system

Abstract

We provide the system. [Solution] Means for digitizing the professional knowledge of educators, A means of accumulating digitized specialized knowledge, A means of transferring accumulated knowledge to new educators when educators are transferred, Methods for collecting and analyzing student learning data, A means of proposing the optimal educational method based on the analysis results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In supporting schools, frequent teacher turnover has an impact, making it difficult to continue specialized education, thereby hindering the growth of students. There is also a problem of interrupted professionalism and a decline in the quality of education, and it is required to overcome these.

Means for Solving the Problems

[0005] This invention provides a means for digitizing and storing the specialized knowledge of educators. Furthermore, it maintains expertise by effectively sharing this digitized knowledge among educators who move between departments. It also includes a means for collecting and analyzing student learning data and proposing optimal teaching methods based on that analysis. This makes it possible to build a system that supports student growth by leveraging the expertise of educators and realizing individualized teaching approaches.

[0006] An "educator" is a person who is responsible for providing specialized education to students in a special needs school.

[0007] "Specialized knowledge" refers to the practical skills and know-how related to special needs education that educators possess.

[0008] "Digitalization" refers to the process of converting information that exists in physical forms, such as on paper or through oral transmission, into electronic data and storing it.

[0009] "Accumulation" refers to the act of gathering and storing information and data over time.

[0010] "Transfer" refers to the act of an educator moving to a different educational institution or a different position.

[0011] "Learning data" refers to information related to education, such as students' learning progress, achievements, and behavioral records.

[0012] "Analysis" is the process of interpreting information and finding meaning in it using collected data.

[0013] "Educational methods" refer to the methods, techniques, and approaches that educators use to achieve specific outcomes.

[0014] A "proposal" is the act of providing guidance for deciding on desirable actions or choices in order to achieve a specific objective. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the 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.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system for digitizing and passing on the professional knowledge of educators, with the aim of maintaining and improving the quality of education. In this system, the terminal, server, and user components cooperate to achieve effective knowledge transfer and personalized education.

[0037] The terminal is a device that provides an interface for educators to input and digitize their professional knowledge. Through the terminal, educators can input data such as successful methods and experiences in special education. This standardizes proven teaching methods and allows them to be shared as knowledge with other educators.

[0038] The server stores and manages the educators' professional knowledge received from terminals as a database. This database contains past success stories and knowledge to address various scenarios in special education. The server also collects student learning data and performs analysis to suggest teaching methods optimized for individual needs. By using AI technology for analysis, it creates customized teaching plans for each student.

[0039] Educators, as users, utilize the teaching methods suggested by the server to implement the most suitable teaching approach for their students. For example, the use of tactile materials may be suggested for students with visual impairments. Users then use these suggestions to adjust their lesson content and provide instruction tailored to the specific needs of their students.

[0040] This system ensures the efficient sharing of educators' professional knowledge, preventing a decline in the quality of education at a school even when educators are transferred. Furthermore, by quickly proposing teaching methods tailored to students' characteristics, it enables the development of individualized education, aiming to improve outcomes in educational settings.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The terminal displays a dedicated login screen for educators, who then log in to the system using their own accounts. Once an educator logs in, they are granted access to data associated with them, and they can begin using the system.

[0044] Step 2:

[0045] The user, an educator, enters their expertise and teaching methods into a form on their terminal. The entered data includes details on successful cases in special education, effective teaching materials, and teaching methods.

[0046] Step 3:

[0047] The terminal formats the specialized knowledge entered by the educator as digital data and sends it to the server using an HTTP POST request. This transmission is designed with secure communication in mind, ensuring that the data is not leaked externally.

[0048] Step 4:

[0049] The server verifies the expert knowledge data received from the terminal, confirms that it is in the correct format, and then saves it to the database. This database records and manages the accumulated knowledge of many educators, step by step.

[0050] Step 5:

[0051] When a request comes in from a terminal indicating that the system wants to check a student's learning progress, the server retrieves relevant learning history and grade information from the database based on the student's ID.

[0052] Step 6:

[0053] The server passes the acquired student learning data to an AI module for data analysis. The AI ​​analyzes the data and selects the most effective teaching methods for that student.

[0054] Step 7:

[0055] The server compiles the AI-generated optimal teaching methods into a proposal and sends a response to the terminal. This allows educators to immediately receive optimized teaching methods.

[0056] Step 8:

[0057] The educator, as the user, reviews the proposed teaching methods displayed on the terminal and develops a teaching plan that reflects them. The educator then incorporates the new methods into their lessons and implements the suggestions in actual teaching activities.

[0058] Through this process, the specialized knowledge of educators is effectively utilized, and education tailored to each individual student is realized.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern education, challenges include the loss of knowledge due to educators' transfers and delays in proposing teaching methods that address students' individual learning needs. In particular, there is a problem with the inability to effectively transfer specialized knowledge to other educators, leading to inconsistencies in the quality of learning. Furthermore, there is a need to quickly provide individually optimized educational plans tailored to each student's learning progress.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes a device for inputting the specialized knowledge of educators, a data management device for storing and managing the inputted knowledge of educators, a knowledge transfer device for transmitting the stored knowledge to new educators in accordance with the transfer of educators, a data analysis device for collecting and analyzing student learning data, and means for presenting individually optimized teaching methods to students based on the analysis results. This makes it possible to efficiently transfer the knowledge of educators and to provide educational plans that quickly respond to the individual needs of students.

[0064] A "device for inputting educators' professional knowledge" is a device that provides an interface for educators to input their teaching methods and experiences as digital data.

[0065] A "data management device for accumulating and managing the knowledge entered by educators" is a system that stores the specialized knowledge entered by educators and manages it as a database so that it can be organized and searched as needed.

[0066] A "knowledge transfer device for transmitting accumulated knowledge to new educators in accordance with the transfer of educators" is a device that has the function of effectively conveying the accumulated knowledge of educators to new educators.

[0067] A "data analysis device for collecting and analyzing student learning data" is a device that collects students' learning history and achievements and performs various analyses based on that data. This makes it possible to understand individual learning trends and challenges.

[0068] "Means for presenting individually optimized educational methods based on analysis results" refers to a function that proposes the most suitable educational methods for each student based on the results of the analysis of learning data.

[0069] A description of embodiments for carrying out this invention will be given.

[0070] In this system, the terminal functions as a device that allows educators to input their professional knowledge. The terminal is equipped with an interface that enables text and voice input, allowing educators to digitize their special education experience and success stories as data. This digitized knowledge is then transmitted to a server.

[0071] The server stores and manages received knowledge data in a cloud database. A remote data storage system is used as the cloud database. Specifically, it classifies and stores knowledge for each educator and provides it to other educators in a format that can be viewed as needed. The server also collects student learning data and analyzes it using AI technology. In this process, software such as Python and TENSORFLOW® are used to analyze the data with machine learning algorithms.

[0072] Educators, as users of the system, receive personalized teaching methods based on the server's analysis. For example, for a visually impaired student, the system might suggest the use of tactile materials, taking into account the student's past performance and behavioral patterns. Such suggestions help educators create more effective lesson plans.

[0073] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "In special education, please suggest methods for visually impaired students to effectively learn information. This student has previously shown improved learning comprehension when using tactile materials." Based on this prompt sentence, the AI ​​will suggest the most suitable materials and methods.

[0074] As described above, this system enables the efficient transfer of knowledge from educators and allows for a rapid response to the individual learning needs of students.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The terminal provides an interface to educators. Educators use the terminal to input their expertise. Specifically, they input successful case studies and teaching experiences in special education as text input or audio data. This input is converted into a digital format and prepared for subsequent processing.

[0078] Step 2:

[0079] The terminal sends the entered digital data to the server. The input data is digitized, and the server receives it and prepares to store it in a cloud database. After receiving the data, the server uses an automatic classification algorithm to categorize the data and assign appropriate labels. This labeling organizes the data so that other educators can efficiently search and refer to it.

[0080] Step 3:

[0081] The server collects and analyzes student learning data. The learning data is obtained from online learning platforms and grading systems, and preprocessed using Python. Data preprocessing includes imputing missing values ​​and normalizing the data. After preprocessing, an AI model is used to analyze learning patterns and performance trends.

[0082] Step 4:

[0083] Based on the AI ​​analysis results, the server proposes the optimal teaching method to the user, the educator. At this stage, the AI ​​model presents the educator with a prompt message and suggests a specific teaching approach. For example, a suggestion such as, "It has been found that this student's understanding will be enhanced by using tactile learning materials," might be generated.

[0084] Step 5:

[0085] The user adjusts the lesson plan based on the suggested teaching methods. The educator uses the information provided by the server as a reference and prepares teaching materials and modifies the lesson content as needed. The educator then inputs feedback on the lesson implementation back into the system via the terminal, and the system uses this feedback as new data points for the next analysis.

[0086] (Application Example 1)

[0087] 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."

[0088] There are challenges in transferring educators' knowledge and providing students with individualized learning experiences. Furthermore, traditional education systems make it difficult to effectively digitize and share educators' specialized knowledge, potentially leading to a decline in the quality of education. Additionally, it is difficult to fully utilize student learning data, making it challenging to provide an optimal educational environment tailored to individual needs.

[0089] 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.

[0090] In this invention, the server includes means for digitizing the professional knowledge of educators, means for constructing the digitized educational knowledge in a virtual space, and educational means for students to learn through virtual experiences. This makes it possible to widely share the knowledge of educators and provide a learning environment tailored to individual students.

[0091] "Educator's professional knowledge" refers to a sophisticated body of knowledge gained from specific teaching methods and experiences possessed by educators.

[0092] "Digitized specialized knowledge" refers to information that has been converted from educators' knowledge into an electronic format and stored.

[0093] "Means of storage" refer to methods and devices for quantitatively collecting and storing information.

[0094] A "virtual space" is a three-dimensional virtual environment created using computer technology.

[0095] A "virtual experience" is an experience obtained through interactive engagement in a virtual environment.

[0096] A "cloud database" is a data storage function located in a remote location and accessible via the internet.

[0097] An "interactive learning experience" is an experience in which students actively participate and learn in a two-way manner.

[0098] An "individualized learning plan" is a learning plan created according to each student's individual learning situation and needs.

[0099] The system implementing this invention digitizes the specialized knowledge of educators and provides it as an educational experience in a virtual space. A terminal is responsible for digitizing the knowledge entered by the educator and transmitting it to a cloud database. This digitized knowledge is stored by a server and analyzed using AI technology. The server uses Unity and Azure Cognitive Services to generate content for providing an interactive learning experience, which is then used for learning in the virtual space.

[0100] The server uses a generative AI model to create personalized learning plans based on a specific student's learning data. This allows students to experience a customized learning path tailored just for them. For example, content incorporating tactile materials and audio feedback may be provided for students with visual impairments. Students can learn in a virtual space using smartphones or head-mounted displays.

[0101] Educators, as users, can implement more interactive and personalized education by utilizing educational methods provided by the server within a virtual space. The generative AI model generates educational content based on the input of prompts. For example, a possible prompt might be, "I want to provide tactile chemistry learning materials that can be safely used in a virtual space for visually impaired students. Please design the materials using feedback tailored to the learning objectives." This system greatly improves the quality and personalization of education.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The terminal receives specialized knowledge entered by educators. It formats the entered knowledge as digital data and processes it into a form that can be sent to a cloud database. The input consists of the educator's methods and experience, and the output is formatted digital data.

[0105] Step 2:

[0106] The server stores digitized expert knowledge transmitted from terminals in a cloud database. This data is preprocessed for AI analysis, organizing information about individual educators as entities. The input is digitized expert knowledge, and the output is stored database entries.

[0107] Step 3:

[0108] The server uses a generative AI model to collect student learning data and perform analysis based on that data. It takes learning history and performance data as input and uses a predictive algorithm to generate the optimal learning path. The output is a customized learning plan.

[0109] Step 4:

[0110] Based on the analysis results, the server generates educational content in a virtual space using Unity and Azure Cognitive Services. It designs an interactive educational environment based on prompts and places the actual learning content in the virtual space. The input consists of a customized learning plan and prompts, and the output is a ready-to-use virtual learning material.

[0111] Step 5:

[0112] Educators, as users, utilize learning content within a virtual space provided by the server to educate students. Based on feedback from the field, educators adjust the content to provide a more effective learning experience. The input is the content within the virtual space, and the output is the students' learning outcomes.

[0113] 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.

[0114] This invention is a system that improves the quality of education by not only digitizing, storing, and sharing the professional knowledge of educators, but also by combining it with an emotion engine to recognize the emotions of users. This system consists of terminal, server, emotion engine, and user components, and supports effective instruction in educational settings.

[0115] The device not only provides an interface for educators to input their expertise, but also features a function that monitors the user's emotions in real time, as detected by an emotion engine. Educators can use the device to monitor students' emotional states along with education-related data.

[0116] The server receives data from the emotion engine and uses it to analyze it in combination with input data from educators. This analysis not only reviews learning data but also derives optimal teaching methods that consider the impact of students' emotional states on learning outcomes. The server utilizes accumulated digital knowledge data and emotion data to create customized teaching plans that address specific emotional states and learning disabilities.

[0117] The emotion engine tracks students' facial expressions, voice tone, and body movements in the educational environment, and analyzes their emotional state from this information. For example, by using facial recognition technology, it can detect in real time whether a student is concentrating or stressed, and send this data to the server.

[0118] The educator, as the user, adjusts individual teaching methods based on the suggested teaching techniques and emotion-based feedback displayed on the device. For example, if the server suggests that relaxation exercises should be introduced for students who are feeling stressed, the educator can make changes during the lesson accordingly and take measures to promote student relaxation.

[0119] This overall system seamlessly integrates the dynamics of education and emotion, enabling educators to adopt more inclusive and adaptive teaching methods, thereby maximizing students' learning experiences and outcomes.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The terminal displays a login screen to the educator, who then logs into the system using their individual account information. After logging in, the educator can access a dedicated interface for entering their own educational data.

[0123] Step 2:

[0124] Educators, as users, input their professional knowledge and past teaching experience into forms on the terminal. They can also input real-time information, such as student reactions and newly acquired insights during lessons.

[0125] Step 3:

[0126] The terminal formats the entered specialized knowledge as digital data, checks for errors, and then sends an HTTP POST request to the server. At this time, a secure protocol is used to ensure data confidentiality.

[0127] Step 4:

[0128] The server verifies the expert knowledge data received from the terminal and stores it in the database. Simultaneously, emotional data received from the emotion engine is also sent to the server and recorded in the database. This allows each student's emotional state to be accumulated over time.

[0129] Step 5:

[0130] The emotion engine calculates emotion values ​​from students' facial expressions and voice analysis, and sends these values ​​to the server via the terminal. The terminal visualizes these emotion values ​​and provides real-time feedback to educators.

[0131] Step 6:

[0132] The educators, as users, adjust their teaching methods based on student emotional data displayed on their devices. If students are experiencing stress, they implement relaxation techniques to create an adaptive learning environment.

[0133] Step 7:

[0134] The server uses accumulated expertise and emotional data to perform analysis with an AI module, generating individually customized educational plans for each student. The results are then sent to the terminal.

[0135] Step 8:

[0136] The terminal displays the educational plan received from the server to the educator. The educator reviews this plan and develops specific lesson activities and teaching strategies. This allows the educator to provide instruction that is tailored to each student's emotions and learning needs.

[0137] This system enables the integration of emotions and knowledge in educational settings, leading to more effective and student-centered education.

[0138] (Example 2)

[0139] 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".

[0140] Traditional education systems lacked sufficient accumulation and sharing of educators' specialized knowledge, and the impact of learners' emotional states on learning quality was not adequately considered. This made it difficult to individually optimize the quality of education, and also led to the problem of losing educational know-how when educators were transferred.

[0141] 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.

[0142] In this invention, the server includes means for digitizing and storing the professional knowledge of educators, means for recognizing and analyzing the emotional state of users, and means for integrating the collected data to generate optimal teaching methods. This enables the efficient storage and sharing of educators' knowledge, and further allows for the individual optimization of learning plans that take into account the emotional state of users.

[0143] "Methods for digitizing educators' professional knowledge" refers to technologies that record and preserve the knowledge and know-how possessed by educators in an electronic format.

[0144] "Means of accumulation" refers to the technology that aggregates digitized data and stores it centrally.

[0145] "Means of transferring accumulated knowledge to new educators following the transfer of educators" refers to systems and technologies that support the sharing of knowledge from existing educators to newly appointed educators.

[0146] "Means for recognizing and analyzing the emotional state of users" refers to technologies that use sensors and software to detect the emotions expressed by learners and analyze that state.

[0147] "Methods for integrating and analyzing collected data" refers to the technology of collecting data from various sources, comprehensively analyzing it, and deriving useful insights.

[0148] "Means for generating optimal educational methods" refers to technologies that analyze data obtained from educators and learners and propose optimized educational methods and plans based on the results.

[0149] This invention is a system that improves the quality of education by digitizing the professional knowledge of educators and comprehensively recognizing and analyzing the emotions of users. This system consists of the following components: a terminal, a server, an emotion engine, and an educator (user).

[0150] The device first provides an interface that allows educators to efficiently input their specialized knowledge digitally. For example, educators can record lesson content using a text editor on the device and input knowledge data by pressing a save button. Furthermore, the device has a built-in camera and microphone that allows it to monitor learners' facial expressions and voice tones in real time during lessons and collect emotional data.

[0151] The emotion engine features advanced algorithms that analyze the learner's emotional state using monitored data such as facial expressions and voice tone. Specifically, it uses facial recognition technology to determine in real time whether the learner is focused or stressed. This analysis result is immediately sent to the server.

[0152] The server integrates knowledge information from educators sent from terminals with data from the emotion engine and performs analysis based on a generative AI model. The server's processing power allows it to combine this data and propose the most suitable teaching methods for learners. By utilizing this generative AI model, it's possible to input prompts such as, "How should I approach a nervous student?" and automatically generate a specific teaching plan.

[0153] The educators, as users, can view these suggestions and feedback on their devices and adjust their teaching methods accordingly. For example, if the server provides a specific example such as "relaxation exercises should be introduced for students who are feeling stressed," educators can incorporate relaxation-promoting activities into their lessons.

[0154] In this way, the present invention aims to improve the educational process by providing a comprehensive educational method that takes into account the accumulated knowledge of educators and the emotional state of learners.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The terminal provides an interface for educators to input specialized knowledge. Input consists of lesson content and teaching materials entered by the educator using a text editor. This data is then converted by the terminal into a digital format and prepared for transmission to the server. The output is formalized digital knowledge data.

[0158] Step 2:

[0159] The device uses its built-in camera and microphone to sense the learner's facial expressions and voice tone in real time during instruction and collect emotional data. In this step, the input is visual and audio data, which is used as information to analyze changes in facial expressions and voice tone. The output is raw facial and audio data to be sent to the emotion engine.

[0160] Step 3:

[0161] The emotion engine analyzes the learner's emotional state based on visual and audio data received from the device. By applying deep learning-based facial recognition technology, it classifies the input visual data into emotional categories (e.g., "concentration," "tension"). Meanwhile, it performs tone analysis on the audio data. The output is the analysis result indicating each learner's emotional state.

[0162] Step 4:

[0163] The server integrates digitized knowledge data transmitted from the terminal with emotion analysis results received from the emotion engine. At this stage, it receives digital knowledge data and emotional state data as input. The server uses a generative AI model to analyze this data and generate optimal teaching methods. The output is a teaching plan or approach proposed to educators.

[0164] Step 5:

[0165] The user, an educator, reviews the lesson plans and emotion-based feedback provided by the server on their terminal. The input consists of all feedback information to improve the educator's teaching methods. Based on this information, the educator adjusts their teaching methods to suit specific situations and implements the optimal approach for the learners. The output is the adjusted lesson plan and teaching methodology.

[0166] (Application Example 2)

[0167] 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".

[0168] Traditional training and customer service operations face the challenge of accurately understanding the emotions and states of individual users and responding appropriately accordingly. Furthermore, particularly in physical stores, there is a lack of means to quickly and accurately recognize the emotions of customers, hindering improvements in customer satisfaction. Improving this situation and enabling more effective training and service delivery is essential.

[0169] 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.

[0170] In this invention, the server includes means for digitizing the professional knowledge of educators, means for recognizing and analyzing emotional states, and means for optimizing customer service based on the analyzed emotional information. This makes it possible to grasp the emotional states of customers and students in real time and provide appropriate services and educational methods.

[0171] An "educator" is an individual or organization whose role is to instruct students in specific specialized knowledge and to support their learning.

[0172] "Means of digitization" refer to technologies and methods that convert analog information and conventional knowledge into electronic format, making them usable for computer processing.

[0173] "Means of accumulating knowledge" refers to the process of storing digitized information and data according to certain standards and keeping them in a state where they can be retrieved as needed.

[0174] "Means for recognizing and analyzing emotional states" refers to technologies and methods for detecting a user's inner emotions from facial expressions, tone of voice, body movements, etc., and for analyzing that information.

[0175] "Methods for optimizing customer service" refer to strategies and approaches that adjust the services and responses provided according to the user's different emotional states in order to increase satisfaction.

[0176] The system implementing this invention consists of a server, a terminal, and an emotion engine. The server is built on the cloud and has the function of digitally storing expert knowledge provided by educators and store clerks. It also receives real-time emotion analysis data transmitted from the emotion engine, analyzes it, and derives appropriate response methods. The server is equipped with a cloud database and artificial intelligence algorithms, which enable data accumulation and analysis.

[0177] The terminals are devices operated by educators and shop staff, and include smart glasses and tablet devices. They include interfaces for receiving user input and can also display emotional states analyzed by an emotion engine. The terminals also provide educators and shop staff with optimal methods for application in educational and customer service settings.

[0178] The emotion engine uses sensor devices such as cameras and microphones to detect the user's facial expressions and voice tone, and analyzes this data to identify the user's emotional state. This analysis is sent to a server, which then provides information to support more appropriate responses. For example, if the emotion engine detects customer stress, the server will suggest specific customer service methods to help the staff relax.

[0179] Examples of prompts to input into the generating AI model include: "The following customer's emotional state has been detected: Facial expression: confused, Tone of voice: anxious. What customer service approach should be suggested to improve this state?" In this way, appropriate responses based on the user's emotional state can be provided at any time.

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The device uses a camera and microphone to capture the user's facial expressions and voice tone. Input data consists of image and audio files, collected by high-precision sensors. Output is raw image and audio data. Each piece of data is used to assess the individual's emotional state.

[0183] Step 2:

[0184] The device uses an emotion engine to analyze the image and audio data collected in Step 1. Here, facial recognition and voice analysis algorithms are used to detect characteristic facial expressions and vocal tones. The input is image and audio data, and the output is the emotional state (e.g., confusion, anxiety) as a result of the analysis. The analysis results are transmitted to the server as a digital signal.

[0185] Step 3:

[0186] The server devises the optimal service and response based on the emotional state received from the emotion engine. This process involves comparing a knowledge base stored in a cloud database with the user's past data. The input is emotional state data, and the output is specific countermeasures appropriate to that state (e.g., suggestions for relaxation).

[0187] Step 4:

[0188] The terminal notifies the user in real time of countermeasures received from the server, and adjusts necessary guidance and customer service accordingly. The input is a suggestion from the server, and the output is an action to the user (e.g., changing customer service methods, providing special services). This allows educators and staff to take immediate action according to the situation, which is expected to improve customer satisfaction.

[0189] Step 5:

[0190] Users input the results and feedback of the countermeasures they have implemented via their devices. The input is feedback information as a result of the countermeasures, and the output is the storage of information in a cloud database and the accumulation of data for future analysis. This provides data that will lead to improvements in future services, and is expected to improve the long-term learning process.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0206] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0207] This invention is a system for digitizing and passing on the professional knowledge of educators, with the aim of maintaining and improving the quality of education. In this system, the terminal, server, and user components cooperate to achieve effective knowledge transfer and personalized education.

[0208] The terminal is a device that provides an interface for educators to input and digitize their professional knowledge. Through the terminal, educators can input data such as successful methods and experiences in special education. This standardizes proven teaching methods and allows them to be shared as knowledge with other educators.

[0209] The server stores and manages the educators' professional knowledge received from terminals as a database. This database contains past success stories and knowledge to address various scenarios in special education. The server also collects student learning data and performs analysis to suggest teaching methods optimized for individual needs. By using AI technology for analysis, it creates customized teaching plans for each student.

[0210] Educators, as users, utilize the teaching methods suggested by the server to implement the most suitable teaching approach for their students. For example, the use of tactile materials may be suggested for students with visual impairments. Users then use these suggestions to adjust their lesson content and provide instruction tailored to the specific needs of their students.

[0211] This system ensures the efficient sharing of educators' professional knowledge, preventing a decline in the quality of education at a school even when educators are transferred. Furthermore, by quickly proposing teaching methods tailored to students' characteristics, it enables the development of individualized education, aiming to improve outcomes in educational settings.

[0212] The following describes the processing flow.

[0213] Step 1:

[0214] The terminal displays a dedicated login screen for educators, who then log in to the system using their own accounts. Once an educator logs in, they are granted access to data associated with them, and they can begin using the system.

[0215] Step 2:

[0216] The user, an educator, enters their expertise and teaching methods into a form on their terminal. The entered data includes details on successful cases in special education, effective teaching materials, and teaching methods.

[0217] Step 3:

[0218] The terminal formats the specialized knowledge entered by the educator as digital data and sends it to the server using an HTTP POST request. This transmission is designed with secure communication in mind, ensuring that the data is not leaked externally.

[0219] Step 4:

[0220] The server verifies the expert knowledge data received from the terminal, confirms that it is in the correct format, and then saves it to the database. This database records and manages the accumulated knowledge of many educators, step by step.

[0221] Step 5:

[0222] When a request comes in from a terminal indicating that the system wants to check a student's learning progress, the server retrieves relevant learning history and grade information from the database based on the student's ID.

[0223] Step 6:

[0224] The server passes the acquired student learning data to an AI module for data analysis. The AI ​​analyzes the data and selects the most effective teaching methods for that student.

[0225] Step 7:

[0226] The server compiles the AI-generated optimal teaching methods into a proposal and sends a response to the terminal. This allows educators to immediately receive optimized teaching methods.

[0227] Step 8:

[0228] The educator, as the user, reviews the proposed teaching methods displayed on the terminal and develops a teaching plan that reflects them. The educator then incorporates the new methods into their lessons and implements the suggestions in actual teaching activities.

[0229] Through this process, the specialized knowledge of educators is effectively utilized, and education tailored to each individual student is realized.

[0230] (Example 1)

[0231] 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."

[0232] In modern education, challenges include the loss of knowledge due to educators' transfers and delays in proposing teaching methods that address students' individual learning needs. In particular, there is a problem with the inability to effectively transfer specialized knowledge to other educators, leading to inconsistencies in the quality of learning. Furthermore, there is a need to quickly provide individually optimized educational plans tailored to each student's learning progress.

[0233] 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.

[0234] In this invention, the server includes a device for inputting the specialized knowledge of educators, a data management device for storing and managing the inputted knowledge of educators, a knowledge transfer device for transmitting the stored knowledge to new educators in accordance with the transfer of educators, a data analysis device for collecting and analyzing student learning data, and means for presenting individually optimized teaching methods to students based on the analysis results. This makes it possible to efficiently transfer the knowledge of educators and to provide educational plans that quickly respond to the individual needs of students.

[0235] A "device for inputting educators' professional knowledge" is a device that provides an interface for educators to input their teaching methods and experiences as digital data.

[0236] A "data management device for accumulating and managing the knowledge entered by educators" is a system that stores the specialized knowledge entered by educators and manages it as a database so that it can be organized and searched as needed.

[0237] A "knowledge transfer device for transmitting accumulated knowledge to new educators in accordance with the transfer of educators" is a device that has the function of effectively conveying the accumulated knowledge of educators to new educators.

[0238] A "data analysis device for collecting and analyzing student learning data" is a device that collects students' learning history and achievements and performs various analyses based on that data. This makes it possible to understand individual learning trends and challenges.

[0239] "Means for presenting individually optimized educational methods based on analysis results" refers to a function that proposes the most suitable educational methods for each student based on the results of the analysis of learning data.

[0240] A description of embodiments for carrying out this invention will be given.

[0241] In this system, the terminal functions as a device that allows educators to input their professional knowledge. The terminal is equipped with an interface that enables text and voice input, allowing educators to digitize their special education experience and success stories as data. This digitized knowledge is then transmitted to a server.

[0242] The server stores and manages received knowledge data in a cloud database. A remote data storage system is used as the cloud database. Specifically, it classifies and stores knowledge for each educator and provides it to other educators in a format that can be viewed as needed. The server also collects student learning data and analyzes it using AI technology. In this process, software such as Python and TensorFlow are used to analyze the data with machine learning algorithms.

[0243] Educators, as users of the system, receive personalized teaching methods based on the server's analysis. For example, for a visually impaired student, the system might suggest the use of tactile materials, taking into account the student's past performance and behavioral patterns. Such suggestions help educators create more effective lesson plans.

[0244] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "In special education, please suggest methods for visually impaired students to effectively learn information. This student has previously shown improved learning comprehension when using tactile materials." Based on this prompt sentence, the AI ​​will suggest the most suitable materials and methods.

[0245] As described above, this system enables the efficient transfer of knowledge from educators and allows for a rapid response to the individual learning needs of students.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The terminal provides an interface to educators. Educators use the terminal to input their expertise. Specifically, they input successful case studies and teaching experiences in special education as text input or audio data. This input is converted into a digital format and prepared for subsequent processing.

[0249] Step 2:

[0250] The terminal sends the entered digital data to the server. The input data is digitized, and the server receives it and prepares to store it in a cloud database. After receiving the data, the server uses an automatic classification algorithm to categorize the data and assign appropriate labels. This labeling organizes the data so that other educators can efficiently search and refer to it.

[0251] Step 3:

[0252] The server collects and analyzes student learning data. The learning data is obtained from online learning platforms and grading systems, and preprocessed using Python. Data preprocessing includes imputing missing values ​​and normalizing the data. After preprocessing, an AI model is used to analyze learning patterns and performance trends.

[0253] Step 4:

[0254] Based on the AI ​​analysis results, the server proposes the optimal teaching method to the user, the educator. At this stage, the AI ​​model presents the educator with a prompt message and suggests a specific teaching approach. For example, a suggestion such as, "It has been found that this student's understanding will be enhanced by using tactile learning materials," might be generated.

[0255] Step 5:

[0256] The user adjusts the lesson plan based on the suggested teaching methods. The educator uses the information provided by the server as a reference and prepares teaching materials and modifies the lesson content as needed. The educator then inputs feedback on the lesson implementation back into the system via the terminal, and the system uses this feedback as new data points for the next analysis.

[0257] (Application Example 1)

[0258] 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."

[0259] There are challenges in transferring educators' knowledge and providing students with individualized learning experiences. Furthermore, traditional education systems make it difficult to effectively digitize and share educators' specialized knowledge, potentially leading to a decline in the quality of education. Additionally, it is difficult to fully utilize student learning data, making it challenging to provide an optimal educational environment tailored to individual needs.

[0260] 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.

[0261] In this invention, the server includes means for digitizing the professional knowledge of educators, means for constructing the digitized educational knowledge in a virtual space, and educational means for students to learn through virtual experiences. This makes it possible to widely share the knowledge of educators and provide a learning environment tailored to individual students.

[0262] "Educator's professional knowledge" refers to a sophisticated body of knowledge gained from specific teaching methods and experiences possessed by educators.

[0263] "Digitized specialized knowledge" refers to information that has been converted from educators' knowledge into an electronic format and stored.

[0264] "Means of storage" refer to methods and devices for quantitatively collecting and storing information.

[0265] A "virtual space" is a three-dimensional virtual environment created using computer technology.

[0266] A "virtual experience" is an experience obtained through interactive engagement in a virtual environment.

[0267] A "cloud database" is a data storage function located in a remote location and accessible via the internet.

[0268] An "interactive learning experience" is an experience in which students actively participate and learn in a two-way manner.

[0269] An "individualized learning plan" is a learning plan created according to each student's individual learning situation and needs.

[0270] The system implementing this invention digitizes the specialized knowledge of educators and provides it as an educational experience in a virtual space. A terminal is responsible for digitizing the knowledge entered by the educator and transmitting it to a cloud database. This digitized knowledge is stored by a server and analyzed using AI technology. The server uses Unity and Azure Cognitive Services to generate content for providing an interactive learning experience, which is then used for learning in the virtual space.

[0271] The server uses a generative AI model to create personalized learning plans based on a specific student's learning data. This allows students to experience a customized learning path tailored just for them. For example, content incorporating tactile materials and audio feedback may be provided for students with visual impairments. Students can learn in a virtual space using smartphones or head-mounted displays.

[0272] Educators, as users, can implement more interactive and personalized education by utilizing educational methods provided by the server within a virtual space. The generative AI model generates educational content based on the input of prompts. For example, a possible prompt might be, "I want to provide tactile chemistry learning materials that can be safely used in a virtual space for visually impaired students. Please design the materials using feedback tailored to the learning objectives." This system greatly improves the quality and personalization of education.

[0273] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0274] Step 1:

[0275] The terminal receives specialized knowledge entered by educators. It formats the entered knowledge as digital data and processes it into a form that can be sent to a cloud database. The input consists of the educator's methods and experience, and the output is formatted digital data.

[0276] Step 2:

[0277] The server stores digitized expert knowledge transmitted from terminals in a cloud database. This data is preprocessed for AI analysis, organizing information about individual educators as entities. The input is digitized expert knowledge, and the output is stored database entries.

[0278] Step 3:

[0279] The server uses a generative AI model to collect student learning data and perform analysis based on that data. It takes learning history and performance data as input and uses a predictive algorithm to generate the optimal learning path. The output is a customized learning plan.

[0280] Step 4:

[0281] Based on the analysis results, the server generates educational content in a virtual space using Unity and Azure Cognitive Services. It designs an interactive educational environment based on prompts and places the actual learning content in the virtual space. The input consists of a customized learning plan and prompts, and the output is a ready-to-use virtual learning material.

[0282] Step 5:

[0283] The educator, who is the user, utilizes the learning content within the virtual space provided by the server to conduct education for the students. Based on the on-site feedback, the educator adjusts the content and endeavors to provide a more effective learning experience. The input is the content within the virtual space, and the output is the learning achievements of the students.

[0284] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0285] The present invention is a system that not only digitizes, accumulates, and shares the specialized knowledge of educators but also combines an emotion engine for recognizing the user's emotions, thereby improving the quality of education. This system is composed of components such as a terminal, a server, an emotion engine, and the user, and supports effective guidance in the educational field.

[0286] The terminal not only has an interface for the educator to input their specialized knowledge but also has a function to monitor in real-time the emotions of the user sensed by the emotion engine. The educator can use the terminal to monitor the emotional state of the students together with the data related to education.

[0287] The server receives data from the emotion engine and analyzes it in combination with the educator's input data based on this. This analysis not only reviews the learning data but also derives an optimal teaching method considering the impact of the students' emotional state on the learning effect. The server utilizes the accumulated digital knowledge data and emotion data to create a customized educational plan for specific emotional states and learning disabilities.

[0288] The emotion engine tracks students' facial expressions, voice tone, and body movements in the educational environment, and analyzes their emotional state from this information. For example, by using facial recognition technology, it can detect in real time whether a student is concentrating or stressed, and send this data to the server.

[0289] The educator, as the user, adjusts individual teaching methods based on the suggested teaching techniques and emotion-based feedback displayed on the device. For example, if the server suggests that relaxation exercises should be introduced for students who are feeling stressed, the educator can make changes during the lesson accordingly and take measures to promote student relaxation.

[0290] This overall system seamlessly integrates the dynamics of education and emotion, enabling educators to adopt more inclusive and adaptive teaching methods, thereby maximizing students' learning experiences and outcomes.

[0291] The following describes the processing flow.

[0292] Step 1:

[0293] The terminal displays a login screen to the educator, who then logs into the system using their individual account information. After logging in, the educator can access a dedicated interface for entering their own educational data.

[0294] Step 2:

[0295] Educators, as users, input their professional knowledge and past teaching experience into forms on the terminal. They can also input real-time information, such as student reactions and newly acquired insights during lessons.

[0296] Step 3:

[0297] The terminal formats the entered specialized knowledge as digital data, checks for errors, and then sends an HTTP POST request to the server. At this time, a secure protocol is used to ensure data confidentiality.

[0298] Step 4:

[0299] The server verifies the expert knowledge data received from the terminal and stores it in the database. Simultaneously, emotional data received from the emotion engine is also sent to the server and recorded in the database. This allows each student's emotional state to be accumulated over time.

[0300] Step 5:

[0301] The emotion engine calculates emotion values ​​from students' facial expressions and voice analysis, and sends these values ​​to the server via the terminal. The terminal visualizes these emotion values ​​and provides real-time feedback to educators.

[0302] Step 6:

[0303] The educators, as users, adjust their teaching methods based on student emotional data displayed on their devices. If students are experiencing stress, they implement relaxation techniques to create an adaptive learning environment.

[0304] Step 7:

[0305] The server uses accumulated expertise and emotional data to perform analysis with an AI module, generating individually customized educational plans for each student. The results are then sent to the terminal.

[0306] Step 8:

[0307] The terminal displays the educational plan received from the server to the educator. The educator reviews this plan and develops specific lesson activities and teaching strategies. This allows the educator to provide instruction that is tailored to each student's emotions and learning needs.

[0308] This system enables the integration of emotions and knowledge in the educational field, providing more effective and student-centered education.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In the conventional education system, the accumulation and sharing of the educator's professional knowledge have not been sufficiently carried out, and furthermore, the impact of the learner's emotional state on the quality of learning has not been fully considered. For this reason, it has been difficult to optimize the quality of education individually, and there has also been a problem of educational know-how being lost due to the transfer of educators.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0313] In this invention, the server includes means for digitizing and accumulating the educator's professional knowledge, means for recognizing and analyzing the emotional state of the user, and means for integrating the collected data to generate an optimal educational method. Thereby, the educator's knowledge can be efficiently accumulated and shared, and furthermore, individual optimization of the learning plan considering the user's emotional state becomes possible.

[0314] The "means for digitizing the educator's professional knowledge" is a technology for electronically recording and storing the knowledge and know-how possessed by the educator.

[0315] The "means for accumulating" refers to a technology that aggregates digitized data and stores it centrally.

[0316] The "means for transmitting the knowledge accumulated due to the transfer of the educator to the new educator" is a system or technology for supporting the sharing of knowledge from the existing educator to the new educator.

[0317] "Means for recognizing and analyzing the emotional state of users" refers to technologies that use sensors and software to detect the emotions expressed by learners and analyze that state.

[0318] "Methods for integrating and analyzing collected data" refers to the technology of collecting data from various sources, comprehensively analyzing it, and deriving useful insights.

[0319] "Means for generating optimal educational methods" refers to technologies that analyze data obtained from educators and learners and propose optimized educational methods and plans based on the results.

[0320] This invention is a system that improves the quality of education by digitizing the professional knowledge of educators and comprehensively recognizing and analyzing the emotions of users. This system consists of the following components: a terminal, a server, an emotion engine, and an educator (user).

[0321] The device first provides an interface that allows educators to efficiently input their specialized knowledge digitally. For example, educators can record lesson content using a text editor on the device and input knowledge data by pressing a save button. Furthermore, the device has a built-in camera and microphone that allows it to monitor learners' facial expressions and voice tones in real time during lessons and collect emotional data.

[0322] The emotion engine features advanced algorithms that analyze the learner's emotional state using monitored data such as facial expressions and voice tone. Specifically, it uses facial recognition technology to determine in real time whether the learner is focused or stressed. This analysis result is immediately sent to the server.

[0323] The server integrates knowledge information from educators sent from terminals with data from the emotion engine and performs analysis based on a generative AI model. The server's processing power allows it to combine this data and propose the most suitable teaching methods for learners. By utilizing this generative AI model, it's possible to input prompts such as, "How should I approach a nervous student?" and automatically generate a specific teaching plan.

[0324] The educators, as users, can view these suggestions and feedback on their devices and adjust their teaching methods accordingly. For example, if the server provides a specific example such as "relaxation exercises should be introduced for students who are feeling stressed," educators can incorporate relaxation-promoting activities into their lessons.

[0325] In this way, the present invention aims to improve the educational process by providing a comprehensive educational method that takes into account the accumulated knowledge of educators and the emotional state of learners.

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The terminal provides an interface for educators to input specialized knowledge. Input consists of lesson content and teaching materials entered by the educator using a text editor. This data is then converted by the terminal into a digital format and prepared for transmission to the server. The output is formalized digital knowledge data.

[0329] Step 2:

[0330] The device uses its built-in camera and microphone to sense the learner's facial expressions and voice tone in real time during instruction and collect emotional data. In this step, the input is visual and audio data, which is used as information to analyze changes in facial expressions and voice tone. The output is raw facial and audio data to be sent to the emotion engine.

[0331] Step 3:

[0332] The emotion engine analyzes the learner's emotional state based on visual and audio data received from the device. By applying deep learning-based facial recognition technology, it classifies the input visual data into emotional categories (e.g., "concentration," "tension"). Meanwhile, it performs tone analysis on the audio data. The output is the analysis result indicating each learner's emotional state.

[0333] Step 4:

[0334] The server integrates digitized knowledge data transmitted from the terminal with emotion analysis results received from the emotion engine. At this stage, it receives digital knowledge data and emotional state data as input. The server uses a generative AI model to analyze this data and generate optimal teaching methods. The output is a teaching plan or approach proposed to educators.

[0335] Step 5:

[0336] The user, an educator, reviews the lesson plans and emotion-based feedback provided by the server on their terminal. The input consists of all feedback information to improve the educator's teaching methods. Based on this information, the educator adjusts their teaching methods to suit specific situations and implements the optimal approach for the learners. The output is the adjusted lesson plan and teaching methodology.

[0337] (Application Example 2)

[0338] 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."

[0339] Traditional training and customer service operations face the challenge of accurately understanding the emotions and states of individual users and responding appropriately accordingly. Furthermore, particularly in physical stores, there is a lack of means to quickly and accurately recognize the emotions of customers, hindering improvements in customer satisfaction. Improving this situation and enabling more effective training and service delivery is essential.

[0340] 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.

[0341] In this invention, the server includes means for digitizing the professional knowledge of educators, means for recognizing and analyzing emotional states, and means for optimizing customer service based on the analyzed emotional information. This makes it possible to grasp the emotional states of customers and students in real time and provide appropriate services and educational methods.

[0342] An "educator" is an individual or organization whose role is to instruct students in specific specialized knowledge and to support their learning.

[0343] "Means of digitization" refer to technologies and methods that convert analog information and conventional knowledge into electronic format, making them usable for computer processing.

[0344] "Means of accumulating knowledge" refers to the process of storing digitized information and data according to certain standards and keeping them in a state where they can be retrieved as needed.

[0345] "Means for recognizing and analyzing emotional states" refers to technologies and methods for detecting a user's inner emotions from facial expressions, tone of voice, body movements, etc., and for analyzing that information.

[0346] "Methods for optimizing customer service" refer to strategies and approaches that adjust the services and responses provided according to the user's different emotional states in order to increase satisfaction.

[0347] The system implementing this invention consists of a server, a terminal, and an emotion engine. The server is built on the cloud and has the function of digitally storing expert knowledge provided by educators and store clerks. It also receives real-time emotion analysis data transmitted from the emotion engine, analyzes it, and derives appropriate response methods. The server is equipped with a cloud database and artificial intelligence algorithms, which enable data accumulation and analysis.

[0348] The terminals are devices operated by educators and shop staff, and include smart glasses and tablet devices. They include interfaces for receiving user input and can also display emotional states analyzed by an emotion engine. The terminals also provide educators and shop staff with optimal methods for application in educational and customer service settings.

[0349] The emotion engine uses sensor devices such as cameras and microphones to detect the user's facial expressions and voice tone, and analyzes this data to identify the user's emotional state. This analysis is sent to a server, which then provides information to support more appropriate responses. For example, if the emotion engine detects customer stress, the server will suggest specific customer service methods to help the staff relax.

[0350] Examples of prompts to input into the generating AI model include: "The following customer's emotional state has been detected: Facial expression: confused, Tone of voice: anxious. What customer service approach should be suggested to improve this state?" In this way, appropriate responses based on the user's emotional state can be provided at any time.

[0351] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0352] Step 1:

[0353] The device uses a camera and microphone to capture the user's facial expressions and voice tone. Input data consists of image and audio files, collected by high-precision sensors. Output is raw image and audio data. Each piece of data is used to assess the individual's emotional state.

[0354] Step 2:

[0355] The device uses an emotion engine to analyze the image and audio data collected in Step 1. Here, facial recognition and voice analysis algorithms are used to detect characteristic facial expressions and vocal tones. The input is image and audio data, and the output is the emotional state (e.g., confusion, anxiety) as a result of the analysis. The analysis results are transmitted to the server as a digital signal.

[0356] Step 3:

[0357] The server devises the optimal service and response based on the emotional state received from the emotion engine. This process involves comparing a knowledge base stored in a cloud database with the user's past data. The input is emotional state data, and the output is specific countermeasures appropriate to that state (e.g., suggestions for relaxation).

[0358] Step 4:

[0359] The terminal notifies the user in real time of countermeasures received from the server, and adjusts necessary guidance and customer service accordingly. The input is a suggestion from the server, and the output is an action to the user (e.g., changing customer service methods, providing special services). This allows educators and staff to take immediate action according to the situation, which is expected to improve customer satisfaction.

[0360] Step 5:

[0361] Users input the results and feedback of the countermeasures they have implemented via their devices. The input is feedback information as a result of the countermeasures, and the output is the storage of information in a cloud database and the accumulation of data for future analysis. This provides data that will lead to improvements in future services, and is expected to improve the long-term learning process.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] [Third Embodiment]

[0366] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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".

[0378] This invention is a system for digitizing and passing on the professional knowledge of educators, with the aim of maintaining and improving the quality of education. In this system, the terminal, server, and user components cooperate to achieve effective knowledge transfer and personalized education.

[0379] The terminal is a device that provides an interface for educators to input and digitize their professional knowledge. Through the terminal, educators can input data such as successful methods and experiences in special education. This standardizes proven teaching methods and allows them to be shared as knowledge with other educators.

[0380] The server stores and manages the educators' professional knowledge received from terminals as a database. This database contains past success stories and knowledge to address various scenarios in special education. The server also collects student learning data and performs analysis to suggest teaching methods optimized for individual needs. By using AI technology for analysis, it creates customized teaching plans for each student.

[0381] Educators, as users, utilize the teaching methods suggested by the server to implement the most suitable teaching approach for their students. For example, the use of tactile materials may be suggested for students with visual impairments. Users then use these suggestions to adjust their lesson content and provide instruction tailored to the specific needs of their students.

[0382] This system ensures the efficient sharing of educators' professional knowledge, preventing a decline in the quality of education at a school even when educators are transferred. Furthermore, by quickly proposing teaching methods tailored to students' characteristics, it enables the development of individualized education, aiming to improve outcomes in educational settings.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] The terminal displays a dedicated login screen for educators, who then log in to the system using their own accounts. Once an educator logs in, they are granted access to data associated with them, and they can begin using the system.

[0386] Step 2:

[0387] The user, an educator, enters their expertise and teaching methods into a form on their terminal. The entered data includes details on successful cases in special education, effective teaching materials, and teaching methods.

[0388] Step 3:

[0389] The terminal formats the specialized knowledge entered by the educator as digital data and sends it to the server using an HTTP POST request. This transmission is designed with secure communication in mind, ensuring that the data is not leaked externally.

[0390] Step 4:

[0391] The server verifies the expert knowledge data received from the terminal, confirms that it is in the correct format, and then saves it to the database. This database records and manages the accumulated knowledge of many educators, step by step.

[0392] Step 5:

[0393] When a request comes in from a terminal indicating that the system wants to check a student's learning progress, the server retrieves relevant learning history and grade information from the database based on the student's ID.

[0394] Step 6:

[0395] The server passes the acquired student learning data to an AI module for data analysis. The AI ​​analyzes the data and selects the most effective teaching methods for that student.

[0396] Step 7:

[0397] The server compiles the AI-generated optimal teaching methods into a proposal and sends a response to the terminal. This allows educators to immediately receive optimized teaching methods.

[0398] Step 8:

[0399] The educator, as the user, reviews the proposed teaching methods displayed on the terminal and develops a teaching plan that reflects them. The educator then incorporates the new methods into their lessons and implements the suggestions in actual teaching activities.

[0400] Through this process, the specialized knowledge of educators is effectively utilized, and education tailored to each individual student is realized.

[0401] (Example 1)

[0402] 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."

[0403] In modern education, challenges include the loss of knowledge due to educators' transfers and delays in proposing teaching methods that address students' individual learning needs. In particular, there is a problem with the inability to effectively transfer specialized knowledge to other educators, leading to inconsistencies in the quality of learning. Furthermore, there is a need to quickly provide individually optimized educational plans tailored to each student's learning progress.

[0404] 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.

[0405] In this invention, the server includes a device for inputting the specialized knowledge of educators, a data management device for storing and managing the inputted knowledge of educators, a knowledge transfer device for transmitting the stored knowledge to new educators in accordance with the transfer of educators, a data analysis device for collecting and analyzing student learning data, and means for presenting individually optimized teaching methods to students based on the analysis results. This makes it possible to efficiently transfer the knowledge of educators and to provide educational plans that quickly respond to the individual needs of students.

[0406] A "device for inputting educators' professional knowledge" is a device that provides an interface for educators to input their teaching methods and experiences as digital data.

[0407] A "data management device for accumulating and managing the knowledge entered by educators" is a system that stores the specialized knowledge entered by educators and manages it as a database so that it can be organized and searched as needed.

[0408] A "knowledge transfer device for transmitting accumulated knowledge to new educators in accordance with the transfer of educators" is a device that has the function of effectively conveying the accumulated knowledge of educators to new educators.

[0409] A "data analysis device for collecting and analyzing student learning data" is a device that collects students' learning history and achievements and performs various analyses based on that data. This makes it possible to understand individual learning trends and challenges.

[0410] "Means for presenting individually optimized educational methods based on analysis results" refers to a function that proposes the most suitable educational methods for each student based on the results of the analysis of learning data.

[0411] A description of embodiments for carrying out this invention will be given.

[0412] In this system, the terminal functions as a device that allows educators to input their professional knowledge. The terminal is equipped with an interface that enables text and voice input, allowing educators to digitize their special education experience and success stories as data. This digitized knowledge is then transmitted to a server.

[0413] The server stores and manages received knowledge data in a cloud database. A remote data storage system is used as the cloud database. Specifically, it classifies and stores knowledge for each educator and provides it to other educators in a format that can be viewed as needed. The server also collects student learning data and analyzes it using AI technology. In this process, software such as Python and TensorFlow are used to analyze the data with machine learning algorithms.

[0414] Educators, as users of the system, receive personalized teaching methods based on the server's analysis. For example, for a visually impaired student, the system might suggest the use of tactile materials, taking into account the student's past performance and behavioral patterns. Such suggestions help educators create more effective lesson plans.

[0415] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "In special education, please suggest methods for visually impaired students to effectively learn information. This student has previously shown improved learning comprehension when using tactile materials." Based on this prompt sentence, the AI ​​will suggest the most suitable materials and methods.

[0416] As described above, this system enables the efficient transfer of knowledge from educators and allows for a rapid response to the individual learning needs of students.

[0417] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0418] Step 1:

[0419] The terminal provides an interface to educators. Educators use the terminal to input their expertise. Specifically, they input successful case studies and teaching experiences in special education as text input or audio data. This input is converted into a digital format and prepared for subsequent processing.

[0420] Step 2:

[0421] The terminal sends the entered digital data to the server. The input data is digitized, and the server receives it and prepares to store it in a cloud database. After receiving the data, the server uses an automatic classification algorithm to categorize the data and assign appropriate labels. This labeling organizes the data so that other educators can efficiently search and refer to it.

[0422] Step 3:

[0423] The server collects and analyzes student learning data. The learning data is obtained from online learning platforms and grading systems, and preprocessed using Python. Data preprocessing includes imputing missing values ​​and normalizing the data. After preprocessing, an AI model is used to analyze learning patterns and performance trends.

[0424] Step 4:

[0425] Based on the AI ​​analysis results, the server proposes the optimal teaching method to the user, the educator. At this stage, the AI ​​model presents the educator with a prompt message and suggests a specific teaching approach. For example, a suggestion such as, "It has been found that this student's understanding will be enhanced by using tactile learning materials," might be generated.

[0426] Step 5:

[0427] The user adjusts the lesson plan based on the suggested teaching methods. The educator uses the information provided by the server as a reference and prepares teaching materials and modifies the lesson content as needed. The educator then inputs feedback on the lesson implementation back into the system via the terminal, and the system uses this feedback as new data points for the next analysis.

[0428] (Application Example 1)

[0429] 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."

[0430] There are challenges in transferring educators' knowledge and providing students with individualized learning experiences. Furthermore, traditional education systems make it difficult to effectively digitize and share educators' specialized knowledge, potentially leading to a decline in the quality of education. Additionally, it is difficult to fully utilize student learning data, making it challenging to provide an optimal educational environment tailored to individual needs.

[0431] 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.

[0432] In this invention, the server includes means for digitizing the professional knowledge of educators, means for constructing the digitized educational knowledge in a virtual space, and educational means for students to learn through virtual experiences. This makes it possible to widely share the knowledge of educators and provide a learning environment tailored to individual students.

[0433] "Educator's professional knowledge" refers to a sophisticated body of knowledge gained from specific teaching methods and experiences possessed by educators.

[0434] "Digitized specialized knowledge" refers to information that has been converted from educators' knowledge into an electronic format and stored.

[0435] "Means of storage" refer to methods and devices for quantitatively collecting and storing information.

[0436] A "virtual space" is a three-dimensional virtual environment created using computer technology.

[0437] A "virtual experience" is an experience obtained through interactive engagement in a virtual environment.

[0438] A "cloud database" is a data storage function located in a remote location and accessible via the internet.

[0439] An "interactive learning experience" is an experience in which students actively participate and learn in a two-way manner.

[0440] An "individualized learning plan" is a learning plan created according to each student's individual learning situation and needs.

[0441] The system implementing this invention digitizes the specialized knowledge of educators and provides it as an educational experience in a virtual space. A terminal is responsible for digitizing the knowledge entered by the educator and transmitting it to a cloud database. This digitized knowledge is stored by a server and analyzed using AI technology. The server uses Unity and Azure Cognitive Services to generate content for providing an interactive learning experience, which is then used for learning in the virtual space.

[0442] The server uses a generative AI model to create personalized learning plans based on a specific student's learning data. This allows students to experience a customized learning path tailored just for them. For example, content incorporating tactile materials and audio feedback may be provided for students with visual impairments. Students can learn in a virtual space using smartphones or head-mounted displays.

[0443] Educators, as users, can implement more interactive and personalized education by utilizing educational methods provided by the server within a virtual space. The generative AI model generates educational content based on the input of prompts. For example, a possible prompt might be, "I want to provide tactile chemistry learning materials that can be safely used in a virtual space for visually impaired students. Please design the materials using feedback tailored to the learning objectives." This system greatly improves the quality and personalization of education.

[0444] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0445] Step 1:

[0446] The terminal receives specialized knowledge entered by educators. It formats the entered knowledge as digital data and processes it into a form that can be sent to a cloud database. The input consists of the educator's methods and experience, and the output is formatted digital data.

[0447] Step 2:

[0448] The server stores digitized expert knowledge transmitted from terminals in a cloud database. This data is preprocessed for AI analysis, organizing information about individual educators as entities. The input is digitized expert knowledge, and the output is stored database entries.

[0449] Step 3:

[0450] The server uses a generative AI model to collect student learning data and perform analysis based on that data. It takes learning history and performance data as input and uses a predictive algorithm to generate the optimal learning path. The output is a customized learning plan.

[0451] Step 4:

[0452] Based on the analysis results, the server generates educational content in a virtual space using Unity and Azure Cognitive Services. It designs an interactive educational environment based on prompts and places the actual learning content in the virtual space. The input consists of a customized learning plan and prompts, and the output is a ready-to-use virtual learning material.

[0453] Step 5:

[0454] Educators, as users, utilize learning content within a virtual space provided by the server to educate students. Based on feedback from the field, educators adjust the content to provide a more effective learning experience. The input is the content within the virtual space, and the output is the students' learning outcomes.

[0455] 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.

[0456] This invention is a system that improves the quality of education by not only digitizing, storing, and sharing the professional knowledge of educators, but also by combining it with an emotion engine to recognize the emotions of users. This system consists of terminal, server, emotion engine, and user components, and supports effective instruction in educational settings.

[0457] The device not only provides an interface for educators to input their expertise, but also features a function that monitors the user's emotions in real time, as detected by an emotion engine. Educators can use the device to monitor students' emotional states along with education-related data.

[0458] The server receives data from the emotion engine and uses it to analyze it in combination with input data from educators. This analysis not only reviews learning data but also derives optimal teaching methods that consider the impact of students' emotional states on learning outcomes. The server utilizes accumulated digital knowledge data and emotion data to create customized teaching plans that address specific emotional states and learning disabilities.

[0459] The emotion engine tracks students' facial expressions, voice tone, and body movements in the educational environment, and analyzes their emotional state from this information. For example, by using facial recognition technology, it can detect in real time whether a student is concentrating or stressed, and send this data to the server.

[0460] The educator, as the user, adjusts individual teaching methods based on the suggested teaching techniques and emotion-based feedback displayed on the device. For example, if the server suggests that relaxation exercises should be introduced for students who are feeling stressed, the educator can make changes during the lesson accordingly and take measures to promote student relaxation.

[0461] This overall system seamlessly integrates the dynamics of education and emotion, enabling educators to adopt more inclusive and adaptive teaching methods, thereby maximizing students' learning experiences and outcomes.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The terminal displays a login screen to the educator, who then logs into the system using their individual account information. After logging in, the educator can access a dedicated interface for entering their own educational data.

[0465] Step 2:

[0466] Educators, as users, input their professional knowledge and past teaching experience into forms on the terminal. They can also input real-time information, such as student reactions and newly acquired insights during lessons.

[0467] Step 3:

[0468] The terminal formats the entered specialized knowledge as digital data, checks for errors, and then sends an HTTP POST request to the server. At this time, a secure protocol is used to ensure data confidentiality.

[0469] Step 4:

[0470] The server verifies the expert knowledge data received from the terminal and stores it in the database. Simultaneously, emotional data received from the emotion engine is also sent to the server and recorded in the database. This allows each student's emotional state to be accumulated over time.

[0471] Step 5:

[0472] The emotion engine calculates emotion values ​​from students' facial expressions and voice analysis, and sends these values ​​to the server via the terminal. The terminal visualizes these emotion values ​​and provides real-time feedback to educators.

[0473] Step 6:

[0474] The educators, as users, adjust their teaching methods based on student emotional data displayed on their devices. If students are experiencing stress, they implement relaxation techniques to create an adaptive learning environment.

[0475] Step 7:

[0476] The server uses accumulated expertise and emotional data to perform analysis with an AI module, generating individually customized educational plans for each student. The results are then sent to the terminal.

[0477] Step 8:

[0478] The terminal displays the educational plan received from the server to the educator. The educator reviews this plan and develops specific lesson activities and teaching strategies. This allows the educator to provide instruction that is tailored to each student's emotions and learning needs.

[0479] This system enables the integration of emotions and knowledge in educational settings, leading to more effective and student-centered education.

[0480] (Example 2)

[0481] 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."

[0482] Traditional education systems lacked sufficient accumulation and sharing of educators' specialized knowledge, and the impact of learners' emotional states on learning quality was not adequately considered. This made it difficult to individually optimize the quality of education, and also led to the problem of losing educational know-how when educators were transferred.

[0483] 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.

[0484] In this invention, the server includes means for digitizing and storing the professional knowledge of educators, means for recognizing and analyzing the emotional state of users, and means for integrating the collected data to generate optimal teaching methods. This enables the efficient storage and sharing of educators' knowledge, and further allows for the individual optimization of learning plans that take into account the emotional state of users.

[0485] "Methods for digitizing educators' professional knowledge" refers to technologies that record and preserve the knowledge and know-how possessed by educators in an electronic format.

[0486] "Means of accumulation" refers to the technology that aggregates digitized data and stores it centrally.

[0487] "Means of transferring accumulated knowledge to new educators following the transfer of educators" refers to systems and technologies that support the sharing of knowledge from existing educators to newly appointed educators.

[0488] "Means for recognizing and analyzing the emotional state of users" refers to technologies that use sensors and software to detect the emotions expressed by learners and analyze that state.

[0489] "Methods for integrating and analyzing collected data" refers to the technology of collecting data from various sources, comprehensively analyzing it, and deriving useful insights.

[0490] "Means for generating optimal educational methods" refers to technologies that analyze data obtained from educators and learners and propose optimized educational methods and plans based on the results.

[0491] This invention is a system that improves the quality of education by digitizing the professional knowledge of educators and comprehensively recognizing and analyzing the emotions of users. This system consists of the following components: a terminal, a server, an emotion engine, and an educator (user).

[0492] The device first provides an interface that allows educators to efficiently input their specialized knowledge digitally. For example, educators can record lesson content using a text editor on the device and input knowledge data by pressing a save button. Furthermore, the device has a built-in camera and microphone that allows it to monitor learners' facial expressions and voice tones in real time during lessons and collect emotional data.

[0493] The emotion engine features advanced algorithms that analyze the learner's emotional state using monitored data such as facial expressions and voice tone. Specifically, it uses facial recognition technology to determine in real time whether the learner is focused or stressed. This analysis result is immediately sent to the server.

[0494] The server integrates knowledge information from educators sent from terminals with data from the emotion engine and performs analysis based on a generative AI model. The server's processing power allows it to combine this data and propose the most suitable teaching methods for learners. By utilizing this generative AI model, it's possible to input prompts such as, "How should I approach a nervous student?" and automatically generate a specific teaching plan.

[0495] The educators, as users, can view these suggestions and feedback on their devices and adjust their teaching methods accordingly. For example, if the server provides a specific example such as "relaxation exercises should be introduced for students who are feeling stressed," educators can incorporate relaxation-promoting activities into their lessons.

[0496] In this way, the present invention aims to improve the educational process by providing a comprehensive educational method that takes into account the accumulated knowledge of educators and the emotional state of learners.

[0497] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0498] Step 1:

[0499] The terminal provides an interface for educators to input specialized knowledge. Input consists of lesson content and teaching materials entered by the educator using a text editor. This data is then converted by the terminal into a digital format and prepared for transmission to the server. The output is formalized digital knowledge data.

[0500] Step 2:

[0501] The device uses its built-in camera and microphone to sense the learner's facial expressions and voice tone in real time during instruction and collect emotional data. In this step, the input is visual and audio data, which is used as information to analyze changes in facial expressions and voice tone. The output is raw facial and audio data to be sent to the emotion engine.

[0502] Step 3:

[0503] The emotion engine analyzes the learner's emotional state based on visual and audio data received from the device. By applying deep learning-based facial recognition technology, it classifies the input visual data into emotional categories (e.g., "concentration," "tension"). Meanwhile, it performs tone analysis on the audio data. The output is the analysis result indicating each learner's emotional state.

[0504] Step 4:

[0505] The server integrates digitized knowledge data transmitted from the terminal with emotion analysis results received from the emotion engine. At this stage, it receives digital knowledge data and emotional state data as input. The server uses a generative AI model to analyze this data and generate optimal teaching methods. The output is a teaching plan or approach proposed to educators.

[0506] Step 5:

[0507] The user, an educator, reviews the lesson plans and emotion-based feedback provided by the server on their terminal. The input consists of all feedback information to improve the educator's teaching methods. Based on this information, the educator adjusts their teaching methods to suit specific situations and implements the optimal approach for the learners. The output is the adjusted lesson plan and teaching methodology.

[0508] (Application Example 2)

[0509] 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."

[0510] Traditional training and customer service operations face the challenge of accurately understanding the emotions and states of individual users and responding appropriately accordingly. Furthermore, particularly in physical stores, there is a lack of means to quickly and accurately recognize the emotions of customers, hindering improvements in customer satisfaction. Improving this situation and enabling more effective training and service delivery is essential.

[0511] 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.

[0512] In this invention, the server includes means for digitizing the professional knowledge of educators, means for recognizing and analyzing emotional states, and means for optimizing customer service based on the analyzed emotional information. This makes it possible to grasp the emotional states of customers and students in real time and provide appropriate services and educational methods.

[0513] An "educator" is an individual or organization whose role is to instruct students in specific specialized knowledge and to support their learning.

[0514] "Means of digitization" refer to technologies and methods that convert analog information and conventional knowledge into electronic format, making them usable for computer processing.

[0515] "Means of accumulating knowledge" refers to the process of storing digitized information and data according to certain standards and keeping them in a state where they can be retrieved as needed.

[0516] "Means for recognizing and analyzing emotional states" refers to technologies and methods for detecting a user's inner emotions from facial expressions, tone of voice, body movements, etc., and for analyzing that information.

[0517] "Methods for optimizing customer service" refer to strategies and approaches that adjust the services and responses provided according to the user's different emotional states in order to increase satisfaction.

[0518] The system implementing this invention consists of a server, a terminal, and an emotion engine. The server is built on the cloud and has the function of digitally storing expert knowledge provided by educators and store clerks. It also receives real-time emotion analysis data transmitted from the emotion engine, analyzes it, and derives appropriate response methods. The server is equipped with a cloud database and artificial intelligence algorithms, which enable data accumulation and analysis.

[0519] The terminals are devices operated by educators and shop staff, and include smart glasses and tablet devices. They include interfaces for receiving user input and can also display emotional states analyzed by an emotion engine. The terminals also provide educators and shop staff with optimal methods for application in educational and customer service settings.

[0520] The emotion engine uses sensor devices such as cameras and microphones to detect the user's facial expressions and voice tone, and analyzes this data to identify the user's emotional state. This analysis is sent to a server, which then provides information to support more appropriate responses. For example, if the emotion engine detects customer stress, the server will suggest specific customer service methods to help the staff relax.

[0521] Examples of prompts to input into the generating AI model include: "The following customer's emotional state has been detected: Facial expression: confused, Tone of voice: anxious. What customer service approach should be suggested to improve this state?" In this way, appropriate responses based on the user's emotional state can be provided at any time.

[0522] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0523] Step 1:

[0524] The device uses a camera and microphone to capture the user's facial expressions and voice tone. Input data consists of image and audio files, collected by high-precision sensors. Output is raw image and audio data. Each piece of data is used to assess the individual's emotional state.

[0525] Step 2:

[0526] The device uses an emotion engine to analyze the image and audio data collected in Step 1. Here, facial recognition and voice analysis algorithms are used to detect characteristic facial expressions and vocal tones. The input is image and audio data, and the output is the emotional state (e.g., confusion, anxiety) as a result of the analysis. The analysis results are transmitted to the server as a digital signal.

[0527] Step 3:

[0528] The server devises the optimal service and response based on the emotional state received from the emotion engine. This process involves comparing a knowledge base stored in a cloud database with the user's past data. The input is emotional state data, and the output is specific countermeasures appropriate to that state (e.g., suggestions for relaxation).

[0529] Step 4:

[0530] The terminal notifies the user in real time of countermeasures received from the server, and adjusts necessary guidance and customer service accordingly. The input is a suggestion from the server, and the output is an action to the user (e.g., changing customer service methods, providing special services). This allows educators and staff to take immediate action according to the situation, which is expected to improve customer satisfaction.

[0531] Step 5:

[0532] Users input the results and feedback of the countermeasures they have implemented via their devices. The input is feedback information as a result of the countermeasures, and the output is the storage of information in a cloud database and the accumulation of data for future analysis. This provides data that will lead to improvements in future services, and is expected to improve the long-term learning process.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] [Fourth Embodiment]

[0537] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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).

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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".

[0550] This invention is a system for digitizing and passing on the professional knowledge of educators, with the aim of maintaining and improving the quality of education. In this system, the terminal, server, and user components cooperate to achieve effective knowledge transfer and personalized education.

[0551] The terminal is a device that provides an interface for educators to input and digitize their professional knowledge. Through the terminal, educators can input data such as successful methods and experiences in special education. This standardizes proven teaching methods and allows them to be shared as knowledge with other educators.

[0552] The server stores and manages the educators' professional knowledge received from terminals as a database. This database contains past success stories and knowledge to address various scenarios in special education. The server also collects student learning data and performs analysis to suggest teaching methods optimized for individual needs. By using AI technology for analysis, it creates customized teaching plans for each student.

[0553] Educators, as users, utilize the teaching methods suggested by the server to implement the most suitable teaching approach for their students. For example, the use of tactile materials may be suggested for students with visual impairments. Users then use these suggestions to adjust their lesson content and provide instruction tailored to the specific needs of their students.

[0554] This system ensures the efficient sharing of educators' professional knowledge, preventing a decline in the quality of education at a school even when educators are transferred. Furthermore, by quickly proposing teaching methods tailored to students' characteristics, it enables the development of individualized education, aiming to improve outcomes in educational settings.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The terminal displays a dedicated login screen for educators, who then log in to the system using their own accounts. Once an educator logs in, they are granted access to data associated with them, and they can begin using the system.

[0558] Step 2:

[0559] The user, an educator, enters their expertise and teaching methods into a form on their terminal. The entered data includes details on successful cases in special education, effective teaching materials, and teaching methods.

[0560] Step 3:

[0561] The terminal formats the specialized knowledge entered by the educator as digital data and sends it to the server using an HTTP POST request. This transmission is designed with secure communication in mind, ensuring that the data is not leaked externally.

[0562] Step 4:

[0563] The server verifies the expert knowledge data received from the terminal, confirms that it is in the correct format, and then saves it to the database. This database records and manages the accumulated knowledge of many educators, step by step.

[0564] Step 5:

[0565] When a request comes in from a terminal indicating that the system wants to check a student's learning progress, the server retrieves relevant learning history and grade information from the database based on the student's ID.

[0566] Step 6:

[0567] The server passes the acquired student learning data to an AI module for data analysis. The AI ​​analyzes the data and selects the most effective teaching methods for that student.

[0568] Step 7:

[0569] The server compiles the AI-generated optimal teaching methods into a proposal and sends a response to the terminal. This allows educators to immediately receive optimized teaching methods.

[0570] Step 8:

[0571] The educator, as the user, reviews the proposed teaching methods displayed on the terminal and develops a teaching plan that reflects them. The educator then incorporates the new methods into their lessons and implements the suggestions in actual teaching activities.

[0572] Through this process, the specialized knowledge of educators is effectively utilized, and education tailored to each individual student is realized.

[0573] (Example 1)

[0574] 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".

[0575] In modern education, challenges include the loss of knowledge due to educators' transfers and delays in proposing teaching methods that address students' individual learning needs. In particular, there is a problem with the inability to effectively transfer specialized knowledge to other educators, leading to inconsistencies in the quality of learning. Furthermore, there is a need to quickly provide individually optimized educational plans tailored to each student's learning progress.

[0576] 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.

[0577] In this invention, the server includes a device for inputting the specialized knowledge of educators, a data management device for storing and managing the inputted knowledge of educators, a knowledge transfer device for transmitting the stored knowledge to new educators in accordance with the transfer of educators, a data analysis device for collecting and analyzing student learning data, and means for presenting individually optimized teaching methods to students based on the analysis results. This makes it possible to efficiently transfer the knowledge of educators and to provide educational plans that quickly respond to the individual needs of students.

[0578] A "device for inputting educators' professional knowledge" is a device that provides an interface for educators to input their teaching methods and experiences as digital data.

[0579] A "data management device for accumulating and managing the knowledge entered by educators" is a system that stores the specialized knowledge entered by educators and manages it as a database so that it can be organized and searched as needed.

[0580] A "knowledge transfer device for transmitting accumulated knowledge to new educators in accordance with the transfer of educators" is a device that has the function of effectively conveying the accumulated knowledge of educators to new educators.

[0581] A "data analysis device for collecting and analyzing student learning data" is a device that collects students' learning history and achievements and performs various analyses based on that data. This makes it possible to understand individual learning trends and challenges.

[0582] "Means for presenting individually optimized educational methods based on analysis results" refers to a function that proposes the most suitable educational methods for each student based on the results of the analysis of learning data.

[0583] A description of embodiments for carrying out this invention will be given.

[0584] In this system, the terminal functions as a device that allows educators to input their professional knowledge. The terminal is equipped with an interface that enables text and voice input, allowing educators to digitize their special education experience and success stories as data. This digitized knowledge is then transmitted to a server.

[0585] The server stores and manages received knowledge data in a cloud database. A remote data storage system is used as the cloud database. Specifically, it classifies and stores knowledge for each educator and provides it to other educators in a format that can be viewed as needed. The server also collects student learning data and analyzes it using AI technology. In this process, software such as Python and TensorFlow are used to analyze the data with machine learning algorithms.

[0586] Educators, as users of the system, receive personalized teaching methods based on the server's analysis. For example, for a visually impaired student, the system might suggest the use of tactile materials, taking into account the student's past performance and behavioral patterns. Such suggestions help educators create more effective lesson plans.

[0587] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "In special education, please suggest methods for visually impaired students to effectively learn information. This student has previously shown improved learning comprehension when using tactile materials." Based on this prompt sentence, the AI ​​will suggest the most suitable materials and methods.

[0588] As described above, this system enables the efficient transfer of knowledge from educators and allows for a rapid response to the individual learning needs of students.

[0589] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0590] Step 1:

[0591] The terminal provides an interface to educators. Educators use the terminal to input their expertise. Specifically, they input successful case studies and teaching experiences in special education as text input or audio data. This input is converted into a digital format and prepared for subsequent processing.

[0592] Step 2:

[0593] The terminal sends the entered digital data to the server. The input data is digitized, and the server receives it and prepares to store it in a cloud database. After receiving the data, the server uses an automatic classification algorithm to categorize the data and assign appropriate labels. This labeling organizes the data so that other educators can efficiently search and refer to it.

[0594] Step 3:

[0595] The server collects and analyzes student learning data. The learning data is obtained from online learning platforms and grading systems, and preprocessed using Python. Data preprocessing includes imputing missing values ​​and normalizing the data. After preprocessing, an AI model is used to analyze learning patterns and performance trends.

[0596] Step 4:

[0597] Based on the AI ​​analysis results, the server proposes the optimal teaching method to the user, the educator. At this stage, the AI ​​model presents the educator with a prompt message and suggests a specific teaching approach. For example, a suggestion such as, "It has been found that this student's understanding will be enhanced by using tactile learning materials," might be generated.

[0598] Step 5:

[0599] The user adjusts the lesson plan based on the suggested teaching methods. The educator uses the information provided by the server as a reference and prepares teaching materials and modifies the lesson content as needed. The educator then inputs feedback on the lesson implementation back into the system via the terminal, and the system uses this feedback as new data points for the next analysis.

[0600] (Application Example 1)

[0601] 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".

[0602] There are challenges in transferring educators' knowledge and providing students with individualized learning experiences. Furthermore, traditional education systems make it difficult to effectively digitize and share educators' specialized knowledge, potentially leading to a decline in the quality of education. Additionally, it is difficult to fully utilize student learning data, making it challenging to provide an optimal educational environment tailored to individual needs.

[0603] 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.

[0604] In this invention, the server includes means for digitizing the professional knowledge of educators, means for constructing the digitized educational knowledge in a virtual space, and educational means for students to learn through virtual experiences. This makes it possible to widely share the knowledge of educators and provide a learning environment tailored to individual students.

[0605] "Educator's professional knowledge" refers to a sophisticated body of knowledge gained from specific teaching methods and experiences possessed by educators.

[0606] "Digitized specialized knowledge" refers to information that has been converted from educators' knowledge into an electronic format and stored.

[0607] "Means of storage" refer to methods and devices for quantitatively collecting and storing information.

[0608] A "virtual space" is a three-dimensional virtual environment created using computer technology.

[0609] A "virtual experience" is an experience obtained through interactive engagement in a virtual environment.

[0610] A "cloud database" is a data storage function located in a remote location and accessible via the internet.

[0611] An "interactive learning experience" is an experience in which students actively participate and learn in a two-way manner.

[0612] An "individualized learning plan" is a learning plan created according to each student's individual learning situation and needs.

[0613] The system implementing this invention digitizes the specialized knowledge of educators and provides it as an educational experience in a virtual space. A terminal is responsible for digitizing the knowledge entered by the educator and transmitting it to a cloud database. This digitized knowledge is stored by a server and analyzed using AI technology. The server uses Unity and Azure Cognitive Services to generate content for providing an interactive learning experience, which is then used for learning in the virtual space.

[0614] The server uses a generative AI model to create personalized learning plans based on a specific student's learning data. This allows students to experience a customized learning path tailored just for them. For example, content incorporating tactile materials and audio feedback may be provided for students with visual impairments. Students can learn in a virtual space using smartphones or head-mounted displays.

[0615] Educators, as users, can implement more interactive and personalized education by utilizing educational methods provided by the server within a virtual space. The generative AI model generates educational content based on the input of prompts. For example, a possible prompt might be, "I want to provide tactile chemistry learning materials that can be safely used in a virtual space for visually impaired students. Please design the materials using feedback tailored to the learning objectives." This system greatly improves the quality and personalization of education.

[0616] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0617] Step 1:

[0618] The terminal receives specialized knowledge entered by educators. It formats the entered knowledge as digital data and processes it into a form that can be sent to a cloud database. The input consists of the educator's methods and experience, and the output is formatted digital data.

[0619] Step 2:

[0620] The server stores digitized expert knowledge transmitted from terminals in a cloud database. This data is preprocessed for AI analysis, organizing information about individual educators as entities. The input is digitized expert knowledge, and the output is stored database entries.

[0621] Step 3:

[0622] The server uses a generative AI model to collect student learning data and perform analysis based on that data. It takes learning history and performance data as input and uses a predictive algorithm to generate the optimal learning path. The output is a customized learning plan.

[0623] Step 4:

[0624] Based on the analysis results, the server generates educational content in a virtual space using Unity and Azure Cognitive Services. It designs an interactive educational environment based on prompts and places the actual learning content in the virtual space. The input consists of a customized learning plan and prompts, and the output is a ready-to-use virtual learning material.

[0625] Step 5:

[0626] Educators, as users, utilize learning content within a virtual space provided by the server to educate students. Based on feedback from the field, educators adjust the content to provide a more effective learning experience. The input is the content within the virtual space, and the output is the students' learning outcomes.

[0627] 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.

[0628] This invention is a system that improves the quality of education by not only digitizing, storing, and sharing the professional knowledge of educators, but also by combining it with an emotion engine to recognize the emotions of users. This system consists of terminal, server, emotion engine, and user components, and supports effective instruction in educational settings.

[0629] The device not only provides an interface for educators to input their expertise, but also features a function that monitors the user's emotions in real time, as detected by an emotion engine. Educators can use the device to monitor students' emotional states along with education-related data.

[0630] The server receives data from the emotion engine and uses it to analyze it in combination with input data from educators. This analysis not only reviews learning data but also derives optimal teaching methods that consider the impact of students' emotional states on learning outcomes. The server utilizes accumulated digital knowledge data and emotion data to create customized teaching plans that address specific emotional states and learning disabilities.

[0631] The emotion engine tracks students' facial expressions, voice tone, and body movements in the educational environment, and analyzes their emotional state from this information. For example, by using facial recognition technology, it can detect in real time whether a student is concentrating or stressed, and send this data to the server.

[0632] The educator, as the user, adjusts individual teaching methods based on the suggested teaching techniques and emotion-based feedback displayed on the device. For example, if the server suggests that relaxation exercises should be introduced for students who are feeling stressed, the educator can make changes during the lesson accordingly and take measures to promote student relaxation.

[0633] This overall system seamlessly integrates the dynamics of education and emotion, enabling educators to adopt more inclusive and adaptive teaching methods, thereby maximizing students' learning experiences and outcomes.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] The terminal displays a login screen to the educator, who then logs into the system using their individual account information. After logging in, the educator can access a dedicated interface for entering their own educational data.

[0637] Step 2:

[0638] Educators, as users, input their professional knowledge and past teaching experience into forms on the terminal. They can also input real-time information, such as student reactions and newly acquired insights during lessons.

[0639] Step 3:

[0640] The terminal formats the entered specialized knowledge as digital data, checks for errors, and then sends an HTTP POST request to the server. At this time, a secure protocol is used to ensure data confidentiality.

[0641] Step 4:

[0642] The server verifies the expert knowledge data received from the terminal and stores it in the database. Simultaneously, emotional data received from the emotion engine is also sent to the server and recorded in the database. This allows each student's emotional state to be accumulated over time.

[0643] Step 5:

[0644] The emotion engine calculates emotion values ​​from students' facial expressions and voice analysis, and sends these values ​​to the server via the terminal. The terminal visualizes these emotion values ​​and provides real-time feedback to educators.

[0645] Step 6:

[0646] The educators, as users, adjust their teaching methods based on student emotional data displayed on their devices. If students are experiencing stress, they implement relaxation techniques to create an adaptive learning environment.

[0647] Step 7:

[0648] The server uses accumulated expertise and emotional data to perform analysis with an AI module, generating individually customized educational plans for each student. The results are then sent to the terminal.

[0649] Step 8:

[0650] The terminal displays the educational plan received from the server to the educator. The educator reviews this plan and develops specific lesson activities and teaching strategies. This allows the educator to provide instruction that is tailored to each student's emotions and learning needs.

[0651] This system enables the integration of emotions and knowledge in educational settings, leading to more effective and student-centered education.

[0652] (Example 2)

[0653] 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".

[0654] Traditional education systems lacked sufficient accumulation and sharing of educators' specialized knowledge, and the impact of learners' emotional states on learning quality was not adequately considered. This made it difficult to individually optimize the quality of education, and also led to the problem of losing educational know-how when educators were transferred.

[0655] 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.

[0656] In this invention, the server includes means for digitizing and storing the professional knowledge of educators, means for recognizing and analyzing the emotional state of users, and means for integrating the collected data to generate optimal teaching methods. This enables the efficient storage and sharing of educators' knowledge, and further allows for the individual optimization of learning plans that take into account the emotional state of users.

[0657] "Methods for digitizing educators' professional knowledge" refers to technologies that record and preserve the knowledge and know-how possessed by educators in an electronic format.

[0658] "Means of accumulation" refers to the technology that aggregates digitized data and stores it centrally.

[0659] "Means of transferring accumulated knowledge to new educators following the transfer of educators" refers to systems and technologies that support the sharing of knowledge from existing educators to newly appointed educators.

[0660] "Means for recognizing and analyzing the emotional state of users" refers to technologies that use sensors and software to detect the emotions expressed by learners and analyze that state.

[0661] "Methods for integrating and analyzing collected data" refers to the technology of collecting data from various sources, comprehensively analyzing it, and deriving useful insights.

[0662] "Means for generating optimal educational methods" refers to technologies that analyze data obtained from educators and learners and propose optimized educational methods and plans based on the results.

[0663] This invention is a system that improves the quality of education by digitizing the professional knowledge of educators and comprehensively recognizing and analyzing the emotions of users. This system consists of the following components: a terminal, a server, an emotion engine, and an educator (user).

[0664] The device first provides an interface that allows educators to efficiently input their specialized knowledge digitally. For example, educators can record lesson content using a text editor on the device and input knowledge data by pressing a save button. Furthermore, the device has a built-in camera and microphone that allows it to monitor learners' facial expressions and voice tones in real time during lessons and collect emotional data.

[0665] The emotion engine features advanced algorithms that analyze the learner's emotional state using monitored data such as facial expressions and voice tone. Specifically, it uses facial recognition technology to determine in real time whether the learner is focused or stressed. This analysis result is immediately sent to the server.

[0666] The server integrates knowledge information from educators sent from terminals with data from the emotion engine and performs analysis based on a generative AI model. The server's processing power allows it to combine this data and propose the most suitable teaching methods for learners. By utilizing this generative AI model, it's possible to input prompts such as, "How should I approach a nervous student?" and automatically generate a specific teaching plan.

[0667] The educators, as users, can view these suggestions and feedback on their devices and adjust their teaching methods accordingly. For example, if the server provides a specific example such as "relaxation exercises should be introduced for students who are feeling stressed," educators can incorporate relaxation-promoting activities into their lessons.

[0668] In this way, the present invention aims to improve the educational process by providing a comprehensive educational method that takes into account the accumulated knowledge of educators and the emotional state of learners.

[0669] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0670] Step 1:

[0671] The terminal provides an interface for educators to input specialized knowledge. Input consists of lesson content and teaching materials entered by the educator using a text editor. This data is then converted by the terminal into a digital format and prepared for transmission to the server. The output is formalized digital knowledge data.

[0672] Step 2:

[0673] The device uses its built-in camera and microphone to sense the learner's facial expressions and voice tone in real time during instruction and collect emotional data. In this step, the input is visual and audio data, which is used as information to analyze changes in facial expressions and voice tone. The output is raw facial and audio data to be sent to the emotion engine.

[0674] Step 3:

[0675] The emotion engine analyzes the learner's emotional state based on visual and audio data received from the device. By applying deep learning-based facial recognition technology, it classifies the input visual data into emotional categories (e.g., "concentration," "tension"). Meanwhile, it performs tone analysis on the audio data. The output is the analysis result indicating each learner's emotional state.

[0676] Step 4:

[0677] The server integrates digitized knowledge data transmitted from the terminal with emotion analysis results received from the emotion engine. At this stage, it receives digital knowledge data and emotional state data as input. The server uses a generative AI model to analyze this data and generate optimal teaching methods. The output is a teaching plan or approach proposed to educators.

[0678] Step 5:

[0679] The user, an educator, reviews the lesson plans and emotion-based feedback provided by the server on their terminal. The input consists of all feedback information to improve the educator's teaching methods. Based on this information, the educator adjusts their teaching methods to suit specific situations and implements the optimal approach for the learners. The output is the adjusted lesson plan and teaching methodology.

[0680] (Application Example 2)

[0681] 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".

[0682] Traditional training and customer service operations face the challenge of accurately understanding the emotions and states of individual users and responding appropriately accordingly. Furthermore, particularly in physical stores, there is a lack of means to quickly and accurately recognize the emotions of customers, hindering improvements in customer satisfaction. Improving this situation and enabling more effective training and service delivery is essential.

[0683] 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.

[0684] In this invention, the server includes means for digitizing the professional knowledge of educators, means for recognizing and analyzing emotional states, and means for optimizing customer service based on the analyzed emotional information. This makes it possible to grasp the emotional states of customers and students in real time and provide appropriate services and educational methods.

[0685] An "educator" is an individual or organization whose role is to instruct students in specific specialized knowledge and to support their learning.

[0686] "Means of digitization" refer to technologies and methods that convert analog information and conventional knowledge into electronic format, making them usable for computer processing.

[0687] "Means of accumulating knowledge" refers to the process of storing digitized information and data according to certain standards and keeping them in a state where they can be retrieved as needed.

[0688] "Means for recognizing and analyzing emotional states" refers to technologies and methods for detecting a user's inner emotions from facial expressions, tone of voice, body movements, etc., and for analyzing that information.

[0689] "Methods for optimizing customer service" refer to strategies and approaches that adjust the services and responses provided according to the user's different emotional states in order to increase satisfaction.

[0690] The system implementing this invention consists of a server, a terminal, and an emotion engine. The server is built on the cloud and has the function of digitally storing expert knowledge provided by educators and store clerks. It also receives real-time emotion analysis data transmitted from the emotion engine, analyzes it, and derives appropriate response methods. The server is equipped with a cloud database and artificial intelligence algorithms, which enable data accumulation and analysis.

[0691] The terminals are devices operated by educators and shop staff, and include smart glasses and tablet devices. They include interfaces for receiving user input and can also display emotional states analyzed by an emotion engine. The terminals also provide educators and shop staff with optimal methods for application in educational and customer service settings.

[0692] The emotion engine uses sensor devices such as cameras and microphones to detect the user's facial expressions and voice tone, and analyzes this data to identify the user's emotional state. This analysis is sent to a server, which then provides information to support more appropriate responses. For example, if the emotion engine detects customer stress, the server will suggest specific customer service methods to help the staff relax.

[0693] Examples of prompts to input into the generating AI model include: "The following customer's emotional state has been detected: Facial expression: confused, Tone of voice: anxious. What customer service approach should be suggested to improve this state?" In this way, appropriate responses based on the user's emotional state can be provided at any time.

[0694] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0695] Step 1:

[0696] The device uses a camera and microphone to capture the user's facial expressions and voice tone. Input data consists of image and audio files, collected by high-precision sensors. Output is raw image and audio data. Each piece of data is used to assess the individual's emotional state.

[0697] Step 2:

[0698] The device uses an emotion engine to analyze the image and audio data collected in Step 1. Here, facial recognition and voice analysis algorithms are used to detect characteristic facial expressions and vocal tones. The input is image and audio data, and the output is the emotional state (e.g., confusion, anxiety) as a result of the analysis. The analysis results are transmitted to the server as a digital signal.

[0699] Step 3:

[0700] The server devises the optimal service and response based on the emotional state received from the emotion engine. This process involves comparing a knowledge base stored in a cloud database with the user's past data. The input is emotional state data, and the output is specific countermeasures appropriate to that state (e.g., suggestions for relaxation).

[0701] Step 4:

[0702] The terminal notifies the user in real time of countermeasures received from the server, and adjusts necessary guidance and customer service accordingly. The input is a suggestion from the server, and the output is an action to the user (e.g., changing customer service methods, providing special services). This allows educators and staff to take immediate action according to the situation, which is expected to improve customer satisfaction.

[0703] Step 5:

[0704] Users input the results and feedback of the countermeasures they have implemented via their devices. The input is feedback information as a result of the countermeasures, and the output is the storage of information in a cloud database and the accumulation of data for future analysis. This provides data that will lead to improvements in future services, and is expected to improve the long-term learning process.

[0705] 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.

[0706] 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.

[0707] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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."

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] The following is further disclosed regarding the embodiments described above.

[0727] (Claim 1)

[0728] Means for digitizing the professional knowledge of educators,

[0729] A means of accumulating digitized specialized knowledge,

[0730] A means of transferring accumulated knowledge to new educators when educators are transferred,

[0731] Methods for collecting and analyzing student learning data,

[0732] A means of proposing the optimal educational method based on the analysis results,

[0733] A system that includes this.

[0734] (Claim 2)

[0735] The system according to claim 1, which uses a cloud database to accumulate the knowledge of educators.

[0736] (Claim 3)

[0737] The system according to claim 1, which generates individualized learning plans based on student progress data.

[0738] "Example 1"

[0739] (Claim 1)

[0740] A device for inputting the specialized knowledge of educators,

[0741] A data management device that stores and manages the knowledge of educators that has been entered,

[0742] A knowledge transfer device for transmitting accumulated knowledge to new educators in accordance with the transfer of educators,

[0743] A data analysis device for collecting and analyzing student learning data,

[0744] A means to present individually optimized educational methods to each student based on the analysis results,

[0745] A system that includes this.

[0746] (Claim 2)

[0747] The system according to claim 1, which uses remote data storage to accumulate the knowledge of educators.

[0748] (Claim 3)

[0749] The system according to claim 1, which generates individualized learning plans based on students' progress information.

[0750] "Application Example 1"

[0751] (Claim 1)

[0752] Means for digitizing the professional knowledge of educators,

[0753] A means of accumulating digitized specialized knowledge,

[0754] A means of transferring accumulated knowledge to new educators when educators are transferred,

[0755] Methods for collecting and analyzing student learning data,

[0756] A means of proposing the optimal educational method based on the analysis results,

[0757] This educational method involves constructing digitized educational knowledge within a virtual space, allowing students to learn through virtual experiences.

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, which uses a cloud database to accumulate the knowledge of educators and provides learning content in a virtual space.

[0761] (Claim 3)

[0762] The system according to claim 1, which generates individualized learning plans through an interactive learning experience in a virtual space based on students' progress data.

[0763] "Example 2 of combining an emotion engine"

[0764] (Claim 1)

[0765] Means for digitizing the professional knowledge of educators,

[0766] A means of accumulating digitized specialized knowledge,

[0767] A means of transferring accumulated knowledge to new educators when educators are transferred,

[0768] A means of recognizing and analyzing the emotional state of users,

[0769] A means of integrating and analyzing collected emotional data and training data,

[0770] A means of generating the optimal educational method based on the analysis results,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, which uses electronic media to store digitized knowledge and emotional data.

[0774] (Claim 3)

[0775] The system according to claim 1, which generates an individualized learning plan based on the user's progress data and emotional state.

[0776] "Application example 2 when combining with an emotional engine"

[0777] (Claim 1)

[0778] Means for digitizing the professional knowledge of educators,

[0779] A means of accumulating digitized specialized knowledge,

[0780] A means of transferring accumulated knowledge to new educators when educators are transferred,

[0781] Methods for collecting and analyzing student learning data,

[0782] A means of proposing the optimal educational method based on the analysis results,

[0783] A means of recognizing and analyzing the user's emotional state,

[0784] A means of optimizing customer service based on analyzed emotional information,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, which uses a cloud database to accumulate the knowledge of educators.

[0788] (Claim 3)

[0789] The system according to claim 1, which generates individualized learning plans based on student progress data and further adjusts the method of service delivery based on the customer's emotional state. [Explanation of Symbols]

[0790] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for digitizing the professional knowledge of educators, A means of accumulating digitized specialized knowledge, A means of transferring accumulated knowledge to new educators when educators are transferred, Methods for collecting and analyzing student learning data, A means of proposing the optimal educational method based on the analysis results, A system that includes this.

2. The system according to claim 1, which uses a cloud database to accumulate the knowledge of educators.

3. The system according to claim 1, which generates individualized learning plans based on student progress data.

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