermore, it generates a technology map showing the relationships between techniques and concepts in different teaching areas, and visualizes these relationships using a Python library. Finally, it analyzes sentiment data obtained from an emotion engine and uses a generative AI model to create prompts, dynamically adjusting the teaching plan in real time.

[0387] Terminal role:

[0388] The terminal visually displays learning information transmitted from the server. This involves implementing an interactive user interface using web technologies, allowing learners to intuitively understand their learning progress and feedback. Technical maps, learning sequences, and feedback are displayed on this interface to aid learner comprehension.

[0389] User roles:

[0390] Users progress through their learning based on a learning plan provided via their device. Based on feedback from the emotion engine, users become aware of their own emotional state and, under that awareness, explore the optimal learning style.

[0391] The role of the emotional engine:

[0392] The emotion engine uses sensors such as cameras and microphones to detect learners' emotions in real time. To achieve this, it employs computer vision and natural language processing technologies to analyze learners' facial expressions and voices. The resulting emotion data is sent to a server and used to adjust the educational plan.

[0393] As a concrete example, if a student experiences stress while learning physics, the emotion engine detects this emotion and reports it to the server. Upon receiving this report, the server adjusts its plan to prioritize biology content in the next session. An example of a prompt might be a command given to the generating AI model: "Please provide appropriate learning adjustments for when a user experiences stress while learning physics."

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] The server collects learner history data. Specifically, it uses a database management system to extract learners' past grades and activity logs. Inputs include learner IDs and activity records for each semester, and output is an individual learner dataset. This dataset is used for profile generation in the next step.

[0397] Step 2:

[0398] The server generates individual educator profiles based on collected historical data. This process uses machine learning algorithms to analyze subjects of expertise and skill levels using the dataset as input. Specifically, k-means clustering is used to divide learners' characteristics into clusters. As output, individual educator profiles are generated.

[0399] Step 3:

[0400] The server generates a technology map that visualizes the relationships between technologies and concepts across teaching disciplines. The input is the generated educator profile and associated teaching data. A Python library (e.g., NetworkX) is used to build and visualize a network showing the relationships between each subject. The output is the technology map, which is used to propose learning sequences in the next step.

[0401] Step 4:

[0402] The emotion engine collects real-time emotional data of the user using sensors such as cameras and microphones. It receives real-time facial images and audio data as input and performs analysis using facial recognition software (e.g., OpenCV) and voice analysis tools (e.g., NLP technology). The output is data representing the user's current emotional state.

[0403] Step 5:

[0404] The server analyzes emotional data and dynamically adjusts the learning plan. Inputs include emotional state data obtained from the emotion engine and the server-side educator profile. A generative AI model generates prompts and proposes a new learning sequence. The output is a refined learning plan, which is then provided to the user.

[0405] Step 6:

[0406] The terminal displays data received from the server. Using specific web technologies (e.g., HTML, CSS, JavaScript), it receives a customized learning plan and technology map as input and displays them on an interactive user screen. The output is an easily viewable interface that supports the user's learning.

[0407] Step 7:

[0408] Users engage in learning activities based on a learning plan provided through their device. Specific actions include answering presented questions and viewing related content. Input consists of receiving instructions from the learning plan via the device, while output includes recording learning progress and new history data.

[0409] (Application Example 2)

[0410] 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."

[0411] Current learning support systems have a problem in that they have difficulty dynamically adjusting learning plans to take into account an individual's emotional state. As a result, learners may experience stress and decreased concentration, which hinders effective learning. There is also the challenge of not being able to visualize the relationships between different educational fields and provide a learning environment that responds to emotional states.

[0412] 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.

[0413] In this invention, the server includes means for collecting learner history data and generating individual learning profiles based on that information; means for generating knowledge maps that visualize the relationships between skills and concepts across different educational fields; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for identifying the learner's emotional state using emotion-detecting sensor technology and providing a learning environment that corresponds to that emotional state. By analyzing the learner's emotional state and dynamically adjusting the individual learning plan, more effective learning becomes possible.

[0414] "Historical data" refers to information about a learner's past learning activities and performance, and is used to generate a profile unique to each individual learner.

[0415] A "learning profile" is a collection of information generated based on historical data that indicates a learner's strengths and weaknesses and their skill level.

[0416] A "knowledge map" is a visual representation of the relationships between skills and concepts across different educational fields, helping to foster cross-domain understanding in learning.

[0417] "Emotional state" refers to the learner's current psychological state, and includes, for example, states such as stress, concentration, and relaxation.

[0418] "Sensor technology" refers to technologies used to detect emotional states, utilizing methods such as facial recognition and voice analysis.

[0419] A "learning sequence" refers to a series of learning steps that learners should follow to achieve a specific educational objective.

[0420] "Dynamic adjustment" is a method of optimization that involves analyzing data in real time and making changes to systems and processes based on the results.

[0421] This educational support system includes servers, terminals, and an emotion engine as its main components.

[0422] The server first collects learner history data and uses this information to generate individual learning profiles. These profiles are created based on the learner's past performance and activities and support learning plans aimed at specific educational goals. Furthermore, the server generates knowledge maps that visualize the relationships between skills and concepts across different educational fields, facilitating cross-domain learning. The server monitors learning progress in real time, dynamically adjusts the learning plan as needed, and provides an optimal learning sequence tailored to each individual learner.

[0423] The terminal plays a role in visually presenting information transmitted from the server to the learner. This includes knowledge maps, learning sequences, and feedback based on sentiment information. This interface makes it easier for learners to understand their own state and promotes self-regulated learning.

[0424] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the learner's emotional state in real time. This emotional data is sent to a server and used to provide the learner with the optimal learning environment. For example, if it detects that the learner is feeling stressed, it can dynamically adapt by switching to relaxing content or learning content that encourages a change of pace.

[0425] As a concrete example, consider a scenario where an elementary school student uses the system to learn at home. If the emotion engine detects a stress response while the student is working on a math problem, the system supports the student by providing simple games or relaxing content. Through this system, the student can continue learning effectively while reducing stress.

[0426] An example of a prompt message is, "Please suggest the best learning support method when the user is under stress." In this way, the system utilizes a generative AI model to provide an individually adapted learning experience.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The server collects learner history data. It receives past learning activity data and performance data as input to obtain basic information for generating individual learning profiles. This data is analyzed to create learning profiles that include strengths and weaknesses.

[0430] Step 2:

[0431] The server generates knowledge maps that visualize the relationships between skills and concepts across different educational disciplines. It takes learning profile data as input and models those relationships to output visual knowledge maps that enable cross-domain learning.

[0432] Step 3:

[0433] The device uses learning profiles and knowledge maps sent from the server to present the optimal learning sequence to the learner. It displays the received information on the interface, visualizing the learning content and steps available to the learner.

[0434] Step 4:

[0435] As the user progresses through the learning process, the server analyzes the learning progress in real time. It receives the learner's activity status as input, dynamically adjusts the learning plan based on the progress, and outputs the results.

[0436] Step 5:

[0437] The emotion engine detects the learner's emotional state in real time. It acquires emotional data as input using a facial recognition camera and microphone, analyzes this data to identify the current emotional state, and sends this information to a server.

[0438] Step 6:

[0439] The server adjusts the learning environment based on emotional data obtained from the emotion engine. If the emotional state is determined to be stressful, it suggests relaxing content or breaks to the learner. The optimized learning environment settings are then sent to the terminal as output.

[0440] Step 7:

[0441] The device presents the learner with the learning environment settings received from the server. It displays content and activities tailored to the learner on the screen, supporting efficient and appropriate learning.

[0442] This series of processes makes it possible to provide a learning experience tailored to the individual needs of each learner. Generative AI models are also used to generate prompts that further optimize the user experience.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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".

[0459] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide an optimized educational experience for learners.

[0460] Server Role

[0461] The server first collects learner history data, including information about past assessments, grades, and learning patterns. Using this data, the server generates an individualized learning profile that reflects the learner's strengths and weaknesses, as well as detailed skill levels. Furthermore, the server generates a skills map to visualize the relationships between skills and concepts across different subjects. This skills map is designed to enable learners to apply skills acquired in one subject to others.

[0462] Based on the generated learning profile and skill map, the server proposes a customized learning sequence to the learner. The proposed sequence is optimized for efficient learning. The server also analyzes the learner's progress in real time and dynamically adjusts the learning plan as needed. This ensures that learners are always provided with an optimal learning environment tailored to their progress.

[0463] Terminal role

[0464] The terminal is a device that visually presents learning plans and skill maps received from the server to the user. Specifically, it displays the user's learning progress and next assignments as graphs and charts. Furthermore, the terminal also plays a role in receiving and responding to user input.

[0465] User roles

[0466] Users independently progress through their learning by following the learning plan displayed on their device. They complete assigned tasks by solving problems and viewing learning materials. Learning progress is recorded on the device in real time and sent to the server for use as analysis material.

[0467] Specific example

[0468] For example, consider a high school student who primarily studies mathematics and science. This student excels at logical operations in mathematics but struggles with physics. Using this system, the server presents a customized learning sequence that applies mathematical skills to solving physics problems. Through the terminal, the user can apply the logical thinking skills cultivated in mathematics to physics experiment problems, developing practical application skills. In this way, the learning experience is optimized to individual needs, enabling efficient knowledge acquisition.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The server collects learner history data. It retrieves learner performance on past assignments, test results, and learning activity logs from the database.

[0472] Step 2:

[0473] The server generates individual learner profiles based on the collected data. It identifies the learner's strongest subjects, skill level, and learning style, and saves them as profiles.

[0474] Step 3:

[0475] The server generates a skill map that visualizes skill relationships between different subjects. It references profiles and graphs common abstract skills, such as math and science.

[0476] Step 4:

[0477] The server suggests the optimal learning sequence for the user based on their skill map and learning profile. It determines learning priorities and plans how the learner should progress through each subject.

[0478] Step 5:

[0479] The terminal visually displays the learning sequence and skill map received from the server. Graphs and charts are used to intuitively communicate the next steps to the learner.

[0480] Step 6:

[0481] Users follow the learning plan displayed on their device, solving problems and studying related materials.

[0482] Step 7:

[0483] The device records the user's learning activities. It saves information such as the progress made on each assignment, the accuracy of the answers, and the time spent on learning.

[0484] Step 8:

[0485] The server analyzes progress data sent from the terminal in real time. It evaluates the user's skill improvement and determines the effectiveness of the learning plan.

[0486] Step 9:

[0487] The server dynamically adjusts the learning plan based on the analysis results. It updates the next learning content and resets the optimized plan according to the user's progress.

[0488] (Example 1)

[0489] 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."

[0490] Conventional educational support systems have struggled to provide uniform learning plans and individually optimize learning progress, making it difficult to deliver effective educational experiences that meet the diverse needs of learners. Furthermore, they lacked real-time adjustments based on learning progress, which prevented improvements in learning efficiency.

[0491] 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.

[0492] In this invention, the server includes means for collecting learner history information and generating individual learning profiles based on that information; means for generating skill mappings that visualize the relationships between abilities and concepts across different educational domains; means for proposing an optimal learning process based on the skill mappings and learning profiles; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for visually displaying the progress status to the learner on a terminal and accepting user input. This makes it possible to provide an efficient and appropriate learning environment that meets individual learning needs.

[0493] "History information" refers to all data related to the learning activities that a learner has undertaken to date, including past grades and behavioral history.

[0494] A "learning profile" is a personalized collection of information generated based on collected historical data, reflecting the learner's strengths and skill levels.

[0495] "Skill mapping" refers to diagrams and representations used to visually display the relationships between abilities and concepts across different educational domains.

[0496] The term "learning process" refers to the sequence and order of learning activities that learners should follow in order to acquire optimal knowledge.

[0497] "Progress analysis" is a process that analyzes the progress of learners' learning activities in real time and evaluates their current position and level of achievement.

[0498] "Visual information" refers to information in a visually easy-to-understand format, such as graphs and charts, provided to learners via a device.

[0499] "User input" refers to information that learners provide to the system through their devices, including feedback and progress reports.

[0500] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide learners with an individualized educational experience.

[0501] Server Role

[0502] The server possesses advanced data processing capabilities, collecting learner history information and generating individual learning profiles. This utilizes a database processing system, applying machine learning algorithms and generative AI models to analyze the information. For example, data analysis tools built in Python are used to identify learner trends from past learning data and formulate individual learning routes. The server also generates skill mappings that visualize competency relationships across different educational domains, and uses this to suggest the optimal learning process for each learner. Real-time progress analysis and dynamic plan adjustments are also included as server functions, with cloud services assisting in the processing and synchronization of real-time data.

[0503] Terminal role

[0504] The terminal functions as a display device that provides learners with information transmitted from the server. For example, a tablet or laptop may be used, displaying the learner's progress and skill mapping as charts. The terminal also collects user feedback and sends it to the server to allow for further adjustments to the learning plan. User input is performed via a graphical user interface.

[0505] User roles

[0506] Users progress through their learning activities based on information presented via their devices. For example, they might follow a displayed roadmap to tackle the next task. Users also report feedback and progress via their devices, which is sent to the server, providing a more personalized learning sequence. This allows learners to develop practical application skills and acquire knowledge efficiently.

[0507] Specific example

[0508] For example, if a high school student is studying mathematics and physics, the server analyzes their past performance and current learning content and suggests ways to solve physics problems that utilize their logical thinking skills in mathematics. This information is presented intuitively to the user via a terminal, assisting them in their actual learning process.

[0509] Example of a prompt

[0510] "A high school student is strong in mathematical logic but struggles to understand physics. How can you provide a learning sequence that allows them to apply their mathematical skills to physics?"

[0511] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0512] Step 1:

[0513] The server collects learner history information. Specifically, it retrieves data such as past learning performance, test results, and study time from a database. The input is the learner ID, and the output is the history information associated with that ID. This collected data serves as the basic information necessary for subsequent profile generation.

[0514] Step 2:

[0515] The server generates individual learning profiles based on collected historical information. This process uses a generative AI model to analyze the acquired historical data and identify learner tendencies. The input is the collected historical information, and the output is a learning profile including the learner's strengths and weaknesses in different subjects and their skill level. The algorithm used for analysis utilizes pattern recognition technology.

[0516] Step 3:

[0517] The server generates skill mappings based on learning profiles. Here, we use the power of a generative AI model to visualize the transferability of skills across different educational domains. The input is the learning profile, and the output is the skill mapping. This mapping shows, for example, how mathematical logical thinking can be applied to solving problems in physics.

[0518] Step 4:

[0519] The server proposes an optimal learning process based on skill mapping and learning profiles. The input is skill mapping and learning profiles, and the output is a learning process optimized for the learner. The generative AI model derives an efficient and personalized learning sequence.

[0520] Step 5:

[0521] The terminal visually presents the proposed learning process and skill mapping to the user. Input is the learning process and skill mapping sent from the server, and output is visual information using charts and graphs. The user interface allows learners to understand their learning progress and the next steps.

[0522] Step 6:

[0523] Users perform learning activities according to the learning process displayed on their device. They report their learning progress and assignment completion status as input to the device. The output consists of learning results and feedback information. This information is sent to the server and used to adjust future learning plans.

[0524] Step 7:

[0525] The server receives user progress data and feedback as input and analyzes learning progress in real time. Based on the analysis results, it dynamically adjusts the learning plan. The output is the updated learning process, which is then provided to the user again via the terminal.

[0526] (Application Example 1)

[0527] 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."

[0528] In online learning environments and delivery operations, there is a need for systems that can propose optimal plans based on the learning or work progress of individual users or delivery personnel. However, existing technologies make it difficult to provide real-time, dynamically personalized, and efficient progress plans. In particular, in mobile operations, the challenge is to constantly propose efficient routes in response to changing traffic conditions.

[0529] 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.

[0530] In this invention, the server includes means for collecting learner and delivery person history data and generating individual profiles based on that information; means for generating maps that visualize the relationships between different operations and objectives; and means for proposing an optimal progression sequence or route based on the map and profiles. This enables the plan to be dynamically adjusted according to the user's progress and traffic conditions, resulting in optimized and efficient achievement.

[0531] "Historical data" refers to a collection of information that shows records of actions and operations performed in the past.

[0532] A "profile" is a dataset that compiles information indicating an individual's characteristics and tendencies.

[0533] A "skill map" is a diagram that visually shows the relationships between different activities and abilities.

[0534] A "sequence" is a list that describes a series of steps or operations in the optimal order.

[0535] "Real-time" refers to a state that immediately reflects the ongoing situation.

[0536] "Dynamic adjustment" means making flexible changes in response to changing circumstances.

[0537] A "mobility profile" is a dataset that shows the characteristics and trends of individual travel.

[0538] A "route" refers to the optimal direction or path from a specific point to a destination.

[0539] To implement this system, the server first collects user history data and generates individual profiles based on it. Specifically, it analyzes past behavioral patterns and characteristics using programming languages ​​such as Python and data analysis libraries (e.g., Pandas and Scikit-learn). Based on the generated profiles, it utilizes KMeans clustering and geopy to create skill maps that visualize the relationships between different activities. The server uses this information to propose the optimal sequence and travel path to the user in real time.

[0540] The terminal uses information provided by the server to visually display optimal learning plans and route information to the user. Specifically, it uses devices such as smartphones and tablets to provide a user-friendly display via a graphical user interface (GUI). The terminal also receives input from the user in real time and sends data to the server, updating information on both sides.

[0541] Users improve their skills and perform tasks efficiently based on the learning plans and route information displayed on their devices. For example, in delivery work, safe and fast routes are recommended based on past driving data. This system can use the following as an example of a prompt: "Please suggest the optimal delivery route considering current traffic conditions and past delivery patterns."

[0542] This allows users to obtain optimal guidance tailored to their characteristics and circumstances, and to enjoy a highly customized experience.

[0543] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0544] Step 1:

[0545] The server collects user history data. This data includes logs of the user's past actions and operations. Based on this, a data analysis library is used to extract specific trends and patterns, generating individual profiles. These profiles summarize the characteristics and trends of past activities.

[0546] Step 2:

[0547] The server utilizes profile data to generate a map that visualizes the relationships between different activities. The input is the individual profiles obtained in Step 1. From these profiles, KMeans clustering is used to group related points and create a skill map as a graph structure. The output is a visually displayable relationship map.

[0548] Step 3:

[0549] The server proposes the optimal progression sequence and travel route based on the skill map and profile. The input here is the skill map and individual profile generated in step 2. Using this information, prompts are input to the generated AI model, taking into account real-time changing conditions, and the optimal plan is determined. As a specific example, the prompt "Please propose the optimal delivery route considering the current traffic conditions and past delivery patterns" is used.

[0550] Step 4:

[0551] The terminal provides users with visual information based on data provided by the server. The input includes optimal route information and plans. This is displayed via a GUI, making it easily understandable and visualized for the user. The output is a user-operable display screen, which the user uses to guide their activities.

[0552] Step 5:

[0553] The user effectively executes their activities using the information displayed on the device. The input here is the visual information on the device. Based on this, the user makes decisions and performs actions, and the results are fed back to the server via the device. The output is the data of the executed results, which is used for optimization in the next process.

[0554] 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.

[0555] This invention aims to provide learners with an optimal learning experience by incorporating an emotion engine into an educational support system. The system consists of a server, terminals, users, and an emotion engine.

[0556] Server Role

[0557] The server first collects learner history data and generates individual learning profiles. Based on the learner's past performance and learning activities, it clarifies their strengths and weaknesses in different subjects and their skill levels. It then enables cross-domain learning by generating skill maps that visualize the relationships between skills and concepts across different educational subjects. Furthermore, the server analyzes emotional data obtained from the emotion engine and adjusts the learning plan accordingly. The emotion engine monitors the learner's emotional state (e.g., excitement, concentration, stress) and modifies the learning sequence in real time based on this.

[0558] Terminal role

[0559] The terminal is a device that visually presents information sent from the server to the learner. By displaying skill maps, learning sequences, and feedback based on sentiment data on the interface, learners can progress through their learning while understanding their own progress.

[0560] User roles

[0561] Users learn according to personalized learning plans provided via their devices. Emotional data acquired by the emotion engine is fed back to the user, allowing learners to explore the optimal learning method while being aware of their own emotional state.

[0562] The role of the emotional engine

[0563] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the user's emotions in real time. The detected emotion information is sent to a server and used to provide a learning environment optimized for the learner's current emotional state.

[0564] Specific example

[0565] Consider a case of a university student studying physics and biology. This student often experiences stress when studying physics but is highly focused on biology. The emotion engine collects data when the user experiences stress while viewing physics content, and the server adjusts the learning plan based on this information. In the next learning session, it prioritizes biology topics and presents content designed to enhance learning motivation. Through this dynamic adaptation, the user can effectively continue to acquire knowledge.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The server collects the learner's past history data. This data includes test results and performance on past learning assignments.

[0569] Step 2:

[0570] The server generates a personalized learning profile for each learner based on the collected data. This profile meticulously records their strengths and learning style.

[0571] Step 3:

[0572] The server generates a skills map. It identifies the relationships between skills and concepts across different subjects and visualizes them in a map.

[0573] Step 4:

[0574] The server designs an optimized learning sequence, taking into account the generated learning profiles and skill maps.

[0575] Step 5:

[0576] The emotion engine collects emotional data in real time from the user's facial expressions and voice. This data includes emotions such as joy, concentration, and stress.

[0577] Step 6:

[0578] The server dynamically adjusts the learning sequence based on emotional data. It analyzes how the user's emotional state affects learning and modifies the learning content as needed.

[0579] Step 7:

[0580] The device visually presents the user with a learning plan and skill map that have been coordinated by the server. The user can then proceed with their learning according to this plan.

[0581] Step 8:

[0582] Users engage in learning activities according to the presented learning plan. They manage their own learning pace while receiving emotional feedback.

[0583] Step 9:

[0584] The device tracks the user's progress and sends information to the server in real time. This allows the server to make further adjustments and always provide the optimal learning environment.

[0585] (Example 2)

[0586] 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."

[0587] Providing a learning experience optimized for each individual learner is difficult, given that learners' motivation to learn changes depending on the situation. In particular, it is necessary to propose an appropriate learning plan while considering the learner's emotional state, but conventional methods do not adequately perform real-time sentiment analysis, resulting in incomplete optimization of learning.

[0588] 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.

[0589] In this invention, the server includes means for accumulating learners' past history and generating individual educator profiles based on that information, means for generating technology maps that visualize the relationships between technologies and concepts across different teaching areas, and means for analyzing emotional states in real time and dynamically adjusting the teaching plan. This makes it possible to provide an optimized learning environment based on learners' historical data and real-time emotional states.

[0590] "A learner's past history" refers to information about the learning activities and performance that a learner has undertaken to date.

[0591] An "educator profile" refers to an individualized learner profile constructed based on information such as the learner's strengths in subjects and skill level.

[0592] "Teaching area" refers to a specific subject or field in which educational activities are conducted.

[0593] "Relevance of technologies and concepts" refers to how common technologies and concepts are related across different areas of instruction.

[0594] A "technology map" refers to a diagram that visually displays the relationships between technologies and concepts across different areas of instruction.

[0595] "Analyzing emotional states in real time" refers to the process of instantly evaluating and digitizing the learner's emotions on the spot.

[0596] "Dynamically adjusting educational plans" refers to flexibly modifying learning plans and sequences in response to analyzed emotional states and learning progress.

[0597] This invention is an educational support system that provides an optimal learning environment for learners, and consists of a server, terminals, users, and an emotion engine.

[0598] Server role:

[0599] The server uses a database management system to collect learners' past history data. Specifically, it uses database software to manage learners' performance and activity logs, thereby generating individual educator profiles. Machine learning algorithms are used to analyze learners' strengths and skill levels in profile generation. Furthermore, it generates a technology map showing the relationships between techniques and concepts in different teaching areas, and visualizes these relationships using a Python library. Finally, it analyzes sentiment data obtained from an emotion engine and uses a generative AI model to create prompts, dynamically adjusting the teaching plan in real time.

[0600] Terminal role:

[0601] The terminal visually displays learning information transmitted from the server. This involves implementing an interactive user interface using web technologies, allowing learners to intuitively understand their learning progress and feedback. Technical maps, learning sequences, and feedback are displayed on this interface to aid learner comprehension.

[0602] User roles:

[0603] Users progress through their learning based on a learning plan provided via their device. Based on feedback from the emotion engine, users become aware of their own emotional state and, under that awareness, explore the optimal learning style.

[0604] The role of the emotional engine:

[0605] The emotion engine uses sensors such as cameras and microphones to detect learners' emotions in real time. To achieve this, it employs computer vision and natural language processing technologies to analyze learners' facial expressions and voices. The resulting emotion data is sent to a server and used to adjust the educational plan.

[0606] As a concrete example, if a student experiences stress while learning physics, the emotion engine detects this emotion and reports it to the server. Upon receiving this report, the server adjusts its plan to prioritize biology content in the next session. An example of a prompt might be a command given to the generating AI model: "Please provide appropriate learning adjustments for when a user experiences stress while learning physics."

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] The server collects learner history data. Specifically, it uses a database management system to extract learners' past grades and activity logs. Inputs include learner IDs and activity records for each semester, and output is an individual learner dataset. This dataset is used for profile generation in the next step.

[0610] Step 2:

[0611] The server generates individual educator profiles based on collected historical data. This process uses machine learning algorithms to analyze subjects of expertise and skill levels using the dataset as input. Specifically, k-means clustering is used to divide learners' characteristics into clusters. As output, individual educator profiles are generated.

[0612] Step 3:

[0613] The server generates a technology map that visualizes the relationships between technologies and concepts across teaching disciplines. The input is the generated educator profile and associated teaching data. A Python library (e.g., NetworkX) is used to build and visualize a network showing the relationships between each subject. The output is the technology map, which is used to propose learning sequences in the next step.

[0614] Step 4:

[0615] The emotion engine collects real-time emotional data of the user using sensors such as cameras and microphones. It receives real-time facial images and audio data as input and performs analysis using facial recognition software (e.g., OpenCV) and voice analysis tools (e.g., NLP technology). The output is data representing the user's current emotional state.

[0616] Step 5:

[0617] The server analyzes emotional data and dynamically adjusts the learning plan. Inputs include emotional state data obtained from the emotion engine and the server-side educator profile. A generative AI model generates prompts and proposes a new learning sequence. The output is a refined learning plan, which is then provided to the user.

[0618] Step 6:

[0619] The terminal displays data received from the server. Using specific web technologies (e.g., HTML, CSS, JavaScript), it receives a customized learning plan and technology map as input and displays them on an interactive user screen. The output is an easily viewable interface that supports the user's learning.

[0620] Step 7:

[0621] Users engage in learning activities based on a learning plan provided through their device. Specific actions include answering presented questions and viewing related content. Input consists of receiving instructions from the learning plan via the device, while output includes recording learning progress and new history data.

[0622] (Application Example 2)

[0623] 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."

[0624] Current learning support systems have a problem in that they have difficulty dynamically adjusting learning plans to take into account an individual's emotional state. As a result, learners may experience stress and decreased concentration, which hinders effective learning. There is also the challenge of not being able to visualize the relationships between different educational fields and provide a learning environment that responds to emotional states.

[0625] 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.

[0626] In this invention, the server includes means for collecting learner history data and generating individual learning profiles based on that information; means for generating knowledge maps that visualize the relationships between skills and concepts across different educational fields; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for identifying the learner's emotional state using emotion-detecting sensor technology and providing a learning environment that corresponds to that emotional state. By analyzing the learner's emotional state and dynamically adjusting the individual learning plan, more effective learning becomes possible.

[0627] "Historical data" refers to information about a learner's past learning activities and performance, and is used to generate a profile unique to each individual learner.

[0628] A "learning profile" is a collection of information generated based on historical data that indicates a learner's strengths and weaknesses and their skill level.

[0629] A "knowledge map" is a visual representation of the relationships between skills and concepts across different educational fields, helping to foster cross-domain understanding in learning.

[0630] "Emotional state" refers to the learner's current psychological state, and includes, for example, states such as stress, concentration, and relaxation.

[0631] "Sensor technology" refers to technologies used to detect emotional states, utilizing methods such as facial recognition and voice analysis.

[0632] A "learning sequence" refers to a series of learning steps that learners should follow to achieve a specific educational objective.

[0633] "Dynamic adjustment" is a method of optimization that involves analyzing data in real time and making changes to systems and processes based on the results.

[0634] This educational support system includes servers, terminals, and an emotion engine as its main components.

[0635] The server first collects learner history data and uses this information to generate individual learning profiles. These profiles are created based on the learner's past performance and activities and support learning plans aimed at specific educational goals. Furthermore, the server generates knowledge maps that visualize the relationships between skills and concepts across different educational fields, facilitating cross-domain learning. The server monitors learning progress in real time, dynamically adjusts the learning plan as needed, and provides an optimal learning sequence tailored to each individual learner.

[0636] The terminal plays a role in visually presenting information transmitted from the server to the learner. This includes knowledge maps, learning sequences, and feedback based on sentiment information. This interface makes it easier for learners to understand their own state and promotes self-regulated learning.

[0637] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the learner's emotional state in real time. This emotional data is sent to a server and used to provide the learner with the optimal learning environment. For example, if it detects that the learner is feeling stressed, it can dynamically adapt by switching to relaxing content or learning content that encourages a change of pace.

[0638] As a concrete example, consider a scenario where an elementary school student uses the system to learn at home. If the emotion engine detects a stress response while the student is working on a math problem, the system supports the student by providing simple games or relaxing content. Through this system, the student can continue learning effectively while reducing stress.

[0639] An example of a prompt message is, "Please suggest the best learning support method when the user is under stress." In this way, the system utilizes a generative AI model to provide an individually adapted learning experience.

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The server collects learner history data. It receives past learning activity data and performance data as input to obtain basic information for generating individual learning profiles. This data is analyzed to create learning profiles that include strengths and weaknesses.

[0643] Step 2:

[0644] The server generates knowledge maps that visualize the relationships between skills and concepts across different educational disciplines. It takes learning profile data as input and models those relationships to output visual knowledge maps that enable cross-domain learning.

[0645] Step 3:

[0646] The device uses learning profiles and knowledge maps sent from the server to present the optimal learning sequence to the learner. It displays the received information on the interface, visualizing the learning content and steps available to the learner.

[0647] Step 4:

[0648] As the user progresses through the learning process, the server analyzes the learning progress in real time. It receives the learner's activity status as input, dynamically adjusts the learning plan based on the progress, and outputs the results.

[0649] Step 5:

[0650] The emotion engine detects the learner's emotional state in real time. It acquires emotional data as input using a facial recognition camera and microphone, analyzes this data to identify the current emotional state, and sends this information to a server.

[0651] Step 6:

[0652] The server adjusts the learning environment based on emotional data obtained from the emotion engine. If the emotional state is determined to be stressful, it suggests relaxing content or breaks to the learner. The optimized learning environment settings are then sent to the terminal as output.

[0653] Step 7:

[0654] The device presents the learner with the learning environment settings received from the server. It displays content and activities tailored to the learner on the screen, supporting efficient and appropriate learning.

[0655] This series of processes makes it possible to provide a learning experience tailored to the individual needs of each learner. Generative AI models are also used to generate prompts that further optimize the user experience.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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".

[0673] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide an optimized educational experience for learners.

[0674] Server Role

[0675] The server first collects learner history data, including information about past assessments, grades, and learning patterns. Using this data, the server generates an individualized learning profile that reflects the learner's strengths and weaknesses, as well as detailed skill levels. Furthermore, the server generates a skills map to visualize the relationships between skills and concepts across different subjects. This skills map is designed to enable learners to apply skills acquired in one subject to others.

[0676] Based on the generated learning profile and skill map, the server proposes a customized learning sequence to the learner. The proposed sequence is optimized for efficient learning. The server also analyzes the learner's progress in real time and dynamically adjusts the learning plan as needed. This ensures that learners are always provided with an optimal learning environment tailored to their progress.

[0677] Terminal role

[0678] The terminal is a device that visually presents learning plans and skill maps received from the server to the user. Specifically, it displays the user's learning progress and next assignments as graphs and charts. Furthermore, the terminal also plays a role in receiving and responding to user input.

[0679] User roles

[0680] Users independently progress through their learning by following the learning plan displayed on their device. They complete assigned tasks by solving problems and viewing learning materials. Learning progress is recorded on the device in real time and sent to the server for use as analysis material.

[0681] Specific example

[0682] For example, consider a high school student who primarily studies mathematics and science. This student excels at logical operations in mathematics but struggles with physics. Using this system, the server presents a customized learning sequence that applies mathematical skills to solving physics problems. Through the terminal, the user can apply the logical thinking skills cultivated in mathematics to physics experiment problems, developing practical application skills. In this way, the learning experience is optimized to individual needs, enabling efficient knowledge acquisition.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The server collects learner history data. It retrieves learner performance on past assignments, test results, and learning activity logs from the database.

[0686] Step 2:

[0687] The server generates individual learner profiles based on the collected data. It identifies the learner's strongest subjects, skill level, and learning style, and saves them as profiles.

[0688] Step 3:

[0689] The server generates a skill map that visualizes skill relationships between different subjects. It references profiles and graphs common abstract skills, such as math and science.

[0690] Step 4:

[0691] The server suggests the optimal learning sequence for the user based on their skill map and learning profile. It determines learning priorities and plans how the learner should progress through each subject.

[0692] Step 5:

[0693] The terminal visually displays the learning sequence and skill map received from the server. Graphs and charts are used to intuitively communicate the next steps to the learner.

[0694] Step 6:

[0695] Users follow the learning plan displayed on their device, solving problems and studying related materials.

[0696] Step 7:

[0697] The device records the user's learning activities. It saves information such as the progress made on each assignment, the accuracy of the answers, and the time spent on learning.

[0698] Step 8:

[0699] The server analyzes progress data sent from the terminal in real time. It evaluates the user's skill improvement and determines the effectiveness of the learning plan.

[0700] Step 9:

[0701] The server dynamically adjusts the learning plan based on the analysis results. It updates the next learning content and resets the optimized plan according to the user's progress.

[0702] (Example 1)

[0703] 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".

[0704] Conventional educational support systems have struggled to provide uniform learning plans and individually optimize learning progress, making it difficult to deliver effective educational experiences that meet the diverse needs of learners. Furthermore, they lacked real-time adjustments based on learning progress, which prevented improvements in learning efficiency.

[0705] 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.

[0706] In this invention, the server includes means for collecting learner history information and generating individual learning profiles based on that information; means for generating skill mappings that visualize the relationships between abilities and concepts across different educational domains; means for proposing an optimal learning process based on the skill mappings and learning profiles; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for visually displaying the progress status to the learner on a terminal and accepting user input. This makes it possible to provide an efficient and appropriate learning environment that meets individual learning needs.

[0707] "History information" refers to all data related to the learning activities that a learner has undertaken to date, including past grades and behavioral history.

[0708] A "learning profile" is a personalized collection of information generated based on collected historical data, reflecting the learner's strengths and skill levels.

[0709] "Skill mapping" refers to diagrams and representations used to visually display the relationships between abilities and concepts across different educational domains.

[0710] The term "learning process" refers to the sequence and order of learning activities that learners should follow in order to acquire optimal knowledge.

[0711] "Progress analysis" is a process that analyzes the progress of learners' learning activities in real time and evaluates their current position and level of achievement.

[0712] "Visual information" refers to information in a visually easy-to-understand format, such as graphs and charts, provided to learners via a device.

[0713] "User input" refers to information that learners provide to the system through their devices, including feedback and progress reports.

[0714] This invention is implemented as an educational support system in which a server, terminal, and user work together to provide learners with an individualized educational experience.

[0715] Server Role

[0716] The server possesses advanced data processing capabilities, collecting learner history information and generating individual learning profiles. This utilizes a database processing system, applying machine learning algorithms and generative AI models to analyze the information. For example, data analysis tools built in Python are used to identify learner trends from past learning data and formulate individual learning routes. The server also generates skill mappings that visualize competency relationships across different educational domains, and uses this to suggest the optimal learning process for each learner. Real-time progress analysis and dynamic plan adjustments are also included as server functions, with cloud services assisting in the processing and synchronization of real-time data.

[0717] Terminal role

[0718] The terminal functions as a display device that provides learners with information transmitted from the server. For example, a tablet or laptop may be used, displaying the learner's progress and skill mapping as charts. The terminal also collects user feedback and sends it to the server to allow for further adjustments to the learning plan. User input is performed via a graphical user interface.

[0719] User roles

[0720] Users progress through their learning activities based on information presented via their devices. For example, they might follow a displayed roadmap to tackle the next task. Users also report feedback and progress via their devices, which is sent to the server, providing a more personalized learning sequence. This allows learners to develop practical application skills and acquire knowledge efficiently.

[0721] Specific example

[0722] For example, if a high school student is studying mathematics and physics, the server analyzes their past performance and current learning content and suggests ways to solve physics problems that utilize their logical thinking skills in mathematics. This information is presented intuitively to the user via a terminal, assisting them in their actual learning process.

[0723] Example of a prompt

[0724] "A high school student is strong in mathematical logic but struggles to understand physics. How can you provide a learning sequence that allows them to apply their mathematical skills to physics?"

[0725] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0726] Step 1:

[0727] The server collects learner history information. Specifically, it retrieves data such as past learning performance, test results, and study time from a database. The input is the learner ID, and the output is the history information associated with that ID. This collected data serves as the basic information necessary for subsequent profile generation.

[0728] Step 2:

[0729] The server generates individual learning profiles based on collected historical information. This process uses a generative AI model to analyze the acquired historical data and identify learner tendencies. The input is the collected historical information, and the output is a learning profile including the learner's strengths and weaknesses in different subjects and their skill level. The algorithm used for analysis utilizes pattern recognition technology.

[0730] Step 3:

[0731] The server generates skill mappings based on learning profiles. Here, we use the power of a generative AI model to visualize the transferability of skills across different educational domains. The input is the learning profile, and the output is the skill mapping. This mapping shows, for example, how mathematical logical thinking can be applied to solving problems in physics.

[0732] Step 4:

[0733] The server proposes an optimal learning process based on skill mapping and learning profiles. The input is skill mapping and learning profiles, and the output is a learning process optimized for the learner. The generative AI model derives an efficient and personalized learning sequence.

[0734] Step 5:

[0735] The terminal visually presents the proposed learning process and skill mapping to the user. Input is the learning process and skill mapping sent from the server, and output is visual information using charts and graphs. The user interface allows learners to understand their learning progress and the next steps.

[0736] Step 6:

[0737] Users perform learning activities according to the learning process displayed on their device. They report their learning progress and assignment completion status as input to the device. The output consists of learning results and feedback information. This information is sent to the server and used to adjust future learning plans.

[0738] Step 7:

[0739] The server receives user progress data and feedback as input and analyzes learning progress in real time. Based on the analysis results, it dynamically adjusts the learning plan. The output is the updated learning process, which is then provided to the user again via the terminal.

[0740] (Application Example 1)

[0741] 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".

[0742] In online learning environments and delivery operations, there is a need for systems that can propose optimal plans based on the learning or work progress of individual users or delivery personnel. However, existing technologies make it difficult to provide real-time, dynamically personalized, and efficient progress plans. In particular, in mobile operations, the challenge is to constantly propose efficient routes in response to changing traffic conditions.

[0743] 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.

[0744] In this invention, the server includes means for collecting learner and delivery person history data and generating individual profiles based on that information; means for generating maps that visualize the relationships between different operations and objectives; and means for proposing an optimal progression sequence or route based on the map and profiles. This enables the plan to be dynamically adjusted according to the user's progress and traffic conditions, resulting in optimized and efficient achievement.

[0745] "Historical data" refers to a collection of information that shows records of actions and operations performed in the past.

[0746] A "profile" is a dataset that compiles information indicating an individual's characteristics and tendencies.

[0747] A "skill map" is a diagram that visually shows the relationships between different activities and abilities.

[0748] A "sequence" is a list that describes a series of steps or operations in the optimal order.

[0749] "Real-time" refers to a state that immediately reflects the ongoing situation.

[0750] "Dynamic adjustment" means making flexible changes in response to changing circumstances.

[0751] A "mobility profile" is a dataset that shows the characteristics and trends of individual travel.

[0752] A "route" refers to the optimal direction or path from a specific point to a destination.

[0753] To implement this system, the server first collects user history data and generates individual profiles based on it. Specifically, it analyzes past behavioral patterns and characteristics using programming languages ​​such as Python and data analysis libraries (e.g., Pandas and Scikit-learn). Based on the generated profiles, it utilizes KMeans clustering and geopy to create skill maps that visualize the relationships between different activities. The server uses this information to propose the optimal sequence and travel path to the user in real time.

[0754] The terminal uses information provided by the server to visually display optimal learning plans and route information to the user. Specifically, it uses devices such as smartphones and tablets to provide a user-friendly display via a graphical user interface (GUI). The terminal also receives input from the user in real time and sends data to the server, updating information on both sides.

[0755] Users improve their skills and perform tasks efficiently based on the learning plans and route information displayed on their devices. For example, in delivery work, safe and fast routes are recommended based on past driving data. This system can use the following as an example of a prompt: "Please suggest the optimal delivery route considering current traffic conditions and past delivery patterns."

[0756] This allows users to obtain optimal guidance tailored to their characteristics and circumstances, and to enjoy a highly customized experience.

[0757] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0758] Step 1:

[0759] The server collects user history data. This data includes logs of the user's past actions and operations. Based on this, a data analysis library is used to extract specific trends and patterns, generating individual profiles. These profiles summarize the characteristics and trends of past activities.

[0760] Step 2:

[0761] The server utilizes profile data to generate a map that visualizes the relationships between different activities. The input is the individual profiles obtained in Step 1. From these profiles, KMeans clustering is used to group related points and create a skill map as a graph structure. The output is a visually displayable relationship map.

[0762] Step 3:

[0763] The server proposes the optimal progression sequence and travel route based on the skill map and profile. The input here is the skill map and individual profile generated in step 2. Using this information, prompts are input to the generated AI model, taking into account real-time changing conditions, and the optimal plan is determined. As a specific example, the prompt "Please propose the optimal delivery route considering the current traffic conditions and past delivery patterns" is used.

[0764] Step 4:

[0765] The terminal provides users with visual information based on data provided by the server. The input includes optimal route information and plans. This is displayed via a GUI, making it easily understandable and visualized for the user. The output is a user-operable display screen, which the user uses to guide their activities.

[0766] Step 5:

[0767] The user effectively executes their activities using the information displayed on the device. The input here is the visual information on the device. Based on this, the user makes decisions and performs actions, and the results are fed back to the server via the device. The output is the data of the executed results, which is used for optimization in the next process.

[0768] 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.

[0769] This invention aims to provide learners with an optimal learning experience by incorporating an emotion engine into an educational support system. The system consists of a server, terminals, users, and an emotion engine.

[0770] Server Role

[0771] The server first collects learner history data and generates individual learning profiles. Based on the learner's past performance and learning activities, it clarifies their strengths and weaknesses in different subjects and their skill levels. It then enables cross-domain learning by generating skill maps that visualize the relationships between skills and concepts across different educational subjects. Furthermore, the server analyzes emotional data obtained from the emotion engine and adjusts the learning plan accordingly. The emotion engine monitors the learner's emotional state (e.g., excitement, concentration, stress) and modifies the learning sequence in real time based on this.

[0772] Terminal role

[0773] The terminal is a device that visually presents information sent from the server to the learner. By displaying skill maps, learning sequences, and feedback based on sentiment data on the interface, learners can progress through their learning while understanding their own progress.

[0774] User roles

[0775] Users learn according to personalized learning plans provided via their devices. Emotional data acquired by the emotion engine is fed back to the user, allowing learners to explore the optimal learning method while being aware of their own emotional state.

[0776] The role of the emotional engine

[0777] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the user's emotions in real time. The detected emotion information is sent to a server and used to provide a learning environment optimized for the learner's current emotional state.

[0778] Specific example

[0779] Consider a case of a university student studying physics and biology. This student often experiences stress when studying physics but is highly focused on biology. The emotion engine collects data when the user experiences stress while viewing physics content, and the server adjusts the learning plan based on this information. In the next learning session, it prioritizes biology topics and presents content designed to enhance learning motivation. Through this dynamic adaptation, the user can effectively continue to acquire knowledge.

[0780] The following describes the processing flow.

[0781] Step 1:

[0782] The server collects the learner's past history data. This data includes test results and performance on past learning assignments.

[0783] Step 2:

[0784] The server generates a personalized learning profile for each learner based on the collected data. This profile meticulously records their strengths and learning style.

[0785] Step 3:

[0786] The server generates a skills map. It identifies the relationships between skills and concepts across different subjects and visualizes them in a map.

[0787] Step 4:

[0788] The server designs an optimized learning sequence, taking into account the generated learning profiles and skill maps.

[0789] Step 5:

[0790] The emotion engine collects emotional data in real time from the user's facial expressions and voice. This data includes emotions such as joy, concentration, and stress.

[0791] Step 6:

[0792] The server dynamically adjusts the learning sequence based on emotional data. It analyzes how the user's emotional state affects learning and modifies the learning content as needed.

[0793] Step 7:

[0794] The device visually presents the user with a learning plan and skill map that have been coordinated by the server. The user can then proceed with their learning according to this plan.

[0795] Step 8:

[0796] Users engage in learning activities according to the presented learning plan. They manage their own learning pace while receiving emotional feedback.

[0797] Step 9:

[0798] The device tracks the user's progress and sends information to the server in real time. This allows the server to make further adjustments and always provide the optimal learning environment.

[0799] (Example 2)

[0800] 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".

[0801] Providing a learning experience optimized for each individual learner is difficult, given that learners' motivation to learn changes depending on the situation. In particular, it is necessary to propose an appropriate learning plan while considering the learner's emotional state, but conventional methods do not adequately perform real-time sentiment analysis, resulting in incomplete optimization of learning.

[0802] 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.

[0803] In this invention, the server includes means for accumulating learners' past history and generating individual educator profiles based on that information, means for generating technology maps that visualize the relationships between technologies and concepts across different teaching areas, and means for analyzing emotional states in real time and dynamically adjusting the teaching plan. This makes it possible to provide an optimized learning environment based on learners' historical data and real-time emotional states.

[0804] "A learner's past history" refers to information about the learning activities and performance that a learner has undertaken to date.

[0805] An "educator profile" refers to an individualized learner profile constructed based on information such as the learner's strengths in subjects and skill level.

[0806] "Teaching area" refers to a specific subject or field in which educational activities are conducted.

[0807] "Relevance of technologies and concepts" refers to how common technologies and concepts are related across different areas of instruction.

[0808] A "technology map" refers to a diagram that visually displays the relationships between technologies and concepts across different areas of instruction.

[0809] "Analyzing emotional states in real time" refers to the process of instantly evaluating and digitizing the learner's emotions on the spot.

[0810] "Dynamically adjusting educational plans" refers to flexibly modifying learning plans and sequences in response to analyzed emotional states and learning progress.

[0811] This invention is an educational support system that provides an optimal learning environment for learners, and consists of a server, terminals, users, and an emotion engine.

[0812] Server role:

[0813] The server uses a database management system to collect learners' past history data. Specifically, it uses database software to manage learners' performance and activity logs, thereby generating individual educator profiles. Machine learning algorithms are used to analyze learners' strengths and skill levels in profile generation. Furthermore, it generates a technology map showing the relationships between techniques and concepts in different teaching areas, and visualizes these relationships using a Python library. Finally, it analyzes sentiment data obtained from an emotion engine and uses a generative AI model to create prompts, dynamically adjusting the teaching plan in real time.

[0814] Terminal role:

[0815] The terminal visually displays learning information transmitted from the server. This involves implementing an interactive user interface using web technologies, allowing learners to intuitively understand their learning progress and feedback. Technical maps, learning sequences, and feedback are displayed on this interface to aid learner comprehension.

[0816] User roles:

[0817] Users progress through their learning based on a learning plan provided via their device. Based on feedback from the emotion engine, users become aware of their own emotional state and, under that awareness, explore the optimal learning style.

[0818] The role of the emotional engine:

[0819] The emotion engine uses sensors such as cameras and microphones to detect learners' emotions in real time. To achieve this, it employs computer vision and natural language processing technologies to analyze learners' facial expressions and voices. The resulting emotion data is sent to a server and used to adjust the educational plan.

[0820] As a concrete example, if a student experiences stress while learning physics, the emotion engine detects this emotion and reports it to the server. Upon receiving this report, the server adjusts its plan to prioritize biology content in the next session. An example of a prompt might be a command given to the generating AI model: "Please provide appropriate learning adjustments for when a user experiences stress while learning physics."

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1:

[0823] The server collects learner history data. Specifically, it uses a database management system to extract learners' past grades and activity logs. Inputs include learner IDs and activity records for each semester, and output is an individual learner dataset. This dataset is used for profile generation in the next step.

[0824] Step 2:

[0825] The server generates individual educator profiles based on collected historical data. This process uses machine learning algorithms to analyze subjects of expertise and skill levels using the dataset as input. Specifically, k-means clustering is used to divide learners' characteristics into clusters. As output, individual educator profiles are generated.

[0826] Step 3:

[0827] The server generates a technology map that visualizes the relationships between technologies and concepts across teaching disciplines. The input is the generated educator profile and associated teaching data. A Python library (e.g., NetworkX) is used to build and visualize a network showing the relationships between each subject. The output is the technology map, which is used to propose learning sequences in the next step.

[0828] Step 4:

[0829] The emotion engine collects real-time emotional data of the user using sensors such as cameras and microphones. It receives real-time facial images and audio data as input and performs analysis using facial recognition software (e.g., OpenCV) and voice analysis tools (e.g., NLP technology). The output is data representing the user's current emotional state.

[0830] Step 5:

[0831] The server analyzes emotional data and dynamically adjusts the learning plan. Inputs include emotional state data obtained from the emotion engine and the server-side educator profile. A generative AI model generates prompts and proposes a new learning sequence. The output is a refined learning plan, which is then provided to the user.

[0832] Step 6:

[0833] The terminal displays data received from the server. Using specific web technologies (e.g., HTML, CSS, JavaScript), it receives a customized learning plan and technology map as input and displays them on an interactive user screen. The output is an easily viewable interface that supports the user's learning.

[0834] Step 7:

[0835] Users engage in learning activities based on a learning plan provided through their device. Specific actions include answering presented questions and viewing related content. Input consists of receiving instructions from the learning plan via the device, while output includes recording learning progress and new history data.

[0836] (Application Example 2)

[0837] 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".

[0838] Current learning support systems have a problem in that they have difficulty dynamically adjusting learning plans to take into account an individual's emotional state. As a result, learners may experience stress and decreased concentration, which hinders effective learning. There is also the challenge of not being able to visualize the relationships between different educational fields and provide a learning environment that responds to emotional states.

[0839] 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.

[0840] In this invention, the server includes means for collecting learner history data and generating individual learning profiles based on that information; means for generating knowledge maps that visualize the relationships between skills and concepts across different educational fields; means for analyzing learning progress in real time and dynamically adjusting the learning plan; and means for identifying the learner's emotional state using emotion-detecting sensor technology and providing a learning environment that corresponds to that emotional state. By analyzing the learner's emotional state and dynamically adjusting the individual learning plan, more effective learning becomes possible.

[0841] "Historical data" refers to information about a learner's past learning activities and performance, and is used to generate a profile unique to each individual learner.

[0842] A "learning profile" is a collection of information generated based on historical data that indicates a learner's strengths and weaknesses and their skill level.

[0843] A "knowledge map" is a visual representation of the relationships between skills and concepts across different educational fields, helping to foster cross-domain understanding in learning.

[0844] "Emotional state" refers to the learner's current psychological state, and includes, for example, states such as stress, concentration, and relaxation.

[0845] "Sensor technology" refers to technologies used to detect emotional states, utilizing methods such as facial recognition and voice analysis.

[0846] A "learning sequence" refers to a series of learning steps that learners should follow to achieve a specific educational objective.

[0847] "Dynamic adjustment" is a method of optimization that involves analyzing data in real time and making changes to systems and processes based on the results.

[0848] This educational support system includes servers, terminals, and an emotion engine as its main components.

[0849] The server first collects learner history data and uses this information to generate individual learning profiles. These profiles are created based on the learner's past performance and activities and support learning plans aimed at specific educational goals. Furthermore, the server generates knowledge maps that visualize the relationships between skills and concepts across different educational fields, facilitating cross-domain learning. The server monitors learning progress in real time, dynamically adjusts the learning plan as needed, and provides an optimal learning sequence tailored to each individual learner.

[0850] The terminal plays a role in visually presenting information transmitted from the server to the learner. This includes knowledge maps, learning sequences, and feedback based on sentiment information. This interface makes it easier for learners to understand their own state and promotes self-regulated learning.

[0851] The emotion engine utilizes sensor technologies such as facial recognition and voice analysis to detect the learner's emotional state in real time. This emotional data is sent to a server and used to provide the learner with the optimal learning environment. For example, if it detects that the learner is feeling stressed, it can dynamically adapt by switching to relaxing content or learning content that encourages a change of pace.

[0852] As a concrete example, consider a scenario where an elementary school student uses the system to learn at home. If the emotion engine detects a stress response while the student is working on a math problem, the system supports the student by providing simple games or relaxing content. Through this system, the student can continue learning effectively while reducing stress.

[0853] An example of a prompt message is, "Please suggest the best learning support method when the user is under stress." In this way, the system utilizes a generative AI model to provide an individually adapted learning experience.

[0854] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0855] Step 1:

[0856] The server collects learner history data. It receives past learning activity data and performance data as input to obtain basic information for generating individual learning profiles. This data is analyzed to create learning profiles that include strengths and weaknesses.

[0857] Step 2:

[0858] The server generates knowledge maps that visualize the relationships between skills and concepts across different educational disciplines. It takes learning profile data as input and models those relationships to output visual knowledge maps that enable cross-domain learning.

[0859] Step 3:

[0860] The device uses learning profiles and knowledge maps sent from the server to present the optimal learning sequence to the learner. It displays the received information on the interface, visualizing the learning content and steps available to the learner.

[0861] Step 4:

[0862] As the user progresses through the learning process, the server analyzes the learning progress in real time. It receives the learner's activity status as input, dynamically adjusts the learning plan based on the progress, and outputs the results.

[0863] Step 5:

[0864] The emotion engine detects the learner's emotional state in real time. It acquires emotional data as input using a facial recognition camera and microphone, analyzes this data to identify the current emotional state, and sends this information to a server.

[0865] Step 6:

[0866] The server adjusts the learning environment based on emotional data obtained from the emotion engine. If the emotional state is determined to be stressful, it suggests relaxing content or breaks to the learner. The optimized learning environment settings are then sent to the terminal as output.

[0867] Step 7:

[0868] The device presents the learner with the learning environment settings received from the server. It displays content and activities tailored to the learner on the screen, supporting efficient and appropriate learning.

[0869] This series of processes makes it possible to provide a learning experience tailored to the individual needs of each learner. Generative AI models are also used to generate prompts that further optimize the user experience.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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."

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] A means for collecting learner history data and generating individual learning profiles based on that information,

[0894] A means of generating a skill map that visualizes the relationships between skills and concepts across different educational subjects,

[0895] A means for proposing an optimal learning sequence based on the aforementioned skill map and learning profile,

[0896] A means to analyze learning progress in real time and dynamically adjust the learning plan,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, which visually displays a graph showing the skill relationships between different educational subjects.

[0900] (Claim 3)

[0901] The system according to claim 1, which evaluates the degree of skill improvement of learners based on collected learning data.

[0902] "Example 1"

[0903] (Claim 1)

[0904] A means for collecting learner history information and generating individual learning profiles based on that information,

[0905] A means of generating skill mappings that visualize the relationships between abilities and concepts across different educational domains,

[0906] A means for proposing an optimal learning process based on the aforementioned skill mapping and learning profile,

[0907] A means to analyze learning progress in real time and dynamically adjust the learning plan,

[0908] A means of visually displaying the learner's progress on a terminal and accepting user input,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which displays visual information representing competency relationships between different educational domains.

[0912] (Claim 3)

[0913] The system according to claim 1, which evaluates the degree of improvement in learners' abilities based on collected learning information.

[0914] "Application Example 1"

[0915] (Claim 1)

[0916] A means for collecting learner history data and generating individual learning profiles based on that information,

[0917] A means of generating a skill map that visualizes the relationships between skills and concepts across different educational subjects,

[0918] A means for proposing an optimal learning sequence based on the aforementioned skill map and learning profile,

[0919] A means to analyze progress in real time and dynamically adjust the plan,

[0920] A means of generating individual travel profiles based on historical data and proposing efficient routes,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, which visually displays graphs showing different skill relationships.

[0924] (Claim 3)

[0925] The system according to claim 1, which evaluates the degree of improvement based on the collected data.

[0926] "Example 2 of combining an emotion engine"

[0927] (Claim 1)

[0928] A means for collecting learners' past history and generating individual educator profiles based on that information,

[0929] A means of generating a technology map that visualizes the relationships between technologies and concepts across different areas of instruction,

[0930] A means for proposing an optimized learning sequence based on the aforementioned technology map and educator profile,

[0931] A means to analyze emotional states in real time and dynamically adjust educational plans,

[0932] A means of detecting educators' emotions using sensor technology and optimizing the learning environment based on that information,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, which visually displays a diagram showing the technical relationships between different areas of instruction.

[0936] (Claim 3)

[0937] The system according to claim 1, which evaluates the degree of skill improvement of educators based on collected educational data.

[0938] "Application example 2 when combining with an emotional engine"

[0939] (Claim 1)

[0940] A means for collecting learner history data and generating individual learning profiles based on that information,

[0941] A means of generating knowledge maps that visualize the relationships between skills and concepts across different educational fields,

[0942] A means for proposing an optimal learning sequence based on the aforementioned knowledge map and learning profile,

[0943] A means to analyze learning progress in real time and dynamically adjust the learning plan,

[0944] A means of identifying a learner's emotional state using emotion-detecting sensor technology and providing a learning environment that corresponds to that emotional state,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, which visually displays a diagram showing the relationships between skills in different educational fields.

[0948] (Claim 3)

[0949] The system according to claim 1, which evaluates the degree of skill improvement of learners based on collected learning data and emotional data. [Explanation of symbols]

[0950] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting learner history data and generating individual learning profiles based on that information, A means of generating a skill map that visualizes the relationships between skills and concepts across different educational subjects, A means for proposing an optimal learning sequence based on the aforementioned skill map and learning profile, A means to analyze progress in real time and dynamically adjust the plan, A means of generating individual travel profiles based on historical data and proposing efficient routes, A system that includes this.

2. The system according to claim 1, which visually displays graphs showing different skill relationships.

3. The system according to claim 1, which evaluates the degree of improvement based on the collected data.

Images