system

Abstract

Provide a system. 【Solution means】 Means for a user to input basic information about the user, Means for a server to generate a profile based on the basic information, Means for presenting a test for measuring the learning ability level of the user from a terminal, Means for a server to analyze the test results and identify the learning ability level of the user, Means for a server to select learning information based on the learning ability level and interests, Means for a terminal to present the selected learning information to the user, Means for transmitting the learning progress data of the user from the terminal to the server, Means for a server to adjust the learning pace based on the learning progress data, Means for setting the learning goal of the user and generating a learning schedule towards the goal, Means for providing an optimized learning experience in cooperation with information on public facilities in an urban environment, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional learning materials are uniformly provided based on age and grade, so there is a problem that it is impossible to select materials suitable for the academic ability and interests of individual children. In addition, due to insufficient motivation for learning, it is difficult for children to clearly set learning goals and maintain a continuous learning desire. Furthermore, an appropriate progress management and an efficient learning pace for achieving goals are not provided, and the problem is also that there are variations in learning outcomes.

Means for Solving the Problems

[0005] This invention provides a system for generating a profile based on a user's basic information and measuring their individual learning ability level. The system includes means for analyzing the user's input information and test results to select appropriate learning information according to their learning ability. Furthermore, it dynamically adjusts the learning pace based on the progress data and generates an efficient learning schedule for the learning goals set by the user. In this way, it provides each user with an appropriate learning experience and enables them to maintain their motivation to learn toward achieving their goals.

[0006] A "user" is someone who accesses the system, enters basic information, and is responsible for setting up and managing the user's learning profile.

[0007] A "server" is a central management device that generates profiles based on information sent by users and identifies and analyzes the user's learning ability level.

[0008] A "terminal" is a device that allows users to learn through an interface, and is a device that administers exams and presents learning content.

[0009] "Learning ability level" refers to the degree of knowledge and skills a user currently possesses, and is measured through testing.

[0010] "Learning content" refers to educational materials selected based on the user's learning ability level and interests, with the aim of improving the user's knowledge and skills.

[0011] "Learning progress data" refers to information about activities and performance recorded by the user during the learning process, which the server receives and analyzes.

[0012] "Learning objectives" are goals for knowledge and skills that users and the user jointly set to achieve within a certain period of time.

[0013] A "learning schedule" is a plan or timeline generated by the server to help the user efficiently achieve their learning goals. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system for carrying out the present invention is a learning management application designed to individually optimize the learning experience of each user. This system functions through the interaction of a server, a terminal, and a user, and provides personalized learning content to each user.

[0036] The user first launches the application on their device and enters the child's basic information when creating a new account. This includes the user's name, age, and areas of interest in learning. The device sends this information to the server, which generates a user profile based on it. This profile is then updated with information about the user's learning history and learning ability level.

[0037] The server generates a test to identify the user's learning ability using their profile and presents it to the user via their device. The user answers the test, and the results are sent back to the server. The server analyzes these results to determine the user's current learning ability level and selects appropriate learning content based on that level.

[0038] The selected learning content is presented on the device in a way that is easy for the user to understand and is engaging. For example, learning materials using animations in a science field that the user is interested in may be presented. Users can monitor their progress and set goals. Goals are set with the aim of acquiring specific skills and knowledge, and a learning schedule is automatically generated by the server accordingly.

[0039] The system continuously records the user's learning progress data and sends it to the server. The server analyzes this data and adjusts the user's learning pace and content as needed. This dynamic adjustment ensures that the user is always working on challenges of an appropriate difficulty level.

[0040] As a concrete example, suppose a user uses this system to learn the basics of arithmetic. Based on the user's input, the server generates a new arithmetic-focused test and presents it to the user via the terminal. From the test results, the server identifies that the user has particular difficulty with multiplication. The server selects content, including videos and interactive games related to the basics of multiplication, and provides it to the user via the terminal. Through this content, the user can see a concrete plan to continue practicing multiplication and work towards the goal of "achieving a complete understanding of multiplication within a specific time."

[0041] Thus, the system implementing the present invention aims to maximize the educational effect on the user through interaction with the user and the terminal. The server plays a central role in managing the individualized learning experience and providing an optimized learning path.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user installs the application on their device and creates a new account. They enter the child's basic information (name, age, subjects of interest, etc.). The device sends the entered information to the server.

[0045] Step 2:

[0046] The server generates a learning profile specifically for the child based on the basic information received. This profile includes fields for saving learning history and progress information that will be added later.

[0047] Step 3:

[0048] The device receives instructions from the server and presents the child with a basic test. The test includes basic questions in subjects such as arithmetic, language arts, and science. The child answers the questions on the device.

[0049] Step 4:

[0050] The device sends the child's test results to the server. The server analyzes the received responses and evaluates the accuracy rate and response time to calculate the child's learning ability level.

[0051] Step 5:

[0052] The server selects appropriate learning content based on the child's learning ability level and interests. The selected content includes various formats such as videos, animations, and interactive games.

[0053] Step 6:

[0054] The device displays learning content provided by the server to the child. The content is designed to be visually appealing and engaging for the child.

[0055] Step 7:

[0056] The device records the child's learning progress (correct answers to problems, learning speed, content viewing time, etc.) and sends that data to the server.

[0057] Step 8:

[0058] The server analyzes learning progress data and dynamically adjusts the learning pace and the next content to be tackled as needed. This ensures that children are always working on tasks of appropriate difficulty.

[0059] Step 9:

[0060] The user sets goals together with the user. The server automatically generates a learning schedule based on the set goals. The schedule includes tasks to be completed and content to be viewed.

[0061] Step 10:

[0062] The device provides story-driven content and interactive activities to enhance children's motivation to learn. The server continuously monitors progress and provides support to help them achieve their goals.

[0063] (Example 1)

[0064] 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."

[0065] In today's educational environment, customized learning experiences tailored to each user's learning ability and interests are crucial. However, conventional learning systems have struggled to provide adaptive learning programs to users. This invention aims to solve these problems and provide a learning experience optimized for each user.

[0066] 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.

[0067] In this invention, the server includes means for designing a test to measure the user's learning ability using a profile-generated AI model, means for analyzing the test results to identify the user's learning ability level, and means for selecting learning information based on the learning ability level and interests. This makes it possible to quickly and effectively provide the user with the most suitable learning program.

[0068] A "user" is an entity that performs input or operations on a system, and refers to a person who has the role of inputting information about the user.

[0069] "User" refers to the subject who receives learning through an educational program, and the person who acquires knowledge through learning content and examinations.

[0070] "Basic information" refers to information about the user's attributes, such as their name, age, and areas of learning interest.

[0071] A "profile" refers to a dataset containing a user's basic information, learning history, and proficiency level.

[0072] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate output for a specific task.

[0073] "Test" refers to an evaluation method designed to measure a user's learning ability and level of understanding.

[0074] "Content" refers to learning information and materials provided to the user.

[0075] The system for implementing the present invention centers around a user, a terminal, and a server, providing a learning experience individually optimized for each user. First, the user launches the learning management software on the terminal and enters their basic information when using the system for the first time. This basic information includes the user's name, age, and areas of interest.

[0076] The device sends this basic information to a server in the cloud. The server receives this information and generates an individual profile of the user. This process also collects information based on the user's past learning history and interests. Based on the generated profile, the server uses a generative AI model to design a test to measure the user's learning ability. The AI ​​model used here is optimized using natural language processing and machine learning algorithms.

[0077] Therefore, the server sends the designed test content back to the terminal, which then presents it to the user. At this point, the user can provide support to the user as they take the test. Once the user answers the test, the results are automatically sent to the server. The server analyzes these results to identify the user's current learning ability level. This analysis utilizes advanced data analysis techniques and is processed immediately.

[0078] Next, the server selects appropriate learning content based on the identified learning ability level and the user's interests. In this process, the generating AI model can generate appropriate learning materials and interactive content according to prompts such as "Suggest a learning method that the user will find most interesting." The selected content is then provided to the user via the device. For example, if the user is interested in multiplication and wants to deepen their understanding of the subject, the server will send multiplication-related animated videos or interactive games to the device.

[0079] Furthermore, the terminal continuously records the user's learning progress, and this data is synchronized with the server. Based on this information, the server adjusts the learning pace and content, providing an environment where users can learn efficiently towards their set learning goals. In this way, the system of the present invention continuously provides an individually optimized learning experience through the cooperation of the user, terminal, and server.

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

[0081] Step 1:

[0082] The user launches the learning management software on their device and creates a new account. During this process, they enter their basic information (name, age, areas of interest, etc.). This information becomes input data for the system.

[0083] Step 2:

[0084] The terminal processes the entered basic information, formats it appropriately, and sends it to the server. The server generates an individual user profile from the received information. This process involves recording information in a database and creating the profile.

[0085] Step 3:

[0086] The server uses a generative AI model to design tests to measure the user's learning ability based on their profile. Specifically, the generative AI model takes profile information as input, generates appropriate test questions, and outputs the results as a test in text or digital format.

[0087] Step 4:

[0088] The server sends the designed test to the terminal, which displays the test to the user. The user assists the other user in answering the test. The terminal records the user's answers as digital data.

[0089] Step 5:

[0090] The terminal sends the recorded test answers to the server. The server uses the answer data as input to perform advanced data analysis and identify the user's current learning ability level. The identified learning ability level becomes output data used within the system.

[0091] Step 6:

[0092] The server takes the user's learning ability level and interests as input and uses a generative AI model to select appropriate learning content. Output content includes animated videos and interactive games. The selected content is then sent to the device.

[0093] Step 7:

[0094] The device provides the user with the received learning content and initiates interaction with the user. For example, if the user is watching an animated video about multiplication, the user can see their viewing status.

[0095] Step 8:

[0096] The device continuously records the user's learning progress data and sends it to the server. The server uses this progress data as input to analyze and adjust the learning pace and content. If necessary, it sends newly selected content to the device.

[0097] Step 9:

[0098] Users can set their learning goals based on information provided by the server. The server automatically generates a corresponding learning schedule based on the set goals. This schedule is output to the terminal for the user to review.

[0099] (Application Example 1)

[0100] 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."

[0101] In modern society, there is a need to provide individually optimized learning experiences while effectively promoting the use of learning resources in public facilities and urban environments. However, conventional learning management systems have limited capabilities in providing learning content based on user profiles and interests, and do not adequately integrate with urban public resources, making it difficult to meet the diverse needs of learners.

[0102] 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.

[0103] In this invention, the server includes means for the user to input basic information about the user, means for the server to generate a profile based on the basic information, and means for providing an optimized learning experience in conjunction with information on public facilities in an urban environment. This makes it possible to realize individually optimized learning effects even in public places and to provide diverse learning opportunities.

[0104] "A means for users to input basic information about themselves" refers to an interface that allows users to input information such as their name, age, and areas of interest in learning, and for the system to receive that information.

[0105] "Means by which the server generates a profile based on the basic information" refers to a function that creates a database for managing each user's learning history and interests based on the entered basic information.

[0106] "A means of presenting a test from a terminal to measure the user's learning ability level" refers to a function that provides the user with an appropriate learning ability assessment test and makes an ability assessment based on that test.

[0107] "A means by which the server analyzes test results and identifies the user's learning ability level" refers to a function that analyzes test response data to quantitatively evaluate the user's learning ability.

[0108] "Means by which the server selects learning information based on learning ability level and interests" refers to an algorithm that automatically selects learning materials that match the user's interests and abilities.

[0109] "A means by which a device presents selected learning information to the user" refers to a function that displays selected learning content on the user's device.

[0110] "Means for transmitting user learning progress data from the terminal to the server" refers to a communication protocol for recording the progress of the user's learning activities and uploading it to a central server.

[0111] "Means by which the server adjusts the learning pace based on learning progress data" refers to a function that automatically adjusts the learning content and time allocation according to the progress made.

[0112] "Means for setting user learning objectives and generating a learning schedule to achieve those objectives" refers to a function that sets specific learning objectives that the user should achieve and creates an optimal timetable to achieve them.

[0113] "A means of providing an optimized learning experience in conjunction with information on public facilities in an urban environment" refers to a function that provides users with learning content tailored to their specific location by linking with public resources within the city.

[0114] The system for implementing this invention is a learning management system designed to individually optimize the learning experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0115] First, the user enters basic information about themselves and their child using the device. This includes data such as name, age, and areas of learning interest. The device sends this basic information to the server, which then generates a user profile based on this information. The generated profile records the user's learning history and interests.

[0116] The server then uses this profile to generate a test to measure the user's learning ability and presents it through the terminal. Once the user answers the test, the results are sent to the server, which analyzes the results to determine the user's learning ability level. Based on that learning ability level and interests, the server selects appropriate learning information, such as video materials or interactive materials, and provides them to the terminal.

[0117] Furthermore, the system can be integrated with public facilities in urban environments. For example, when a learner visits a specific library, learning content related to that location is displayed on the user's device. In addition, videos about the history and characteristics of the facility are provided, allowing learners to absorb new knowledge on the spot.

[0118] The server continuously records the user's learning progress and dynamically adjusts the learning pace and schedule based on the shared data. Once the user sets their learning goals, it automatically generates a suitable learning schedule. Furthermore, progress data is stored on cloud servers (such as AWS® and Azure®), and machine learning models (such as Python TENSORFLOW®) are used for analysis.

[0119] For example, if a user expresses interest in the field of "history," the application is designed to provide them with detailed information about historical events and exhibits related to a museum they visit in the city.

[0120] An example of a prompt message is: "Based on the learning area you entered, please suggest the latest and most appropriate learning materials. Please consider the user's level and interests."

[0121] In this way, the user's learning experience can be optimized to their individual needs and, by linking with resources throughout the city, can provide a richer learning environment.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The user enters their basic information using a device. This data includes the user's name, age, and areas of study they are interested in. The device sends this information to a server, which uses it as basic data for creating a profile.

[0125] Step 2:

[0126] The server generates a user profile based on the basic information it receives. This profile records the user's learning history and interests, and the generated AI model analyzes this data to create a personalized learning plan. The output provides basic data for evaluating learning ability.

[0127] Step 3:

[0128] Based on the profile generated by the server, a test is created to measure the user's learning ability. The test content is customized with prompts to be optimized for the user. The created test is presented to the user via the terminal, and they are prompted to answer.

[0129] Step 4:

[0130] The user answers the test, and the device sends the results to the server. The server analyzes the received test results to identify the user's learning ability. It receives the test answer data as input and evaluates the user's learning ability level as output.

[0131] Step 5:

[0132] The server selects appropriate learning information based on the user's learning ability level and interests. At this stage, a machine learning model is used to automatically select learning materials that match the user's interests and abilities. The selected learning content is sent to the device and presented to the user.

[0133] Step 6:

[0134] The device displays selected learning information to the user. This information includes video and interactive learning materials designed to encourage the user to engage in learning. The output provides visually easy-to-understand learning materials.

[0135] Step 7:

[0136] The server records the user's learning progress and dynamically adjusts the learning pace and schedule based on the progress data. It automatically updates the next learning content according to the progress, providing a plan that is suitable for the user.

[0137] Step 8:

[0138] The server interacts with information on public facilities in urban environments to provide users with an optimized learning experience. For example, when a user visits a specific facility, relevant learning content is displayed on their device, expanding learning opportunities. By interacting with public facility data, the server provides learning content tailored to the specific location.

[0139] 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.

[0140] One embodiment of the present invention is a learning management system that incorporates emotion recognition technology to optimize individual user learning and further personalize the learning experience. This system includes an emotion engine that recognizes and analyzes the user's emotions in real time, in addition to user input information and learning progress data.

[0141] The user installs the application on their device and initially enters user information. This information includes name, age, and learning interests. The device sends this information to a server, which uses it to create a user profile. The device also has built-in input devices such as a camera and microphone, which work in conjunction with an emotion engine to monitor the user's facial expressions, tone of voice, language patterns, etc., and detect their emotional state.

[0142] The server designs a test to measure the user's learning ability level and presents it to the user via the terminal. Once the user completes the test, the terminal returns the results to the server. The server analyzes the results to determine the learning ability level and, further, combines this with emotional data obtained from the emotion engine to select more suitable learning content.

[0143] The device displays selected learning content while an emotion engine tracks the user's reactions in real time. For example, if the server determines that the user is confused, it can use that data to lower the difficulty level or add supplementary explanations. Conversely, if positive emotions such as joy or excitement are recognized, the system can either continue the current learning path or introduce more challenging content.

[0144] As a concrete example, suppose a child is learning math and shows a confused expression when faced with a particular problem. The emotion engine detects this expression and sends data to the server. Based on the child's emotional state and learning progress, the server instructs the system to add hints to the problem or present visual aids. Such dynamic adjustments reduce stress for the user and allow them to learn more comfortably.

[0145] This system not only tracks the user's learning progress but also aims for optimal learning outcomes by adaptively adjusting learning content and approaches using emotional data. This optimizes the learning experience according to the user's emotions and concentration levels, providing a highly satisfying learning process.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user installs a learning management application on their device and enters their basic information (name, age, subjects of interest, etc.). The entered information is then sent from the device to the server.

[0149] Step 2:

[0150] The server generates a learning profile for the user based on the basic information received. The profile includes a structure that also records the user's learning history and sentiment data.

[0151] Step 3:

[0152] The terminal, following instructions from the server, presents a basic test to measure the user's learning ability level. The user answers the test questions, and the terminal sends the answer data to the server.

[0153] Step 4:

[0154] The server analyzes the received test results to identify the user's learning ability level. It also evaluates the user's emotional state by linking with emotion engine data from the terminal.

[0155] Step 5:

[0156] The server selects the most suitable learning content based on the user's learning ability level and emotional state. This selection may include content with a storyline or interactive elements. The selection results are delivered to the device.

[0157] Step 6:

[0158] The device displays learning content delivered from the server to the user. During the presentation, an emotion engine monitors the user's facial expressions and voice in real time and reports their emotional state to the server.

[0159] Step 7:

[0160] The server analyzes emotional data and dynamically adjusts the learning content as needed. For example, if it detects that the user is dissatisfied, it will instruct the device to lower the difficulty level of the content or change the presentation method.

[0161] Step 8:

[0162] The device adjusts and continues to present learning content based on instructions from the server. This process is repeated until the user's emotional state improves.

[0163] Step 9:

[0164] The user sets learning goals together with the user. The server generates a learning schedule based on the set goals and continuously monitors the progress.

[0165] Step 10:

[0166] The device continuously presents story-driven learning content and interactive activities aimed at achieving learning goals, encouraging active user participation. The server periodically evaluates overall learning progress and sentiment data, adjusting the entire system as needed.

[0167] (Example 2)

[0168] 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".

[0169] In optimizing individualized learning, it was difficult to provide an adaptive learning experience because the user's emotional state could not be reflected in real time. Furthermore, there was no means to adjust the learning content based on the learner's physiological responses and emotions, resulting in missed opportunities to improve learning efficiency.

[0170] 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.

[0171] In this invention, the server includes means for analyzing test results and collected emotional data to identify the user's learning ability level and emotional state; means for selecting optimal learning information based on the learning ability level and emotional data; and means for automatically adjusting the difficulty level of the learning information and adding supplementary details according to the emotional data. This makes it possible to provide an adaptive and personalized learning experience that responds to the user's emotional changes.

[0172] A "user" is an individual or group that uses a system to learn.

[0173] "Basic information" refers to data about the user, such as name, age, and learning interests.

[0174] A "profile" is a unique dataset that a server generates based on a user's basic information.

[0175] A "test" refers to a set of problems or tasks designed by a server and presented by a device to measure the user's learning ability level.

[0176] A "terminal" is a device used by a user to interact with a system, and may include input devices such as cameras and microphones.

[0177] An "emotion engine" is software that uses data from the device's camera, microphone, and other sources to analyze the user's emotional state in real time.

[0178] "Emotional data" refers to information about emotions extracted from a user's facial expressions, tone of voice, language patterns, and other similar data.

[0179] "Learning information" refers to educational content and materials selected by the server based on the user's learning ability level and emotional state.

[0180] A "learning schedule" refers to the plan generated by the server to help the user achieve their learning goals.

[0181] A "generative AI model" is a type of artificial intelligence that generates patterns and outputs based on large amounts of data, and in this invention, it is mainly used for selecting training information.

[0182] A "prompt sentence" is a sentence or phrase that is input into a generative AI model, and its role is to elicit a specific response or output.

[0183] This invention provides a learning management system incorporating emotion recognition technology. The user first installs an application on their device and enters basic personal information as part of the initial setup. This basic information includes the user's name, age, and learning interests. The device transmits this information to the server using a secure communication protocol. The server generates a user-specific profile based on the received information.

[0184] The system uses a test designed by the server to measure the user's learning ability. The terminal presents this test to the user and receives their answers. Simultaneously, the terminal is equipped with a camera and microphone to collect emotional data in real time through the user's facial expressions and voice. An emotion engine analyzes this data to determine the user's emotional state.

[0185] The server comprehensively analyzes test results and emotional data to understand the user's learning ability level and emotional state. Using a generative AI model, it selects the optimal learning information based on this data. The terminal presents this information to the user and continues to monitor emotional data throughout the learning process. The system can adjust the difficulty level of the learning information or add supplementary information in response to changes in the user's emotions.

[0186] For example, when a student is taking a history lesson, if the emotion engine detects a decrease in the user's interest from their facial expressions, the server will help increase their motivation by lowering the difficulty level or incorporating visual materials related to the question. An example of a prompt might be: "Analyze the emotions the user feels towards a specific mathematical formula and suggest learning content that corresponds to those emotions."

[0187] This invention enables effective learning by personalizing the user's learning experience and flexibly adapting it to their emotional state.

[0188] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0189] Step 1:

[0190] The user enters basic information into the device.

[0191] Input: Basic information such as name, age, and learning interests.

[0192] Operation: The user enters personal information into an input form on the application screen.

[0193] Output: The input information is organized within the terminal and ready to be sent to the next step.

[0194] Step 2:

[0195] The device sends basic information to the server.

[0196] Input: Basic information organized in Step 1.

[0197] Operation: The terminal uses a secure protocol such as HTTPS to send basic information to the server.

[0198] Output: The server generates a user profile based on the information received.

[0199] Step 3:

[0200] The server designs the tests for measuring learning ability.

[0201] Input: User profile information.

[0202] Operation: The server uses a generated AI model to create tests for measuring individualized learning ability.

[0203] Output: Test data is generated and sent to the terminal.

[0204] Step 4:

[0205] The device presents the user with a test.

[0206] Input: Test data sent from the server.

[0207] Operation: The device displays test questions on the screen and provides an interface for the user to answer them.

[0208] Output: User response data is collected.

[0209] Step 5:

[0210] The device collects emotional data.

[0211] Input: Real-time facial and voice data of the user.

[0212] Operation: Uses the device's camera and microphone to record the user's facial expressions and voice tone.

[0213] Output: Raw emotional data is generated for analysis by the emotion engine.

[0214] Step 6:

[0215] The device sends test results and emotional data to the server.

[0216] Input: User response data and unprocessed sentiment data.

[0217] Operation: The terminal securely transmits this data to the server.

[0218] Output: Test results and sentiment data are passed for use by the server.

[0219] Step 7:

[0220] The server analyzes the test results and sentiment data.

[0221] Input: Test results and sentiment data.

[0222] Operation: The server performs statistical analysis of test results and analyzes emotional data using an emotion engine.

[0223] Output: Optimized learning ability level and emotional state data are obtained based on the analysis results.

[0224] Step 8:

[0225] The server selects the learning information.

[0226] Input: Learning ability level and emotional state data.

[0227] Operation: The generative AI model selects the most suitable training information for the user.

[0228] Output: Optimal learning information data is sent to the device.

[0229] Step 9:

[0230] The device presents learning information to the user.

[0231] Input: Training information data sent from the server.

[0232] Operation: The device provides the user with selected learning content and begins learning.

[0233] Output: Learning is successfully delivered, improving the user experience.

[0234] Step 10:

[0235] The server provides feedback based on user sentiment data.

[0236] Input: Real-time user sentiment data.

[0237] Operation: The server monitors emotional changes and adjusts the learned information as needed.

[0238] Output: Pre-tuned content designed to maximize learning effectiveness for the user.

[0239] (Application Example 2)

[0240] 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".

[0241] In learning and recreational activities for the elderly, there is a problem in providing appropriate stimuli tailored to each individual's emotional state. Conventional learning systems cannot take individual emotional states into account and are limited to providing uniform learning information, which limits learning effectiveness and motivation maintenance.

[0242] 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.

[0243] In this invention, the server includes means for analyzing the user's emotional state using emotion recognition technology and adaptively adjusting learning information based on the emotional state; means for presenting learning information with a narrative structure to the user and flexibly adjusting its order; and means for changing the content of the interaction according to the user's emotional state. This makes it possible to provide a deeply personalized learning experience tailored to the user's emotions.

[0244] "User" refers to individuals who engage in individual learning or recreational activities on the system.

[0245] "Basic information" refers to personal data such as name, age, and interests that users enter.

[0246] A "profile" refers to a dataset generated based on a user's basic information and used to optimize learning and recreational activities.

[0247] "Tests" refer to a set of tasks or problems provided to measure the user's learning ability level.

[0248] "Learning information" refers to educational or entertainment content that is selected and provided based on the user's learning ability level and interests.

[0249] "Learning progress data" refers to records of how users utilize the system, and includes information indicating their learning pace and progress.

[0250] "Emotion recognition technology" refers to technology that analyzes a user's facial expressions, tone of voice, and language patterns to identify their emotional state.

[0251] "Storytelling" refers to the characteristic of incorporating elements of a consistent narrative, developing learning information not merely as a list of facts, but as a story.

[0252] This invention is a learning support system for the elderly that utilizes emotion recognition technology, and the server operates in conjunction with terminals such as smart glasses. Specific embodiments are described below.

[0253] The server first generates a profile based on the basic information entered by the user. This involves using the camera and microphone equipped on the device to collect the user's facial expressions and voice in real time, and then applying emotion recognition technology to analyze their emotional state. For this analysis, emotion recognition APIs such as Amazon Rekognition or Microsoft® Azure Emotion API are used.

[0254] The device selects and displays the most appropriate learning information on the screen based on the user's emotional state. This allows the user to continue having an engaging learning experience without feeling bored or uncomfortable. Learning progress data is sent to the server each time, and the server analyzes this data to adjust the learning pace.

[0255] For example, if a user is wearing smart glasses and their facial expression is detected as relaxed, the server will select and present a lecture video or music program suitable for a relaxed state. This selection process utilizes automated prompt generation. An example of a prompt might be, "If an elderly person shows interest in local history, what kind of video should be recommended?"

[0256] This system makes it possible to provide optimized learning content tailored to the user's emotions, creating a more personalized learning environment.

[0257] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0258] Step 1:

[0259] The user puts on smart glasses and enters basic information into the terminal. Here, the user enters profile data such as name, age, and areas of interest, and the terminal sends this information to the server. The entered data is sent in text format and recorded in a database on the server.

[0260] Step 2:

[0261] The device's camera and microphone are used to collect the user's facial expressions and voice. The device sends this data to an emotion recognition API in real time for analysis of the emotional state. This analysis process uses a facial recognition algorithm to identify an emotion category (e.g., joy, sadness, interest) and returns the result to the device.

[0262] Step 3:

[0263] The server selects the most suitable learning content based on the analyzed sentiment data. Here, a recommendation system that considers the user's interests and emotional state is used to select the most appropriate content from multiple candidates. The selected learning content is sent back to the device and displayed on the screen.

[0264] Step 4:

[0265] The device presents learning content to the user. Specifically, selected videos, text materials, and interactive quizzes are displayed. The user views this content and performs simple input operations according to the instructions. User feedback and learning progress are then sent back to the server.

[0266] Step 5:

[0267] The server analyzes the user's learning progress data and adjusts the learning pace and the next content. Using the aggregated progress and sentiment data, the server determines the difficulty level of the next content and sends feedback to the user. By testing with a generative AI model, prompts for the next learning session are automatically generated, enabling further personalization.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] [Second Embodiment]

[0272] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0273] 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.

[0274] 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).

[0275] 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.

[0276] 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.

[0277] 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).

[0278] 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.

[0279] 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.

[0280] The specific processing program 56 is an example of the "program" according to the technology of the present 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 operating as the specific processing unit 290 according to the specific processing program 56 executed by the processor 28 on the RAM 30.

[0281] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the specific processing unit 290.

[0282] In the smart glasses 214, the processor 46 performs reception / output processing. The storage 50 stores a 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 operating as the control unit 46A according to the reception / output program 60 executed by the processor 46 on the RAM 48.

[0283] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] The system for implementing the present invention is a learning management application designed to individually optimize the learning experience of users. This system functions through the interaction of the server, the terminal, and the user, and provides personalized learning content for each user.

[0285] The user first launches the application on the terminal and enters the basic information of the child when creating a new account. This includes the user's name, age, learning subjects of interest, etc. The terminal sends this information to the server, and the server generates a user profile based on this information. Information regarding the user's learning history and learning ability level is added to this profile.

[0286] The server generates a test to identify the user's learning ability using the profile and presents it to the user via the terminal. The user answers the test, and the results are sent back to the server again. The server analyzes these results to identify the user's current learning ability level and selects appropriate learning content based on it.

[0287] The selected learning content is easy for the user to understand and interesting, and is presented from the terminal. For example, teaching materials using animations in the scientific field that the user is interested in may be presented. The user can monitor their progress and set goals. The goals are set aiming at the acquisition of specific skills and knowledge, and a learning schedule is automatically generated by the server according to them.

[0288] The system continuously records the user's learning progress data and sends it to the server. The server analyzes this data and adjusts the user's learning pace and content as necessary. With this dynamic adjustment, the user can always work on tasks of appropriate difficulty.

[0289] As a concrete example, suppose a user uses this system to learn the basics of arithmetic. Based on the user's input, the server generates a new arithmetic-focused test and presents it to the user via the terminal. From the test results, the server identifies that the user has particular difficulty with multiplication. The server selects content, including videos and interactive games related to the basics of multiplication, and provides it to the user via the terminal. Through this content, the user can see a concrete plan to continue practicing multiplication and work towards the goal of "achieving a complete understanding of multiplication within a specific time."

[0290] Thus, the system implementing the present invention aims to maximize the educational effect on the user through interaction with the user and the terminal. The server plays a central role in managing the individualized learning experience and providing an optimized learning path.

[0291] The following describes the processing flow.

[0292] Step 1:

[0293] The user installs the application on their device and creates a new account. They enter the child's basic information (name, age, subjects of interest, etc.). The device sends the entered information to the server.

[0294] Step 2:

[0295] The server generates a learning profile specifically for the child based on the basic information received. This profile includes fields for saving learning history and progress information that will be added later.

[0296] Step 3:

[0297] The device receives instructions from the server and presents the child with a basic test. The test includes basic questions in subjects such as arithmetic, language arts, and science. The child answers the questions on the device.

[0298] Step 4:

[0299] The terminal sends the child's test results to the server. The server analyzes the received answers and evaluates the correct answer rate and response time to calculate the child's learning ability level.

[0300] Step 5:

[0301] The server selects appropriate learning content based on the learning ability level and the child's interests. The selected content includes various forms such as videos, animations, and interactive games.

[0302] Step 6:

[0303] The terminal presents the learning content provided by the server to the child. The content is designed to be visually appealing and attract the child's interest.

[0304] Step 7:

[0305] The terminal records the child's learning progress (correct answers to questions, learning speed, viewing time of content, etc.) and sends the data to the server.

[0306] Step 8:

[0307] The server analyzes the learning progress data and dynamically adjusts the learning pace and the content to work on next as needed. This enables the child to always work on tasks of appropriate difficulty.

[0308] Step 9:

[0309] The user sets goals with the user. The server automatically generates a learning schedule based on the set goals. The schedule includes tasks to be achieved and content to be viewed.

[0310] Step 10:

[0311] The device provides story-driven content and interactive activities to enhance children's motivation to learn. The server continuously monitors progress and provides support to help them achieve their goals.

[0312] (Example 1)

[0313] 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".

[0314] In today's educational environment, customized learning experiences tailored to each user's learning ability and interests are crucial. However, conventional learning systems have struggled to provide adaptive learning programs to users. This invention aims to solve these problems and provide a learning experience optimized for each user.

[0315] 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.

[0316] In this invention, the server includes means for designing a test to measure the user's learning ability using a profile-generated AI model, means for analyzing the test results to identify the user's learning ability level, and means for selecting learning information based on the learning ability level and interests. This makes it possible to quickly and effectively provide the user with the most suitable learning program.

[0317] A "user" is an entity that performs input or operations on a system, and refers to a person who has the role of inputting information about the user.

[0318] "User" refers to the subject who receives learning through an educational program, and the person who acquires knowledge through learning content and examinations.

[0319] "Basic information" refers to information about the user's attributes, such as their name, age, and areas of learning interest.

[0320] A "profile" refers to a dataset containing a user's basic information, learning history, and proficiency level.

[0321] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate output for a specific task.

[0322] "Test" refers to an evaluation method designed to measure a user's learning ability and level of understanding.

[0323] "Content" refers to learning information and materials provided to the user.

[0324] The system for implementing the present invention centers around a user, a terminal, and a server, providing a learning experience individually optimized for each user. First, the user launches the learning management software on the terminal and enters their basic information when using the system for the first time. This basic information includes the user's name, age, and areas of interest.

[0325] The device sends this basic information to a server in the cloud. The server receives this information and generates an individual profile of the user. This process also collects information based on the user's past learning history and interests. Based on the generated profile, the server uses a generative AI model to design a test to measure the user's learning ability. The AI ​​model used here is optimized using natural language processing and machine learning algorithms.

[0326] Therefore, the server sends the designed test content back to the terminal, which then presents it to the user. At this point, the user can provide support to the user as they take the test. Once the user answers the test, the results are automatically sent to the server. The server analyzes these results to identify the user's current learning ability level. This analysis utilizes advanced data analysis techniques and is processed immediately.

[0327] Next, the server selects appropriate learning content based on the identified learning ability level and the user's interests. In this process, the generating AI model can generate appropriate learning materials and interactive content according to prompts such as "Suggest a learning method that the user will find most interesting." The selected content is then provided to the user via the device. For example, if the user is interested in multiplication and wants to deepen their understanding of the subject, the server will send multiplication-related animated videos or interactive games to the device.

[0328] Furthermore, the terminal continuously records the user's learning progress, and this data is synchronized with the server. Based on this information, the server adjusts the learning pace and content, providing an environment where users can learn efficiently towards their set learning goals. In this way, the system of the present invention continuously provides an individually optimized learning experience through the cooperation of the user, terminal, and server.

[0329] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0330] Step 1:

[0331] The user launches the learning management software on their device and creates a new account. During this process, they enter their basic information (name, age, areas of interest, etc.). This information becomes input data for the system.

[0332] Step 2:

[0333] The terminal processes the entered basic information, formats it appropriately, and sends it to the server. The server generates an individual user profile from the received information. This process involves recording information in a database and creating the profile.

[0334] Step 3:

[0335] The server uses a generative AI model to design tests to measure the user's learning ability based on their profile. Specifically, the generative AI model takes profile information as input, generates appropriate test questions, and outputs the results as a test in text or digital format.

[0336] Step 4:

[0337] The server sends the designed test to the terminal, which displays the test to the user. The user assists the other user in answering the test. The terminal records the user's answers as digital data.

[0338] Step 5:

[0339] The terminal sends the recorded test answers to the server. The server uses the answer data as input to perform advanced data analysis and identify the user's current learning ability level. The identified learning ability level becomes output data used within the system.

[0340] Step 6:

[0341] The server takes the user's learning ability level and interests as input and uses a generative AI model to select appropriate learning content. Output content includes animated videos and interactive games. The selected content is then sent to the device.

[0342] Step 7:

[0343] The device provides the user with the received learning content and initiates interaction with the user. For example, if the user is watching an animated video about multiplication, the user can see their viewing status.

[0344] Step 8:

[0345] The device continuously records the user's learning progress data and sends it to the server. The server uses this progress data as input to analyze and adjust the learning pace and content. If necessary, it sends newly selected content to the device.

[0346] Step 9:

[0347] Users can set their learning goals based on information provided by the server. The server automatically generates a corresponding learning schedule based on the set goals. This schedule is output to the terminal for the user to review.

[0348] (Application Example 1)

[0349] 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."

[0350] In modern society, there is a need to provide individually optimized learning experiences while effectively promoting the use of learning resources in public facilities and urban environments. However, conventional learning management systems have limited capabilities in providing learning content based on user profiles and interests, and do not adequately integrate with urban public resources, making it difficult to meet the diverse needs of learners.

[0351] 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.

[0352] In this invention, the server includes means for the user to input basic information about the user, means for the server to generate a profile based on the basic information, and means for providing an optimized learning experience in conjunction with information on public facilities in an urban environment. This makes it possible to realize individually optimized learning effects even in public places and to provide diverse learning opportunities.

[0353] "A means for users to input basic information about themselves" refers to an interface that allows users to input information such as their name, age, and areas of interest in learning, and for the system to receive that information.

[0354] "Means by which the server generates a profile based on the basic information" refers to a function that creates a database for managing each user's learning history and interests based on the entered basic information.

[0355] "A means of presenting a test from a terminal to measure the user's learning ability level" refers to a function that provides the user with an appropriate learning ability assessment test and makes an ability assessment based on that test.

[0356] "A means by which the server analyzes test results and identifies the user's learning ability level" refers to a function that analyzes test response data to quantitatively evaluate the user's learning ability.

[0357] "Means by which the server selects learning information based on learning ability level and interests" refers to an algorithm that automatically selects learning materials that match the user's interests and abilities.

[0358] "A means by which a device presents selected learning information to the user" refers to a function that displays selected learning content on the user's device.

[0359] "Means for transmitting user learning progress data from the terminal to the server" refers to a communication protocol for recording the progress of the user's learning activities and uploading it to a central server.

[0360] "Means by which the server adjusts the learning pace based on learning progress data" refers to a function that automatically adjusts the learning content and time allocation according to the progress made.

[0361] "Means for setting user learning objectives and generating a learning schedule to achieve those objectives" refers to a function that sets specific learning objectives that the user should achieve and creates an optimal timetable to achieve them.

[0362] "A means of providing an optimized learning experience in conjunction with information on public facilities in an urban environment" refers to a function that provides users with learning content tailored to their specific location by linking with public resources within the city.

[0363] The system for implementing this invention is a learning management system designed to individually optimize the learning experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0364] First, the user enters basic information about themselves and their child using the device. This includes data such as name, age, and areas of learning interest. The device sends this basic information to the server, which then generates a user profile based on this information. The generated profile records the user's learning history and interests.

[0365] The server then uses this profile to generate a test to measure the user's learning ability and presents it through the terminal. Once the user answers the test, the results are sent to the server, which analyzes the results to determine the user's learning ability level. Based on that learning ability level and interests, the server selects appropriate learning information, such as video materials or interactive materials, and provides them to the terminal.

[0366] Furthermore, the system can be integrated with public facilities in urban environments. For example, when a learner visits a specific library, learning content related to that location is displayed on the user's device. In addition, videos about the history and characteristics of the facility are provided, allowing learners to absorb new knowledge on the spot.

[0367] The server continuously records the user's learning progress and dynamically adjusts the learning pace and schedule based on the shared data. Once the user sets their learning goals, it automatically generates a suitable learning schedule. Furthermore, progress data is stored on cloud servers (such as AWS and Azure), and machine learning models (such as Python TensorFlow) are used for analysis.

[0368] For example, if a user expresses interest in the field of "history," the application is designed to provide them with detailed information about historical events and exhibits related to a museum they visit in the city.

[0369] An example of a prompt message is: "Based on the learning area you entered, please suggest the latest and most appropriate learning materials. Please consider the user's level and interests."

[0370] In this way, the user's learning experience can be optimized to their individual needs and, by linking with resources throughout the city, can provide a richer learning environment.

[0371] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0372] Step 1:

[0373] The user enters their basic information using a device. This data includes the user's name, age, and areas of study they are interested in. The device sends this information to a server, which uses it as basic data for creating a profile.

[0374] Step 2:

[0375] The server generates a user profile based on the basic information it receives. This profile records the user's learning history and interests, and the generated AI model analyzes this data to create a personalized learning plan. The output provides basic data for evaluating learning ability.

[0376] Step 3:

[0377] Based on the profile generated by the server, a test is created to measure the user's learning ability. The test content is customized with prompts to be optimized for the user. The created test is presented to the user via the terminal, and they are prompted to answer.

[0378] Step 4:

[0379] The user answers the test, and the device sends the results to the server. The server analyzes the received test results to identify the user's learning ability. It receives the test answer data as input and evaluates the user's learning ability level as output.

[0380] Step 5:

[0381] The server selects appropriate learning information based on the user's learning ability level and interests. At this stage, a machine learning model is used to automatically select learning materials that match the user's interests and abilities. The selected learning content is sent to the device and presented to the user.

[0382] Step 6:

[0383] The device displays selected learning information to the user. This information includes video and interactive learning materials designed to encourage the user to engage in learning. The output provides visually easy-to-understand learning materials.

[0384] Step 7:

[0385] The server records the user's learning progress and dynamically adjusts the learning pace and schedule based on the progress data. It automatically updates the next learning content according to the progress, providing a plan that is suitable for the user.

[0386] Step 8:

[0387] The server interacts with information on public facilities in urban environments to provide users with an optimized learning experience. For example, when a user visits a specific facility, relevant learning content is displayed on their device, expanding learning opportunities. By interacting with public facility data, the server provides learning content tailored to the specific location.

[0388] 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.

[0389] One embodiment of the present invention is a learning management system that incorporates emotion recognition technology to optimize individual user learning and further personalize the learning experience. This system includes an emotion engine that recognizes and analyzes the user's emotions in real time, in addition to user input information and learning progress data.

[0390] The user installs the application on their device and initially enters user information. This information includes name, age, and learning interests. The device sends this information to a server, which uses it to create a user profile. The device also has built-in input devices such as a camera and microphone, which work in conjunction with an emotion engine to monitor the user's facial expressions, tone of voice, language patterns, etc., and detect their emotional state.

[0391] The server designs a test to measure the user's learning ability level and presents it to the user via the terminal. Once the user completes the test, the terminal returns the results to the server. The server analyzes the results to determine the learning ability level and, further, combines this with emotional data obtained from the emotion engine to select more suitable learning content.

[0392] The device displays selected learning content while an emotion engine tracks the user's reactions in real time. For example, if the server determines that the user is confused, it can use that data to lower the difficulty level or add supplementary explanations. Conversely, if positive emotions such as joy or excitement are recognized, the system can either continue the current learning path or introduce more challenging content.

[0393] As a concrete example, suppose a child is learning math and shows a confused expression when faced with a particular problem. The emotion engine detects this expression and sends data to the server. Based on the child's emotional state and learning progress, the server instructs the system to add hints to the problem or present visual aids. Such dynamic adjustments reduce stress for the user and allow them to learn more comfortably.

[0394] This system not only tracks the user's learning progress but also aims for optimal learning outcomes by adaptively adjusting learning content and approaches using emotional data. This optimizes the learning experience according to the user's emotions and concentration levels, providing a highly satisfying learning process.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The user installs a learning management application on their device and enters their basic information (name, age, subjects of interest, etc.). The entered information is then sent from the device to the server.

[0398] Step 2:

[0399] The server generates a learning profile for the user based on the basic information received. The profile includes a structure that also records the user's learning history and sentiment data.

[0400] Step 3:

[0401] The terminal, following instructions from the server, presents a basic test to measure the user's learning ability level. The user answers the test questions, and the terminal sends the answer data to the server.

[0402] Step 4:

[0403] The server analyzes the received test results to identify the user's learning ability level. It also evaluates the user's emotional state by linking with emotion engine data from the terminal.

[0404] Step 5:

[0405] The server selects the most suitable learning content based on the user's learning ability level and emotional state. This selection may include content with a storyline or interactive elements. The selection results are delivered to the device.

[0406] Step 6:

[0407] The device displays learning content delivered from the server to the user. During the presentation, an emotion engine monitors the user's facial expressions and voice in real time and reports their emotional state to the server.

[0408] Step 7:

[0409] The server analyzes emotional data and dynamically adjusts the learning content as needed. For example, if it detects that the user is dissatisfied, it will instruct the device to lower the difficulty level of the content or change the presentation method.

[0410] Step 8:

[0411] The device adjusts and continues to present learning content based on instructions from the server. This process is repeated until the user's emotional state improves.

[0412] Step 9:

[0413] The user sets learning goals together with the user. The server generates a learning schedule based on the set goals and continuously monitors the progress.

[0414] Step 10:

[0415] The device continuously presents story-driven learning content and interactive activities aimed at achieving learning goals, encouraging active user participation. The server periodically evaluates overall learning progress and sentiment data, adjusting the entire system as needed.

[0416] (Example 2)

[0417] 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".

[0418] In optimizing individualized learning, it was difficult to provide an adaptive learning experience because the user's emotional state could not be reflected in real time. Furthermore, there was no means to adjust the learning content based on the learner's physiological responses and emotions, resulting in missed opportunities to improve learning efficiency.

[0419] 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.

[0420] In this invention, the server includes means for analyzing test results and collected emotional data to identify the user's learning ability level and emotional state; means for selecting optimal learning information based on the learning ability level and emotional data; and means for automatically adjusting the difficulty level of the learning information and adding supplementary details according to the emotional data. This makes it possible to provide an adaptive and personalized learning experience that responds to the user's emotional changes.

[0421] A "user" is an individual or group that uses a system to learn.

[0422] "Basic information" refers to data about the user, such as name, age, and learning interests.

[0423] A "profile" is a unique dataset that a server generates based on a user's basic information.

[0424] A "test" refers to a set of problems or tasks designed by a server and presented by a device to measure the user's learning ability level.

[0425] A "terminal" is a device used by a user to interact with a system, and may include input devices such as cameras and microphones.

[0426] An "emotion engine" is software that uses data from the device's camera, microphone, and other sources to analyze the user's emotional state in real time.

[0427] "Emotional data" refers to information about emotions extracted from a user's facial expressions, tone of voice, language patterns, and other similar data.

[0428] "Learning information" refers to educational content and materials selected by the server based on the user's learning ability level and emotional state.

[0429] A "learning schedule" refers to the plan generated by the server to help the user achieve their learning goals.

[0430] A "generative AI model" is a type of artificial intelligence that generates patterns and outputs based on large amounts of data, and in this invention, it is mainly used for selecting training information.

[0431] A "prompt sentence" is a sentence or phrase that is input into a generative AI model, and its role is to elicit a specific response or output.

[0432] This invention provides a learning management system incorporating emotion recognition technology. The user first installs an application on their device and enters basic personal information as part of the initial setup. This basic information includes the user's name, age, and learning interests. The device transmits this information to the server using a secure communication protocol. The server generates a user-specific profile based on the received information.

[0433] The system uses a test designed by the server to measure the user's learning ability. The terminal presents this test to the user and receives their answers. Simultaneously, the terminal is equipped with a camera and microphone to collect emotional data in real time through the user's facial expressions and voice. An emotion engine analyzes this data to determine the user's emotional state.

[0434] The server comprehensively analyzes test results and emotional data to understand the user's learning ability level and emotional state. Using a generative AI model, it selects the optimal learning information based on this data. The terminal presents this information to the user and continues to monitor emotional data throughout the learning process. The system can adjust the difficulty level of the learning information or add supplementary information in response to changes in the user's emotions.

[0435] For example, when a student is taking a history lesson, if the emotion engine detects a decrease in the user's interest from their facial expressions, the server will help increase their motivation by lowering the difficulty level or incorporating visual materials related to the question. An example of a prompt might be: "Analyze the emotions the user feels towards a specific mathematical formula and suggest learning content that corresponds to those emotions."

[0436] This invention enables effective learning by personalizing the user's learning experience and flexibly adapting it to their emotional state.

[0437] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0438] Step 1:

[0439] The user enters basic information into the device.

[0440] Input: Basic information such as name, age, and learning interests.

[0441] Operation: The user enters personal information into an input form on the application screen.

[0442] Output: The input information is organized within the terminal and ready to be sent to the next step.

[0443] Step 2:

[0444] The device sends basic information to the server.

[0445] Input: Basic information organized in Step 1.

[0446] Operation: The terminal uses a secure protocol such as HTTPS to send basic information to the server.

[0447] Output: The server generates a user profile based on the information received.

[0448] Step 3:

[0449] The server designs the tests for measuring learning ability.

[0450] Input: User profile information.

[0451] Operation: The server uses a generated AI model to create tests for measuring individualized learning ability.

[0452] Output: Test data is generated and sent to the terminal.

[0453] Step 4:

[0454] The device presents the user with a test.

[0455] Input: Test data sent from the server.

[0456] Operation: The device displays test questions on the screen and provides an interface for the user to answer them.

[0457] Output: User response data is collected.

[0458] Step 5:

[0459] The device collects emotional data.

[0460] Input: Real-time facial and voice data of the user.

[0461] Operation: Uses the device's camera and microphone to record the user's facial expressions and voice tone.

[0462] Output: Raw emotional data is generated for analysis by the emotion engine.

[0463] Step 6:

[0464] The device sends test results and emotional data to the server.

[0465] Input: User response data and unprocessed sentiment data.

[0466] Operation: The terminal securely transmits this data to the server.

[0467] Output: Test results and sentiment data are passed for use by the server.

[0468] Step 7:

[0469] The server analyzes the test results and sentiment data.

[0470] Input: Test results and sentiment data.

[0471] Operation: The server performs statistical analysis of test results and analyzes emotional data using an emotion engine.

[0472] Output: Optimized learning ability level and emotional state data are obtained based on the analysis results.

[0473] Step 8:

[0474] The server selects the learning information.

[0475] Input: Learning ability level and emotional state data.

[0476] Operation: The generative AI model selects the most suitable training information for the user.

[0477] Output: Optimal learning information data is sent to the device.

[0478] Step 9:

[0479] The device presents learning information to the user.

[0480] Input: Training information data sent from the server.

[0481] Operation: The device provides the user with selected learning content and begins learning.

[0482] Output: Learning is successfully delivered, improving the user experience.

[0483] Step 10:

[0484] The server provides feedback based on user sentiment data.

[0485] Input: Real-time user sentiment data.

[0486] Operation: The server monitors emotional changes and adjusts the learned information as needed.

[0487] Output: Pre-tuned content designed to maximize learning effectiveness for the user.

[0488] (Application Example 2)

[0489] 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."

[0490] In learning and recreational activities for the elderly, there is a problem in providing appropriate stimuli tailored to each individual's emotional state. Conventional learning systems cannot take individual emotional states into account and are limited to providing uniform learning information, which limits learning effectiveness and motivation maintenance.

[0491] 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.

[0492] In this invention, the server includes means for analyzing the user's emotional state using emotion recognition technology and adaptively adjusting learning information based on the emotional state; means for presenting learning information with a narrative structure to the user and flexibly adjusting its order; and means for changing the content of the interaction according to the user's emotional state. This makes it possible to provide a deeply personalized learning experience tailored to the user's emotions.

[0493] "User" refers to individuals who engage in individual learning or recreational activities on the system.

[0494] "Basic information" refers to personal data such as name, age, and interests that users enter.

[0495] A "profile" refers to a dataset generated based on a user's basic information and used to optimize learning and recreational activities.

[0496] "Tests" refer to a set of tasks or problems provided to measure the user's learning ability level.

[0497] "Learning information" refers to educational or entertainment content that is selected and provided based on the user's learning ability level and interests.

[0498] "Learning progress data" refers to records of how users utilize the system, and includes information indicating their learning pace and progress.

[0499] "Emotion recognition technology" refers to technology that analyzes a user's facial expressions, tone of voice, and language patterns to identify their emotional state.

[0500] "Storytelling" refers to the characteristic of incorporating elements of a consistent narrative, developing learning information not merely as a list of facts, but as a story.

[0501] This invention is a learning support system for the elderly that utilizes emotion recognition technology, and the server operates in conjunction with terminals such as smart glasses. Specific embodiments are described below.

[0502] The server first generates a profile based on the basic information entered by the user. This involves using the camera and microphone equipped on the device to collect the user's facial expressions and voice in real time, and then applying emotion recognition technology to analyze their emotional state. For this analysis, emotion recognition APIs such as Amazon Rekognition or Microsoft Azure Emotion API are used.

[0503] The device selects and displays the most appropriate learning information on the screen based on the user's emotional state. This allows the user to continue having an engaging learning experience without feeling bored or uncomfortable. Learning progress data is sent to the server each time, and the server analyzes this data to adjust the learning pace.

[0504] For example, if a user is wearing smart glasses and their facial expression is detected as relaxed, the server will select and present a lecture video or music program suitable for a relaxed state. This selection process utilizes automated prompt generation. An example of a prompt might be, "If an elderly person shows interest in local history, what kind of video should be recommended?"

[0505] This system makes it possible to provide optimized learning content tailored to the user's emotions, creating a more personalized learning environment.

[0506] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0507] Step 1:

[0508] The user puts on smart glasses and enters basic information into the terminal. Here, the user enters profile data such as name, age, and areas of interest, and the terminal sends this information to the server. The entered data is sent in text format and recorded in a database on the server.

[0509] Step 2:

[0510] The device's camera and microphone are used to collect the user's facial expressions and voice. The device sends this data to an emotion recognition API in real time for analysis of the emotional state. This analysis process uses a facial recognition algorithm to identify an emotion category (e.g., joy, sadness, interest) and returns the result to the device.

[0511] Step 3:

[0512] The server selects the most suitable learning content based on the analyzed sentiment data. Here, a recommendation system that considers the user's interests and emotional state is used to select the most appropriate content from multiple candidates. The selected learning content is sent back to the device and displayed on the screen.

[0513] Step 4:

[0514] The device presents learning content to the user. Specifically, selected videos, text materials, and interactive quizzes are displayed. The user views this content and performs simple input operations according to the instructions. User feedback and learning progress are then sent back to the server.

[0515] Step 5:

[0516] The server analyzes the user's learning progress data and adjusts the learning pace and the next content. Using the aggregated progress and sentiment data, the server determines the difficulty level of the next content and sends feedback to the user. By testing with a generative AI model, prompts for the next learning session are automatically generated, enabling further personalization.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] [Third Embodiment]

[0521] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0522] 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.

[0523] 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).

[0524] 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.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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".

[0533] The system for carrying out the present invention is a learning management application designed to individually optimize the learning experience of each user. This system functions through the interaction of a server, a terminal, and a user, and provides personalized learning content to each user.

[0534] The user first launches the application on their device and enters the child's basic information when creating a new account. This includes the user's name, age, and areas of interest in learning. The device sends this information to the server, which generates a user profile based on it. This profile is then updated with information about the user's learning history and learning ability level.

[0535] The server generates a test to identify the user's learning ability using their profile and presents it to the user via their device. The user answers the test, and the results are sent back to the server. The server analyzes these results to determine the user's current learning ability level and selects appropriate learning content based on that level.

[0536] The selected learning content is presented on the device in a way that is easy for the user to understand and is engaging. For example, learning materials using animations in a science field that the user is interested in may be presented. Users can monitor their progress and set goals. Goals are set with the aim of acquiring specific skills and knowledge, and a learning schedule is automatically generated by the server accordingly.

[0537] The system continuously records the user's learning progress data and sends it to the server. The server analyzes this data and adjusts the user's learning pace and content as needed. This dynamic adjustment ensures that the user is always working on challenges of an appropriate difficulty level.

[0538] As a concrete example, suppose a user uses this system to learn the basics of arithmetic. Based on the user's input, the server generates a new arithmetic-focused test and presents it to the user via the terminal. From the test results, the server identifies that the user has particular difficulty with multiplication. The server selects content, including videos and interactive games related to the basics of multiplication, and provides it to the user via the terminal. Through this content, the user can see a concrete plan to continue practicing multiplication and work towards the goal of "achieving a complete understanding of multiplication within a specific time."

[0539] Thus, the system implementing the present invention aims to maximize the educational effect on the user through interaction with the user and the terminal. The server plays a central role in managing the individualized learning experience and providing an optimized learning path.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] The user installs the application on their device and creates a new account. They enter the child's basic information (name, age, subjects of interest, etc.). The device sends the entered information to the server.

[0543] Step 2:

[0544] The server generates a learning profile specifically for the child based on the basic information received. This profile includes fields for saving learning history and progress information that will be added later.

[0545] Step 3:

[0546] The device receives instructions from the server and presents the child with a basic test. The test includes basic questions in subjects such as arithmetic, language arts, and science. The child answers the questions on the device.

[0547] Step 4:

[0548] The device sends the child's test results to the server. The server analyzes the received responses and evaluates the accuracy rate and response time to calculate the child's learning ability level.

[0549] Step 5:

[0550] The server selects appropriate learning content based on the child's learning ability level and interests. The selected content includes various formats such as videos, animations, and interactive games.

[0551] Step 6:

[0552] The device displays learning content provided by the server to the child. The content is designed to be visually appealing and engaging for the child.

[0553] Step 7:

[0554] The device records the child's learning progress (correct answers to problems, learning speed, content viewing time, etc.) and sends that data to the server.

[0555] Step 8:

[0556] The server analyzes learning progress data and dynamically adjusts the learning pace and the next content to be tackled as needed. This ensures that children are always working on tasks of appropriate difficulty.

[0557] Step 9:

[0558] The user sets goals together with the user. The server automatically generates a learning schedule based on the set goals. The schedule includes tasks to be completed and content to be viewed.

[0559] Step 10:

[0560] The device provides story-driven content and interactive activities to enhance children's motivation to learn. The server continuously monitors progress and provides support to help them achieve their goals.

[0561] (Example 1)

[0562] 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."

[0563] In today's educational environment, customized learning experiences tailored to each user's learning ability and interests are crucial. However, conventional learning systems have struggled to provide adaptive learning programs to users. This invention aims to solve these problems and provide a learning experience optimized for each user.

[0564] 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.

[0565] In this invention, the server includes means for designing a test to measure the user's learning ability using a profile-generated AI model, means for analyzing the test results to identify the user's learning ability level, and means for selecting learning information based on the learning ability level and interests. This makes it possible to quickly and effectively provide the user with the most suitable learning program.

[0566] A "user" is an entity that performs input or operations on a system, and refers to a person who has the role of inputting information about the user.

[0567] "User" refers to the subject who receives learning through an educational program, and the person who acquires knowledge through learning content and examinations.

[0568] "Basic information" refers to information about the user's attributes, such as their name, age, and areas of learning interest.

[0569] A "profile" refers to a dataset containing a user's basic information, learning history, and proficiency level.

[0570] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate output for a specific task.

[0571] "Test" refers to an evaluation method designed to measure a user's learning ability and level of understanding.

[0572] "Content" refers to learning information and materials provided to the user.

[0573] The system for implementing the present invention centers around a user, a terminal, and a server, providing a learning experience individually optimized for each user. First, the user launches the learning management software on the terminal and enters their basic information when using the system for the first time. This basic information includes the user's name, age, and areas of interest.

[0574] The device sends this basic information to a server in the cloud. The server receives this information and generates an individual profile of the user. This process also collects information based on the user's past learning history and interests. Based on the generated profile, the server uses a generative AI model to design a test to measure the user's learning ability. The AI ​​model used here is optimized using natural language processing and machine learning algorithms.

[0575] Therefore, the server sends the designed test content back to the terminal, which then presents it to the user. At this point, the user can provide support to the user as they take the test. Once the user answers the test, the results are automatically sent to the server. The server analyzes these results to identify the user's current learning ability level. This analysis utilizes advanced data analysis techniques and is processed immediately.

[0576] Next, the server selects appropriate learning content based on the identified learning ability level and the user's interests. In this process, the generating AI model can generate appropriate learning materials and interactive content according to prompts such as "Suggest a learning method that the user will find most interesting." The selected content is then provided to the user via the device. For example, if the user is interested in multiplication and wants to deepen their understanding of the subject, the server will send multiplication-related animated videos or interactive games to the device.

[0577] Furthermore, the terminal continuously records the user's learning progress, and this data is synchronized with the server. Based on this information, the server adjusts the learning pace and content, providing an environment where users can learn efficiently towards their set learning goals. In this way, the system of the present invention continuously provides an individually optimized learning experience through the cooperation of the user, terminal, and server.

[0578] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0579] Step 1:

[0580] The user launches the learning management software on their device and creates a new account. During this process, they enter their basic information (name, age, areas of interest, etc.). This information becomes input data for the system.

[0581] Step 2:

[0582] The terminal processes the entered basic information, formats it appropriately, and sends it to the server. The server generates an individual user profile from the received information. This process involves recording information in a database and creating the profile.

[0583] Step 3:

[0584] The server uses a generative AI model to design tests to measure the user's learning ability based on their profile. Specifically, the generative AI model takes profile information as input, generates appropriate test questions, and outputs the results as a test in text or digital format.

[0585] Step 4:

[0586] The server sends the designed test to the terminal, which displays the test to the user. The user assists the other user in answering the test. The terminal records the user's answers as digital data.

[0587] Step 5:

[0588] The terminal sends the recorded test answers to the server. The server uses the answer data as input to perform advanced data analysis and identify the user's current learning ability level. The identified learning ability level becomes output data used within the system.

[0589] Step 6:

[0590] The server takes the user's learning ability level and interests as input and uses a generative AI model to select appropriate learning content. Output content includes animated videos and interactive games. The selected content is then sent to the device.

[0591] Step 7:

[0592] The device provides the user with the received learning content and initiates interaction with the user. For example, if the user is watching an animated video about multiplication, the user can see their viewing status.

[0593] Step 8:

[0594] The device continuously records the user's learning progress data and sends it to the server. The server uses this progress data as input to analyze and adjust the learning pace and content. If necessary, it sends newly selected content to the device.

[0595] Step 9:

[0596] Users can set their learning goals based on information provided by the server. The server automatically generates a corresponding learning schedule based on the set goals. This schedule is output to the terminal for the user to review.

[0597] (Application Example 1)

[0598] 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."

[0599] In modern society, there is a need to provide individually optimized learning experiences while effectively promoting the use of learning resources in public facilities and urban environments. However, conventional learning management systems have limited capabilities in providing learning content based on user profiles and interests, and do not adequately integrate with urban public resources, making it difficult to meet the diverse needs of learners.

[0600] 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.

[0601] In this invention, the server includes means for the user to input basic information about the user, means for the server to generate a profile based on the basic information, and means for providing an optimized learning experience in conjunction with information on public facilities in an urban environment. This makes it possible to realize individually optimized learning effects even in public places and to provide diverse learning opportunities.

[0602] "A means for users to input basic information about themselves" refers to an interface that allows users to input information such as their name, age, and areas of interest in learning, and for the system to receive that information.

[0603] "Means by which the server generates a profile based on the basic information" refers to a function that creates a database for managing each user's learning history and interests based on the entered basic information.

[0604] "A means of presenting a test from a terminal to measure the user's learning ability level" refers to a function that provides the user with an appropriate learning ability assessment test and makes an ability assessment based on that test.

[0605] "A means by which the server analyzes test results and identifies the user's learning ability level" refers to a function that analyzes test response data to quantitatively evaluate the user's learning ability.

[0606] "Means by which the server selects learning information based on learning ability level and interests" refers to an algorithm that automatically selects learning materials that match the user's interests and abilities.

[0607] "A means by which a device presents selected learning information to the user" refers to a function that displays selected learning content on the user's device.

[0608] "Means for transmitting user learning progress data from the terminal to the server" refers to a communication protocol for recording the progress of the user's learning activities and uploading it to a central server.

[0609] "Means by which the server adjusts the learning pace based on learning progress data" refers to a function that automatically adjusts the learning content and time allocation according to the progress made.

[0610] "Means for setting user learning objectives and generating a learning schedule to achieve those objectives" refers to a function that sets specific learning objectives that the user should achieve and creates an optimal timetable to achieve them.

[0611] "A means of providing an optimized learning experience in conjunction with information on public facilities in an urban environment" refers to a function that provides users with learning content tailored to their specific location by linking with public resources within the city.

[0612] The system for implementing this invention is a learning management system designed to individually optimize the learning experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0613] First, the user enters basic information about themselves and their child using the device. This includes data such as name, age, and areas of learning interest. The device sends this basic information to the server, which then generates a user profile based on this information. The generated profile records the user's learning history and interests.

[0614] The server then uses this profile to generate a test to measure the user's learning ability and presents it through the terminal. Once the user answers the test, the results are sent to the server, which analyzes the results to determine the user's learning ability level. Based on that learning ability level and interests, the server selects appropriate learning information, such as video materials or interactive materials, and provides them to the terminal.

[0615] Furthermore, the system can be integrated with public facilities in urban environments. For example, when a learner visits a specific library, learning content related to that location is displayed on the user's device. In addition, videos about the history and characteristics of the facility are provided, allowing learners to absorb new knowledge on the spot.

[0616] The server continuously records the user's learning progress and dynamically adjusts the learning pace and schedule based on the shared data. Once the user sets their learning goals, it automatically generates a suitable learning schedule. Furthermore, progress data is stored on cloud servers (such as AWS and Azure), and machine learning models (such as Python TensorFlow) are used for analysis.

[0617] For example, if a user expresses interest in the field of "history," the application is designed to provide them with detailed information about historical events and exhibits related to a museum they visit in the city.

[0618] An example of a prompt message is: "Based on the learning area you entered, please suggest the latest and most appropriate learning materials. Please consider the user's level and interests."

[0619] In this way, the user's learning experience can be optimized to their individual needs and, by linking with resources throughout the city, can provide a richer learning environment.

[0620] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0621] Step 1:

[0622] The user enters their basic information using a device. This data includes the user's name, age, and areas of study they are interested in. The device sends this information to a server, which uses it as basic data for creating a profile.

[0623] Step 2:

[0624] The server generates a user profile based on the basic information it receives. This profile records the user's learning history and interests, and the generated AI model analyzes this data to create a personalized learning plan. The output provides basic data for evaluating learning ability.

[0625] Step 3:

[0626] Based on the profile generated by the server, a test is created to measure the user's learning ability. The test content is customized with prompts to be optimized for the user. The created test is presented to the user via the terminal, and they are prompted to answer.

[0627] Step 4:

[0628] The user answers the test, and the device sends the results to the server. The server analyzes the received test results to identify the user's learning ability. It receives the test answer data as input and evaluates the user's learning ability level as output.

[0629] Step 5:

[0630] The server selects appropriate learning information based on the user's learning ability level and interests. At this stage, a machine learning model is used to automatically select learning materials that match the user's interests and abilities. The selected learning content is sent to the device and presented to the user.

[0631] Step 6:

[0632] The device displays selected learning information to the user. This information includes video and interactive learning materials designed to encourage the user to engage in learning. The output provides visually easy-to-understand learning materials.

[0633] Step 7:

[0634] The server records the user's learning progress and dynamically adjusts the learning pace and schedule based on the progress data. It automatically updates the next learning content according to the progress, providing a plan that is suitable for the user.

[0635] Step 8:

[0636] The server interacts with information on public facilities in urban environments to provide users with an optimized learning experience. For example, when a user visits a specific facility, relevant learning content is displayed on their device, expanding learning opportunities. By interacting with public facility data, the server provides learning content tailored to the specific location.

[0637] 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.

[0638] One embodiment of the present invention is a learning management system that incorporates emotion recognition technology to optimize individual user learning and further personalize the learning experience. This system includes an emotion engine that recognizes and analyzes the user's emotions in real time, in addition to user input information and learning progress data.

[0639] The user installs the application on their device and initially enters user information. This information includes name, age, and learning interests. The device sends this information to a server, which uses it to create a user profile. The device also has built-in input devices such as a camera and microphone, which work in conjunction with an emotion engine to monitor the user's facial expressions, tone of voice, language patterns, etc., and detect their emotional state.

[0640] The server designs a test to measure the user's learning ability level and presents it to the user via the terminal. Once the user completes the test, the terminal returns the results to the server. The server analyzes the results to determine the learning ability level and, further, combines this with emotional data obtained from the emotion engine to select more suitable learning content.

[0641] The device displays selected learning content while an emotion engine tracks the user's reactions in real time. For example, if the server determines that the user is confused, it can use that data to lower the difficulty level or add supplementary explanations. Conversely, if positive emotions such as joy or excitement are recognized, the system can either continue the current learning path or introduce more challenging content.

[0642] As a concrete example, suppose a child is learning math and shows a confused expression when faced with a particular problem. The emotion engine detects this expression and sends data to the server. Based on the child's emotional state and learning progress, the server instructs the system to add hints to the problem or present visual aids. Such dynamic adjustments reduce stress for the user and allow them to learn more comfortably.

[0643] This system not only tracks the user's learning progress but also aims for optimal learning outcomes by adaptively adjusting learning content and approaches using emotional data. This optimizes the learning experience according to the user's emotions and concentration levels, providing a highly satisfying learning process.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The user installs a learning management application on their device and enters their basic information (name, age, subjects of interest, etc.). The entered information is then sent from the device to the server.

[0647] Step 2:

[0648] The server generates a learning profile for the user based on the basic information received. The profile includes a structure that also records the user's learning history and sentiment data.

[0649] Step 3:

[0650] The terminal, following instructions from the server, presents a basic test to measure the user's learning ability level. The user answers the test questions, and the terminal sends the answer data to the server.

[0651] Step 4:

[0652] The server analyzes the received test results to identify the user's learning ability level. It also evaluates the user's emotional state by linking with emotion engine data from the terminal.

[0653] Step 5:

[0654] The server selects the most suitable learning content based on the user's learning ability level and emotional state. This selection may include content with a storyline or interactive elements. The selection results are delivered to the device.

[0655] Step 6:

[0656] The device displays learning content delivered from the server to the user. During the presentation, an emotion engine monitors the user's facial expressions and voice in real time and reports their emotional state to the server.

[0657] Step 7:

[0658] The server analyzes emotional data and dynamically adjusts the learning content as needed. For example, if it detects that the user is dissatisfied, it will instruct the device to lower the difficulty level of the content or change the presentation method.

[0659] Step 8:

[0660] The device adjusts and continues to present learning content based on instructions from the server. This process is repeated until the user's emotional state improves.

[0661] Step 9:

[0662] The user sets learning goals together with the user. The server generates a learning schedule based on the set goals and continuously monitors the progress.

[0663] Step 10:

[0664] The device continuously presents story-driven learning content and interactive activities aimed at achieving learning goals, encouraging active user participation. The server periodically evaluates overall learning progress and sentiment data, adjusting the entire system as needed.

[0665] (Example 2)

[0666] 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."

[0667] In optimizing individualized learning, it was difficult to provide an adaptive learning experience because the user's emotional state could not be reflected in real time. Furthermore, there was no means to adjust the learning content based on the learner's physiological responses and emotions, resulting in missed opportunities to improve learning efficiency.

[0668] 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.

[0669] In this invention, the server includes means for analyzing test results and collected emotional data to identify the user's learning ability level and emotional state; means for selecting optimal learning information based on the learning ability level and emotional data; and means for automatically adjusting the difficulty level of the learning information and adding supplementary details according to the emotional data. This makes it possible to provide an adaptive and personalized learning experience that responds to the user's emotional changes.

[0670] A "user" is an individual or group that uses a system to learn.

[0671] "Basic information" refers to data about the user, such as name, age, and learning interests.

[0672] A "profile" is a unique dataset that a server generates based on a user's basic information.

[0673] A "test" refers to a set of problems or tasks designed by a server and presented by a device to measure the user's learning ability level.

[0674] A "terminal" is a device used by a user to interact with a system, and may include input devices such as cameras and microphones.

[0675] An "emotion engine" is software that uses data from the device's camera, microphone, and other sources to analyze the user's emotional state in real time.

[0676] "Emotional data" refers to information about emotions extracted from a user's facial expressions, tone of voice, language patterns, and other similar data.

[0677] "Learning information" refers to educational content and materials selected by the server based on the user's learning ability level and emotional state.

[0678] A "learning schedule" refers to the plan generated by the server to help the user achieve their learning goals.

[0679] A "generative AI model" is a type of artificial intelligence that generates patterns and outputs based on large amounts of data, and in this invention, it is mainly used for selecting training information.

[0680] A "prompt sentence" is a sentence or phrase that is input into a generative AI model, and its role is to elicit a specific response or output.

[0681] This invention provides a learning management system incorporating emotion recognition technology. The user first installs an application on their device and enters basic personal information as part of the initial setup. This basic information includes the user's name, age, and learning interests. The device transmits this information to the server using a secure communication protocol. The server generates a user-specific profile based on the received information.

[0682] The system uses a test designed by the server to measure the user's learning ability. The terminal presents this test to the user and receives their answers. Simultaneously, the terminal is equipped with a camera and microphone to collect emotional data in real time through the user's facial expressions and voice. An emotion engine analyzes this data to determine the user's emotional state.

[0683] The server comprehensively analyzes test results and emotional data to understand the user's learning ability level and emotional state. Using a generative AI model, it selects the optimal learning information based on this data. The terminal presents this information to the user and continues to monitor emotional data throughout the learning process. The system can adjust the difficulty level of the learning information or add supplementary information in response to changes in the user's emotions.

[0684] For example, when a student is taking a history lesson, if the emotion engine detects a decrease in the user's interest from their facial expressions, the server will help increase their motivation by lowering the difficulty level or incorporating visual materials related to the question. An example of a prompt might be: "Analyze the emotions the user feels towards a specific mathematical formula and suggest learning content that corresponds to those emotions."

[0685] This invention enables effective learning by personalizing the user's learning experience and flexibly adapting it to their emotional state.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] The user enters basic information into the device.

[0689] Input: Basic information such as name, age, and learning interests.

[0690] Operation: The user enters personal information into an input form on the application screen.

[0691] Output: The input information is organized within the terminal and ready to be sent to the next step.

[0692] Step 2:

[0693] The device sends basic information to the server.

[0694] Input: Basic information organized in Step 1.

[0695] Operation: The terminal uses a secure protocol such as HTTPS to send basic information to the server.

[0696] Output: The server generates a user profile based on the information received.

[0697] Step 3:

[0698] The server designs the tests for measuring learning ability.

[0699] Input: User profile information.

[0700] Operation: The server uses a generated AI model to create tests for measuring individualized learning ability.

[0701] Output: Test data is generated and sent to the terminal.

[0702] Step 4:

[0703] The device presents the user with a test.

[0704] Input: Test data sent from the server.

[0705] Operation: The device displays test questions on the screen and provides an interface for the user to answer them.

[0706] Output: User response data is collected.

[0707] Step 5:

[0708] The device collects emotional data.

[0709] Input: Real-time facial and voice data of the user.

[0710] Operation: Uses the device's camera and microphone to record the user's facial expressions and voice tone.

[0711] Output: Raw emotional data is generated for analysis by the emotion engine.

[0712] Step 6:

[0713] The device sends test results and emotional data to the server.

[0714] Input: User response data and unprocessed sentiment data.

[0715] Operation: The terminal securely transmits this data to the server.

[0716] Output: Test results and sentiment data are passed for use by the server.

[0717] Step 7:

[0718] The server analyzes the test results and sentiment data.

[0719] Input: Test results and sentiment data.

[0720] Operation: The server performs statistical analysis of test results and analyzes emotional data using an emotion engine.

[0721] Output: Optimized learning ability level and emotional state data are obtained based on the analysis results.

[0722] Step 8:

[0723] The server selects the learning information.

[0724] Input: Learning ability level and emotional state data.

[0725] Operation: The generative AI model selects the most suitable training information for the user.

[0726] Output: Optimal learning information data is sent to the device.

[0727] Step 9:

[0728] The device presents learning information to the user.

[0729] Input: Training information data sent from the server.

[0730] Operation: The device provides the user with selected learning content and begins learning.

[0731] Output: Learning is successfully delivered, improving the user experience.

[0732] Step 10:

[0733] The server provides feedback based on user sentiment data.

[0734] Input: Real-time user sentiment data.

[0735] Operation: The server monitors emotional changes and adjusts the learned information as needed.

[0736] Output: Pre-tuned content designed to maximize learning effectiveness for the user.

[0737] (Application Example 2)

[0738] 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."

[0739] In learning and recreational activities for the elderly, there is a problem in providing appropriate stimuli tailored to each individual's emotional state. Conventional learning systems cannot take individual emotional states into account and are limited to providing uniform learning information, which limits learning effectiveness and motivation maintenance.

[0740] 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.

[0741] In this invention, the server includes means for analyzing the user's emotional state using emotion recognition technology and adaptively adjusting learning information based on the emotional state; means for presenting learning information with a narrative structure to the user and flexibly adjusting its order; and means for changing the content of the interaction according to the user's emotional state. This makes it possible to provide a deeply personalized learning experience tailored to the user's emotions.

[0742] "User" refers to individuals who engage in individual learning or recreational activities on the system.

[0743] "Basic information" refers to personal data such as name, age, and interests that users enter.

[0744] A "profile" refers to a dataset generated based on a user's basic information and used to optimize learning and recreational activities.

[0745] "Tests" refer to a set of tasks or problems provided to measure the user's learning ability level.

[0746] "Learning information" refers to educational or entertainment content that is selected and provided based on the user's learning ability level and interests.

[0747] "Learning progress data" refers to records of how users utilize the system, and includes information indicating their learning pace and progress.

[0748] "Emotion recognition technology" refers to technology that analyzes a user's facial expressions, tone of voice, and language patterns to identify their emotional state.

[0749] "Storytelling" refers to the characteristic of incorporating elements of a consistent narrative, developing learning information not merely as a list of facts, but as a story.

[0750] This invention is a learning support system for the elderly that utilizes emotion recognition technology, and the server operates in conjunction with terminals such as smart glasses. Specific embodiments are described below.

[0751] The server first generates a profile based on the basic information entered by the user. This involves using the camera and microphone equipped on the device to collect the user's facial expressions and voice in real time, and then applying emotion recognition technology to analyze their emotional state. For this analysis, emotion recognition APIs such as Amazon Rekognition or Microsoft Azure Emotion API are used.

[0752] The device selects and displays the most appropriate learning information on the screen based on the user's emotional state. This allows the user to continue having an engaging learning experience without feeling bored or uncomfortable. Learning progress data is sent to the server each time, and the server analyzes this data to adjust the learning pace.

[0753] For example, if a user is wearing smart glasses and their facial expression is detected as relaxed, the server will select and present a lecture video or music program suitable for a relaxed state. This selection process utilizes automated prompt generation. An example of a prompt might be, "If an elderly person shows interest in local history, what kind of video should be recommended?"

[0754] This system makes it possible to provide optimized learning content tailored to the user's emotions, creating a more personalized learning environment.

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The user puts on smart glasses and enters basic information into the terminal. Here, the user enters profile data such as name, age, and areas of interest, and the terminal sends this information to the server. The entered data is sent in text format and recorded in a database on the server.

[0758] Step 2:

[0759] The device's camera and microphone are used to collect the user's facial expressions and voice. The device sends this data to an emotion recognition API in real time for analysis of the emotional state. This analysis process uses a facial recognition algorithm to identify an emotion category (e.g., joy, sadness, interest) and returns the result to the device.

[0760] Step 3:

[0761] The server selects the most suitable learning content based on the analyzed sentiment data. Here, a recommendation system that considers the user's interests and emotional state is used to select the most appropriate content from multiple candidates. The selected learning content is sent back to the device and displayed on the screen.

[0762] Step 4:

[0763] The device presents learning content to the user. Specifically, selected videos, text materials, and interactive quizzes are displayed. The user views this content and performs simple input operations according to the instructions. User feedback and learning progress are then sent back to the server.

[0764] Step 5:

[0765] The server analyzes the user's learning progress data and adjusts the learning pace and the next content. Using the aggregated progress and sentiment data, the server determines the difficulty level of the next content and sends feedback to the user. By testing with a generative AI model, prompts for the next learning session are automatically generated, enabling further personalization.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] [Fourth Embodiment]

[0770] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0771] 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.

[0772] 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).

[0773] 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.

[0774] 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.

[0775] 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).

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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".

[0783] The system for carrying out the present invention is a learning management application designed to individually optimize the learning experience of each user. This system functions through the interaction of a server, a terminal, and a user, and provides personalized learning content to each user.

[0784] The user first launches the application on their device and enters the child's basic information when creating a new account. This includes the user's name, age, and areas of interest in learning. The device sends this information to the server, which generates a user profile based on it. This profile is then updated with information about the user's learning history and learning ability level.

[0785] The server generates a test to identify the user's learning ability using their profile and presents it to the user via their device. The user answers the test, and the results are sent back to the server. The server analyzes these results to determine the user's current learning ability level and selects appropriate learning content based on that level.

[0786] The selected learning content is presented on the device in a way that is easy for the user to understand and is engaging. For example, learning materials using animations in a science field that the user is interested in may be presented. Users can monitor their progress and set goals. Goals are set with the aim of acquiring specific skills and knowledge, and a learning schedule is automatically generated by the server accordingly.

[0787] The system continuously records the user's learning progress data and sends it to the server. The server analyzes this data and adjusts the user's learning pace and content as needed. This dynamic adjustment ensures that the user is always working on challenges of an appropriate difficulty level.

[0788] As a concrete example, suppose a user uses this system to learn the basics of arithmetic. Based on the user's input, the server generates a new arithmetic-focused test and presents it to the user via the terminal. From the test results, the server identifies that the user has particular difficulty with multiplication. The server selects content, including videos and interactive games related to the basics of multiplication, and provides it to the user via the terminal. Through this content, the user can see a concrete plan to continue practicing multiplication and work towards the goal of "achieving a complete understanding of multiplication within a specific time."

[0789] Thus, the system implementing the present invention aims to maximize the educational effect on the user through interaction with the user and the terminal. The server plays a central role in managing the individualized learning experience and providing an optimized learning path.

[0790] The following describes the processing flow.

[0791] Step 1:

[0792] The user installs the application on their device and creates a new account. They enter the child's basic information (name, age, subjects of interest, etc.). The device sends the entered information to the server.

[0793] Step 2:

[0794] The server generates a learning profile specifically for the child based on the basic information received. This profile includes fields for saving learning history and progress information that will be added later.

[0795] Step 3:

[0796] The device receives instructions from the server and presents the child with a basic test. The test includes basic questions in subjects such as arithmetic, language arts, and science. The child answers the questions on the device.

[0797] Step 4:

[0798] The device sends the child's test results to the server. The server analyzes the received responses and evaluates the accuracy rate and response time to calculate the child's learning ability level.

[0799] Step 5:

[0800] The server selects appropriate learning content based on the child's learning ability level and interests. The selected content includes various formats such as videos, animations, and interactive games.

[0801] Step 6:

[0802] The device displays learning content provided by the server to the child. The content is designed to be visually appealing and engaging for the child.

[0803] Step 7:

[0804] The device records the child's learning progress (correct answers to problems, learning speed, content viewing time, etc.) and sends that data to the server.

[0805] Step 8:

[0806] The server analyzes learning progress data and dynamically adjusts the learning pace and the next content to be tackled as needed. This ensures that children are always working on tasks of appropriate difficulty.

[0807] Step 9:

[0808] The user sets goals together with the user. The server automatically generates a learning schedule based on the set goals. The schedule includes tasks to be completed and content to be viewed.

[0809] Step 10:

[0810] The device provides story-driven content and interactive activities to enhance children's motivation to learn. The server continuously monitors progress and provides support to help them achieve their goals.

[0811] (Example 1)

[0812] 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".

[0813] In today's educational environment, customized learning experiences tailored to each user's learning ability and interests are crucial. However, conventional learning systems have struggled to provide adaptive learning programs to users. This invention aims to solve these problems and provide a learning experience optimized for each user.

[0814] 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.

[0815] In this invention, the server includes means for designing a test to measure the user's learning ability using a profile-generated AI model, means for analyzing the test results to identify the user's learning ability level, and means for selecting learning information based on the learning ability level and interests. This makes it possible to quickly and effectively provide the user with the most suitable learning program.

[0816] A "user" is an entity that performs input or operations on a system, and refers to a person who has the role of inputting information about the user.

[0817] "User" refers to the subject who receives learning through an educational program, and the person who acquires knowledge through learning content and examinations.

[0818] "Basic information" refers to information about the user's attributes, such as their name, age, and areas of learning interest.

[0819] A "profile" refers to a dataset containing a user's basic information, learning history, and proficiency level.

[0820] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate output for a specific task.

[0821] "Test" refers to an evaluation method designed to measure a user's learning ability and level of understanding.

[0822] "Content" refers to learning information and materials provided to the user.

[0823] The system for implementing the present invention centers around a user, a terminal, and a server, providing a learning experience individually optimized for each user. First, the user launches the learning management software on the terminal and enters their basic information when using the system for the first time. This basic information includes the user's name, age, and areas of interest.

[0824] The device sends this basic information to a server in the cloud. The server receives this information and generates an individual profile of the user. This process also collects information based on the user's past learning history and interests. Based on the generated profile, the server uses a generative AI model to design a test to measure the user's learning ability. The AI ​​model used here is optimized using natural language processing and machine learning algorithms.

[0825] Therefore, the server sends the designed test content back to the terminal, which then presents it to the user. At this point, the user can provide support to the user as they take the test. Once the user answers the test, the results are automatically sent to the server. The server analyzes these results to identify the user's current learning ability level. This analysis utilizes advanced data analysis techniques and is processed immediately.

[0826] Next, the server selects appropriate learning content based on the identified learning ability level and the user's interests. In this process, the generating AI model can generate appropriate learning materials and interactive content according to prompts such as "Suggest a learning method that the user will find most interesting." The selected content is then provided to the user via the device. For example, if the user is interested in multiplication and wants to deepen their understanding of the subject, the server will send multiplication-related animated videos or interactive games to the device.

[0827] Furthermore, the terminal continuously records the user's learning progress, and this data is synchronized with the server. Based on this information, the server adjusts the learning pace and content, providing an environment where users can learn efficiently towards their set learning goals. In this way, the system of the present invention continuously provides an individually optimized learning experience through the cooperation of the user, terminal, and server.

[0828] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0829] Step 1:

[0830] The user launches the learning management software on their device and creates a new account. During this process, they enter their basic information (name, age, areas of interest, etc.). This information becomes input data for the system.

[0831] Step 2:

[0832] The terminal processes the entered basic information, formats it appropriately, and sends it to the server. The server generates an individual user profile from the received information. This process involves recording information in a database and creating the profile.

[0833] Step 3:

[0834] The server uses a generative AI model to design tests to measure the user's learning ability based on their profile. Specifically, the generative AI model takes profile information as input, generates appropriate test questions, and outputs the results as a test in text or digital format.

[0835] Step 4:

[0836] The server sends the designed test to the terminal, which displays the test to the user. The user assists the other user in answering the test. The terminal records the user's answers as digital data.

[0837] Step 5:

[0838] The terminal sends the recorded test answers to the server. The server uses the answer data as input to perform advanced data analysis and identify the user's current learning ability level. The identified learning ability level becomes output data used within the system.

[0839] Step 6:

[0840] The server takes the user's learning ability level and interests as input and uses a generative AI model to select appropriate learning content. Output content includes animated videos and interactive games. The selected content is then sent to the device.

[0841] Step 7:

[0842] The device provides the user with the received learning content and initiates interaction with the user. For example, if the user is watching an animated video about multiplication, the user can see their viewing status.

[0843] Step 8:

[0844] The device continuously records the user's learning progress data and sends it to the server. The server uses this progress data as input to analyze and adjust the learning pace and content. If necessary, it sends newly selected content to the device.

[0845] Step 9:

[0846] Users can set their learning goals based on information provided by the server. The server automatically generates a corresponding learning schedule based on the set goals. This schedule is output to the terminal for the user to review.

[0847] (Application Example 1)

[0848] 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".

[0849] In modern society, there is a need to provide individually optimized learning experiences while effectively promoting the use of learning resources in public facilities and urban environments. However, conventional learning management systems have limited capabilities in providing learning content based on user profiles and interests, and do not adequately integrate with urban public resources, making it difficult to meet the diverse needs of learners.

[0850] 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.

[0851] In this invention, the server includes means for the user to input basic information about the user, means for the server to generate a profile based on the basic information, and means for providing an optimized learning experience in conjunction with information on public facilities in an urban environment. This makes it possible to realize individually optimized learning effects even in public places and to provide diverse learning opportunities.

[0852] "A means for users to input basic information about themselves" refers to an interface that allows users to input information such as their name, age, and areas of interest in learning, and for the system to receive that information.

[0853] "Means by which the server generates a profile based on the basic information" refers to a function that creates a database for managing each user's learning history and interests based on the entered basic information.

[0854] "A means of presenting a test from a terminal to measure the user's learning ability level" refers to a function that provides the user with an appropriate learning ability assessment test and makes an ability assessment based on that test.

[0855] "A means by which the server analyzes test results and identifies the user's learning ability level" refers to a function that analyzes test response data to quantitatively evaluate the user's learning ability.

[0856] "Means by which the server selects learning information based on learning ability level and interests" refers to an algorithm that automatically selects learning materials that match the user's interests and abilities.

[0857] "A means by which a device presents selected learning information to the user" refers to a function that displays selected learning content on the user's device.

[0858] "Means for transmitting user learning progress data from the terminal to the server" refers to a communication protocol for recording the progress of the user's learning activities and uploading it to a central server.

[0859] "Means by which the server adjusts the learning pace based on learning progress data" refers to a function that automatically adjusts the learning content and time allocation according to the progress made.

[0860] "Means for setting user learning objectives and generating a learning schedule to achieve those objectives" refers to a function that sets specific learning objectives that the user should achieve and creates an optimal timetable to achieve them.

[0861] "A means of providing an optimized learning experience in conjunction with information on public facilities in an urban environment" refers to a function that provides users with learning content tailored to their specific location by linking with public resources within the city.

[0862] The system for implementing this invention is a learning management system designed to individually optimize the learning experience. This system mainly consists of three elements: a server, a terminal, and a user.

[0863] First, the user enters basic information about themselves and their child using the device. This includes data such as name, age, and areas of learning interest. The device sends this basic information to the server, which then generates a user profile based on this information. The generated profile records the user's learning history and interests.

[0864] The server then uses this profile to generate a test to measure the user's learning ability and presents it through the terminal. Once the user answers the test, the results are sent to the server, which analyzes the results to determine the user's learning ability level. Based on that learning ability level and interests, the server selects appropriate learning information, such as video materials or interactive materials, and provides them to the terminal.

[0865] Furthermore, the system can be integrated with public facilities in urban environments. For example, when a learner visits a specific library, learning content related to that location is displayed on the user's device. In addition, videos about the history and characteristics of the facility are provided, allowing learners to absorb new knowledge on the spot.

[0866] The server continuously records the user's learning progress and dynamically adjusts the learning pace and schedule based on the shared data. Once the user sets their learning goals, it automatically generates a suitable learning schedule. Furthermore, progress data is stored on cloud servers (such as AWS and Azure), and machine learning models (such as Python TensorFlow) are used for analysis.

[0867] For example, if a user expresses interest in the field of "history," the application is designed to provide them with detailed information about historical events and exhibits related to a museum they visit in the city.

[0868] An example of a prompt message is: "Based on the learning area you entered, please suggest the latest and most appropriate learning materials. Please consider the user's level and interests."

[0869] In this way, the user's learning experience can be optimized to their individual needs and, by linking with resources throughout the city, can provide a richer learning environment.

[0870] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0871] Step 1:

[0872] The user enters their basic information using a device. This data includes the user's name, age, and areas of study they are interested in. The device sends this information to a server, which uses it as basic data for creating a profile.

[0873] Step 2:

[0874] The server generates a user profile based on the basic information it receives. This profile records the user's learning history and interests, and the generated AI model analyzes this data to create a personalized learning plan. The output provides basic data for evaluating learning ability.

[0875] Step 3:

[0876] Based on the profile generated by the server, a test is created to measure the user's learning ability. The test content is customized with prompts to be optimized for the user. The created test is presented to the user via the terminal, and they are prompted to answer.

[0877] Step 4:

[0878] The user answers the test, and the device sends the results to the server. The server analyzes the received test results to identify the user's learning ability. It receives the test answer data as input and evaluates the user's learning ability level as output.

[0879] Step 5:

[0880] The server selects appropriate learning information based on the user's learning ability level and interests. At this stage, a machine learning model is used to automatically select learning materials that match the user's interests and abilities. The selected learning content is sent to the device and presented to the user.

[0881] Step 6:

[0882] The device displays selected learning information to the user. This information includes video and interactive learning materials designed to encourage the user to engage in learning. The output provides visually easy-to-understand learning materials.

[0883] Step 7:

[0884] The server records the user's learning progress and dynamically adjusts the learning pace and schedule based on the progress data. It automatically updates the next learning content according to the progress, providing a plan that is suitable for the user.

[0885] Step 8:

[0886] The server interacts with information on public facilities in urban environments to provide users with an optimized learning experience. For example, when a user visits a specific facility, relevant learning content is displayed on their device, expanding learning opportunities. By interacting with public facility data, the server provides learning content tailored to the specific location.

[0887] 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.

[0888] One embodiment of the present invention is a learning management system that incorporates emotion recognition technology to optimize individual user learning and further personalize the learning experience. This system includes an emotion engine that recognizes and analyzes the user's emotions in real time, in addition to user input information and learning progress data.

[0889] The user installs the application on their device and initially enters user information. This information includes name, age, and learning interests. The device sends this information to a server, which uses it to create a user profile. The device also has built-in input devices such as a camera and microphone, which work in conjunction with an emotion engine to monitor the user's facial expressions, tone of voice, language patterns, etc., and detect their emotional state.

[0890] The server designs a test to measure the user's learning ability level and presents it to the user via the terminal. Once the user completes the test, the terminal returns the results to the server. The server analyzes the results to determine the learning ability level and, further, combines this with emotional data obtained from the emotion engine to select more suitable learning content.

[0891] The device displays selected learning content while an emotion engine tracks the user's reactions in real time. For example, if the server determines that the user is confused, it can use that data to lower the difficulty level or add supplementary explanations. Conversely, if positive emotions such as joy or excitement are recognized, the system can either continue the current learning path or introduce more challenging content.

[0892] As a concrete example, suppose a child is learning math and shows a confused expression when faced with a particular problem. The emotion engine detects this expression and sends data to the server. Based on the child's emotional state and learning progress, the server instructs the system to add hints to the problem or present visual aids. Such dynamic adjustments reduce stress for the user and allow them to learn more comfortably.

[0893] This system not only tracks the user's learning progress but also aims for optimal learning outcomes by adaptively adjusting learning content and approaches using emotional data. This optimizes the learning experience according to the user's emotions and concentration levels, providing a highly satisfying learning process.

[0894] The following describes the processing flow.

[0895] Step 1:

[0896] The user installs a learning management application on their device and enters their basic information (name, age, subjects of interest, etc.). The entered information is then sent from the device to the server.

[0897] Step 2:

[0898] The server generates a learning profile for the user based on the basic information received. The profile includes a structure that also records the user's learning history and sentiment data.

[0899] Step 3:

[0900] The terminal, following instructions from the server, presents a basic test to measure the user's learning ability level. The user answers the test questions, and the terminal sends the answer data to the server.

[0901] Step 4:

[0902] The server analyzes the received test results to identify the user's learning ability level. It also evaluates the user's emotional state by linking with emotion engine data from the terminal.

[0903] Step 5:

[0904] The server selects the most suitable learning content based on the user's learning ability level and emotional state. This selection may include content with a storyline or interactive elements. The selection results are delivered to the device.

[0905] Step 6:

[0906] The device displays learning content delivered from the server to the user. During the presentation, an emotion engine monitors the user's facial expressions and voice in real time and reports their emotional state to the server.

[0907] Step 7:

[0908] The server analyzes emotional data and dynamically adjusts the learning content as needed. For example, if it detects that the user is dissatisfied, it will instruct the device to lower the difficulty level of the content or change the presentation method.

[0909] Step 8:

[0910] The device adjusts and continues to present learning content based on instructions from the server. This process is repeated until the user's emotional state improves.

[0911] Step 9:

[0912] The user sets learning goals together with the user. The server generates a learning schedule based on the set goals and continuously monitors the progress.

[0913] Step 10:

[0914] The device continuously presents story-driven learning content and interactive activities aimed at achieving learning goals, encouraging active user participation. The server periodically evaluates overall learning progress and sentiment data, adjusting the entire system as needed.

[0915] (Example 2)

[0916] 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".

[0917] In optimizing individualized learning, it was difficult to provide an adaptive learning experience because the user's emotional state could not be reflected in real time. Furthermore, there was no means to adjust the learning content based on the learner's physiological responses and emotions, resulting in missed opportunities to improve learning efficiency.

[0918] 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.

[0919] In this invention, the server includes means for analyzing test results and collected emotional data to identify the user's learning ability level and emotional state; means for selecting optimal learning information based on the learning ability level and emotional data; and means for automatically adjusting the difficulty level of the learning information and adding supplementary details according to the emotional data. This makes it possible to provide an adaptive and personalized learning experience that responds to the user's emotional changes.

[0920] A "user" is an individual or group that uses a system to learn.

[0921] "Basic information" refers to data about the user, such as name, age, and learning interests.

[0922] A "profile" is a unique dataset that a server generates based on a user's basic information.

[0923] A "test" refers to a set of problems or tasks designed by a server and presented by a device to measure the user's learning ability level.

[0924] A "terminal" is a device used by a user to interact with a system, and may include input devices such as cameras and microphones.

[0925] An "emotion engine" is software that uses data from the device's camera, microphone, and other sources to analyze the user's emotional state in real time.

[0926] "Emotional data" refers to information about emotions extracted from a user's facial expressions, tone of voice, language patterns, and other similar data.

[0927] "Learning information" refers to educational content and materials selected by the server based on the user's learning ability level and emotional state.

[0928] A "learning schedule" refers to the plan generated by the server to help the user achieve their learning goals.

[0929] A "generative AI model" is a type of artificial intelligence that generates patterns and outputs based on large amounts of data, and in this invention, it is mainly used for selecting training information.

[0930] A "prompt sentence" is a sentence or phrase that is input into a generative AI model, and its role is to elicit a specific response or output.

[0931] This invention provides a learning management system incorporating emotion recognition technology. The user first installs an application on their device and enters basic personal information as part of the initial setup. This basic information includes the user's name, age, and learning interests. The device transmits this information to the server using a secure communication protocol. The server generates a user-specific profile based on the received information.

[0932] The system uses a test designed by the server to measure the user's learning ability. The terminal presents this test to the user and receives their answers. Simultaneously, the terminal is equipped with a camera and microphone to collect emotional data in real time through the user's facial expressions and voice. An emotion engine analyzes this data to determine the user's emotional state.

[0933] The server comprehensively analyzes test results and emotional data to understand the user's learning ability level and emotional state. Using a generative AI model, it selects the optimal learning information based on this data. The terminal presents this information to the user and continues to monitor emotional data throughout the learning process. The system can adjust the difficulty level of the learning information or add supplementary information in response to changes in the user's emotions.

[0934] For example, when a student is taking a history lesson, if the emotion engine detects a decrease in the user's interest from their facial expressions, the server will help increase their motivation by lowering the difficulty level or incorporating visual materials related to the question. An example of a prompt might be: "Analyze the emotions the user feels towards a specific mathematical formula and suggest learning content that corresponds to those emotions."

[0935] This invention enables effective learning by personalizing the user's learning experience and flexibly adapting it to their emotional state.

[0936] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0937] Step 1:

[0938] The user enters basic information into the device.

[0939] Input: Basic information such as name, age, and learning interests.

[0940] Operation: The user enters personal information into an input form on the application screen.

[0941] Output: The input information is organized within the terminal and ready to be sent to the next step.

[0942] Step 2:

[0943] The device sends basic information to the server.

[0944] Input: Basic information organized in Step 1.

[0945] Operation: The terminal uses a secure protocol such as HTTPS to send basic information to the server.

[0946] Output: The server generates a user profile based on the information received.

[0947] Step 3:

[0948] The server designs the tests for measuring learning ability.

[0949] Input: User profile information.

[0950] Operation: The server uses a generated AI model to create tests for measuring individualized learning ability.

[0951] Output: Test data is generated and sent to the terminal.

[0952] Step 4:

[0953] The device presents the user with a test.

[0954] Input: Test data sent from the server.

[0955] Operation: The device displays test questions on the screen and provides an interface for the user to answer them.

[0956] Output: User response data is collected.

[0957] Step 5:

[0958] The device collects emotional data.

[0959] Input: Real-time facial and voice data of the user.

[0960] Operation: Uses the device's camera and microphone to record the user's facial expressions and voice tone.

[0961] Output: Raw emotional data is generated for analysis by the emotion engine.

[0962] Step 6:

[0963] The device sends test results and emotional data to the server.

[0964] Input: User response data and unprocessed sentiment data.

[0965] Operation: The terminal securely transmits this data to the server.

[0966] Output: Test results and sentiment data are passed for use by the server.

[0967] Step 7:

[0968] The server analyzes the test results and sentiment data.

[0969] Input: Test results and sentiment data.

[0970] Operation: The server performs statistical analysis of test results and analyzes emotional data using an emotion engine.

[0971] Output: Optimized learning ability level and emotional state data are obtained based on the analysis results.

[0972] Step 8:

[0973] The server selects the learning information.

[0974] Input: Learning ability level and emotional state data.

[0975] Operation: The generative AI model selects the most suitable training information for the user.

[0976] Output: Optimal learning information data is sent to the device.

[0977] Step 9:

[0978] The device presents learning information to the user.

[0979] Input: Training information data sent from the server.

[0980] Operation: The device provides the user with selected learning content and begins learning.

[0981] Output: Learning is successfully delivered, improving the user experience.

[0982] Step 10:

[0983] The server provides feedback based on user sentiment data.

[0984] Input: Real-time user sentiment data.

[0985] Operation: The server monitors emotional changes and adjusts the learned information as needed.

[0986] Output: Pre-tuned content designed to maximize learning effectiveness for the user.

[0987] (Application Example 2)

[0988] 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".

[0989] In learning and recreational activities for the elderly, there is a problem in providing appropriate stimuli tailored to each individual's emotional state. Conventional learning systems cannot take individual emotional states into account and are limited to providing uniform learning information, which limits learning effectiveness and motivation maintenance.

[0990] 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.

[0991] In this invention, the server includes means for analyzing the user's emotional state using emotion recognition technology and adaptively adjusting learning information based on the emotional state; means for presenting learning information with a narrative structure to the user and flexibly adjusting its order; and means for changing the content of the interaction according to the user's emotional state. This makes it possible to provide a deeply personalized learning experience tailored to the user's emotions.

[0992] "User" refers to individuals who engage in individual learning or recreational activities on the system.

[0993] "Basic information" refers to personal data such as name, age, and interests that users enter.

[0994] A "profile" refers to a dataset generated based on a user's basic information and used to optimize learning and recreational activities.

[0995] "Tests" refer to a set of tasks or problems provided to measure the user's learning ability level.

[0996] "Learning information" refers to educational or entertainment content that is selected and provided based on the user's learning ability level and interests.

[0997] "Learning progress data" refers to records of how users utilize the system, and includes information indicating their learning pace and progress.

[0998] "Emotion recognition technology" refers to technology that analyzes a user's facial expressions, tone of voice, and language patterns to identify their emotional state.

[0999] "Storytelling" refers to the characteristic of incorporating elements of a consistent narrative, developing learning information not merely as a list of facts, but as a story.

[1000] This invention is a learning support system for the elderly that utilizes emotion recognition technology, and the server operates in conjunction with terminals such as smart glasses. Specific embodiments are described below.

[1001] The server first generates a profile based on the basic information entered by the user. This involves using the camera and microphone equipped on the device to collect the user's facial expressions and voice in real time, and then applying emotion recognition technology to analyze their emotional state. For this analysis, emotion recognition APIs such as Amazon Rekognition or Microsoft Azure Emotion API are used.

[1002] The device selects and displays the most appropriate learning information on the screen based on the user's emotional state. This allows the user to continue having an engaging learning experience without feeling bored or uncomfortable. Learning progress data is sent to the server each time, and the server analyzes this data to adjust the learning pace.

[1003] For example, if a user is wearing smart glasses and their facial expression is detected as relaxed, the server will select and present a lecture video or music program suitable for a relaxed state. This selection process utilizes automated prompt generation. An example of a prompt might be, "If an elderly person shows interest in local history, what kind of video should be recommended?"

[1004] This system makes it possible to provide optimized learning content tailored to the user's emotions, creating a more personalized learning environment.

[1005] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1006] Step 1:

[1007] The user puts on smart glasses and enters basic information into the terminal. Here, the user enters profile data such as name, age, and areas of interest, and the terminal sends this information to the server. The entered data is sent in text format and recorded in a database on the server.

[1008] Step 2:

[1009] The device's camera and microphone are used to collect the user's facial expressions and voice. The device sends this data to an emotion recognition API in real time for analysis of the emotional state. This analysis process uses a facial recognition algorithm to identify an emotion category (e.g., joy, sadness, interest) and returns the result to the device.

[1010] Step 3:

[1011] The server selects the most suitable learning content based on the analyzed sentiment data. Here, a recommendation system that considers the user's interests and emotional state is used to select the most appropriate content from multiple candidates. The selected learning content is sent back to the device and displayed on the screen.

[1012] Step 4:

[1013] The device presents learning content to the user. Specifically, selected videos, text materials, and interactive quizzes are displayed. The user views this content and performs simple input operations according to the instructions. User feedback and learning progress are then sent back to the server.

[1014] Step 5:

[1015] The server analyzes the user's learning progress data and adjusts the learning pace and the next content. Using the aggregated progress and sentiment data, the server determines the difficulty level of the next content and sends feedback to the user. By testing with a generative AI model, prompts for the next learning session are automatically generated, enabling further personalization.

[1016] 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.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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."

[1025] 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.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] The following is further disclosed regarding the embodiments described above.

[1038] (Claim 1)

[1039] A means for users to input basic information about themselves,

[1040] A means by which the server generates a profile based on the basic information,

[1041] A means of presenting a test from a terminal to measure the user's learning ability level,

[1042] The server analyzes the test results and identifies the user's learning ability level.

[1043] A means by which the server selects learning information based on learning ability level and interests,

[1044] A means by which the terminal presents selected learning information to the user,

[1045] A means of sending user learning progress data from the device to the server,

[1046] A means by which the server adjusts the learning pace based on learning progress data,

[1047] A means for setting learning objectives for the user and generating a learning schedule to achieve those objectives,

[1048] A system that includes this.

[1049] (Claim 2)

[1050] The system according to claim 1, further comprising means for presenting learning information having a narrative structure to the user.

[1051] (Claim 3)

[1052] The system according to claim 1, further comprising means for analyzing information entered by the user and learning progress data, and for providing interactive activities.

[1053] "Example 1"

[1054] (Claim 1)

[1055] A means for users to input basic information about themselves,

[1056] A means by which the server generates a profile based on the basic information,

[1057] A means of designing a test to measure a user's learning ability using a generative AI model based on a profile,

[1058] The means by which the device presents the test to the user,

[1059] The server analyzes the test results and identifies the user's learning ability level.

[1060] A means by which the server selects learning information based on learning ability level and interests,

[1061] A means by which the terminal presents selected learning information to the user,

[1062] A means of sending user learning progress data from the device to the server,

[1063] A means by which the server adjusts the learning pace based on learning progress data,

[1064] A means for setting learning objectives for the user and generating a learning schedule to achieve those objectives,

[1065] A system that includes this.

[1066] (Claim 2)

[1067] The system according to claim 1, further comprising means for selecting and presenting narrative-based learning information to the user using an AI model generated based on profile and learning progress data.

[1068] (Claim 3)

[1069] The system according to claim 1, further comprising means for analyzing user-inputted information and learning progress data and utilizing a generative AI model to provide interactive activities.

[1070] "Application Example 1"

[1071] (Claim 1)

[1072] A means for users to input basic information about themselves,

[1073] A means by which the server generates a profile based on the basic information,

[1074] A means of presenting a test from a terminal to measure the user's learning ability level,

[1075] The server analyzes the test results and identifies the user's learning ability level.

[1076] A means by which the server selects learning information based on learning ability level and interests,

[1077] A means by which the terminal presents selected learning information to the user,

[1078] A means of sending user learning progress data from the device to the server,

[1079] A means by which the server adjusts the learning pace based on learning progress data,

[1080] A means for setting learning objectives for the user and generating a learning schedule to achieve those objectives,

[1081] A means of providing an optimized learning experience in conjunction with information from public facilities in an urban environment,

[1082] A system that includes this.

[1083] (Claim 2)

[1084] The system according to claim 1, further comprising means for presenting learning information having a narrative structure to the user.

[1085] (Claim 3)

[1086] The system according to claim 1, further comprising means for analyzing information entered by the user and learning progress data, and for providing interactive activities.

[1087] "Example 2 of combining an emotion engine"

[1088] (Claim 1)

[1089] A means for users to input basic information about themselves,

[1090] A means by which the server generates a profile based on the basic information,

[1091] A means of presenting a test from a terminal to measure the user's learning ability level,

[1092] The terminal includes means for collecting the user's facial expressions and voice data using a camera and voice input device,

[1093] The server analyzes test results and collected emotional data to identify the user's learning ability level and emotional state.

[1094] A means for the server to select optimal learning information based on learning ability level and emotional data,

[1095] A means of presenting selected learning information to the user via a terminal and monitoring the user's response in real time using an emotion engine,

[1096] A means by which the server automatically adjusts the difficulty level of learning information based on sentiment data and adds supplementary details,

[1097] A means for transmitting the user's learning progress and emotional data from the device to the server,

[1098] A means by which the server generates and adjusts the learning schedule based on learning progress data,

[1099] A system that includes this.

[1100] (Claim 2)

[1101] The system according to claim 1, further comprising means for selecting learning information using the user's learning ability and emotional data by utilizing a generative AI model.

[1102] (Claim 3)

[1103] The system according to claim 1, further comprising means for analyzing information entered by the user and learning progress data, and providing interactive activities based on emotional data.

[1104] "Application example 2 when combining with an emotional engine"

[1105] (Claim 1)

[1106] A means for users to input basic information about themselves,

[1107] A means by which the server generates a profile based on the basic information,

[1108] A means of presenting a test from a terminal to measure the user's learning ability level,

[1109] The server analyzes the test results and identifies the user's learning ability level.

[1110] A means by which the server selects learning information based on learning ability level and interests,

[1111] A means by which the terminal presents selected learning information to the user,

[1112] A means of sending user learning progress data from the device to the server,

[1113] A means by which the server adjusts the learning pace based on learning progress data,

[1114] A means for setting learning objectives for the user and generating a learning schedule to achieve those objectives,

[1115] A means for analyzing the user's emotional state using emotion recognition technology and adaptively adjusting the learned information based on that emotional state,

[1116] A system that includes this.

[1117] (Claim 2)

[1118] The system according to claim 1, further comprising means for presenting learning information having a narrative structure to the user, and capable of flexibly adjusting the order in which the information is presented based on emotion recognition data.

[1119] (Claim 3)

[1120] The system according to claim 1, further comprising means for analyzing information and learning progress data entered by the user, and for providing interactive activities, the system is capable of changing the content of the interaction according to the user's emotional state. [Explanation of symbols]

[1121] 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 users to input basic information about themselves, A means by which the server generates a profile based on the basic information, A means of presenting a test from a terminal to measure the user's learning ability level, The server analyzes the test results and identifies the user's learning ability level. A means by which the server selects learning information based on learning ability level and interests, A means by which the terminal presents selected learning information to the user, A means of sending user learning progress data from the device to the server, A means by which the server adjusts the learning pace based on learning progress data, A means for setting learning objectives for the user and generating a learning schedule to achieve those objectives, A means of providing an optimized learning experience in conjunction with information from public facilities in an urban environment, A system that includes this.

2. The system according to claim 1, further comprising means for presenting learning information having a narrative structure to the user.

3. The system according to claim 1, further comprising means for analyzing information entered by the user and learning progress data, and for providing interactive activities.

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