system

Abstract

We provide the system. [Solution] A means for receiving authentication information from a user and performing authentication processing, A means for automatically generating educational materials using a generation device, A means for analyzing user questions and generating responses using a natural language processing device, Means for sending the generated response to the user, A means of recording user behavior and analyzing that data, A system that includes means for generating educational improvement suggestions for users based on analysis results.

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 modern educational settings, there is a problem that teachers cannot effectively utilize information and communication technology (ICT) because the support regarding ICT is insufficient. This problem arises from the lack of skills and personnel of ICT supporters, and further from the difficulty of arranging supporters due to budget constraints. Also, due to the lack of appropriate ICT support, the quality and efficiency of education may decline.

Means for Solving the Problems

[0005] This invention provides a means for user authentication using authentication information from users and for automatically generating educational materials using a generation device. In addition, it utilizes a natural language processing device to analyze user questions and generate appropriate responses, thereby quickly resolving ICT-related problems. This system records and analyzes user behavior and makes educational improvement suggestions based on that analysis, thereby realizing efficient ICT support. This provides an environment in which teachers can effectively utilize ICT, and aims to improve the quality of education.

[0006] "Users" refers to teachers and support staff who operate the system and utilize its functions and services.

[0007] "Authentication information" refers to information necessary to verify the user's identity and authority, and mainly includes the username and password.

[0008] "Authentication process" refers to the process of verifying access rights to the system based on authentication information provided by the user.

[0009] A "generation device" is a device that has the function of automatically generating educational materials and content based on specified conditions.

[0010] "Educational materials" are materials containing information to support educational activities, and can be in various formats such as text, images, and videos.

[0011] A "natural language processing system" is a device that analyzes questions and instructions from users and generates appropriate responses and solutions.

[0012] "Questions" refer to doubts that users have about the system or questions they have regarding ICT tools.

[0013] "Response" refers to the answer or information that a natural language processing system generates in response to a question.

[0014] "Recording actions" refers to the process of continuously saving a user's system usage history and operation details.

[0015] The "means for analyzing data" is means for analyzing the recorded behavior data of users to identify trends and issues.

[0016] The "education improvement proposal" refers to recommendations provided to improve educational content and methods based on the behavior data of users.

Brief Description of Drawings

[0017] [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 apparatus 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 apparatus 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 apparatus 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 apparatus 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system designed to streamline ICT support in educational settings, primarily for ease of use by users (teachers and support staff). This system specializes in processing information and providing responses through communication between a server, terminals, and users.

[0039] When the system starts, the user first enters login information from their terminal to authenticate. The server verifies the user's identity based on the received authentication information and grants access. After authentication, the user can create educational materials and ask questions about ICT via their terminal.

[0040] For example, if a user needs educational materials for a new unit, they select the content and format of the materials they want to generate on their device and send a request to the server. The server uses a generation device to automatically create the specified materials and send them back to the user's device. This automated generation allows teachers to prepare high-quality teaching materials in a short amount of time.

[0041] Furthermore, if a user has a question about operating an ICT tool, they can input the question from their terminal and send it to the server. The server uses a natural language processing system to analyze the question and generates an appropriate response from its knowledge base. The generated response is quickly returned to the terminal, allowing the user to receive support in real time.

[0042] Furthermore, the server continuously records user activity history and analyzes this data to provide suggestions for improving education. This allows users to efficiently improve their teaching approach while using the system. Specifically, it identifies frequently used tools and recurring problems, and provides training materials and solutions to address them, thereby improving the quality of education.

[0043] The implementation of this system will enable educational institutions to maximize the use of ICT and solve various challenges they face in the field of education.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters their login information into the terminal. The terminal sends the entered username and password to the server.

[0047] Step 2:

[0048] Based on the login information received by the server, it accesses an internal database and performs user authentication. If authentication is successful, the server generates an authentication token and sends it back to the terminal.

[0049] Step 3:

[0050] The user enters a request for material generation on their device. They select the specific type and content of the material and specify the desired format for generation.

[0051] Step 4:

[0052] The terminal sends a request to the server to generate educational materials. The server receives the request and starts the generation device.

[0053] Step 5:

[0054] The server automatically creates content based on the selected learning materials using a generation device. It also performs data processing based on the content and format of the learning materials.

[0055] Step 6:

[0056] The server sends the generated educational materials to the user's device. The user then reviews the materials via the device and prepares them for use in class.

[0057] Step 7:

[0058] The user enters a question about an ICT tool into the terminal. The question is then sent to the server.

[0059] Step 8:

[0060] The server receives a question and uses a natural language processing unit to analyze the intent of the question. Based on the analysis results, it generates an appropriate response from a knowledge base.

[0061] Step 9:

[0062] The server sends the generated response to the terminal. The user can then check the response in real time via the terminal and use it to help with the use of ICT tools.

[0063] Step 10:

[0064] The server logs user activity and system usage. This data is then analyzed to prepare for generating personalized educational improvement suggestions for the user.

[0065] (Example 1)

[0066] 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."

[0067] There is a challenge in efficiently utilizing ICT in educational settings. In particular, the time and effort required to create educational materials and resolve questions about ICT tools is a significant problem. Furthermore, there is a lack of mechanisms to effectively analyze user behavior and propose timely improvements to educational policies.

[0068] 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.

[0069] In this invention, the server includes means for receiving and verifying identification information from a user, means for automatically generating learning materials using a generation mechanism, and means for analyzing user inquiries and generating responses using a natural language processing mechanism. This makes it possible to quickly provide educational materials and resolve user questions in real time. Furthermore, by analyzing user operation trends and making appropriate educational improvement suggestions, the quality of education can be improved.

[0070] "Users" refer to individuals who use the system to generate educational materials or ask questions about ICT.

[0071] "Identification information" refers to information used to identify an individual when a user accesses the system.

[0072] "Verification process" refers to the process of determining whether the received identification information is accurate and whether the user has the right to access it.

[0073] A "generation mechanism" refers to a device or process that automatically creates learning materials based on given conditions.

[0074] "Learning materials" refer to information resources created to support educational activities.

[0075] A "natural language processing mechanism" refers to a technology that analyzes user inquiries in their original human language and generates appropriate responses.

[0076] An "inquiry" refers to a question or request for information that a user makes to the system.

[0077] "Response" refers to the answer or information that the system provides in response to a user's inquiry.

[0078] "Operational tendencies" refer to the patterns and frequency of how users utilize the system.

[0079] "Educational improvement suggestions" refer to specific advice and information to make users' current educational activities more effective.

[0080] This invention provides a system for efficiently utilizing ICT in educational settings. The system operates primarily based on communication between a server, terminals, and users. Specifically, terminals can be computers or tablets, and the server can be a cloud-based server system.

[0081] The server receives identification information sent by the user and performs verification using the database. Once verification is complete, the user's session is established, and other functions become available. For generating learning materials, software called a generative AI model is used to automatically create content according to the user's requests. Prompts are used during the generation process to provide more specific material content.

[0082] The terminal generates prompt messages based on user input and sends them to the server. For example, when creating math teaching materials for elementary school students, a prompt message such as "Please create basic math teaching materials for elementary school students. The content should be addition and subtraction, and it should be fun to use with lots of illustrations" might be generated. This prompt message is processed by the server's AI model, and the specified teaching materials are created.

[0083] Furthermore, the server utilizes natural language processing technology to analyze user inquiries. When a user enters a question about an ICT tool, the terminal sends that question to the server. The server analyzes the question, consults its knowledge base, and generates an appropriate response. This response is quickly returned to the terminal, allowing the user to obtain information in real time.

[0084] The server also continuously records user activity history and analyzes it to generate suggestions for educational improvements. This analysis reveals which tools are used most frequently and what problems occur often, and based on this, it provides appropriate teaching materials and training information.

[0085] In this way, the system supports educational activities and enables teachers and support staff to create teaching materials and solve problems more efficiently.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user enters their login information using their device. This information includes a user ID and password. The entered information is encrypted within the device and securely transmitted to the server.

[0089] Step 2:

[0090] The server compares the received login information with the database and performs user verification. If the database response confirms the user's existence and the matching of the authentication information, it generates a success message and sends it to the terminal. Upon receiving the success message, the user can proceed to the next step.

[0091] Step 3:

[0092] The user enters a prompt message via the device to create learning materials, and the device sends that prompt message to the server as a request for the generation AI model. For example, a prompt message such as "Please create basic math learning materials" might be entered.

[0093] Step 4:

[0094] The server inputs the received prompt message into the generating AI model, which automatically generates training materials based on the specified conditions. During this process, the generating model understands the request through natural language processing and creates and outputs corresponding text and images. The generated materials are then sent back to the terminal in digital format.

[0095] Step 5:

[0096] If a user has a question about how to use an ICT tool, they enter the question into their terminal and send it to the server. The question is in natural language format and includes content such as, "How do I use this function of this tool?"

[0097] Step 6:

[0098] The server uses natural language processing technology to analyze the input question and generates an appropriate response by referring to a knowledge database. The generated response is immediately sent to the terminal, and the user can resolve their question by confirming it.

[0099] Step 7:

[0100] The server records operation history and periodically analyzes it to understand user usage trends. Based on the analysis, educational improvement suggestions are generated and sent to the user via the terminal. This allows users to receive feedback to improve their own educational approach.

[0101] (Application Example 1)

[0102] 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."

[0103] In modern brick-and-mortar stores, customers need to obtain information about products and services quickly and accurately. Furthermore, there is a lack of effective means for sales staff to provide information efficiently and improve customer satisfaction. Continuous staff training utilizing customer inquiry response history is also a challenge. There is a need for a system that can address these issues.

[0104] 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.

[0105] In this invention, the server includes means for quickly acquiring information using a portable information terminal used by the user, means for analyzing user questions using a natural language processing device and generating responses, and means for recording the user's response history and optimizing further information provision. This enables sales staff in physical stores to provide accurate and timely information to customers and improve services that meet specific needs.

[0106] A "user" is an individual or organization that acquires or provides information through this system.

[0107] "Authentication information" refers to identification data used by users when accessing the system, and includes usernames, passwords, and other security information.

[0108] "Authentication processing" is the process of verifying the user's identity using received authentication information and determining whether or not to grant access.

[0109] A "generation device" is a computer system that automatically creates information resources based on requests.

[0110] "Information resources" refer to data or materials provided in response to user requests, and exist in various forms.

[0111] A "natural language processing system" is a technology or device that enables computers to understand and analyze language that humans normally use.

[0112] A "response" is information or an answer generated in response to a question from a user.

[0113] "Recording actions" is the process of saving a history of how a user interacts with the system.

[0114] "Data analysis" is the process of extracting useful information from collected data and identifying patterns.

[0115] An "educational improvement proposal" is advice or a plan based on analysis results aimed at improving users' skills or optimizing methods.

[0116] A "portable information terminal" is an electronic device that is easy to carry and can acquire information via communication.

[0117] "Recording response history" means saving the details of the responses provided to the user.

[0118] "Optimizing information provision" is the process of providing the most relevant information based on the user's past behavior and requests.

[0119] As a form for implementing the invention, this system is an information provision and analysis system designed to support sales staff in physical stores. Efficient information transmission and customer service are achieved through the collaboration of a server, terminal, and user.

[0120] The server receives authentication information from the portable information terminal used by the salesperson and performs appropriate authentication processing. Once authentication is complete, the salesperson communicates with the server in real time and processes customer questions using a natural language processing device. This device utilizes OpenAI's GPT series and other technologies to analyze the collected questions, automatically generate appropriate responses, and send them to the terminal.

[0121] The salesperson, as a user of the system, uses the responses received to explain things to the customer and provide detailed information about the products and services. This allows the salesperson to answer customer questions quickly and accurately.

[0122] Furthermore, the server records the history of responses made by salespeople and performs data analysis. Based on this, the server optimizes information delivery by suggesting more effective methods of providing information and generating training materials for salespeople. This improves the quality of employee training.

[0123] For example, if a customer asks, "What are the features of this product?", the salesperson enters the question into the system using a mobile device. The system quickly analyzes the question and generates a response such as, "This product has an energy-saving design and a long-lasting battery." An example of a prompt could be, "Please provide information so that the customer can understand the features of this product."

[0124] As described above, this invention provides an effective means for improving the information provision capabilities of sales staff in physical stores.

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] The terminal receives the user's authentication information and sends it to the server. The input here is a username and password, and the server performs an authentication process using this authentication information as data calculation. The terminal receives a response indicating whether authentication was successful or failed.

[0128] Step 2:

[0129] After successful authentication, the user inputs a question from the customer using a terminal. The input sent from the terminal to the server is a question written in natural language. The server receives the input question and converts it into a prompt sentence format using a natural language analyzer. Through this process, the question data is analyzed and processed, forming the input necessary for response generation.

[0130] Step 3:

[0131] The server sends data to a generative AI model (e.g., the GPT series) using prompt messages. Based on this input, the model performs data calculations and generates human-readable response messages. The output is a specific answer to the user's question.

[0132] Step 4:

[0133] The generated response is sent from the server to the terminal. The terminal receives it and displays it to the user (salesperson). The input here is the generated response, and the terminal processes the data to prepare it for the salesperson to explain to the customer. The output is the response information in a format that the user can use immediately.

[0134] Step 5:

[0135] The user provides information to the customer based on the response received. Once the customer interaction is complete, the terminal sends the interaction history to the server. This history is later analyzed for system optimization. The input is interaction history data, which is analyzed on the server and output as basic data for optimizing information provision and creating training materials.

[0136] 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.

[0137] This invention combines an emotion engine with an ICT support system for educational activities, aiming to provide more personalized educational support by recognizing users' emotions and utilizing that information. This system enables real-time emotion analysis through the emotion engine while exchanging information between the server, terminal, and user.

[0138] To operate the system, users first log in on their terminal and send the necessary authentication information to the server. The server then performs authentication based on this information and verifies the user's identity. After authentication is complete, users can make various requests through their terminal. For example, several functions are available, such as automatic generation of educational materials and asking questions about ICT tools.

[0139] The emotion engine acquires emotional data from users as they operate their devices, and the server recognizes their emotional state based on this data. The emotion engine identifies the user's emotions through speech recognition, text analysis, and analysis of user behavior patterns. The results of this emotion analysis are used to adjust the content of learning materials and change the format of responses.

[0140] For example, if a user is confused and stressed by operating a complex ICT tool, the emotion engine recognizes that emotion. Based on this information, the server provides more user-friendly and concise explanations. Furthermore, for particularly complex content, it generates step-by-step instructional materials to reduce the user's burden.

[0141] Furthermore, data collected by the emotion engine is logged along with user behavior and used for data analysis on the server. Based on the results of this analysis, personalized educational improvement suggestions are generated. These suggestions, which take into account the user's emotions, allow teachers to improve and adapt their lessons more flexibly.

[0142] In this way, the introduction of an emotion engine elevates the system beyond mere technical support, enabling emotionally resonant educational support. This creates a more comfortable and effective educational environment.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user displays a login screen on their device and enters their username and password. The device then sends this information to the server.

[0146] Step 2:

[0147] The server authenticates the user by matching the received authentication information against the database. If approved, the server generates an authentication token and returns it to the device.

[0148] Step 3:

[0149] Before the user generates learning materials or submits questions, the device activates its emotion engine and begins analyzing the user's voice, input, and operation patterns.

[0150] Step 4:

[0151] The server receives user emotion data analyzed by the emotion engine. This data includes the emotional state the user is experiencing (e.g., stress, anxiety, satisfaction, etc.).

[0152] Step 5:

[0153] The user requests the generation of educational materials from their device. Specifically, they select and input the type and content of the educational materials they want to generate.

[0154] Step 6:

[0155] The device sends this request to the server. Based on the request content and sentiment data, the server uses a generator to produce the most suitable educational materials.

[0156] Step 7:

[0157] The server sends the generated learning materials, along with comments and advice tailored to the user's emotional state, to the user's device. The user reviews this and incorporates it into their educational activities.

[0158] Step 8:

[0159] When a user enters a question about an ICT tool, the terminal sends it to the server. The server uses a natural language processing system to analyze the question.

[0160] Step 9:

[0161] The server generates an appropriate response based on the analysis results. In doing so, it takes into account the user's emotional state and adjusts the format and tone of the response accordingly.

[0162] Step 10:

[0163] Based on user activity history and sentiment data, the server records behavior and stores it in a database. This data is used for later analysis.

[0164] Step 11:

[0165] The server analyzes the recorded data and generates emotionally sensitive educational improvement suggestions for the user. These suggestions are then presented to the user via their device.

[0166] (Example 2)

[0167] 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".

[0168] In recent years, there has been a growing demand for individualized support in educational activities, but conventional systems have difficulty adapting to the individual emotional states of users. Furthermore, there are challenges in generating educational improvement suggestions that take users' emotions into account in real time.

[0169] 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.

[0170] In this invention, the server includes means for receiving authentication information from a user and performing authentication processing; means for automatically generating educational materials using a generation device; means for analyzing questions from the user and generating responses using a natural language processing device; means for analyzing the user's emotions using an emotion engine and reflecting that data in the educational materials; and means for recording the user's behavior and emotion data and analyzing that data. This makes it possible to provide personalized educational support that is tailored to the user's emotional state.

[0171] "Authentication information" refers to data used to verify the user's identity, and includes passwords, user IDs, and other similar information.

[0172] "Authentication processing" is the process of verifying the user's identity based on the authentication information received.

[0173] A "generation device" refers to a mechanism or program for automatically creating educational materials.

[0174] "Educational materials" refer to information and learning materials intended for use by users in their studies, and may be provided in multiple formats.

[0175] A "natural language processing system" refers to the technology and software used to analyze user questions and generate appropriate responses.

[0176] An "emotion engine" refers to a tool or algorithm that analyzes a user's voice and behavioral data to identify their emotional state.

[0177] "Emotional data" refers to information about the user's emotional state, obtained by the emotion engine.

[0178] "Behavioral data" refers to information about user actions and behavior within the system.

[0179] "Educational improvement suggestions" are specific instructions or proposed changes provided to improve users' learning.

[0180] The "modes for carrying out the invention" described herein are systems that realize emotion-based individualized support in educational activities. This system consists of information exchange between a server, a terminal, and a user.

[0181] The user initiates the login process on their device, entering authentication information such as a password and user ID. The device sends this authentication information to the server, which then performs an authentication process to verify the user's identity based on the received information. This allows the user to access various functions on the system.

[0182] A generation device is used to automatically generate educational materials. This device includes a generation AI model and automates the process of creating educational materials. For example, if a prompt such as "Generate educational materials on mathematical concepts that the user wants to understand from the basics" is entered into the generation AI model, the corresponding educational materials will be automatically generated.

[0183] The natural language processing (NLP) system plays the role of analyzing questions from the user. When a user inputs a question through a terminal, the NLP analyzes the question, and the server generates an appropriate response. This allows the user to learn efficiently.

[0184] The emotion engine operates to recognize the user's emotional data. The device sends voice and behavioral data acquired during user interaction to the emotion engine. The emotion engine analyzes this data using speech recognition and behavioral pattern analysis to identify the user's emotional state.

[0185] Furthermore, the server adjusts the content of educational materials according to the user's emotional state. This emotion-based adjustment of materials makes it possible to create a more user-friendly learning environment.

[0186] For example, if the emotion engine determines that a user is confused and stressed by operating an ICT tool, the server will use that emotion data to provide a more concise and user-friendly explanation. Based on the emotional state, the prompt will read, "Generate a simple guide to the ICT tool that is easy for the user to understand," and appropriate materials will be created. This allows the user to use the system more comfortably.

[0187] Through this approach, it becomes possible to provide personalized educational support that is attentive to the user's emotions, resulting in an effective learning experience.

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

[0189] Step 1:

[0190] The user opens a secure login screen on their device and enters their username and password as authentication credentials. The entered authentication credentials are sent from the device to the server. The server verifies the user's identity by comparing the credentials with a database. If the verification is successful, the server sends a login success notification to the device, and the user is granted access to the system.

[0191] Step 2:

[0192] To utilize the system's built-in material generation function, users form a generation request from their terminal. This request includes the target learning content and corresponding prompts for the generation AI model. The terminal sends this to the server, which processes the prompts via a generation device and automatically generates the corresponding educational materials. This process searches for relevant information from the input prompts and outputs the educational materials based on that information.

[0193] Step 3:

[0194] Users input their questions or points of confusion using a terminal. These questions are sent to a natural language processing system. The server receives this input, performs natural language processing to analyze the intent of the question, and generates an answer based on the results. The generated answer is then returned to the user via the terminal. This allows users to receive immediate answers to their specific questions.

[0195] Step 4:

[0196] While the user is operating the device, it sends voice and behavioral data to the emotion engine in real time. The emotion engine on the server processes this data and analyzes the user's emotional state. Through voice recognition and behavioral pattern analysis, it identifies emotions such as whether the user is stressed or relaxed, and records this as emotion data in the engine.

[0197] Step 5:

[0198] The server adjusts the content of educational materials in real time based on the acquired sentiment data. If a user is experiencing stress, it can generate more accessible and concise materials, and present them in a step-by-step format. Specific examples of this include rewriting the text within the materials in simpler terms and adding audio guides.

[0199] Step 6:

[0200] The server logs user learning progress and sentiment data, and performs data analysis based on this information. The results of this analysis are used to analyze user learning patterns and generate personalized educational improvement suggestions. Teachers can then utilize these suggestions to provide customized educational support tailored to each user.

[0201] (Application Example 2)

[0202] 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".

[0203] Traditional educational support systems failed to consider the feelings of users, providing only uniform support and thus failing to achieve effective individualized education. Furthermore, when users experienced anxiety or stress, appropriate support and security measures were not always provided immediately.

[0204] 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.

[0205] In this invention, the server includes a device that receives authentication data from users and performs authentication processing, a device that automatically generates educational materials, a device that analyzes user inquiries using a natural language processing device and generates responses, and a device that analyzes the user's emotional state in real time and adjusts responses and suggestions based on that analysis. This enables educational support and security measures tailored to the individual emotional state of each user.

[0206] "Authentication data" refers to information necessary for verifying a user's identity and granting access permissions.

[0207] "Educational materials" are content provided to support users' learning and instruction in educational activities.

[0208] A "natural language processing system" is a technology that analyzes text input in human language to understand its meaning and intent.

[0209] A "response" is a reply or instruction generated in response to a user's question or request.

[0210] "Action" refers to all actions and operations that a user performs when interacting with a system and device.

[0211] "Emotional state" refers to information that indicates the user's psychological state or mood.

[0212] "Real-time analysis" is a process that evaluates data and obtains results immediately without delay.

[0213] "Security measures" refer to the measures and actions taken to guarantee the safety of users.

[0214] This invention provides an educational support system equipped with an emotion engine. The system is primarily operated around a server, a terminal, and a user. First, the terminal receives authentication data from the user, and the server verifies the user's identity based on this authentication data. If authentication is successful, the terminal automatically generates educational materials via a generation device. This allows the system to provide users with educational content in various formats, such as text and audio information.

[0215] The server also uses a natural language processing system to analyze user inquiries and generate appropriate responses in real time. These responses are immediately presented to the user via their terminal, supporting their learning and problem-solving. Furthermore, the server continuously records user activity, analyzes this data in detail, and provides suggestions for further educational improvements. This results in a personalized learning experience for the user.

[0216] Through interaction with the user, the emotion engine analyzes the user's emotional state in real time. This analysis technology utilizes analytical models such as the Google® Cloud Natural Language API. This makes it possible to instantly detect when the user is feeling anxious or stressed and adjust responses and suggestions accordingly.

[0217] For example, if the emotion engine detects that a user is feeling anxious while out alone at night, the system will display a relaxing message on the device to provide reassurance. It can also automatically send alerts to emergency contacts if necessary.

[0218] An example of a prompt in a generative AI model is the question, "If you were out alone at night and felt anxious, what kind of support would make you feel at ease?" In this way, it is possible to achieve flexible responses that are tailored to the user's emotional state.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The terminal receives authentication data entered by the user. The terminal sends the received authentication data to the server, which verifies the user's identity based on the authentication data. The server checks the integrity of the authentication data and outputs a success or failure result.

[0222] Step 2:

[0223] Upon successful authentication, the server activates the generation device and automatically generates educational materials in the required format. For data processing, it retrieves materials from the system's learning content database and generates educational materials in a format suitable for the content (e.g., text, audio). The generated materials are sent to the terminal and displayed on the user's learning screen.

[0224] Step 3:

[0225] When a user operates a terminal and enters a question, the terminal sends the question data to the server. The server analyzes the question using a natural language processing system, extracts keywords, and performs contextual analysis to generate an appropriate response. The generated response is sent back to the terminal and displayed to the user.

[0226] Step 4:

[0227] The terminal collects user operation data and sends the operation log to the server. Based on the operation log, the server analyzes the user's behavior patterns and performs data calculations to detect changes. As a result, it creates educational improvement suggestions and provides feedback to enhance the user's learning experience.

[0228] Step 5:

[0229] While the user is using the device, it collects data related to the user's emotional state in real time. The collected data is sent to a server and analyzed by an emotion engine. Sentiment analysis is performed using data such as voice tone and input speed, and content and responses are adjusted depending on whether the user is experiencing anxiety or stress. For example, appropriate measures are taken, such as providing a relaxing message.

[0230] Step 6:

[0231] If an anomaly is detected as a result of sentiment analysis, the device will be automatically configured to send an alert to emergency contacts. This will take action to enhance the user's sense of safety and security. After that, the success of the security response will be confirmed and recorded in the log.

[0232] 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.

[0233] 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 those described above. 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 shown 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.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] This invention is a system designed to streamline ICT support in educational settings, primarily for ease of use by users (teachers and support staff). This system specializes in processing information and providing responses through communication between a server, terminals, and users.

[0249] When the system starts, the user first enters login information from their terminal to authenticate. The server verifies the user's identity based on the received authentication information and grants access. After authentication, the user can create educational materials and ask questions about ICT via their terminal.

[0250] For example, if a user needs educational materials for a new unit, they select the content and format of the materials they want to generate on their device and send a request to the server. The server uses a generation device to automatically create the specified materials and send them back to the user's device. This automated generation allows teachers to prepare high-quality teaching materials in a short amount of time.

[0251] Furthermore, if a user has a question about operating an ICT tool, they can input the question from their terminal and send it to the server. The server uses a natural language processing system to analyze the question and generates an appropriate response from its knowledge base. The generated response is quickly returned to the terminal, allowing the user to receive support in real time.

[0252] Furthermore, the server continuously records user activity history and analyzes this data to provide suggestions for improving education. This allows users to efficiently improve their teaching approach while using the system. Specifically, it identifies frequently used tools and recurring problems, and provides training materials and solutions to address them, thereby improving the quality of education.

[0253] The implementation of this system will enable educational institutions to maximize the use of ICT and solve various challenges they face in the field of education.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user enters their login information into the terminal. The terminal sends the entered username and password to the server.

[0257] Step 2:

[0258] Based on the login information received by the server, it accesses an internal database and performs user authentication. If authentication is successful, the server generates an authentication token and sends it back to the terminal.

[0259] Step 3:

[0260] The user enters a request for material generation on their device. They select the specific type and content of the material and specify the desired format for generation.

[0261] Step 4:

[0262] The terminal sends a request to the server to generate educational materials. The server receives the request and starts the generation device.

[0263] Step 5:

[0264] The server automatically creates content based on the selected learning materials using a generation device. It also performs data processing based on the content and format of the learning materials.

[0265] Step 6:

[0266] The server sends the generated educational materials to the user's device. The user then reviews the materials via the device and prepares them for use in class.

[0267] Step 7:

[0268] The user enters a question about an ICT tool into the terminal. The question is then sent to the server.

[0269] Step 8:

[0270] The server receives a question and uses a natural language processing unit to analyze the intent of the question. Based on the analysis results, it generates an appropriate response from a knowledge base.

[0271] Step 9:

[0272] The server sends the generated response to the terminal. The user can then check the response in real time via the terminal and use it to help with the use of ICT tools.

[0273] Step 10:

[0274] The server logs user activity history and system usage. This data is then analyzed to prepare for generating personalized educational improvement suggestions for the user.

[0275] (Example 1)

[0276] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] There is a challenge in efficiently utilizing ICT in educational settings. In particular, the time and effort required to create educational materials and resolve questions about ICT tools is a significant problem. Furthermore, there is a lack of mechanisms to effectively analyze user behavior and propose timely improvements to educational policies.

[0278] 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.

[0279] In this invention, the server includes means for receiving and verifying identification information from a user, means for automatically generating learning materials using a generation mechanism, and means for analyzing user inquiries and generating responses using a natural language processing mechanism. This makes it possible to quickly provide educational materials and resolve user questions in real time. Furthermore, by analyzing user operation trends and making appropriate educational improvement suggestions, the quality of education can be improved.

[0280] "Users" refer to individuals who use the system to generate educational materials or ask questions about ICT.

[0281] "Identification information" refers to information used to identify an individual when a user accesses the system.

[0282] "Verification process" refers to the process of determining whether the received identification information is accurate and whether the user has the right to access it.

[0283] The "generation mechanism" refers to a device or process that automatically creates learning materials based on given conditions.

[0284] The "learning materials" refer to information resources created to support educational activities.

[0285] The "natural language processing mechanism" refers to a technology that analyzes a user's inquiry as it is in human language and generates an appropriate response.

[0286] An "inquiry" refers to a question or information request made by a user to the system.

[0287] A "response" refers to an answer or information provided by the system to the user's inquiry.

[0288] The "operation tendency" refers to the pattern and frequency of how a user uses the system.

[0289] An "education improvement proposal" refers to specific advice or information for making the user's current educational activities more effective.

[0290] This invention provides a system for efficiently using ICT in educational settings. The system mainly operates based on communication among a server, a terminal, and a user. As specific hardware, a computer, a tablet, etc. can be used for the terminal, and a cloud-based server system can be used for the server.

[0291] The server receives the identification information sent from the user and performs a confirmation process using a database. When the confirmation is completed, the user's session is established and other functions become available. For the generation of learning materials, software called a generation AI model is utilized to automatically create content according to the user's request. By using a prompt sentence in the generation process, more specific material content is provided.

[0292] The terminal generates prompt messages based on user input and sends them to the server. For example, when creating math teaching materials for elementary school students, a prompt message such as "Please create basic math teaching materials for elementary school students. The content should be addition and subtraction, and it should be fun to use with lots of illustrations" might be generated. This prompt message is processed by the server's AI model, and the specified teaching materials are created.

[0293] Furthermore, the server utilizes natural language processing technology to analyze user inquiries. When a user enters a question about an ICT tool, the terminal sends that question to the server. The server analyzes the question, consults its knowledge base, and generates an appropriate response. This response is quickly returned to the terminal, allowing the user to obtain information in real time.

[0294] The server also continuously records user activity history and analyzes it to generate suggestions for educational improvements. This analysis reveals which tools are used most frequently and what problems occur often, and based on this, it provides appropriate teaching materials and training information.

[0295] In this way, the system supports educational activities and enables teachers and support staff to create teaching materials and solve problems more efficiently.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The user enters their login information using their device. This information includes a user ID and password. The entered information is encrypted within the device and securely transmitted to the server.

[0299] Step 2:

[0300] The server collates the received login information with the database and performs user verification. At this time, if the existence of the user and the match of the authentication information are confirmed in the response from the database, a message of successful authentication is generated and sent to the terminal. The user who has received the success message can proceed to the next step.

[0301] Step 3:

[0302] The user inputs a prompt sentence for creating learning materials via the terminal, and the terminal sends the prompt sentence to the server as a request for the generation AI model. For example, a prompt sentence such as "Please create a basic arithmetic teaching material" is input.

[0303] Step 4:

[0304] The server inputs the received prompt sentence into the generation AI model and automatically generates learning materials based on the specified conditions. At this time, the generation model understands the requested content through natural language processing, creates and outputs corresponding text and images. The generated materials are sent back to the terminal in digital form.

[0305] Step 5:

[0306] If the user has a question about how to use the ICT tool, the user inputs the question into the terminal and sends it to the server. The question is in the form of natural language and includes content such as "How do I use this function of this tool?"

[0307] Step 6:

[0308] The server analyzes the input question using natural language processing technology, refers to the knowledge database, and generates an appropriate response. The generated response is immediately sent to the terminal, and the user can resolve the doubts by checking it.

[0309] Step 7:

[0310] The server records operation history and periodically analyzes it to understand user usage trends. Based on the analysis, educational improvement suggestions are generated and sent to the user via the terminal. This allows users to receive feedback to improve their own educational approach.

[0311] (Application Example 1)

[0312] 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."

[0313] In modern brick-and-mortar stores, customers need to obtain information about products and services quickly and accurately. Furthermore, there is a lack of effective means for sales staff to provide information efficiently and improve customer satisfaction. Continuous staff training utilizing customer response records is also a challenge. There is a need for a system that can address these issues.

[0314] 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.

[0315] In this invention, the server includes means for quickly acquiring information using a portable information terminal used by the user, means for analyzing user questions using a natural language processing device and generating responses, and means for recording the user's response history and optimizing further information provision. This enables sales staff in physical stores to provide accurate and timely information to customers and improve services that meet specific needs.

[0316] A "user" is an individual or organization that acquires or provides information through this system.

[0317] "Authentication information" refers to identification data used by users when accessing the system, and includes usernames, passwords, and other security information.

[0318] "Authentication processing" is the process of verifying the user's identity using received authentication information and determining whether or not to grant access.

[0319] A "generation device" is a computer system that automatically creates information resources based on requests.

[0320] "Information resources" refer to data or materials provided in response to user requests, and exist in various forms.

[0321] A "natural language processing system" is a technology or device that enables computers to understand and analyze language that humans normally use.

[0322] A "response" is information or an answer generated in response to a question from a user.

[0323] "Recording actions" is the process of saving a history of how a user interacts with the system.

[0324] "Data analysis" is the process of extracting useful information from collected data and identifying patterns.

[0325] An "educational improvement proposal" is advice or a plan based on analysis results aimed at improving users' skills or optimizing methods.

[0326] A "portable information terminal" is an electronic device that is easy to carry and can acquire information via communication.

[0327] "Recording response history" means saving the details of the responses provided to the user.

[0328] "Optimizing information provision" is the process of providing the most relevant information based on the user's past behavior and requests.

[0329] As a form for implementing the invention, this system is an information provision and analysis system designed to support sales staff in physical stores. Efficient information transmission and customer service are achieved through the collaboration of a server, terminal, and user.

[0330] The server receives authentication information from the portable information terminal used by the salesperson and performs appropriate authentication processing. Once authentication is complete, the salesperson communicates with the server in real time, and the customer's questions are processed by a natural language processing system. This system utilizes OpenAI's GPT series and other technologies to analyze the collected questions, automatically generate appropriate responses, and send them to the terminal.

[0331] The salesperson, as a user of the system, uses the responses received to explain things to the customer and provide detailed information about the products and services. This allows the salesperson to answer customer questions quickly and accurately.

[0332] Furthermore, the server records the history of responses made by salespeople and performs data analysis. Based on this, the server optimizes information delivery by suggesting more effective methods of providing information and generating training materials for salespeople. This improves the quality of employee training.

[0333] For example, if a customer asks, "What are the features of this product?", the salesperson enters the question into the system using a mobile device. The system quickly analyzes the question and generates a response such as, "This product has an energy-saving design and a long-lasting battery." An example of a prompt could be, "Please provide information so that the customer can understand the features of this product."

[0334] As described above, this invention provides an effective means for improving the information provision capabilities of sales staff in physical stores.

[0335] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0336] Step 1:

[0337] The terminal receives the user's authentication information and sends it to the server. The input here is a username and password, and the server performs an authentication process using this authentication information as data calculation. The terminal receives a response indicating whether the authentication was successful or failed.

[0338] Step 2:

[0339] After successful authentication, the user inputs a question from the customer using a terminal. The input sent from the terminal to the server is a question written in natural language. The server receives the input question and converts it into a prompt sentence format using a natural language analyzer. Through this process, the question data is analyzed and processed, forming the input necessary for response generation.

[0340] Step 3:

[0341] The server sends data to a generative AI model (e.g., the GPT series) using prompt messages. Based on this input, the model performs data calculations and generates human-readable response messages. The output is a specific answer to the user's question.

[0342] Step 4:

[0343] The generated response is sent from the server to the terminal. The terminal receives it and displays it to the user (salesperson). The input here is the generated response, and the terminal processes the data to prepare it for the salesperson to explain to the customer. The output is the response information in a format that the user can use immediately.

[0344] Step 5:

[0345] The user provides information to the customer based on the response received. Once the customer interaction is complete, the terminal sends the interaction history to the server. This history is later analyzed for system optimization. The input is interaction history data, which is analyzed on the server and output as basic data for optimizing information provision and creating training materials.

[0346] 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.

[0347] This invention combines an emotion engine with an ICT support system for educational activities, aiming to provide more personalized educational support by recognizing users' emotions and utilizing that information. This system enables real-time emotion analysis through the emotion engine while exchanging information between the server, terminal, and user.

[0348] To operate the system, users first log in on their terminal and send the necessary authentication information to the server. The server then performs authentication based on this information and verifies the user's identity. After authentication is complete, users can make various requests through their terminal. For example, several functions are available, such as automatic generation of educational materials and asking questions about ICT tools.

[0349] The emotion engine acquires emotional data from users as they operate their devices, and the server recognizes their emotional state based on this data. The emotion engine identifies the user's emotions through speech recognition, text analysis, and analysis of user behavior patterns. The results of this emotion analysis are used to adjust the content of learning materials and change the format of responses.

[0350] For example, if a user is confused and stressed by operating a complex ICT tool, the emotion engine recognizes that emotion. Based on this information, the server provides more user-friendly and concise explanations. Furthermore, for particularly complex content, it generates step-by-step instructional materials to reduce the user's burden.

[0351] Furthermore, data collected by the emotion engine is logged along with user behavior and used for data analysis on the server. Based on the results of this analysis, personalized educational improvement suggestions are generated. These suggestions, which take into account the user's emotions, allow teachers to improve and adapt their lessons more flexibly.

[0352] In this way, the introduction of an emotion engine elevates the system beyond mere technical support, enabling emotionally resonant educational support. This creates a more comfortable and effective educational environment.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The user displays a login screen on their device and enters their username and password. The device then sends this information to the server.

[0356] Step 2:

[0357] The server authenticates the user by matching the received authentication information against the database. If approved, the server generates an authentication token and returns it to the device.

[0358] Step 3:

[0359] Before the user generates learning materials or submits questions, the device activates its emotion engine and begins analyzing the user's voice, input, and operation patterns.

[0360] Step 4:

[0361] The server receives user emotion data analyzed by the emotion engine. This data includes the emotional state the user is expressing (e.g., stress, anxiety, satisfaction, etc.).

[0362] Step 5:

[0363] The user requests the generation of educational materials from their device. Specifically, they select and input the type and content of the educational materials they want to generate.

[0364] Step 6:

[0365] The device sends this request to the server. Based on the request content and sentiment data, the server uses a generator to produce the most suitable educational materials.

[0366] Step 7:

[0367] The server sends the generated learning materials, along with comments and advice tailored to the user's emotional state, to the user's device. The user reviews this and incorporates it into their educational activities.

[0368] Step 8:

[0369] When a user enters a question about an ICT tool, the terminal sends it to the server. The server uses a natural language processing system to analyze the question.

[0370] Step 9:

[0371] The server generates an appropriate response based on the analysis results. In doing so, it takes into account the user's emotional state and adjusts the format and tone of the response accordingly.

[0372] Step 10:

[0373] Based on user activity history and sentiment data, the server records behavior and stores it in a database. This data is used for later analysis.

[0374] Step 11:

[0375] The server analyzes the recorded data and generates emotionally sensitive educational improvement suggestions for the user. These suggestions are then presented to the user via their device.

[0376] (Example 2)

[0377] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0378] In recent years, there has been a growing demand for individualized support in educational activities, but conventional systems have difficulty adapting to the individual emotional states of users. Furthermore, there are challenges in generating educational improvement suggestions that take users' emotions into account in real time.

[0379] 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.

[0380] In this invention, the server includes means for receiving authentication information from a user and performing authentication processing; means for automatically generating educational materials using a generation device; means for analyzing questions from the user and generating responses using a natural language processing device; means for analyzing the user's emotions using an emotion engine and reflecting that data in the educational materials; and means for recording the user's behavior and emotion data and analyzing that data. This makes it possible to provide personalized educational support that is tailored to the user's emotional state.

[0381] "Authentication information" refers to data used to verify the user's identity, and includes passwords, user IDs, and other similar information.

[0382] "Authentication processing" is the process of verifying the user's identity based on the authentication information received.

[0383] A "generation device" refers to a mechanism or program for automatically creating educational materials.

[0384] "Educational materials" refer to information and learning materials intended for use by users in their studies, and may be provided in multiple formats.

[0385] A "natural language processing system" refers to the technology and software used to analyze user questions and generate appropriate responses.

[0386] An "emotion engine" refers to a tool or algorithm that analyzes a user's voice and behavioral data to identify their emotional state.

[0387] "Emotional data" refers to information about the user's emotional state, obtained by the emotion engine.

[0388] "Behavioral data" refers to information about user actions and behavior within the system.

[0389] "Educational improvement suggestions" are specific instructions or proposed changes provided to improve users' learning.

[0390] The "modes for carrying out the invention" described herein are systems that realize emotion-based individualized support in educational activities. This system consists of information exchange between a server, a terminal, and a user.

[0391] The user initiates the login process on their device, entering authentication information such as a password and user ID. The device sends this authentication information to the server, which then performs an authentication process to verify the user's identity based on the received information. This allows the user to access various functions on the system.

[0392] A generation device is used to automatically generate educational materials. This device includes a generation AI model and automates the process of creating educational materials. For example, if a prompt such as "Generate educational materials on mathematical concepts that the user wants to understand from the basics" is entered into the generation AI model, the corresponding educational materials will be automatically generated.

[0393] The natural language processing (NLP) system plays the role of analyzing questions from the user. When a user inputs a question through a terminal, the NLP analyzes the question, and the server generates an appropriate response. This allows the user to learn efficiently.

[0394] The emotion engine operates to recognize the user's emotional data. The device sends voice and behavioral data acquired during user interaction to the emotion engine. The emotion engine analyzes this data using speech recognition and behavioral pattern analysis to identify the user's emotional state.

[0395] Furthermore, the server adjusts the content of educational materials according to the user's emotional state. This emotion-based adjustment of materials makes it possible to create a more user-friendly learning environment.

[0396] For example, if the emotion engine determines that a user is confused and stressed by operating an ICT tool, the server will use that emotion data to provide a more concise and user-friendly explanation. Based on the emotional state, the prompt will read, "Generate a simple guide to the ICT tool that is easy for the user to understand," and appropriate materials will be created. This allows the user to use the system more comfortably.

[0397] Through this approach, it becomes possible to provide personalized educational support that is attentive to the user's emotions, resulting in an effective learning experience.

[0398] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0399] Step 1:

[0400] The user opens a secure login screen on their device and enters their username and password as authentication credentials. The entered authentication credentials are sent from the device to the server. The server verifies the user's identity by comparing the credentials with a database. If the verification is successful, the server sends a login success notification to the device, and the user is granted access to the system.

[0401] Step 2:

[0402] To utilize the system's built-in material generation function, users form a generation request from their terminal. This request includes the target learning content and corresponding prompts for the generation AI model. The terminal sends this to the server, which processes the prompts via a generation device and automatically generates the corresponding educational materials. This process searches for relevant information from the input prompts and outputs the educational materials based on that information.

[0403] Step 3:

[0404] Users input their questions or points of confusion using a terminal. These questions are sent to a natural language processing system. The server receives this input, performs natural language processing to analyze the intent of the question, and generates an answer based on the results. The generated answer is then returned to the user via the terminal. This allows users to receive immediate answers to their specific questions.

[0405] Step 4:

[0406] While the user is operating the device, it sends voice and behavioral data to the emotion engine in real time. The emotion engine on the server processes this data and analyzes the user's emotional state. Through voice recognition and behavioral pattern analysis, it identifies emotions such as whether the user is stressed or relaxed, and records this as emotion data in the engine.

[0407] Step 5:

[0408] The server adjusts the content of educational materials in real time based on the acquired sentiment data. If a user is experiencing stress, it can generate more accessible and concise materials, and present them in a step-by-step format. Specific examples of this include rewriting the text within the materials in simpler terms and adding audio guides.

[0409] Step 6:

[0410] The server logs user learning progress and sentiment data, and performs data analysis based on this information. The results of this analysis are used to analyze user learning patterns and generate personalized educational improvement suggestions. Teachers can then utilize these suggestions to provide customized educational support tailored to each user.

[0411] (Application Example 2)

[0412] 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."

[0413] Traditional educational support systems failed to consider the feelings of users, providing only uniform support and thus failing to achieve effective individualized education. Furthermore, when users experienced anxiety or stress, appropriate support and security measures were not always provided immediately.

[0414] 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.

[0415] In this invention, the server includes a device that receives authentication data from users and performs authentication processing, a device that automatically generates educational materials, a device that analyzes user inquiries using a natural language processing device and generates responses, and a device that analyzes the user's emotional state in real time and adjusts responses and suggestions based on that analysis. This enables educational support and security measures tailored to the individual emotional state of each user.

[0416] "Authentication data" refers to information necessary for verifying a user's identity and granting access permissions.

[0417] "Educational materials" are content provided to support users' learning and instruction in educational activities.

[0418] A "natural language processing system" is a technology that analyzes text input in human language to understand its meaning and intent.

[0419] A "response" is a reply or instruction generated in response to a user's question or request.

[0420] "Action" refers to all actions and operations that a user performs when interacting with a system and device.

[0421] "Emotional state" refers to information that indicates the user's psychological state or mood.

[0422] "Real-time analysis" is a process that evaluates data and obtains results immediately without delay.

[0423] "Security measures" refer to the measures and actions taken to guarantee the safety of users.

[0424] This invention provides an educational support system equipped with an emotion engine. The system is primarily operated around a server, a terminal, and a user. First, the terminal receives authentication data from the user, and the server verifies the user's identity based on this authentication data. If authentication is successful, the terminal automatically generates educational materials via a generation device. This allows the system to provide users with educational content in various formats, such as text and audio information.

[0425] The server also uses a natural language processing system to analyze user inquiries and generate appropriate responses in real time. These responses are immediately presented to the user via their terminal, supporting their learning and problem-solving. Furthermore, the server continuously records user activity, analyzes this data in detail, and provides suggestions for further educational improvements. This results in a personalized learning experience for the user.

[0426] Through interaction with the user, the emotion engine analyzes the user's emotional state in real time. This analysis technology utilizes analytical models such as the Google Cloud Natural Language API. This allows for immediate detection when the user is feeling anxious or stressed, and enables the system to adjust responses and suggestions accordingly.

[0427] For example, if the emotion engine detects that a user is feeling anxious while out alone at night, the system will display a relaxing message on the device to provide reassurance. It can also automatically send alerts to emergency contacts if necessary.

[0428] An example of a prompt in a generative AI model is the question, "If you were out alone at night and felt anxious, what kind of support would make you feel at ease?" In this way, it is possible to achieve flexible responses that are tailored to the user's emotional state.

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The terminal receives authentication data entered by the user. The terminal sends the received authentication data to the server, which verifies the user's identity based on the authentication data. The server checks the integrity of the authentication data and outputs a success or failure result.

[0432] Step 2:

[0433] Upon successful authentication, the server activates the generation device and automatically generates educational materials in the required format. For data processing, it retrieves materials from the system's learning content database and generates educational materials in a format suitable for the content (e.g., text, audio). The generated materials are sent to the terminal and displayed on the user's learning screen.

[0434] Step 3:

[0435] When a user operates a terminal and enters a question, the terminal sends the question data to the server. The server analyzes the question using a natural language processing system, extracts keywords, and performs contextual analysis to generate an appropriate response. The generated response is sent back to the terminal and displayed to the user.

[0436] Step 4:

[0437] The terminal collects user operation data and sends the operation log to the server. Based on the operation log, the server analyzes the user's behavior patterns and performs data calculations to detect changes. As a result, it creates educational improvement suggestions and provides feedback to enhance the user's learning experience.

[0438] Step 5:

[0439] While the user is using the device, it collects data related to the user's emotional state in real time. The collected data is sent to a server and analyzed by an emotion engine. Sentiment analysis is performed using data such as voice tone and input speed, and content and responses are adjusted depending on whether the user is experiencing anxiety or stress. For example, appropriate measures are taken, such as providing a relaxing message.

[0440] Step 6:

[0441] If an anomaly is detected as a result of sentiment analysis, the device will be automatically configured to send an alert to emergency contacts. This will take action to enhance the user's sense of safety and security. After that, the success of the security response will be confirmed and recorded in the log.

[0442] 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.

[0443] 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 those described above. 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 shown 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.

[0444] 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.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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".

[0458] This invention is a system designed to streamline ICT support in educational settings, primarily for ease of use by users (teachers and support staff). This system specializes in processing information and providing responses through communication between a server, terminals, and users.

[0459] When the system starts, the user first enters login information from their terminal to authenticate. The server verifies the user's identity based on the received authentication information and grants access. After authentication, the user can create educational materials and ask questions about ICT via their terminal.

[0460] For example, if a user needs educational materials for a new unit, they select the content and format of the materials they want to generate on their device and send a request to the server. The server uses a generation device to automatically create the specified materials and send them back to the user's device. This automated generation allows teachers to prepare high-quality teaching materials in a short amount of time.

[0461] Furthermore, if a user has a question about operating an ICT tool, they can input the question from their terminal and send it to the server. The server uses a natural language processing system to analyze the question and generates an appropriate response from its knowledge base. The generated response is quickly returned to the terminal, allowing the user to receive support in real time.

[0462] Furthermore, the server continuously records user activity history and analyzes this data to provide suggestions for improving education. This allows users to efficiently improve their teaching approach while using the system. Specifically, it identifies frequently used tools and recurring problems, and provides training materials and solutions to address them, thereby improving the quality of education.

[0463] The implementation of this system will enable educational institutions to maximize the use of ICT and solve various challenges they face in the field of education.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The user enters their login information into the terminal. The terminal sends the entered username and password to the server.

[0467] Step 2:

[0468] Based on the login information received by the server, it accesses an internal database and performs user authentication. If authentication is successful, the server generates an authentication token and sends it back to the terminal.

[0469] Step 3:

[0470] The user enters a request for material generation on their device. They select the specific type and content of the material and specify the desired format for generation.

[0471] Step 4:

[0472] The terminal sends a request to the server to generate educational materials. The server receives the request and starts the generation device.

[0473] Step 5:

[0474] The server automatically creates content based on the selected learning materials using a generation device. It also performs data processing based on the content and format of the learning materials.

[0475] Step 6:

[0476] The server sends the generated educational materials to the user's device. The user then reviews the materials via the device and prepares them for use in class.

[0477] Step 7:

[0478] The user enters a question about an ICT tool into the terminal. The question is then sent to the server.

[0479] Step 8:

[0480] The server receives a question and uses a natural language processing unit to analyze the intent of the question. Based on the analysis results, it generates an appropriate response from a knowledge base.

[0481] Step 9:

[0482] The server sends the generated response to the terminal. The user can then check the response in real time via the terminal and use it to help with the use of ICT tools.

[0483] Step 10:

[0484] The server logs user activity history and system usage. This data is then analyzed to prepare for generating personalized educational improvement suggestions for the user.

[0485] (Example 1)

[0486] 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."

[0487] There is a challenge in efficiently utilizing ICT in educational settings. In particular, the time and effort required to create educational materials and resolve questions about ICT tools is a significant problem. Furthermore, there is a lack of mechanisms to effectively analyze user behavior and propose timely improvements to educational policies.

[0488] 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.

[0489] In this invention, the server includes means for receiving and verifying identification information from a user, means for automatically generating learning materials using a generation mechanism, and means for analyzing user inquiries and generating responses using a natural language processing mechanism. This makes it possible to quickly provide educational materials and resolve user questions in real time. Furthermore, by analyzing user operation trends and making appropriate educational improvement suggestions, the quality of education can be improved.

[0490] "Users" refer to individuals who use the system to generate educational materials or ask questions about ICT.

[0491] "Identification information" refers to information used to identify an individual when a user accesses the system.

[0492] "Verification process" refers to the process of determining whether the received identification information is accurate and whether the user has the right to access it.

[0493] A "generation mechanism" refers to a device or process that automatically creates learning materials based on given conditions.

[0494] "Learning materials" refer to information resources created to support educational activities.

[0495] A "natural language processing mechanism" refers to a technology that analyzes user inquiries in their original human language form and generates appropriate responses.

[0496] An "inquiry" refers to a question or request for information that a user makes to the system.

[0497] "Response" refers to the answer or information that the system provides in response to a user's inquiry.

[0498] "Operational tendencies" refer to the patterns and frequency of how users utilize the system.

[0499] "Educational improvement suggestions" refer to specific advice and information to make users' current educational activities more effective.

[0500] This invention provides a system for efficiently utilizing ICT in educational settings. The system operates primarily based on communication between a server, terminals, and users. Specifically, terminals can be computers or tablets, and the server can be a cloud-based server system.

[0501] The server receives identification information sent by the user and performs verification using the database. Once verification is complete, the user's session is established, and other functions become available. For generating learning materials, software called a generative AI model is used to automatically create content according to the user's requests. Prompts are used during the generation process to provide more specific material content.

[0502] The terminal generates prompt messages based on user input and sends them to the server. For example, when creating math teaching materials for elementary school students, a prompt message such as "Please create basic math teaching materials for elementary school students. The content should be addition and subtraction, and it should be fun to use with lots of illustrations" might be generated. This prompt message is processed by the server's AI model, and the specified teaching materials are created.

[0503] Furthermore, the server utilizes natural language processing technology to analyze user inquiries. When a user enters a question about an ICT tool, the terminal sends that question to the server. The server analyzes the question, consults its knowledge base, and generates an appropriate response. This response is quickly returned to the terminal, allowing the user to obtain information in real time.

[0504] The server also continuously records user activity history and analyzes it to generate suggestions for educational improvements. This analysis reveals which tools are used most frequently and what problems occur often, and based on this, it provides appropriate teaching materials and training information.

[0505] In this way, the system supports educational activities and enables teachers and support staff to create teaching materials and solve problems more efficiently.

[0506] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0507] Step 1:

[0508] The user enters their login information using their device. This information includes a user ID and password. The entered information is encrypted within the device and securely transmitted to the server.

[0509] Step 2:

[0510] The server compares the received login information with the database and performs user verification. If the database response confirms the user's existence and the matching of the authentication information, it generates a success message and sends it to the terminal. Upon receiving the success message, the user can proceed to the next step.

[0511] Step 3:

[0512] The user enters a prompt message via the device to create learning materials, and the device sends that prompt message to the server as a request for the generation AI model. For example, a prompt message such as "Please create basic math learning materials" might be entered.

[0513] Step 4:

[0514] The server inputs the received prompt message into the generating AI model, which automatically generates training materials based on the specified conditions. During this process, the generating model understands the request through natural language processing and creates and outputs corresponding text and images. The generated materials are then sent back to the terminal in digital format.

[0515] Step 5:

[0516] If a user has a question about how to use an ICT tool, they type the question into their terminal and send it to the server. The question is in natural language format and includes content such as, "How do I use this function of this tool?"

[0517] Step 6:

[0518] The server uses natural language processing technology to analyze the input question and generates an appropriate response by referring to a knowledge database. The generated response is immediately sent to the terminal, and the user can resolve their question by confirming it.

[0519] Step 7:

[0520] The server records operation history and periodically analyzes it to understand user usage trends. Based on the analysis, educational improvement suggestions are generated and sent to the user via the terminal. This allows users to receive feedback to improve their own educational approach.

[0521] (Application Example 1)

[0522] 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."

[0523] In modern brick-and-mortar stores, customers need to obtain information about products and services quickly and accurately. Furthermore, there is a lack of effective means for sales staff to provide information efficiently and improve customer satisfaction. Continuous staff training utilizing customer response records is also a challenge. There is a need for a system that can address these issues.

[0524] 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.

[0525] In this invention, the server includes means for quickly acquiring information using a portable information terminal used by the user, means for analyzing user questions using a natural language processing device and generating responses, and means for recording the user's response history and optimizing further information provision. This enables sales staff in physical stores to provide accurate and timely information to customers and improve services that meet specific needs.

[0526] A "user" is an individual or organization that acquires or provides information through this system.

[0527] "Authentication information" refers to identification data used by users when accessing the system, and includes usernames, passwords, and other security information.

[0528] "Authentication processing" is the process of verifying the user's identity using received authentication information and determining whether or not to grant access.

[0529] A "generation device" is a computer system that automatically creates information resources based on requests.

[0530] "Information resources" refer to data or materials provided in response to user requests, and exist in various forms.

[0531] A "natural language processing system" is a technology or device that enables computers to understand and analyze language that humans normally use.

[0532] A "response" is information or an answer generated in response to a question from a user.

[0533] "Recording actions" is the process of saving a history of how a user interacts with the system.

[0534] "Data analysis" is the process of extracting useful information from collected data and identifying patterns.

[0535] An "educational improvement proposal" is advice or a plan based on analysis results aimed at improving users' skills or optimizing methods.

[0536] A "portable information terminal" is an electronic device that is easy to carry and can acquire information via communication.

[0537] "Recording response history" means saving the details of the responses provided to the user.

[0538] "Optimizing information provision" is the process of providing the most relevant information based on the user's past behavior and requests.

[0539] As a form for implementing the invention, this system is an information provision and analysis system designed to support sales staff in physical stores. Efficient information transmission and customer service are achieved through the collaboration of a server, terminal, and user.

[0540] The server receives authentication information from the portable information terminal used by the salesperson and performs appropriate authentication processing. Once authentication is complete, the salesperson communicates with the server in real time, and the customer's questions are processed by a natural language processing system. This system utilizes OpenAI's GPT series and other technologies to analyze the collected questions, automatically generate appropriate responses, and send them to the terminal.

[0541] The salesperson, as a user of the system, uses the responses received to explain things to the customer and provide detailed information about the products and services. This allows the salesperson to answer customer questions quickly and accurately.

[0542] Furthermore, the server records the history of responses made by salespeople and performs data analysis. Based on this, the server optimizes information delivery by suggesting more effective methods of providing information and generating training materials for salespeople. This improves the quality of employee training.

[0543] For example, if a customer asks, "What are the features of this product?", the salesperson enters the question into the system using a mobile device. The system quickly analyzes the question and generates a response such as, "This product has an energy-saving design and a long-lasting battery." An example of a prompt could be, "Please provide information so that the customer can understand the features of this product."

[0544] As described above, this invention provides an effective means for improving the information provision capabilities of sales staff in physical stores.

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The terminal receives the user's authentication information and sends it to the server. The input here is a username and password, and the server performs an authentication process using this authentication information as data calculation. The terminal receives a response indicating whether the authentication was successful or failed.

[0548] Step 2:

[0549] After successful authentication, the user inputs a question from the customer using a terminal. The input sent from the terminal to the server is a question written in natural language. The server receives the input question and converts it into a prompt sentence format using a natural language analyzer. Through this process, the question data is analyzed and processed, forming the input necessary for response generation.

[0550] Step 3:

[0551] The server sends data to a generative AI model (e.g., the GPT series) using prompt messages. Based on this input, the model performs data calculations and generates human-readable response messages. The output is a specific answer to the user's question.

[0552] Step 4:

[0553] The generated response is sent from the server to the terminal. The terminal receives it and displays it to the user (salesperson). The input here is the generated response, and the terminal processes the data to prepare it for the salesperson to explain to the customer. The output is the response information in a format that the user can use immediately.

[0554] Step 5:

[0555] The user provides information to the customer based on the response received. Once the customer interaction is complete, the terminal sends the interaction history to the server. This history is later analyzed for system optimization. The input is interaction history data, which is analyzed on the server and output as basic data for optimizing information provision and creating training materials.

[0556] 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.

[0557] This invention combines an emotion engine with an ICT support system for educational activities, aiming to provide more personalized educational support by recognizing users' emotions and utilizing that information. This system enables real-time emotion analysis through the emotion engine while exchanging information between the server, terminal, and user.

[0558] To operate the system, users first log in on their terminal and send the necessary authentication information to the server. The server then performs authentication based on this information and verifies the user's identity. After authentication is complete, users can make various requests through their terminal. For example, several functions are available, such as automatic generation of educational materials and asking questions about ICT tools.

[0559] The emotion engine acquires emotional data from users as they operate their devices, and the server recognizes their emotional state based on this data. The emotion engine identifies the user's emotions through speech recognition, text analysis, and analysis of user behavior patterns. The results of this emotion analysis are used to adjust the content of learning materials and change the format of responses.

[0560] For example, if a user is confused and stressed by operating a complex ICT tool, the emotion engine recognizes that emotion. Based on this information, the server provides more user-friendly and concise explanations. Furthermore, for particularly complex content, it generates step-by-step instructional materials to reduce the user's burden.

[0561] Furthermore, data collected by the emotion engine is logged along with user behavior and used for data analysis on the server. Based on the results of this analysis, personalized educational improvement suggestions are generated. These suggestions, which take into account the user's emotions, allow teachers to improve and adapt their lessons more flexibly.

[0562] In this way, the introduction of an emotion engine elevates the system beyond mere technical support, enabling emotionally resonant educational support. This creates a more comfortable and effective educational environment.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The user displays a login screen on their device and enters their username and password. The device then sends this information to the server.

[0566] Step 2:

[0567] The server authenticates the user by matching the received authentication information against the database. If approved, the server generates an authentication token and returns it to the device.

[0568] Step 3:

[0569] Before the user generates learning materials or submits questions, the device activates its emotion engine and begins analyzing the user's voice, input, and operation patterns.

[0570] Step 4:

[0571] The server receives user emotion data analyzed by the emotion engine. This data includes the emotional state the user is expressing (e.g., stress, anxiety, satisfaction, etc.).

[0572] Step 5:

[0573] The user requests the generation of educational materials from their device. Specifically, they select and input the type and content of the educational materials they want to generate.

[0574] Step 6:

[0575] The device sends this request to the server. Based on the request content and sentiment data, the server uses a generator to produce the most suitable educational materials.

[0576] Step 7:

[0577] The server sends the generated learning materials, along with comments and advice tailored to the user's emotional state, to the user's device. The user reviews this and incorporates it into their educational activities.

[0578] Step 8:

[0579] When a user enters a question about an ICT tool, the terminal sends it to the server. The server uses a natural language processing system to analyze the question.

[0580] Step 9:

[0581] The server generates an appropriate response based on the analysis results. In doing so, it takes into account the user's emotional state and adjusts the format and tone of the response accordingly.

[0582] Step 10:

[0583] Based on user activity history and sentiment data, the server records behavior and stores it in a database. This data is used for later analysis.

[0584] Step 11:

[0585] The server analyzes the recorded data and generates emotionally sensitive educational improvement suggestions for the user. These suggestions are then presented to the user via their device.

[0586] (Example 2)

[0587] 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."

[0588] In recent years, there has been a growing demand for individualized support in educational activities, but conventional systems have difficulty adapting to the individual emotional states of users. Furthermore, there are challenges in generating educational improvement suggestions that take users' emotions into account in real time.

[0589] 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.

[0590] In this invention, the server includes means for receiving authentication information from a user and performing authentication processing; means for automatically generating educational materials using a generation device; means for analyzing questions from the user and generating responses using a natural language processing device; means for analyzing the user's emotions using an emotion engine and reflecting that data in the educational materials; and means for recording the user's behavior and emotion data and analyzing that data. This makes it possible to provide personalized educational support that is tailored to the user's emotional state.

[0591] "Authentication information" refers to data used to verify the user's identity, and includes passwords, user IDs, and other similar information.

[0592] "Authentication processing" is the process of verifying the user's identity based on the authentication information received.

[0593] A "generation device" refers to a mechanism or program for automatically creating educational materials.

[0594] "Educational materials" refer to information and learning materials intended for use by users in their studies, and may be provided in multiple formats.

[0595] A "natural language processing system" refers to the technology and software used to analyze user questions and generate appropriate responses.

[0596] An "emotion engine" refers to a tool or algorithm that analyzes a user's voice and behavioral data to identify their emotional state.

[0597] "Emotional data" refers to information about the user's emotional state, obtained by the emotion engine.

[0598] "Behavioral data" refers to information about user actions and behavior within the system.

[0599] "Educational improvement suggestions" are specific instructions or proposed changes provided to improve users' learning.

[0600] The "modes for carrying out the invention" described herein are systems that realize emotion-based individualized support in educational activities. This system consists of information exchange between a server, a terminal, and a user.

[0601] The user initiates the login process on their device, entering authentication information such as a password and user ID. The device sends this authentication information to the server, which then performs an authentication process to verify the user's identity based on the received information. This allows the user to access various functions on the system.

[0602] A generation device is used to automatically generate educational materials. This device includes a generation AI model and automates the process of creating educational materials. For example, if a prompt such as "Generate educational materials on mathematical concepts that the user wants to understand from the basics" is entered into the generation AI model, the corresponding educational materials will be automatically generated.

[0603] The natural language processing (NLP) system plays the role of analyzing questions from the user. When a user inputs a question through a terminal, the NLP analyzes the question, and the server generates an appropriate response. This allows the user to learn efficiently.

[0604] The emotion engine operates to recognize the user's emotional data. The device sends voice and behavioral data acquired during user interaction to the emotion engine. The emotion engine analyzes this data using speech recognition and behavioral pattern analysis to identify the user's emotional state.

[0605] Furthermore, the server adjusts the content of educational materials according to the user's emotional state. This emotion-based adjustment of materials makes it possible to create a more user-friendly learning environment.

[0606] For example, if the emotion engine determines that a user is confused and stressed by operating an ICT tool, the server will use that emotion data to provide a more concise and user-friendly explanation. Based on the emotional state, the prompt will read, "Generate a simple guide to the ICT tool that is easy for the user to understand," and appropriate materials will be created. This allows the user to use the system more comfortably.

[0607] Through this approach, it becomes possible to provide personalized educational support that is attentive to the user's emotions, resulting in an effective learning experience.

[0608] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0609] Step 1:

[0610] The user opens a secure login screen on their device and enters their username and password as authentication credentials. The entered authentication credentials are sent from the device to the server. The server verifies the user's identity by comparing the credentials with a database. If the verification is successful, the server sends a login success notification to the device, and the user is granted access to the system.

[0611] Step 2:

[0612] To utilize the system's built-in material generation function, users form a generation request from their terminal. This request includes the target learning content and corresponding prompts for the generation AI model. The terminal sends this to the server, which processes the prompts via a generation device and automatically generates the corresponding educational materials. This process searches for relevant information from the input prompts and outputs the educational materials based on that information.

[0613] Step 3:

[0614] Users input their questions or points of confusion using a terminal. These questions are sent to a natural language processing system. The server receives this input, performs natural language processing to analyze the intent of the question, and generates an answer based on the results. The generated answer is then returned to the user via the terminal. This allows users to receive immediate answers to their specific questions.

[0615] Step 4:

[0616] While the user is operating the device, it sends voice and behavioral data to the emotion engine in real time. The emotion engine on the server processes this data and analyzes the user's emotional state. Through voice recognition and behavioral pattern analysis, it identifies emotions such as whether the user is stressed or relaxed, and records this as emotion data in the engine.

[0617] Step 5:

[0618] The server adjusts the content of educational materials in real time based on the acquired sentiment data. If a user is experiencing stress, it can generate more accessible and concise materials, and present them in a step-by-step format. Specific examples of this include rewriting the text within the materials in simpler terms and adding audio guides.

[0619] Step 6:

[0620] The server logs user learning progress and sentiment data, and performs data analysis based on this information. The results of this analysis are used to analyze user learning patterns and generate personalized educational improvement suggestions. Teachers can then utilize these suggestions to provide customized educational support tailored to each user.

[0621] (Application Example 2)

[0622] 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."

[0623] Traditional educational support systems failed to consider the feelings of users, providing only uniform support and thus failing to achieve effective individualized education. Furthermore, when users experienced anxiety or stress, appropriate support and security measures were not always provided immediately.

[0624] 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.

[0625] In this invention, the server includes a device that receives authentication data from users and performs authentication processing, a device that automatically generates educational materials, a device that analyzes user inquiries using a natural language processing device and generates responses, and a device that analyzes the user's emotional state in real time and adjusts responses and suggestions based on that analysis. This enables educational support and security measures tailored to the individual emotional state of each user.

[0626] "Authentication data" refers to information necessary for verifying a user's identity and granting access permissions.

[0627] "Educational materials" are content provided to support users' learning and instruction in educational activities.

[0628] A "natural language processing system" is a technology that analyzes text input in human language to understand its meaning and intent.

[0629] A "response" is a reply or instruction generated in response to a user's question or request.

[0630] "Action" refers to all actions and operations that a user performs when interacting with a system and device.

[0631] "Emotional state" refers to information that indicates the user's psychological state or mood.

[0632] "Real-time analysis" is a process that evaluates data and obtains results immediately without delay.

[0633] "Security measures" refer to the measures and actions taken to guarantee the safety of users.

[0634] This invention provides an educational support system equipped with an emotion engine. The system is primarily operated around a server, a terminal, and a user. First, the terminal receives authentication data from the user, and the server verifies the user's identity based on this authentication data. If authentication is successful, the terminal automatically generates educational materials via a generation device. This allows the system to provide users with educational content in various formats, such as text and audio information.

[0635] The server also uses a natural language processing system to analyze user inquiries and generate appropriate responses in real time. These responses are immediately presented to the user via their terminal, supporting their learning and problem-solving. Furthermore, the server continuously records user activity, analyzes this data in detail, and provides suggestions for further educational improvements. This results in a personalized learning experience for the user.

[0636] Through interaction with the user, the emotion engine analyzes the user's emotional state in real time. This analysis technology utilizes analytical models such as the Google Cloud Natural Language API. This allows for immediate detection when the user is feeling anxious or stressed, and enables the system to adjust responses and suggestions accordingly.

[0637] For example, if the emotion engine detects that a user is feeling anxious while out alone at night, the system will display a relaxing message on the device to provide reassurance. It can also automatically send alerts to emergency contacts if necessary.

[0638] An example of a prompt in a generative AI model is the question, "If you were out alone at night and felt anxious, what kind of support would make you feel at ease?" In this way, it is possible to achieve flexible responses that are tailored to the user's emotional state.

[0639] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0640] Step 1:

[0641] The terminal receives authentication data entered by the user. The terminal sends the received authentication data to the server, which verifies the user's identity based on the authentication data. The server checks the integrity of the authentication data and outputs a success or failure result.

[0642] Step 2:

[0643] Upon successful authentication, the server activates the generation device and automatically generates educational materials in the required format. For data processing, it retrieves materials from the system's learning content database and generates educational materials in a format suitable for the content (e.g., text, audio). The generated materials are sent to the terminal and displayed on the user's learning screen.

[0644] Step 3:

[0645] When a user operates a terminal and enters a question, the terminal sends the question data to the server. The server analyzes the question using a natural language processing system, extracts keywords, and performs contextual analysis to generate an appropriate response. The generated response is sent back to the terminal and displayed to the user.

[0646] Step 4:

[0647] The terminal collects user operation data and sends the operation log to the server. Based on the operation log, the server analyzes the user's behavior patterns and performs data calculations to detect changes. As a result, it creates educational improvement suggestions and provides feedback to enhance the user's learning experience.

[0648] Step 5:

[0649] While the user is using the device, it collects data related to the user's emotional state in real time. The collected data is sent to a server and analyzed by an emotion engine. Sentiment analysis is performed using data such as voice tone and input speed, and content and responses are adjusted depending on whether the user is experiencing anxiety or stress. For example, appropriate measures are taken, such as providing a relaxing message.

[0650] Step 6:

[0651] If an anomaly is detected as a result of sentiment analysis, the device will be automatically configured to send an alert to emergency contacts. This will take action to enhance the user's sense of safety and security. After that, the success of the security response will be confirmed and recorded in the log.

[0652] 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.

[0653] 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 those described above. 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 shown 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.

[0654] 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.

[0655] [Fourth Embodiment]

[0656] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0657] 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.

[0658] 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).

[0659] 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.

[0660] 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.

[0661] 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).

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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".

[0669] This invention is a system designed to streamline ICT support in educational settings, primarily for ease of use by users (teachers and support staff). This system specializes in processing information and providing responses through communication between a server, terminals, and users.

[0670] When the system starts, the user first enters login information from their terminal to authenticate. The server verifies the user's identity based on the received authentication information and grants access. After authentication, the user can create educational materials and ask questions about ICT via their terminal.

[0671] For example, if a user needs educational materials for a new unit, they select the content and format of the materials they want to generate on their device and send a request to the server. The server uses a generation device to automatically create the specified materials and send them back to the user's device. This automated generation allows teachers to prepare high-quality teaching materials in a short amount of time.

[0672] Furthermore, if a user has a question about operating an ICT tool, they can input the question from their terminal and send it to the server. The server uses a natural language processing system to analyze the question and generates an appropriate response from its knowledge base. The generated response is quickly returned to the terminal, allowing the user to receive support in real time.

[0673] Furthermore, the server continuously records user activity history and analyzes this data to provide suggestions for improving education. This allows users to efficiently improve their teaching approach while using the system. Specifically, it identifies frequently used tools and recurring problems, and provides training materials and solutions to address them, thereby improving the quality of education.

[0674] The implementation of this system will enable educational institutions to maximize the use of ICT and solve various challenges they face in the field of education.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The user enters their login information into the terminal. The terminal sends the entered username and password to the server.

[0678] Step 2:

[0679] Based on the login information received by the server, it accesses an internal database and performs user authentication. If authentication is successful, the server generates an authentication token and sends it back to the terminal.

[0680] Step 3:

[0681] The user enters a request for material generation on their device. They select the specific type and content of the material and specify the desired format for generation.

[0682] Step 4:

[0683] The terminal sends a request to the server to generate educational materials. The server receives the request and starts the generation device.

[0684] Step 5:

[0685] The server automatically creates content based on the selected learning materials using a generation device. It also performs data processing based on the content and format of the learning materials.

[0686] Step 6:

[0687] The server sends the generated educational materials to the user's device. The user then reviews the materials via the device and prepares them for use in class.

[0688] Step 7:

[0689] The user enters a question about an ICT tool into the terminal. The question is then sent to the server.

[0690] Step 8:

[0691] The server receives a question and uses a natural language processing unit to analyze the intent of the question. Based on the analysis results, it generates an appropriate response from a knowledge base.

[0692] Step 9:

[0693] The server sends the generated response to the terminal. The user can then check the response in real time via the terminal and use it to help with the use of ICT tools.

[0694] Step 10:

[0695] The server logs user activity history and system usage. This data is then analyzed to prepare for generating personalized educational improvement suggestions for the user.

[0696] (Example 1)

[0697] 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".

[0698] There is a challenge in efficiently utilizing ICT in educational settings. In particular, the time and effort required to create educational materials and resolve questions about ICT tools is a significant problem. Furthermore, there is a lack of mechanisms to effectively analyze user behavior and propose timely improvements to educational policies.

[0699] 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.

[0700] In this invention, the server includes means for receiving and verifying identification information from a user, means for automatically generating learning materials using a generation mechanism, and means for analyzing user inquiries and generating responses using a natural language processing mechanism. This makes it possible to quickly provide educational materials and resolve user questions in real time. Furthermore, by analyzing user operation trends and making appropriate educational improvement suggestions, the quality of education can be improved.

[0701] "Users" refer to individuals who use the system to generate educational materials or ask questions about ICT.

[0702] "Identification information" refers to information used to identify an individual when a user accesses the system.

[0703] "Verification process" refers to the process of determining whether the received identification information is accurate and whether the user has the right to access it.

[0704] A "generation mechanism" refers to a device or process that automatically creates learning materials based on given conditions.

[0705] "Learning materials" refer to information resources created to support educational activities.

[0706] A "natural language processing mechanism" refers to a technology that analyzes user inquiries in their original human language form and generates appropriate responses.

[0707] An "inquiry" refers to a question or request for information that a user makes to the system.

[0708] "Response" refers to the answer or information that the system provides in response to a user's inquiry.

[0709] "Operational tendencies" refer to the patterns and frequency of how users utilize the system.

[0710] "Educational improvement suggestions" refer to specific advice and information to make users' current educational activities more effective.

[0711] This invention provides a system for efficiently utilizing ICT in educational settings. The system operates primarily based on communication between a server, terminals, and users. Specifically, terminals can be computers or tablets, and the server can be a cloud-based server system.

[0712] The server receives identification information sent by the user and performs verification using the database. Once verification is complete, the user's session is established, and other functions become available. For generating learning materials, software called a generative AI model is used to automatically create content according to the user's requests. Prompts are used during the generation process to provide more specific material content.

[0713] The terminal generates prompt messages based on user input and sends them to the server. For example, when creating math teaching materials for elementary school students, a prompt message such as "Please create basic math teaching materials for elementary school students. The content should be addition and subtraction, and it should be fun to use with lots of illustrations" might be generated. This prompt message is processed by the server's AI model, and the specified teaching materials are created.

[0714] Furthermore, the server utilizes natural language processing technology to analyze user inquiries. When a user enters a question about an ICT tool, the terminal sends that question to the server. The server analyzes the question, consults its knowledge base, and generates an appropriate response. This response is quickly returned to the terminal, allowing the user to obtain information in real time.

[0715] The server also continuously records user activity history and analyzes it to generate suggestions for educational improvements. This analysis reveals which tools are used most frequently and what problems occur often, and based on this, it provides appropriate teaching materials and training information.

[0716] In this way, the system supports educational activities and enables teachers and support staff to create teaching materials and solve problems more efficiently.

[0717] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0718] Step 1:

[0719] The user enters their login information using their device. This information includes a user ID and password. The entered information is encrypted within the device and securely transmitted to the server.

[0720] Step 2:

[0721] The server compares the received login information with the database and performs user verification. If the database response confirms the user's existence and the matching of the authentication information, it generates a success message and sends it to the terminal. Upon receiving the success message, the user can proceed to the next step.

[0722] Step 3:

[0723] The user enters a prompt message via the device to create learning materials, and the device sends that prompt message to the server as a request for the generation AI model. For example, a prompt message such as "Please create basic math learning materials" might be entered.

[0724] Step 4:

[0725] The server inputs the received prompt message into the generating AI model, which automatically generates training materials based on the specified conditions. During this process, the generating model understands the request through natural language processing and creates and outputs corresponding text and images. The generated materials are then sent back to the terminal in digital format.

[0726] Step 5:

[0727] If a user has a question about how to use an ICT tool, they type the question into their terminal and send it to the server. The question is in natural language format and includes content such as, "How do I use this function of this tool?"

[0728] Step 6:

[0729] The server uses natural language processing technology to analyze the input question and generates an appropriate response by referring to a knowledge database. The generated response is immediately sent to the terminal, and the user can resolve their question by confirming it.

[0730] Step 7:

[0731] The server records operation history and periodically analyzes it to understand user usage trends. Based on the analysis, educational improvement suggestions are generated and sent to the user via the terminal. This allows users to receive feedback to improve their own educational approach.

[0732] (Application Example 1)

[0733] 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".

[0734] In modern brick-and-mortar stores, customers need to obtain information about products and services quickly and accurately. Furthermore, there is a lack of effective means for sales staff to provide information efficiently and improve customer satisfaction. Continuous staff training utilizing customer response records is also a challenge. There is a need for a system that can address these issues.

[0735] 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.

[0736] In this invention, the server includes means for quickly acquiring information using a portable information terminal used by the user, means for analyzing user questions using a natural language processing device and generating responses, and means for recording the user's response history and optimizing further information provision. This enables sales staff in physical stores to provide accurate and timely information to customers and improve services that meet specific needs.

[0737] A "user" is an individual or organization that acquires or provides information through this system.

[0738] "Authentication information" refers to identification data used by users when accessing the system, and includes usernames, passwords, and other security information.

[0739] "Authentication processing" is the process of verifying the user's identity using received authentication information and determining whether or not to grant access.

[0740] A "generation device" is a computer system that automatically creates information resources based on requests.

[0741] "Information resources" refer to data or materials provided in response to user requests, and exist in various forms.

[0742] A "natural language processing system" is a technology or device that enables computers to understand and analyze language that humans normally use.

[0743] A "response" is information or an answer generated in response to a question from a user.

[0744] "Recording actions" is the process of saving a history of how a user interacts with the system.

[0745] "Data analysis" is the process of extracting useful information from collected data and identifying patterns.

[0746] An "educational improvement proposal" is advice or a plan based on analysis results aimed at improving users' skills or optimizing methods.

[0747] A "portable information terminal" is an electronic device that is easy to carry and can acquire information via communication.

[0748] "Recording response history" means saving the details of the responses provided to the user.

[0749] "Optimizing information provision" is the process of providing the most relevant information based on the user's past behavior and requests.

[0750] As a form for implementing the invention, this system is an information provision and analysis system designed to support sales staff in physical stores. Efficient information transmission and customer service are achieved through the collaboration of a server, terminal, and user.

[0751] The server receives authentication information from the portable information terminal used by the salesperson and performs appropriate authentication processing. Once authentication is complete, the salesperson communicates with the server in real time, and the customer's questions are processed by a natural language processing system. This system utilizes OpenAI's GPT series and other technologies to analyze the collected questions, automatically generate appropriate responses, and send them to the terminal.

[0752] The salesperson, as a user of the system, uses the responses received to explain things to the customer and provide detailed information about the products and services. This allows the salesperson to answer customer questions quickly and accurately.

[0753] Furthermore, the server records the history of responses made by salespeople and performs data analysis. Based on this, the server optimizes information delivery by suggesting more effective methods of providing information and generating training materials for salespeople. This improves the quality of employee training.

[0754] For example, if a customer asks, "What are the features of this product?", the salesperson enters the question into the system using a mobile device. The system quickly analyzes the question and generates a response such as, "This product has an energy-saving design and a long-lasting battery." An example of a prompt could be, "Please provide information so that the customer can understand the features of this product."

[0755] As described above, this invention provides an effective means for improving the information provision capabilities of sales staff in physical stores.

[0756] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0757] Step 1:

[0758] The terminal receives the user's authentication information and sends it to the server. The input here is a username and password, and the server performs an authentication process using this authentication information as data calculation. The terminal receives a response indicating whether the authentication was successful or failed.

[0759] Step 2:

[0760] After successful authentication, the user inputs a question from the customer using a terminal. The input sent from the terminal to the server is a question written in natural language. The server receives the input question and converts it into a prompt sentence format using a natural language analyzer. Through this process, the question data is analyzed and processed, forming the input necessary for response generation.

[0761] Step 3:

[0762] The server sends data to a generative AI model (e.g., the GPT series) using prompt messages. Based on this input, the model performs data calculations and generates human-readable response messages. The output is a specific answer to the user's question.

[0763] Step 4:

[0764] The generated response is sent from the server to the terminal. The terminal receives it and displays it to the user (salesperson). The input here is the generated response, and the terminal processes the data to prepare it for the salesperson to explain to the customer. The output is the response information in a format that the user can use immediately.

[0765] Step 5:

[0766] The user provides information to the customer based on the response received. Once the customer interaction is complete, the terminal sends the interaction history to the server. This history is later analyzed for system optimization. The input is interaction history data, which is analyzed on the server and output as basic data for optimizing information provision and creating training materials.

[0767] 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.

[0768] This invention combines an emotion engine with an ICT support system for educational activities, aiming to provide more personalized educational support by recognizing users' emotions and utilizing that information. This system enables real-time emotion analysis through the emotion engine while exchanging information between the server, terminal, and user.

[0769] To operate the system, users first log in on their terminal and send the necessary authentication information to the server. The server then performs authentication based on this information and verifies the user's identity. After authentication is complete, users can make various requests through their terminal. For example, several functions are available, such as automatic generation of educational materials and asking questions about ICT tools.

[0770] The emotion engine acquires emotional data from users as they operate their devices, and the server recognizes their emotional state based on this data. The emotion engine identifies the user's emotions through speech recognition, text analysis, and analysis of user behavior patterns. The results of this emotion analysis are used to adjust the content of learning materials and change the format of responses.

[0771] For example, if a user is confused and stressed by operating a complex ICT tool, the emotion engine recognizes that emotion. Based on this information, the server provides more user-friendly and concise explanations. Furthermore, for particularly complex content, it generates step-by-step instructional materials to reduce the user's burden.

[0772] Furthermore, data collected by the emotion engine is logged along with user behavior and used for data analysis on the server. Based on the results of this analysis, personalized educational improvement suggestions are generated. These suggestions, which take into account the user's emotions, allow teachers to improve and adapt their lessons more flexibly.

[0773] In this way, the introduction of an emotion engine elevates the system beyond mere technical support, enabling emotionally resonant educational support. This creates a more comfortable and effective educational environment.

[0774] The following describes the processing flow.

[0775] Step 1:

[0776] The user displays a login screen on their device and enters their username and password. The device then sends this information to the server.

[0777] Step 2:

[0778] The server authenticates the user by matching the received authentication information against the database. If approved, the server generates an authentication token and returns it to the device.

[0779] Step 3:

[0780] Before the user generates learning materials or submits questions, the device activates its emotion engine and begins analyzing the user's voice, input, and operation patterns.

[0781] Step 4:

[0782] The server receives user emotion data analyzed by the emotion engine. This data includes the emotional state the user is expressing (e.g., stress, anxiety, satisfaction, etc.).

[0783] Step 5:

[0784] The user requests the generation of educational materials from their device. Specifically, they select and input the type and content of the educational materials they want to generate.

[0785] Step 6:

[0786] The device sends this request to the server. Based on the request content and sentiment data, the server uses a generator to produce the most suitable educational materials.

[0787] Step 7:

[0788] The server sends the generated learning materials, along with comments and advice tailored to the user's emotional state, to the user's device. The user reviews this and incorporates it into their educational activities.

[0789] Step 8:

[0790] When a user enters a question about an ICT tool, the terminal sends it to the server. The server uses a natural language processing system to analyze the question.

[0791] Step 9:

[0792] The server generates an appropriate response based on the analysis results. In doing so, it takes into account the user's emotional state and adjusts the format and tone of the response accordingly.

[0793] Step 10:

[0794] Based on user activity history and sentiment data, the server records behavior and stores it in a database. This data is used for later analysis.

[0795] Step 11:

[0796] The server analyzes the recorded data and generates emotionally sensitive educational improvement suggestions for the user. These suggestions are then presented to the user via their device.

[0797] (Example 2)

[0798] 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".

[0799] In recent years, there has been a growing demand for individualized support in educational activities, but conventional systems have difficulty adapting to the individual emotional states of users. Furthermore, there are challenges in generating educational improvement suggestions that take users' emotions into account in real time.

[0800] 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.

[0801] In this invention, the server includes means for receiving authentication information from a user and performing authentication processing; means for automatically generating educational materials using a generation device; means for analyzing questions from the user and generating responses using a natural language processing device; means for analyzing the user's emotions using an emotion engine and reflecting that data in the educational materials; and means for recording the user's behavior and emotion data and analyzing that data. This makes it possible to provide personalized educational support that is tailored to the user's emotional state.

[0802] "Authentication information" refers to data used to verify the user's identity, and includes passwords, user IDs, and other similar information.

[0803] "Authentication processing" is the process of verifying the user's identity based on the authentication information received.

[0804] A "generation device" refers to a mechanism or program for automatically creating educational materials.

[0805] "Educational materials" refer to information and learning materials intended for use by users in their studies, and may be provided in multiple formats.

[0806] A "natural language processing system" refers to the technology and software used to analyze user questions and generate appropriate responses.

[0807] An "emotion engine" refers to a tool or algorithm that analyzes a user's voice and behavioral data to identify their emotional state.

[0808] "Emotional data" refers to information about the user's emotional state, obtained by the emotion engine.

[0809] "Behavioral data" refers to information about user actions and behavior within the system.

[0810] "Educational improvement suggestions" are specific instructions or proposed changes provided to improve users' learning.

[0811] The "modes for carrying out the invention" described herein are systems that realize emotion-based individualized support in educational activities. This system consists of information exchange between a server, a terminal, and a user.

[0812] The user initiates the login process on their device, entering authentication information such as a password and user ID. The device sends this authentication information to the server, which then performs an authentication process to verify the user's identity based on the received information. This allows the user to access various functions on the system.

[0813] A generation device is used to automatically generate educational materials. This device includes a generation AI model and automates the process of creating educational materials. For example, if a prompt such as "Generate educational materials on mathematical concepts that the user wants to understand from the basics" is entered into the generation AI model, the corresponding educational materials will be automatically generated.

[0814] The natural language processing (NLP) system plays the role of analyzing questions from the user. When a user inputs a question through a terminal, the NLP analyzes the question, and the server generates an appropriate response. This allows the user to learn efficiently.

[0815] The emotion engine operates to recognize the user's emotional data. The device sends voice and behavioral data acquired during user interaction to the emotion engine. The emotion engine analyzes this data using speech recognition and behavioral pattern analysis to identify the user's emotional state.

[0816] Furthermore, the server adjusts the content of educational materials according to the user's emotional state. This emotion-based adjustment of materials makes it possible to create a more user-friendly learning environment.

[0817] For example, if the emotion engine determines that a user is confused and stressed by operating an ICT tool, the server will use that emotion data to provide a more concise and user-friendly explanation. Based on the emotional state, the prompt will read, "Generate a simple guide to the ICT tool that is easy for the user to understand," and appropriate materials will be created. This allows the user to use the system more comfortably.

[0818] Through this approach, it becomes possible to provide personalized educational support that is attentive to the user's emotions, resulting in an effective learning experience.

[0819] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0820] Step 1:

[0821] The user opens a secure login screen on their device and enters their username and password as authentication credentials. The entered authentication credentials are sent from the device to the server. The server verifies the user's identity by comparing the credentials with a database. If the verification is successful, the server sends a login success notification to the device, and the user is granted access to the system.

[0822] Step 2:

[0823] To utilize the system's built-in material generation function, users form a generation request from their terminal. This request includes the target learning content and corresponding prompts for the generation AI model. The terminal sends this to the server, which processes the prompts via a generation device and automatically generates the corresponding educational materials. This process searches for relevant information from the input prompts and outputs the educational materials based on that information.

[0824] Step 3:

[0825] Users input their questions or points of confusion using a terminal. These questions are sent to a natural language processing system. The server receives this input, performs natural language processing to analyze the intent of the question, and generates an answer based on the results. The generated answer is then returned to the user via the terminal. This allows users to receive immediate answers to their specific questions.

[0826] Step 4:

[0827] While the user is operating the device, it sends voice and behavioral data to the emotion engine in real time. The emotion engine on the server processes this data and analyzes the user's emotional state. Through voice recognition and behavioral pattern analysis, it identifies emotions such as whether the user is stressed or relaxed, and records this as emotion data in the engine.

[0828] Step 5:

[0829] The server adjusts the content of educational materials in real time based on the acquired sentiment data. If a user is experiencing stress, it can generate more accessible and concise materials, and present them in a step-by-step format. Specific examples of this include rewriting the text within the materials in simpler terms and adding audio guides.

[0830] Step 6:

[0831] The server logs user learning progress and sentiment data, and performs data analysis based on this information. The results of this analysis are used to analyze user learning patterns and generate personalized educational improvement suggestions. Teachers can then utilize these suggestions to provide customized educational support tailored to each user.

[0832] (Application Example 2)

[0833] 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".

[0834] Traditional educational support systems failed to consider the feelings of users, providing only uniform support and thus failing to achieve effective individualized education. Furthermore, when users experienced anxiety or stress, appropriate support and security measures were not always provided immediately.

[0835] 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.

[0836] In this invention, the server includes a device that receives authentication data from users and performs authentication processing, a device that automatically generates educational materials, a device that analyzes user inquiries using a natural language processing device and generates responses, and a device that analyzes the user's emotional state in real time and adjusts responses and suggestions based on that analysis. This enables educational support and security measures tailored to the individual emotional state of each user.

[0837] "Authentication data" refers to information necessary for verifying a user's identity and granting access permissions.

[0838] "Educational materials" are content provided to support users' learning and instruction in educational activities.

[0839] A "natural language processing system" is a technology that analyzes text input in human language to understand its meaning and intent.

[0840] A "response" is a reply or instruction generated in response to a user's question or request.

[0841] "Action" refers to all actions and operations that a user performs when interacting with a system and device.

[0842] "Emotional state" refers to information that indicates the user's psychological state or mood.

[0843] "Real-time analysis" is a process that evaluates data and obtains results immediately without delay.

[0844] "Security measures" refer to the measures and actions taken to guarantee the safety of users.

[0845] This invention provides an educational support system equipped with an emotion engine. The system is primarily operated around a server, a terminal, and a user. First, the terminal receives authentication data from the user, and the server verifies the user's identity based on this authentication data. If authentication is successful, the terminal automatically generates educational materials via a generation device. This allows the system to provide users with educational content in various formats, such as text and audio information.

[0846] The server also uses a natural language processing system to analyze user inquiries and generate appropriate responses in real time. These responses are immediately presented to the user via their terminal, supporting their learning and problem-solving. Furthermore, the server continuously records user activity, analyzes this data in detail, and provides suggestions for further educational improvements. This results in a personalized learning experience for the user.

[0847] Through interaction with the user, the emotion engine analyzes the user's emotional state in real time. This analysis technology utilizes analytical models such as the Google Cloud Natural Language API. This allows for immediate detection when the user is feeling anxious or stressed, and enables the system to adjust responses and suggestions accordingly.

[0848] For example, if the emotion engine detects that a user is feeling anxious while out alone at night, the system will display a relaxing message on the device to provide reassurance. It can also automatically send alerts to emergency contacts if necessary.

[0849] An example of a prompt in a generative AI model is the question, "If you were out alone at night and felt anxious, what kind of support would make you feel at ease?" In this way, it is possible to achieve flexible responses that are tailored to the user's emotional state.

[0850] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0851] Step 1:

[0852] The terminal receives authentication data entered by the user. The terminal sends the received authentication data to the server, which verifies the user's identity based on the authentication data. The server checks the integrity of the authentication data and outputs a success or failure result.

[0853] Step 2:

[0854] Upon successful authentication, the server activates the generation device and automatically generates educational materials in the required format. For data processing, it retrieves materials from the system's learning content database and generates educational materials in a format suitable for the content (e.g., text, audio). The generated materials are sent to the terminal and displayed on the user's learning screen.

[0855] Step 3:

[0856] When a user operates a terminal and enters a question, the terminal sends the question data to the server. The server analyzes the question using a natural language processing system, extracts keywords, and performs contextual analysis to generate an appropriate response. The generated response is sent back to the terminal and displayed to the user.

[0857] Step 4:

[0858] The terminal collects user operation data and sends the operation log to the server. Based on the operation log, the server analyzes the user's behavior patterns and performs data calculations to detect changes. As a result, it creates educational improvement suggestions and provides feedback to enhance the user's learning experience.

[0859] Step 5:

[0860] While the user is using the device, it collects data related to the user's emotional state in real time. The collected data is sent to a server and analyzed by an emotion engine. Sentiment analysis is performed using data such as voice tone and input speed, and content and responses are adjusted depending on whether the user is experiencing anxiety or stress. For example, appropriate measures are taken, such as providing a relaxing message.

[0861] Step 6:

[0862] If an anomaly is detected as a result of sentiment analysis, the device will be automatically configured to send an alert to emergency contacts. This will take action to enhance the user's sense of safety and security. After that, the success of the security response will be confirmed and recorded in the log.

[0863] 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.

[0864] 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 those described above. 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 shown 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.

[0865] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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."

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] The following is further disclosed regarding the embodiments described above.

[0885] (Claim 1)

[0886] A means for receiving authentication information from a user and performing authentication processing,

[0887] A means for automatically generating educational materials using a generation device,

[0888] A means for analyzing user questions and generating responses using a natural language processing device,

[0889] Means for sending the generated response to the user,

[0890] A means of recording user behavior and analyzing that data,

[0891] A system that includes means for generating educational improvement suggestions for users based on analysis results.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the aforementioned educational materials are provided in multiple media formats.

[0894] (Claim 3)

[0895] The system according to claim 1, wherein the response is generated in real time.

[0896] "Example 1"

[0897] (Claim 1)

[0898] A means for receiving identification information from a user and performing verification processing,

[0899] A means for automatically generating learning materials using a generation mechanism,

[0900] A means for analyzing user inquiries and generating responses using a natural language processing mechanism,

[0901] Means for communicating the generated response to the user,

[0902] A means of recording user behavior and analyzing that information,

[0903] A means for generating educational improvement suggestions for users based on analysis results,

[0904] A system that includes means to analyze user behavior patterns and provide effective training content.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein the learning materials are provided in a variety of formats.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the above response is generated immediately.

[0909] "Application Example 1"

[0910] (Claim 1)

[0911] A means for receiving authentication information from a user and performing authentication processing,

[0912] A means for automatically generating information resources using a generation device,

[0913] A means for analyzing user questions and generating responses using a natural language processing device,

[0914] Means for sending the generated response to the user,

[0915] A means of recording user behavior and analyzing that data,

[0916] A means of generating improvement suggestions for users based on the analysis results,

[0917] A means by which users can quickly obtain information using portable information terminals,

[0918] A system that includes means for recording user response history and optimizing the provision of further information.

[0919] (Claim 2)

[0920] The system according to claim 1, wherein the information resources are provided in multiple media formats.

[0921] (Claim 3)

[0922] The system according to claim 1, wherein the response is generated in real time.

[0923] "Example 2 of combining an emotion engine"

[0924] (Claim 1)

[0925] A means for receiving authentication information from a user and performing authentication processing,

[0926] A means for automatically generating educational materials using a generation device,

[0927] A means for analyzing user questions and generating responses using a natural language processing device,

[0928] Means for sending the generated response to the user,

[0929] A means of analyzing users' emotions using an emotion engine and reflecting that data in educational materials,

[0930] A means of recording user behavior and emotional data and analyzing that data,

[0931] A system that includes means for generating educational improvement suggestions based on the user's emotional state, using analysis results.

[0932] (Claim 2)

[0933] The system according to claim 1, wherein the aforementioned educational materials are provided in multiple media formats.

[0934] (Claim 3)

[0935] The system according to claim 1, wherein the response is generated in real time.

[0936] "Application example 2 when combining with an emotional engine"

[0937] (Claim 1)

[0938] A device that receives authentication data from users and performs authentication processing,

[0939] A device that automatically generates educational materials using a generation device,

[0940] A device that uses a natural language processing system to analyze user inquiries and generate responses,

[0941] A device that transmits the generated response to the user,

[0942] A device that records user actions and analyzes that data,

[0943] A device that generates educational improvement suggestions for users based on analysis results,

[0944] A system that includes a device that analyzes the user's emotional state in real time and adjusts responses and suggestions based on that analysis.

[0945] (Claim 2)

[0946] The system according to claim 1, wherein the aforementioned educational materials are provided in multiple media formats, and data obtained from sentiment analysis is applied to improve security.

[0947] (Claim 3)

[0948] The system according to claim 1, wherein the response is generated in real time and an automatic alert is issued based on an abnormal emotional state. [Explanation of Symbols]

[0949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving authentication information from a user and performing authentication processing, A means for automatically generating educational materials using a generation device, A means for analyzing user questions and generating responses using a natural language processing device, Means for sending the generated response to the user, A means of recording user behavior and analyzing that data, A system that includes means for generating educational improvement suggestions for users based on analysis results.

2. The system according to claim 1, wherein the aforementioned educational materials are provided in multiple media formats.

3. The system according to claim 1, wherein the response is generated in real time.

Images