system

Abstract

We provide the system. [Solution] A means of providing an interface for users to input information about historical subjects and select subjects of interest, Information processing means via a natural language processing engine for generating a response to the user based on selected historical objects, Output means for adjusting and sending responses and annotations to be displayed to the user, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In historical learning, in the conventional method using teaching materials and exhibits, there is a problem that it is difficult for students and general users to sustain their interest in historical events and figures. Also, there are problems such as difficulty in providing multilingual support and content tailored to individual learning for effectively understanding related information. 【Means for Solving the Problems】 【0005】 This invention provides a system that allows users to interact with historical subjects of their choice. Specifically, it introduces means to provide an interface for users to input information about historical subjects and select subjects of interest. Furthermore, it includes information processing means that generate responses about the selected historical subjects using a natural language processing engine, and output means for displaying these responses to the user. In addition, the system integrates translation means to enable multilingual support and data management means to generate user learning profiles, thereby providing appropriate content tailored to individual learning needs. 【0006】 A "user" is an individual or group that uses the system to obtain information about historical subjects and engage in dialogue with them. 【0007】 A "historical subject" is a collection of information that includes the cultural, social, and political context related to past events or people. 【0008】 An "interface" is a means by which a user inputs information into a system and retrieves the results, and it consists of input areas and selection items on the screen. 【0009】 A "natural language processing engine" is a processing system that generates appropriate responses to user questions and inputs, and includes technologies such as language analysis and generation functions. 【0010】 An "information processing means" is a computational processing unit used to generate answers and information based on historical objects, based on user input. 【0011】 "Output means" refers to a device or function for displaying or providing processed information to the user. 【0012】 "Multilingual support" refers to a feature that enables a system to interact with users in multiple languages, including support from translation engines. 【0013】 "Data management means" refers to technologies that include functions for recording a user's usage history and learning content, generating a learning profile based on that data, and individually optimizing the learning experience. [Brief explanation of the drawing] 【0014】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. 【Embodiments for Carrying Out the Invention】 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc. 【0018】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 This invention provides a system for users to interactively acquire information related to historical objects. The system consists of a server, a terminal, and a user, and each component performs the following functions. 【0036】 First, the user accesses the system using a terminal and selects a historical subject of interest. The terminal receives input information from the user and sends it to the server. 【0037】 The server generates a data model of the selected historical subject based on the information it receives. This data model incorporates a natural language processing engine, which processes user input and generates appropriate responses. The responses are tailored based on the historical context and the characteristics of the selected person. 【0038】 The generated response is sent by the server to the terminal and displayed to the user. Along with the display, annotations and additional information about the relevant historical context are also provided. This is an important element for enhancing the educational effect. 【0039】 The system supports multilingual interaction depending on the user's settings. If the user changes the language, the server translates the response into the specified language and sends it to the terminal in the appropriate format. 【0040】 Furthermore, the terminal sends data from the user's interaction session to the server, which then uses this data to create a user learning profile. This profile is then used when the user revisits the system to provide a personalized learning experience. 【0041】 For example, if a user wants to retrieve information related to "19th-century scientists" from their device, the server will suggest the most relevant scientists and topics for discussion. If the user selects a specific scientist and asks a question, a response based on that scientist's perspective will be generated and displayed to the user through their device. This makes it possible to learn about historical conflicts and scientific discoveries from a practical perspective. 【0042】 In this way, the system aims to make history learning more interactive and meaningful, and to sustainably engage users' interest. 【0043】 The following describes the processing flow. 【0044】 Step 1: 【0045】 The user logs into the terminal and interacts with the interface to select historical subjects of interest. The terminal then sends the selected information to the server. 【0046】 Step 2: 【0047】 The server retrieves a data model of the selected historical object based on the information received from the user. This model includes known information about the object and its relevant historical context. 【0048】 Step 3: 【0049】 The server uses a natural language processing engine to generate responses to user questions and requests. This engine analyzes information within the data model and forms answers in the appropriate context. 【0050】 Step 4: 【0051】 The server attaches annotations and additional historical explanations related to the generated response and sends it to the terminal. This allows the user to gain a deeper understanding. 【0052】 Step 5: 【0053】 The terminal visually displays the responses and annotations sent from the server to the user. The user can then use this information to ask further questions or select other historical subjects. 【0054】 Step 6: 【0055】 When a user utilizes multilingual support, the device notifies the server of the selected language setting. The server translates the response into the specified language and resends it to the device. 【0056】 Step 7: 【0057】 When a user wishes to end a conversation, a command to end the conversation is sent from the terminal to the server. The server saves the conversation history and incorporates it into the user's learning profile. 【0058】 Step 8: 【0059】 Based on the saved learning profile, the server prepares to suggest the most relevant historical subjects and information for the next time the user logs in. 【0060】 (Example 1) 【0061】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0062】 Traditional history learning systems have made it difficult for users to search for specific historical information and efficiently obtain related detailed information. Furthermore, the availability of multilingual information and the provision of customized learning experiences tailored to individual users have been limited. Additionally, interactive systems have struggled to accumulate and utilize individual user learning profiles. 【0063】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0064】 In this invention, the server includes display means for the user to input information related to a specific historical context and select items of interest; analysis means using natural language processing technology to generate a response for the user based on the selected information; and output means for adjusting and communicating the response and supplementary information displayed to the user. This enables efficient information acquisition, multilingual search capabilities, provision of a learning experience tailored to the user, and utilization of the user's learning history to help with future learning. 【0065】 A "user" is an individual or group that operates the system and acquires information, utilizing the functions provided by the system for learning or information retrieval. 【0066】 "Historical context" refers to the social, cultural, and political environment and conditions of a particular period, and forms the basis for understanding the context of historical information. 【0067】 A "display means" is an interface provided for users to visually confirm and select information, and it assists in communication between the system and the user. 【0068】 "Natural language processing technology" is a technique for understanding human language and generating responses, and it is a method used by computers to analyze text data and imitate human speech. 【0069】 An "analysis tool" is a function designed to generate an appropriate response based on received information, and is a process for facilitating data analysis and understanding. 【0070】 "Output means" refers to a method of providing analyzed information or responses to users, and is a mechanism for transmitting messages through visual, auditory, or other means. 【0071】 A "knowledge engineering model" is a theoretical framework for solving concrete problems by utilizing abstract knowledge, and is a technology that is effectively applied in the generation and processing of information. 【0072】 A "learning profile" is personalized learning data built based on a user's past interactions and learning history, and is used to improve their next learning experience. 【0073】 This invention provides a system for users to interactively acquire historical information, and consists of three components: a server, a terminal, and a user. 【0074】 First, the user accesses the system using a terminal. This terminal is a personal computer or mobile device equipped with a browser, which receives user input through its user interface and allows the user to select historical subjects of interest. The entered request is then sent to the server via the internet connection. 【0075】 The server consists of a computing device equipped with a high-performance processor and sufficient memory. The server receives user requests and analyzes the information using a generative AI model (e.g., a large-scale language model). Using natural language processing techniques, it generates a data model based on the input and constructs an appropriate response. The server performs this processing using open-source natural language processing engines or proprietary software. 【0076】 The generated response is sent from the server to the terminal, which then displays it to the user. The response includes annotations and relevant information that supplement the historical context, allowing the user to gain a deeper understanding. For example, if the user enters the prompt, "Please tell me about 19th-century scientists," the server will identify the relevant scientists and provide detailed information about them. 【0077】 Furthermore, the system features multilingual support, allowing the server to translate responses into the user's specified language according to their settings. When the user uses the system again, the terminal sends past conversation data to the server, which then uses this data to create a learning profile for the user. This makes it possible to provide a learning experience that is individually optimized for each user. 【0078】 As a concrete example of its use, by using the prompt "I want to learn more about Renaissance cultural figures," users can obtain information about relevant cultural figures and proceed with their learning based on that information. In this way, the system is a powerful tool for users to deepen their historical knowledge. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The user uses a terminal to enter text-based prompts to identify historical subjects or topics of interest. The terminal receives this input, formats the string data selected by the user, and prepares it for transmission to the server. The prompt text as input data is transferred to the server. 【0082】 Step 2: 【0083】 The server receives a prompt message from the user and begins data analysis using a generative AI model. The server processes the prompt message, searches relevant databases, and extracts appropriate information fragments. During this process, the natural language processing engine analyzes the meaning of the text and prepares to construct a highly relevant response. The output is the determination of the contextual data necessary for response generation. 【0084】 Step 3: 【0085】 The server generates a specific natural language response based on contextual data created by the generative AI model. During this process, data processing is performed to improve the quality of the response by referring to historical data and supplementary information. The generated response is adjusted according to the user's settings. The generated response is then provided as the final output. 【0086】 Step 4: 【0087】 The server sends the generated response to the terminal. The terminal visually displays the received data to the user, also providing corresponding historical context and annotation information on the screen. Specifically, the terminal renders the received data in an appropriate format, such as HTML, to make the information easy for the user to understand. In this process, the output response and annotated data are presented on the user's screen. 【0088】 Step 5: 【0089】 After the interaction ends, the device sends user interaction and selection data as feedback to the server. The server receives the transmitted data and manages it to update the user's unique learning profile. The saved learning data is used to provide personalized services in the next interaction. As output, the latest learning profile is generated, enabling an optimized experience for the user. 【0090】 (Application Example 1) 【0091】 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." 【0092】 Traditional history education often relies on static materials, making it difficult to maintain user interest. Furthermore, providing history education in multiple languages ​​and individually optimized learning experiences has been challenging, highlighting the need to address diverse learning needs. 【0093】 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. 【0094】 In this invention, the server includes a user interface means for the user to input historical information and select items of interest, a natural language processing processor means for generating responses to the user based on the selected historical items, and means for providing historical information via voice in an educational artifact and facilitating learning through dialogue. This allows the user to have an interactive and engaging learning experience and enables history learning that meets diverse learning needs. 【0095】 A "user interface means" is an interface used by a user to input historical information and select items of interest. 【0096】 A "natural language processing processor" is a device that generates a response to the user based on selected historical items. 【0097】 An "educational artifact" is a device that provides historical information through audio and facilitates learning through dialogue. 【0098】 An "information output means" is a device for adjusting and transmitting responses and annotations displayed to the user. 【0099】 "Multilingual translation" refers to a translation function that enables the acquisition of information in multiple languages. 【0100】 A "data profile" is a user-specific learning profile generated based on conversational data saved for individual learning. 【0101】 To implement this invention, the system is constructed based on the following configuration: The server receives information from the user and generates a response based on selected historical items using a natural language processing engine. The server sends the generated response to the terminal and provides it to the user. The server can also utilize a multilingual translation function to generate a response in the language selected by the user. 【0102】 The specific hardware for this system will be an educational artifact equipped with a microphone for voice input and a speaker for output. The software will be developed using Python, controlled by the Robotics Operating System (ROS). The API will utilize Google Cloud's Speech-to-Text to convert speech to text and Dialogflow for natural language processing. A generative AI model will be used to generate responses. The server will then convert the generated responses into speech using Google Cloud Text-to-Speech and provide them to the user via a terminal. 【0103】 For example, if a user asks the device, "Tell me about 19th-century scientists," the server performs natural language processing and generates a response such as, "Representative scientists who were active in the 19th century include A, among others. Which scientist would you like to learn about?" 【0104】 An example of a prompt would be: "Please provide information about 19th-century scientists. Please include specific examples and the impact of those scientists." 【0105】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0106】 Step 1: 【0107】 The user enters a question via voice into the educational artifact. This voice input is then sent to the server. 【0108】 Step 2: 【0109】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text. The input is audio data, and the output is text data. 【0110】 Step 3: 【0111】 The server sends the converted text data to Dialogflow, where it interprets the user's intent. The input is text data, and the output is data about the user's intent and requests. 【0112】 Step 4: 【0113】 The server generates an appropriate response using a generative AI model based on the interpreted intent data. The input is the user's intent data, and the output is a response in natural language. 【0114】 Step 5: 【0115】 The server converts the generated response into speech data using Google Cloud Text-to-Speech. The input is text response data, and the output is speech data. 【0116】 Step 6: 【0117】 The server sends audio data to the terminal and provides it to the user as an audio response. The terminal plays the audio response, thereby conveying the information to the user. 【0118】 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. 【0119】 This invention provides a system that, when a user interacts with a historical object, combines an emotion engine to recognize the user's emotional state and optimize the content of the interaction. 【0120】 This system consists primarily of a server, a terminal, and a user. The user logs into the terminal and selects a historical subject of interest. The terminal sends the user's selection information to the server, retrieving the necessary data from it. The server invokes a data model related to the selected historical subject and uses a natural language processing engine to generate a response based on the user's question. 【0121】 The server is equipped with an emotion engine that analyzes features such as the user's voice tone, input speed, and selected phrases to recognize the user's emotional state in real time. This emotion information influences the generated response, creating a customized response with a tone and content that matches the user's emotions. 【0122】 For example, if a user is engaging in a conversation related to a historical war, and feelings of anger or sadness are detected, the server will generate a correspondingly careful and empathetic response. On the other hand, if the user is expressing excitement or joy, the response will be more lively and positive. 【0123】 The generated response and its annotations are sent to the terminal and displayed visually to the user. Sentimental information may also be provided as visual feedback, allowing the user to understand how the system is responding based on their own emotional state. 【0124】 Furthermore, this system creates a learning profile that reflects the user's emotional state and uses that profile in subsequent interactions to provide the user with the most meaningful and stress-free learning experience. In this way, users can enjoy a rich learning environment optimized for their own emotional state, going beyond mere information acquisition. 【0125】 The following describes the processing flow. 【0126】 Step 1: 【0127】 The user accesses the terminal and logs into the system. After logging in, they select a historical subject of interest from the interface on the terminal. Based on this, the terminal sends the selection information to the server. 【0128】 Step 2: 【0129】 Based on the information received, the server loads data on the selected historical subject. This data includes background information related to the subject and response patterns to common questions. 【0130】 Step 3: 【0131】 The user enters a question about a historical subject from their terminal. The terminal sends this question to the server, which uses a natural language processing engine to generate an appropriate response. 【0132】 Step 4: 【0133】 Simultaneously, if the device receives voice input from the user, it sends that voice data to the server. The server uses an emotion engine to analyze the user's emotions based on factors such as the tone and speed of their voice. 【0134】 Step 5: 【0135】 The server adjusts the tone and content of the responses it generates based on the user's emotional state. For example, if the emotion engine detects anger in the user, the server will select a response with a calm and empathetic tone. 【0136】 Step 6: 【0137】 The server sends a reconciled response, along with annotations and additional information corresponding to the analyzed sentiment, to the terminal. The terminal then displays these to the user. 【0138】 Step 7: 【0139】 If the user continues the conversation, the server records the user's sentiment history to help analyze new questions. If the conversation ends, the data from the entire conversation is reflected in the user's learning profile, which can be used to improve future conversations. 【0140】 Step 8: 【0141】 The next time you log in, the server will be prepared to recommend individually optimized topics and relevant information to your device based on your past sentiment history and learning profile. 【0142】 (Example 2) 【0143】 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". 【0144】 The problem that this invention aims to solve is to provide a system that can recognize a user's emotions and generate responses adapted to their emotional state in interactions with historical or other subject areas. Current dialogue systems lack the functionality to effectively customize responses according to the user's emotions, which can result in an unsatisfactory user experience. Furthermore, there is the problem that it is not easy to generate individualized learning profiles based on the user's past interactions. 【0145】 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. 【0146】 In this invention, the server includes means for providing a dialogue interface for the user to input information about a target area and select an area of ​​interest; information processing means via natural language processing technology for generating a response to the user based on the selected target area; and means including an emotion analysis engine for acquiring and analyzing the user's emotional state. This enables the generation of adaptive responses in accordance with the user's emotions and the provision of a learning experience optimized for each user. 【0147】 A "user" refers to an individual or group that interacts with a system. 【0148】 A "target area" refers to a historical or other topic that the user is interested in and wishes to learn about through interaction with the system. 【0149】 A "conversational interface" refers to a user interface that a user uses to input information and select items of interest. 【0150】 "Natural language processing technology" refers to a technique used to generate appropriate responses based on user input, and it describes the computer's ability to understand and process human language. 【0151】 "Information processing means" refers to methods and means for processing information related to a selected target area and generating a response in accordance with the user's request. 【0152】 A "sentiment analysis engine" refers to the technology and devices used to acquire and analyze a user's emotional state. 【0153】 "Response" refers to the provision of information or instructions that a system issues based on user input. 【0154】 A "personalized learning profile" refers to a user-specific information profile created based on the user's past conversation data and emotional information, designed to provide a personalized experience in subsequent interactions. 【0155】 The system in this invention consists primarily of a user, a terminal, and a server, which work together to optimize interaction with the user. Specific methods for carrying out the invention include the following: 【0156】 The terminal provides an interface for users to log into the system and select a target area. When a user selects a topic of interest, such as "Medieval European Culture," that information is sent from the terminal to the server. The terminal can be implemented as a web browser or a dedicated application, making it easy to connect with users. 【0157】 The server operates to retrieve necessary information from relevant databases based on information received from the terminal. Natural language processing techniques are used for information processing. Specifically, natural language processing libraries are used to implement generative AI models. For example, a generative AI model is used to generate responses based on user questions. In addition, a sentiment analysis engine analyzes user input and voice data to identify emotional states. For this analysis, acoustic analysis software to extract voice features and natural language processing tools to analyze the sentiment of text are used. 【0158】 The responses generated based on the analysis are adjusted to match the user's emotions in terms of tone and content. For example, if the emotion analysis engine determines that the user is excited by a question about medieval European culture, the response will be customized to a lively and engaging tone. 【0159】 Ultimately, the server sends a refined response back to the terminal, which then displays it in the user interface. This process allows the user to receive information tailored to their emotions and gain new learning opportunities. 【0160】 An example of a prompt might be, "Please tell me more about medieval European culture. I'd also like to hear about any episodes that particularly excited you." In this way, the system continuously analyzes the user's emotional state, making the learning experience more personalized and meaningful. 【0161】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0162】 Step 1: 【0163】 The user logs into the device. The user enters login information through an interface accessible on the device. The device receives this information, queries the database to authenticate the user, and outputs the authentication result. 【0164】 Step 2: 【0165】 The user selects a subject area of ​​interest, such as "Medieval European Culture," through the terminal's interface. The selected information is treated as input data, and the terminal sends it to the server. After transmission, the server receives this selection information and uses it as a basis for retrieving related data. 【0166】 Step 3: 【0167】 Based on the selection information received by the server, it retrieves relevant historical data from the database. The server then queries the database, processes the necessary information, and outputs it. This processing includes filtering and organizing the data. 【0168】 Step 4: 【0169】 The server analyzes the acquired data using a generative AI model. Specific questions and requests from the user are input to the generative AI model as prompts, and the model outputs a response. This results in a response that includes detailed explanations that meet the user's expectations. 【0170】 Step 5: 【0171】 The server utilizes an emotion analysis engine to analyze the user's emotional state. It receives user input text and voice data, analyzes voice features and emotion-based keywords in the text, and outputs the user's emotional state. This analysis result is used to customize the tone of the response. 【0172】 Step 6: 【0173】 Based on the analysis results, the server adjusts the generated response, customizing it to match the user's emotions in terms of tone and content. This data processing involves restructuring the text and adjusting the tone. The adjusted response data is then prepared and output. 【0174】 Step 7: 【0175】 The server sends the adjusted response data to the terminal. The terminal receives this data and displays it in the user interface. This allows the user to visually confirm information that matches their emotions. 【0176】 Step 8: 【0177】 The user receives information displayed through the device, deepening their understanding and asking new questions. This information reception acts as new input for the next interaction. The user's responses and actions are used as new input information in the next interaction. 【0178】 (Application Example 2) 【0179】 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". 【0180】 When users access target information and optimize their learning experience through interaction with that information, it is necessary to dynamically adjust the content of the dialogue while considering the user's emotional state. However, existing systems lack the means to grasp user emotions and generate appropriate responses based on them, resulting in a limited quality of user experience. This invention aims to solve these problems and provide a more meaningful and user-friendly learning environment. 【0181】 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. 【0182】 In this invention, the server includes an input / output device for the user to input information about a target and select a target of interest, a data processing device via a natural language processing system for generating a response to the user based on the selected target, and an emotion analysis device for analyzing the user's voice and input to obtain emotion information. This makes it possible to recognize the user's emotional state in real time and generate a response accordingly. 【0183】 An "input / output device" is a device that receives user selection information and instructions and provides content to display to the user. 【0184】 A "natural language processing system" is a system that provides data processing technology to understand user questions and requests and generate appropriate responses. 【0185】 A "data processing device" is a device that processes necessary information based on a selected object and provides it to the user. 【0186】 An "emotion analysis device" is a device that uses technology to analyze a user's voice or input and detect the user's emotional state. 【0187】 A "language conversion device" is a device that translates dialogue into different languages, enabling users to obtain information in multiple languages. 【0188】 A "data storage device" is a device that saves data generated during a conversation and uses it for future conversations. 【0189】 This invention is a system that enables interactive dialogue with users in a physical store environment. Users input information about objects and select objects of interest using a smartphone or smart glasses. The terminal sends the user's selection information to a server, which retrieves the necessary data. 【0190】 The server generates responses based on selected subjects via a natural language processing system. Specifically, it uses software such as Google Cloud's Natural Language API and IBM's Watson® Tone Analyzer to analyze the emotional state of voice and text received from the user. This allows the emotion analysis device to detect the user's emotions in real time and provide appropriate responses accordingly. 【0191】 Furthermore, it utilizes the GPT model from OpenAI®, a generative AI model, to generate natural language responses that match the user's questions. The generated responses are visually displayed to the user on the device, allowing the user to have a more meaningful conversational experience that is appropriate to their emotional state. 【0192】 This system is also used to generate learning profiles for specific users. By analyzing data accumulated through interactions with the user and applying it to subsequent interactions, it efficiently provides users with customized information. 【0193】 As a concrete example, when using a museum exhibit, it is possible to use the app to ask a question such as, "Could you tell me more about the background of this exhibit?" The system will respond in a way that is tailored to the user's interests and emotions, such as, "This exhibit was created in XX year, and it has the following background from that era. Is there anything else you would like to know?" 【0194】 An example of a prompt for a generative AI model might be something like, "Please tell me about the historical background of the exhibit. The questioner is interested and is seeking detailed information." 【0195】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0196】 Step 1: 【0197】 The user uses a smartphone or smart glasses to select a historical subject of interest. The device receives the user's selection information as input and sends this information to the server. This prepares the server to begin retrieving the necessary data. 【0198】 Step 2: 【0199】 The server retrieves relevant data based on the target information sent by the user. Here, it extracts detailed information related to the target from the database and performs preprocessing for natural language processing. The output of this process becomes the input data for the natural language processing engine. 【0200】 Step 3: 【0201】 On the server, the natural language processing system uses a natural language processing engine to generate responses to questions posed by the user. Here, a generative AI model is utilized to output natural language responses that match the questions, and these responses are then sent to the next processing step. 【0202】 Step 4: 【0203】 The emotion analyzer acquires emotion data from the user's voice or text input and adjusts the response based on that emotion. In this step, the user's input is analyzed and the tone and content are converted to suit the emotion. The input to this process is the user's emotion information, and the output is the adjusted response. 【0204】 Step 5: 【0205】 The server sends a coordinated response to the terminal, which then displays the response to the user. The output device visually presents the response to the user, providing a means for the user to verify the information. This allows the user to obtain information about historical subjects of interest in a form that is optimally tailored to their emotions. 【0206】 Step 6: 【0207】 The server stores data obtained through user interaction in a data storage device and uses it to inform future interactions and learning profiles. This allows for more customized information to be provided to the user in subsequent interactions. The input is the interaction data, and the output is the updated learning profile. 【0208】 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. 【0209】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0210】 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. 【0211】 [Second Embodiment] 【0212】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0213】 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. 【0214】 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). 【0215】 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. 【0216】 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. 【0217】 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). 【0218】 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. 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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". 【0224】 This invention provides a system for users to interactively acquire information related to historical objects. The system consists of a server, a terminal, and a user, and each component performs the following functions. 【0225】 First, the user accesses the system using a terminal and selects a historical subject of interest. The terminal receives input information from the user and sends it to the server. 【0226】 The server generates a data model of the selected historical subject based on the information it receives. This data model incorporates a natural language processing engine, which processes user input and generates appropriate responses. The responses are tailored based on the historical context and the characteristics of the selected person. 【0227】 The generated response is sent by the server to the terminal and displayed to the user. Along with the display, annotations and additional information about the relevant historical context are also provided. This is an important element for enhancing the educational effect. 【0228】 The system supports multilingual interaction depending on the user's settings. If the user changes the language, the server translates the response into the specified language and sends it to the terminal in the appropriate format. 【0229】 Furthermore, the terminal sends data from the user's interaction session to the server, which then uses this data to create a user learning profile. This profile is then used when the user revisits the system to provide a personalized learning experience. 【0230】 For example, if a user wants to retrieve information related to "19th-century scientists" from their device, the server will suggest the most relevant scientists and topics for discussion. If the user selects a specific scientist and asks a question, a response based on that scientist's perspective will be generated and displayed to the user through their device. This makes it possible to learn about historical conflicts and scientific discoveries from a practical perspective. 【0231】 In this way, the system aims to make history learning more interactive and meaningful, and to sustainably engage users' interest. 【0232】 The following describes the processing flow. 【0233】 Step 1: 【0234】 The user logs into the terminal and interacts with the interface to select historical subjects of interest. The terminal then sends the selected information to the server. 【0235】 Step 2: 【0236】 The server retrieves a data model of the selected historical object based on the information received from the user. This model includes known information about the object and its relevant historical context. 【0237】 Step 3: 【0238】 The server uses a natural language processing engine to generate responses to user questions and requests. This engine analyzes information within the data model and forms answers in the appropriate context. 【0239】 Step 4: 【0240】 The server attaches annotations and additional historical explanations related to the generated response and sends it to the terminal. This allows the user to gain a deeper understanding. 【0241】 Step 5: 【0242】 The terminal visually displays the responses and annotations sent from the server to the user. The user can then use this information to ask further questions or select other historical subjects. 【0243】 Step 6: 【0244】 When a user utilizes multilingual support, the device notifies the server of the selected language setting. The server translates the response into the specified language and resends it to the device. 【0245】 Step 7: 【0246】 When a user wishes to end a conversation, a command to end the conversation is sent from the terminal to the server. The server saves the conversation history and incorporates it into the user's learning profile. 【0247】 Step 8: 【0248】 Based on the saved learning profile, the server prepares to suggest the most relevant historical subjects and information for the next time the user logs in. 【0249】 (Example 1) 【0250】 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." 【0251】 Traditional history learning systems have made it difficult for users to search for specific historical information and efficiently obtain related detailed information. Furthermore, the availability of multilingual information and the provision of customized learning experiences tailored to individual users have been limited. Additionally, interactive systems have struggled to accumulate and utilize individual user learning profiles. 【0252】 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. 【0253】 In this invention, the server includes display means for the user to input information related to a specific historical context and select items of interest; analysis means using natural language processing technology to generate a response for the user based on the selected information; and output means for adjusting and communicating the response and supplementary information displayed to the user. This enables efficient information acquisition, multilingual search capabilities, provision of a learning experience tailored to the user, and utilization of the user's learning history to help with future learning. 【0254】 A "user" is an individual or group that operates the system and acquires information, utilizing the functions provided by the system for learning or information retrieval. 【0255】 "Historical context" refers to the social, cultural, and political environment and conditions of a particular period, and forms the basis for understanding the context of historical information. 【0256】 A "display means" is an interface provided for users to visually confirm and select information, and it assists in communication between the system and the user. 【0257】 "Natural language processing technology" is a technique for understanding human language and generating responses, and it is a method used by computers to analyze text data and imitate human speech. 【0258】 An "analysis tool" is a function designed to generate an appropriate response based on received information, and is a process for facilitating data analysis and understanding. 【0259】 "Output means" refers to a method of providing analyzed information or responses to users, and is a mechanism for transmitting messages through visual, auditory, or other means. 【0260】 A "knowledge engineering model" is a theoretical framework for solving concrete problems by utilizing abstract knowledge, and is a technology that is effectively applied in the generation and processing of information. 【0261】 A "learning profile" is personalized learning data built based on a user's past interactions and learning history, and is used to improve their next learning experience. 【0262】 This invention provides a system for users to interactively acquire historical information, and consists of three components: a server, a terminal, and a user. 【0263】 First, the user accesses the system using a terminal. This terminal is a personal computer or mobile device equipped with a browser, which receives user input through its user interface and allows the user to select historical subjects of interest. The entered request is then sent to the server via the internet connection. 【0264】 The server consists of a computing device equipped with a high-performance processor and sufficient memory. The server receives user requests and analyzes the information using a generative AI model (e.g., a large-scale language model). Using natural language processing techniques, it generates a data model based on the input and constructs an appropriate response. The server performs this processing using open-source natural language processing engines or proprietary software. 【0265】 The generated response is sent from the server to the terminal, which then displays it to the user. The response includes annotations and relevant information that supplement the historical context, allowing the user to gain a deeper understanding. For example, if the user enters the prompt, "Please tell me about 19th-century scientists," the server will identify the relevant scientists and provide detailed information about them. 【0266】 Furthermore, the system features multilingual support, allowing the server to translate responses into the user's specified language according to their settings. When the user uses the system again, the terminal sends past conversation data to the server, which then uses this data to create a learning profile for the user. This makes it possible to provide a learning experience that is individually optimized for each user. 【0267】 As a concrete example of its use, by using the prompt "I want to learn more about Renaissance cultural figures," users can obtain information about relevant cultural figures and proceed with their learning based on that information. In this way, the system is a powerful tool for users to deepen their historical knowledge. 【0268】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0269】 Step 1: 【0270】 The user uses a terminal to enter text-based prompts to identify historical subjects or topics of interest. The terminal receives this input, formats the string data selected by the user, and prepares it for transmission to the server. The prompt text as input data is transferred to the server. 【0271】 Step 2: 【0272】 The server receives a prompt message from the user and begins data analysis using a generative AI model. The server processes the prompt message, searches relevant databases, and extracts appropriate information fragments. During this process, the natural language processing engine analyzes the meaning of the text and prepares to construct a highly relevant response. The output is the determination of the contextual data necessary for response generation. 【0273】 Step 3: 【0274】 The server generates a specific natural language response based on contextual data created by the generative AI model. During this process, data processing is performed to improve the quality of the response by referring to historical data and supplementary information. The generated response is adjusted according to the user's settings. The generated response is then provided as the final output. 【0275】 Step 4: 【0276】 The server sends the generated response to the terminal. The terminal visually displays the received data to the user, also providing corresponding historical context and annotation information on the screen. Specifically, the terminal renders the received data in an appropriate format, such as HTML, to make the information easy for the user to understand. In this process, the output response and annotated data are presented on the user's screen. 【0277】 Step 5: 【0278】 After the conversation ends, the terminal sends the user's interaction and selection data to the server as feedback. The server receives the transmitted data and performs data management to update the user-specific learning profile. The saved learning data is utilized to provide personalized services in the next conversation. As an output, the latest learning profile is generated, enabling an optimized experience for the user. 【0279】 (Application Example 1) 【0280】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0281】 Conventional historical learning often uses static teaching materials and has difficulty maintaining the user's interest. In addition, due to the difficulty of providing historical learning in multiple languages and an individually optimized learning experience, there is a need to meet diverse learning needs. 【0282】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0283】 In this invention, the server includes user interface means for the user to input historical information and select items of interest, natural language processing processor means for generating a response to the user based on the selected historical items, and means for providing historical information by voice in educational artifacts and promoting learning through conversation. As a result, the user can receive an interactive and interesting learning experience, enabling historical learning that meets diverse learning needs. 【0284】 The "user interface means" is an interface used by the user to input historical information and select items of interest. 【0285】 The "Natural Language Processing Processor Means" is a device that generates responses to users based on selected historical items. 【0286】 The "Educational Artifact" is a device that provides historical information by voice and promotes learning through dialogue. 【0287】 The "Information Output Means" is a device for adjusting and transmitting responses and annotations to be displayed to the user. 【0288】 "Multilingual Translation" is a translation function that enables information acquisition in multiple languages. 【0289】 The "Data Profile" is a learning profile for each user generated based on dialogue data saved for individual learning. 【0290】 To implement this invention, a system is constructed based on the following configuration. The server receives information from the user and generates a response based on selected historical items using a natural language processing engine. The server transmits the generated response to the terminal and provides it to the user. Also, the server can utilize the multilingual translation function to generate responses in the language selected by the user. 【0291】 As specific hardware for constructing this system, an educational artifact equipped with a microphone for voice input and a speaker for output is used. As software, Python is used to develop a program controlled by the Robotics Operating System (ROS). As an API, Google Cloud's Speech-to-Text is used to convert voice to text, and Dialogflow is used for natural language processing. For generating responses, a generative AI model is utilized. The server vocalizes the generated response using Google Cloud Text-to-Speech and provides it to the user through the terminal. 【0292】 For example, if a user asks the device, "Tell me about 19th-century scientists," the server performs natural language processing and generates a response such as, "Representative scientists who were active in the 19th century include A, among others. Which scientist would you like to learn about?" 【0293】 An example of a prompt would be: "Please provide information about 19th-century scientists. Please include specific examples and the impact of those scientists." 【0294】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0295】 Step 1: 【0296】 The user enters a question via voice into the educational artifact. This voice input is then sent to the server. 【0297】 Step 2: 【0298】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text. The input is audio data, and the output is text data. 【0299】 Step 3: 【0300】 The server sends the converted text data to Dialogflow, where it interprets the user's intent. The input is text data, and the output is data about the user's intent and requests. 【0301】 Step 4: 【0302】 The server generates an appropriate response using a generative AI model based on the interpreted intent data. The input is the user's intent data, and the output is a response in natural language. 【0303】 Step 5: 【0304】 The server converts the generated response into audio data using Google Cloud Text-to-Speech. The input is the text response data, and the output is the audio data. 【0305】 Step 6: 【0306】 The server transmits the audio data to the terminal and provides it to the user as an audio response. By playing the audio response on the terminal, information is conveyed to the user. 【0307】 Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion. 【0308】 The present invention provides a system that, when a user interacts with a historical object, combines an emotion engine to recognize the user's emotional state and optimize the dialogue content. 【0309】 This system has a server, a terminal, and a user as main components. The user logs in to the terminal and selects a historical object of interest. The terminal transmits the user's selection information to the server to obtain necessary data from the server. The server calls a data model related to the selected historical object and generates a response based on the user's question using a natural language processing engine. 【0310】 The server is equipped with an emotion engine that analyzes features such as the user's voice tone, input speed, and selected phrases to recognize the user's emotional state in real time. This emotion information affects the response to be generated and creates a customized response with a tone and content that match the user's emotion. 【0311】 For example, if a user is engaging in a conversation related to a historical war, and feelings of anger or sadness are detected, the server will generate a correspondingly careful and empathetic response. On the other hand, if the user is expressing excitement or joy, the response will be more lively and positive. 【0312】 The generated response and its annotations are sent to the terminal and displayed visually to the user. Sentimental information may also be provided as visual feedback, allowing the user to understand how the system is responding based on their own emotional state. 【0313】 Furthermore, this system creates a learning profile that reflects the user's emotional state and uses that profile in subsequent interactions to provide the user with the most meaningful and stress-free learning experience. In this way, users can enjoy a rich learning environment optimized for their own emotional state, going beyond mere information acquisition. 【0314】 The following describes the processing flow. 【0315】 Step 1: 【0316】 The user accesses the terminal and logs into the system. After logging in, they select a historical subject of interest from the interface on the terminal. Based on this, the terminal sends the selection information to the server. 【0317】 Step 2: 【0318】 Based on the information received, the server loads data on the selected historical subject. This data includes background information related to the subject and response patterns to common questions. 【0319】 Step 3: 【0320】 The user enters a question about a historical subject from their terminal. The terminal sends this question to the server, which uses a natural language processing engine to generate an appropriate response. 【0321】 Step 4: 【0322】 Simultaneously, if the device receives voice input from the user, it sends that voice data to the server. The server uses an emotion engine to analyze the user's emotions based on factors such as the tone and speed of their voice. 【0323】 Step 5: 【0324】 The server adjusts the tone and content of the responses it generates based on the user's emotional state. For example, if the emotion engine detects anger in the user, the server will select a response with a calm and empathetic tone. 【0325】 Step 6: 【0326】 The server sends a reconciled response, along with annotations and additional information corresponding to the analyzed sentiment, to the terminal. The terminal then displays these to the user. 【0327】 Step 7: 【0328】 If the user continues the conversation, the server records the user's sentiment history to help analyze new questions. If the conversation ends, the data from the entire conversation is reflected in the user's learning profile, which can be used to improve future conversations. 【0329】 Step 8: 【0330】 The next time you log in, the server will be prepared to recommend individually optimized topics and relevant information to your device based on your past sentiment history and learning profile. 【0331】 (Example 2) 【0332】 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". 【0333】 The problem that this invention aims to solve is to provide a system that can recognize a user's emotions and generate responses adapted to their emotional state in interactions with historical or other subject areas. Current dialogue systems lack the functionality to effectively customize responses according to the user's emotions, which can result in an unsatisfactory user experience. Furthermore, there is the problem that it is not easy to generate individualized learning profiles based on the user's past interactions. 【0334】 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. 【0335】 In this invention, the server includes means for providing a dialogue interface for the user to input information about a target area and select an area of ​​interest; information processing means via natural language processing technology for generating a response to the user based on the selected target area; and means including an emotion analysis engine for acquiring and analyzing the user's emotional state. This enables the generation of adaptive responses in accordance with the user's emotions and the provision of a learning experience optimized for each user. 【0336】 A "user" refers to an individual or group that interacts with a system. 【0337】 A "target area" refers to a historical or other topic that the user is interested in and wishes to learn about through interaction with the system. 【0338】 A "conversational interface" refers to a user interface that a user uses to input information and select items of interest. 【0339】 "Natural language processing technology" refers to a technique used to generate appropriate responses based on user input, and it describes the computer's ability to understand and process human language. 【0340】 "Information processing means" refers to methods and means for processing information related to a selected target area and generating a response in accordance with the user's request. 【0341】 A "sentiment analysis engine" refers to the technology and devices used to acquire and analyze a user's emotional state. 【0342】 "Response" refers to the provision of information or instructions that a system issues based on user input. 【0343】 A "personalized learning profile" refers to a user-specific information profile created based on the user's past conversation data and emotional information, designed to provide a personalized experience in subsequent interactions. 【0344】 The system in this invention consists primarily of a user, a terminal, and a server, which work together to optimize interaction with the user. Specific methods for carrying out the invention include the following: 【0345】 The terminal provides an interface for users to log into the system and select a target area. When a user selects a topic of interest, such as "Medieval European Culture," that information is sent from the terminal to the server. The terminal can be implemented as a web browser or a dedicated application, making it easy to connect with users. 【0346】 The server operates to retrieve necessary information from relevant databases based on information received from the terminal. Natural language processing techniques are used for information processing. Specifically, natural language processing libraries are used to implement generative AI models. For example, a generative AI model is used to generate responses based on user questions. In addition, a sentiment analysis engine analyzes user input and voice data to identify emotional states. For this analysis, acoustic analysis software to extract voice features and natural language processing tools to analyze the sentiment of text are used. 【0347】 The responses generated based on the analysis are adjusted to match the user's emotions in terms of tone and content. For example, if the emotion analysis engine determines that the user is excited by a question about medieval European culture, the response will be customized to a lively and engaging tone. 【0348】 Ultimately, the server sends a refined response back to the terminal, which then displays it in the user interface. This process allows the user to receive information tailored to their emotions and gain new learning opportunities. 【0349】 An example of a prompt might be, "Please tell me more about medieval European culture. I'd also like to hear about any episodes that particularly excited you." In this way, the system continuously analyzes the user's emotional state, making the learning experience more personalized and meaningful. 【0350】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0351】 Step 1: 【0352】 The user logs into the device. The user enters login information through an interface accessible on the device. The device receives this information, queries the database to authenticate the user, and outputs the authentication result. 【0353】 Step 2: 【0354】 The user selects a subject area of ​​interest, such as "Medieval European Culture," through the terminal's interface. The selected information is treated as input data, and the terminal sends it to the server. After transmission, the server receives this selection information and uses it as a basis for retrieving related data. 【0355】 Step 3: 【0356】 Based on the selection information received by the server, it retrieves relevant historical data from the database. The server then queries the database, processes the necessary information, and outputs it. This processing includes filtering and organizing the data. 【0357】 Step 4: 【0358】 The server analyzes the acquired data using a generative AI model. Specific questions and requests from the user are input to the generative AI model as prompts, and the model outputs a response. This results in a response that includes detailed explanations that meet the user's expectations. 【0359】 Step 5: 【0360】 The server utilizes an emotion analysis engine to analyze the user's emotional state. It receives user input text and voice data, analyzes voice features and emotion-based keywords in the text, and outputs the user's emotional state. This analysis result is used to customize the tone of the response. 【0361】 Step 6: 【0362】 Based on the analysis results, the server adjusts the generated response, customizing it to match the user's emotions in terms of tone and content. This data processing involves restructuring the text and adjusting the tone. The adjusted response data is then prepared and output. 【0363】 Step 7: 【0364】 The server sends the adjusted response data to the terminal. The terminal receives this data and displays it in the user interface. This allows the user to visually confirm information that matches their emotions. 【0365】 Step 8: 【0366】 The user receives information displayed through the device, deepening their understanding and asking new questions. This information reception acts as new input for the next interaction. The user's responses and actions are used as new input information in the next interaction. 【0367】 (Application Example 2) 【0368】 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." 【0369】 When users access target information and optimize their learning experience through interaction with that information, it is necessary to dynamically adjust the content of the dialogue while considering the user's emotional state. However, existing systems lack the means to grasp user emotions and generate appropriate responses based on them, resulting in a limited quality of user experience. This invention aims to solve these problems and provide a more meaningful and user-friendly learning environment. 【0370】 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. 【0371】 In this invention, the server includes an input / output device for the user to input information about a target and select a target of interest, a data processing device via a natural language processing system for generating a response to the user based on the selected target, and an emotion analysis device for analyzing the user's voice and input to obtain emotion information. This makes it possible to recognize the user's emotional state in real time and generate a response accordingly. 【0372】 An "input / output device" is a device that receives user selection information and instructions and provides content to display to the user. 【0373】 A "natural language processing system" is a system that provides data processing technology to understand user questions and requests and generate appropriate responses. 【0374】 A "data processing device" is a device that processes necessary information based on a selected object and provides it to the user. 【0375】 An "emotion analysis device" is a device that uses technology to analyze a user's voice or input and detect the user's emotional state. 【0376】 A "language conversion device" is a device that translates dialogue into different languages, enabling users to obtain information in multiple languages. 【0377】 A "data storage device" is a device that saves data generated during a conversation and uses it for future conversations. 【0378】 This invention is a system that enables interactive dialogue with users in a physical store environment. Users input information about objects and select objects of interest using a smartphone or smart glasses. The terminal sends the user's selection information to a server, which retrieves the necessary data. 【0379】 The server generates responses based on selected subjects via a natural language processing system. Specifically, it uses software such as Google Cloud's Natural Language API and IBM's Watson Tone Analyzer to analyze the emotional state of voice and text received from the user. This allows the emotion analysis device to detect the user's emotions in real time and provide appropriate responses accordingly. 【0380】 Furthermore, it utilizes OpenAI's GPT model, a generative AI model, to generate natural language responses that match the user's questions. The generated responses are visually displayed to the user on the device, allowing the user to have a more meaningful conversational experience that is appropriate to their emotional state. 【0381】 This system is also used to generate learning profiles for specific users. By analyzing data accumulated through interactions with the user and applying it to subsequent interactions, it efficiently provides users with customized information. 【0382】 As a concrete example, when using a museum exhibit, it is possible to use the app to ask a question such as, "Could you tell me more about the background of this exhibit?" The system will respond in a way that is tailored to the user's interests and emotions, such as, "This exhibit was created in XX year, and it has the following background from that era. Is there anything else you would like to know?" 【0383】 An example of a prompt for a generative AI model might be something like, "Please tell me about the historical background of the exhibit. The questioner is interested and is seeking detailed information." 【0384】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0385】 Step 1: 【0386】 The user uses a smartphone or smart glasses to select a historical subject of interest. The device receives the user's selection information as input and sends this information to the server. This prepares the server to begin retrieving the necessary data. 【0387】 Step 2: 【0388】 The server retrieves relevant data based on the target information sent by the user. Here, it extracts detailed information related to the target from the database and performs preprocessing for natural language processing. The output of this process becomes the input data for the natural language processing engine. 【0389】 Step 3: 【0390】 On the server, the natural language processing system uses a natural language processing engine to generate responses to questions posed by the user. Here, a generative AI model is utilized to output natural language responses that match the questions, and these responses are then sent to the next processing step. 【0391】 Step 4: 【0392】 The emotion analyzer acquires emotion data from the user's voice or text input and adjusts the response based on that emotion. In this step, the user's input is analyzed and the tone and content are converted to suit the emotion. The input to this process is the user's emotion information, and the output is the adjusted response. 【0393】 Step 5: 【0394】 The server sends a coordinated response to the terminal, which then displays the response to the user. The output device visually presents the response to the user, providing a means for the user to verify the information. This allows the user to obtain information about historical subjects of interest in a form that is optimally tailored to their emotions. 【0395】 Step 6: 【0396】 The server stores data obtained through user interaction in a data storage device and uses it to inform future interactions and learning profiles. This allows for more customized information to be provided to the user in subsequent interactions. The input is the interaction data, and the output is the updated learning profile. 【0397】 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. 【0398】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0399】 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. 【0400】 [Third Embodiment] 【0401】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0402】 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. 【0403】 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). 【0404】 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. 【0405】 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. 【0406】 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). 【0407】 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. 【0408】 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. 【0409】 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. 【0410】 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. 【0411】 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. 【0412】 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". 【0413】 This invention provides a system for users to interactively acquire information related to historical objects. The system consists of a server, a terminal, and a user, and each component performs the following functions. 【0414】 First, the user accesses the system using a terminal and selects a historical subject of interest. The terminal receives input information from the user and sends it to the server. 【0415】 The server generates a data model of the selected historical subject based on the information it receives. This data model incorporates a natural language processing engine, which processes user input and generates appropriate responses. The responses are tailored based on the historical context and the characteristics of the selected person. 【0416】 The generated response is sent by the server to the terminal and displayed to the user. Along with the display, annotations and additional information about the relevant historical context are also provided. This is an important element for enhancing the educational effect. 【0417】 The system supports multilingual interaction depending on the user's settings. If the user changes the language, the server translates the response into the specified language and sends it to the terminal in the appropriate format. 【0418】 Furthermore, the terminal sends data from the user's interaction session to the server, which then uses this data to create a user learning profile. This profile is then used when the user revisits the system to provide a personalized learning experience. 【0419】 For example, if a user wants to retrieve information related to "19th-century scientists" from their device, the server will suggest the most relevant scientists and topics for discussion. If the user selects a specific scientist and asks a question, a response based on that scientist's perspective will be generated and displayed to the user through their device. This makes it possible to learn about historical conflicts and scientific discoveries from a practical perspective. 【0420】 In this way, the system aims to make history learning more interactive and meaningful, and to sustainably engage users' interest. 【0421】 The following describes the processing flow. 【0422】 Step 1: 【0423】 The user logs into the terminal and interacts with the interface to select historical subjects of interest. The terminal then sends the selected information to the server. 【0424】 Step 2: 【0425】 The server retrieves a data model of the selected historical object based on the information received from the user. This model includes known information about the object and its relevant historical context. 【0426】 Step 3: 【0427】 The server uses a natural language processing engine to generate responses to user questions and requests. This engine analyzes information within the data model and forms answers in the appropriate context. 【0428】 Step 4: 【0429】 The server attaches annotations and additional historical explanations related to the generated response and sends it to the terminal. This allows the user to gain a deeper understanding. 【0430】 Step 5: 【0431】 The terminal visually displays the responses and annotations sent from the server to the user. The user can then use this information to ask further questions or select other historical subjects. 【0432】 Step 6: 【0433】 When a user utilizes multilingual support, the device notifies the server of the selected language setting. The server translates the response into the specified language and resends it to the device. 【0434】 Step 7: 【0435】 When a user wishes to end a conversation, a command to end the conversation is sent from the terminal to the server. The server saves the conversation history and incorporates it into the user's learning profile. 【0436】 Step 8: 【0437】 Based on the saved learning profile, the server prepares to suggest the most relevant historical subjects and information for the next time the user logs in. 【0438】 (Example 1) 【0439】 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." 【0440】 Traditional history learning systems have made it difficult for users to search for specific historical information and efficiently obtain related detailed information. Furthermore, the availability of multilingual information and the provision of customized learning experiences tailored to individual users have been limited. Additionally, interactive systems have struggled to accumulate and utilize individual user learning profiles. 【0441】 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. 【0442】 In this invention, the server includes display means for the user to input information related to a specific historical context and select items of interest; analysis means using natural language processing technology to generate a response for the user based on the selected information; and output means for adjusting and communicating the response and supplementary information displayed to the user. This enables efficient information acquisition, multilingual search capabilities, provision of a learning experience tailored to the user, and utilization of the user's learning history to help with future learning. 【0443】 A "user" is an individual or group that operates the system and acquires information, utilizing the functions provided by the system for learning or information retrieval. 【0444】 "Historical context" refers to the social, cultural, and political environment and conditions of a particular period, and forms the basis for understanding the context of historical information. 【0445】 A "display means" is an interface provided for users to visually confirm and select information, and it assists in communication between the system and the user. 【0446】 "Natural language processing technology" is a technique for understanding human language and generating responses, and it is a method used by computers to analyze text data and imitate human speech. 【0447】 An "analysis tool" is a function designed to generate an appropriate response based on received information, and is a process for facilitating data analysis and understanding. 【0448】 "Output means" refers to a method of providing analyzed information or responses to users, and is a mechanism for transmitting messages through visual, auditory, or other means. 【0449】 A "knowledge engineering model" is a theoretical framework for solving concrete problems by utilizing abstract knowledge, and is a technology that is effectively applied in the generation and processing of information. 【0450】 A "learning profile" is personalized learning data built based on a user's past interactions and learning history, and is used to improve their next learning experience. 【0451】 This invention provides a system for users to interactively acquire historical information, and consists of three components: a server, a terminal, and a user. 【0452】 First, the user accesses the system using a terminal. This terminal is a personal computer or mobile device equipped with a browser, which receives user input through its user interface and allows the user to select historical subjects of interest. The entered request is then sent to the server via the internet connection. 【0453】 The server consists of a computing device equipped with a high-performance processor and sufficient memory. The server receives user requests and analyzes the information using a generative AI model (e.g., a large-scale language model). Using natural language processing techniques, it generates a data model based on the input and constructs an appropriate response. The server performs this processing using open-source natural language processing engines or proprietary software. 【0454】 The generated response is sent from the server to the terminal, which then displays it to the user. The response includes annotations and relevant information that supplement the historical context, allowing the user to gain a deeper understanding. For example, if the user enters the prompt, "Please tell me about 19th-century scientists," the server will identify the relevant scientists and provide detailed information about them. 【0455】 Furthermore, the system features multilingual support, allowing the server to translate responses into the user's specified language according to their settings. When the user uses the system again, the terminal sends past conversation data to the server, which then uses this data to create a learning profile for the user. This makes it possible to provide a learning experience that is individually optimized for each user. 【0456】 As a concrete example of its use, by using the prompt "I want to learn more about Renaissance cultural figures," users can obtain information about relevant cultural figures and proceed with their learning based on that information. In this way, the system is a powerful tool for users to deepen their historical knowledge. 【0457】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0458】 Step 1: 【0459】 The user uses a terminal to enter text-based prompts to identify historical subjects or topics of interest. The terminal receives this input, formats the string data selected by the user, and prepares it for transmission to the server. The prompt text as input data is transferred to the server. 【0460】 Step 2: 【0461】 The server receives a prompt message from the user and begins data analysis using a generative AI model. The server processes the prompt message, searches relevant databases, and extracts appropriate information fragments. During this process, the natural language processing engine analyzes the meaning of the text and prepares to construct a highly relevant response. The output is the determination of the contextual data necessary for response generation. 【0462】 Step 3: 【0463】 The server generates a specific natural language response based on contextual data created by the generative AI model. During this process, data processing is performed to improve the quality of the response by referring to historical data and supplementary information. The generated response is adjusted according to the user's settings. The generated response is then provided as the final output. 【0464】 Step 4: 【0465】 The server sends the generated response to the terminal. The terminal visually displays the received data to the user, also providing corresponding historical context and annotation information on the screen. Specifically, the terminal renders the received data in an appropriate format, such as HTML, to make the information easy for the user to understand. In this process, the output response and annotated data are presented on the user's screen. 【0466】 Step 5: 【0467】 After the interaction ends, the device sends user interaction and selection data as feedback to the server. The server receives the transmitted data and manages it to update the user's unique learning profile. The saved learning data is used to provide personalized services in the next interaction. As output, the latest learning profile is generated, enabling an optimized experience for the user. 【0468】 (Application Example 1) 【0469】 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." 【0470】 Traditional history education often relies on static materials, making it difficult to maintain user interest. Furthermore, providing history education in multiple languages ​​and individually optimized learning experiences has been challenging, highlighting the need to address diverse learning needs. 【0471】 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. 【0472】 In this invention, the server includes a user interface means for the user to input historical information and select items of interest, a natural language processing processor means for generating responses to the user based on the selected historical items, and means for providing historical information via voice in an educational artifact and facilitating learning through dialogue. This allows the user to have an interactive and engaging learning experience and enables history learning that meets diverse learning needs. 【0473】 A "user interface means" is an interface used by a user to input historical information and select items of interest. 【0474】 A "natural language processing processor" is a device that generates a response to the user based on selected historical items. 【0475】 An "educational artifact" is a device that provides historical information through audio and facilitates learning through dialogue. 【0476】 An "information output means" is a device for adjusting and transmitting responses and annotations displayed to the user. 【0477】 "Multilingual translation" refers to a translation function that enables the acquisition of information in multiple languages. 【0478】 A "data profile" is a user-specific learning profile generated based on conversational data saved for individual learning. 【0479】 To implement this invention, the system is constructed based on the following configuration: The server receives information from the user and generates a response based on selected historical items using a natural language processing engine. The server sends the generated response to the terminal and provides it to the user. The server can also utilize a multilingual translation function to generate a response in the language selected by the user. 【0480】 The specific hardware for this system will be an educational artifact equipped with a microphone for voice input and a speaker for output. The software will be developed using Python, controlled by the Robotics Operating System (ROS). The API will utilize Google Cloud's Speech-to-Text to convert speech to text and Dialogflow for natural language processing. A generative AI model will be used to generate responses. The server will then convert the generated responses into speech using Google Cloud Text-to-Speech and provide them to the user via a terminal. 【0481】 For example, if a user asks the device, "Tell me about 19th-century scientists," the server performs natural language processing and generates a response such as, "Representative scientists who were active in the 19th century include A, among others. Which scientist would you like to learn about?" 【0482】 An example of a prompt would be: "Please provide information about 19th-century scientists. Please include specific examples and the impact of those scientists." 【0483】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0484】 Step 1: 【0485】 The user enters a question via voice into the educational artifact. This voice input is then sent to the server. 【0486】 Step 2: 【0487】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text. The input is audio data, and the output is text data. 【0488】 Step 3: 【0489】 The server sends the converted text data to Dialogflow, where it interprets the user's intent. The input is text data, and the output is data about the user's intent and requests. 【0490】 Step 4: 【0491】 The server generates an appropriate response using a generative AI model based on the interpreted intent data. The input is the user's intent data, and the output is a response in natural language. 【0492】 Step 5: 【0493】 The server converts the generated response into speech data using Google Cloud Text-to-Speech. The input is text response data, and the output is speech data. 【0494】 Step 6: 【0495】 The server sends audio data to the terminal and provides it to the user as an audio response. The terminal plays the audio response, thereby conveying the information to the user. 【0496】 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. 【0497】 This invention provides a system that, when a user interacts with a historical object, combines an emotion engine to recognize the user's emotional state and optimize the content of the interaction. 【0498】 This system consists primarily of a server, a terminal, and a user. The user logs into the terminal and selects a historical subject of interest. The terminal sends the user's selection information to the server, retrieving the necessary data from it. The server invokes a data model related to the selected historical subject and uses a natural language processing engine to generate a response based on the user's question. 【0499】 The server is equipped with an emotion engine that analyzes features such as the user's voice tone, input speed, and selected phrases to recognize the user's emotional state in real time. This emotion information influences the generated response, creating a customized response with a tone and content that matches the user's emotions. 【0500】 For example, if a user is engaging in a conversation related to a historical war, and feelings of anger or sadness are detected, the server will generate a correspondingly careful and empathetic response. On the other hand, if the user is expressing excitement or joy, the response will be more lively and positive. 【0501】 The generated response and its annotations are sent to the terminal and displayed visually to the user. Sentimental information may also be provided as visual feedback, allowing the user to understand how the system is responding based on their own emotional state. 【0502】 Furthermore, this system creates a learning profile that reflects the user's emotional state and uses that profile in subsequent interactions to provide the user with the most meaningful and stress-free learning experience. In this way, users can enjoy a rich learning environment optimized for their own emotional state, going beyond mere information acquisition. 【0503】 The following describes the processing flow. 【0504】 Step 1: 【0505】 The user accesses the terminal and logs into the system. After logging in, they select a historical subject of interest from the interface on the terminal. Based on this, the terminal sends the selection information to the server. 【0506】 Step 2: 【0507】 Based on the information received, the server loads data on the selected historical subject. This data includes background information related to the subject and response patterns to common questions. 【0508】 Step 3: 【0509】 The user enters a question about a historical subject from their terminal. The terminal sends this question to the server, which uses a natural language processing engine to generate an appropriate response. 【0510】 Step 4: 【0511】 Simultaneously, if the device receives voice input from the user, it sends that voice data to the server. The server uses an emotion engine to analyze the user's emotions based on factors such as the tone and speed of their voice. 【0512】 Step 5: 【0513】 The server adjusts the tone and content of the responses it generates based on the user's emotional state. For example, if the emotion engine detects anger in the user, the server will select a response with a calm and empathetic tone. 【0514】 Step 6: 【0515】 The server sends a reconciled response, along with annotations and additional information corresponding to the analyzed sentiment, to the terminal. The terminal then displays these to the user. 【0516】 Step 7: 【0517】 If the user continues the conversation, the server records the user's sentiment history to help analyze new questions. If the conversation ends, the data from the entire conversation is reflected in the user's learning profile, which can be used to improve future conversations. 【0518】 Step 8: 【0519】 The next time you log in, the server will be prepared to recommend individually optimized topics and relevant information to your device based on your past sentiment history and learning profile. 【0520】 (Example 2) 【0521】 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." 【0522】 The problem that this invention aims to solve is to provide a system that can recognize a user's emotions and generate responses adapted to their emotional state in interactions with historical or other subject areas. Current dialogue systems lack the functionality to effectively customize responses according to the user's emotions, which can result in an unsatisfactory user experience. Furthermore, there is the problem that it is not easy to generate individualized learning profiles based on the user's past interactions. 【0523】 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. 【0524】 In this invention, the server includes means for providing a dialogue interface for the user to input information about a target area and select an area of ​​interest; information processing means via natural language processing technology for generating a response to the user based on the selected target area; and means including an emotion analysis engine for acquiring and analyzing the user's emotional state. This enables the generation of adaptive responses in accordance with the user's emotions and the provision of a learning experience optimized for each user. 【0525】 A "user" refers to an individual or group that interacts with a system. 【0526】 A "target area" refers to a historical or other topic that the user is interested in and wishes to learn about through interaction with the system. 【0527】 A "conversational interface" refers to a user interface that a user uses to input information and select items of interest. 【0528】 "Natural language processing technology" refers to a technique used to generate appropriate responses based on user input, and it describes the computer's ability to understand and process human language. 【0529】 "Information processing means" refers to methods and means for processing information related to a selected target area and generating a response in accordance with the user's request. 【0530】 A "sentiment analysis engine" refers to the technology and devices used to acquire and analyze a user's emotional state. 【0531】 "Response" refers to the provision of information or instructions that a system issues based on user input. 【0532】 A "personalized learning profile" refers to a user-specific information profile created based on the user's past conversation data and emotional information, designed to provide a personalized experience in subsequent interactions. 【0533】 The system in this invention consists primarily of a user, a terminal, and a server, which work together to optimize interaction with the user. Specific methods for carrying out the invention include the following: 【0534】 The terminal provides an interface for users to log into the system and select a target area. When a user selects a topic of interest, such as "Medieval European Culture," that information is sent from the terminal to the server. The terminal can be implemented as a web browser or a dedicated application, making it easy to connect with users. 【0535】 The server operates to retrieve necessary information from relevant databases based on information received from the terminal. Natural language processing techniques are used for information processing. Specifically, natural language processing libraries are used to implement generative AI models. For example, a generative AI model is used to generate responses based on user questions. In addition, a sentiment analysis engine analyzes user input and voice data to identify emotional states. For this analysis, acoustic analysis software to extract voice features and natural language processing tools to analyze the sentiment of text are used. 【0536】 The responses generated based on the analysis are adjusted to match the user's emotions in terms of tone and content. For example, if the emotion analysis engine determines that the user is excited by a question about medieval European culture, the response will be customized to a lively and engaging tone. 【0537】 Ultimately, the server sends a refined response back to the terminal, which then displays it in the user interface. This process allows the user to receive information tailored to their emotions and gain new learning opportunities. 【0538】 An example of a prompt might be, "Please tell me more about medieval European culture. I'd also like to hear about any episodes that particularly excited you." In this way, the system continuously analyzes the user's emotional state, making the learning experience more personalized and meaningful. 【0539】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0540】 Step 1: 【0541】 The user logs into the device. The user enters login information through an interface accessible on the device. The device receives this information, queries the database to authenticate the user, and outputs the authentication result. 【0542】 Step 2: 【0543】 The user selects a subject area of ​​interest, such as "Medieval European Culture," through the terminal's interface. The selected information is treated as input data, and the terminal sends it to the server. After transmission, the server receives this selection information and uses it as a basis for retrieving related data. 【0544】 Step 3: 【0545】 Based on the selection information received by the server, it retrieves relevant historical data from the database. The server then queries the database, processes the necessary information, and outputs it. This processing includes filtering and organizing the data. 【0546】 Step 4: 【0547】 The server analyzes the acquired data using a generative AI model. Specific questions and requests from the user are input to the generative AI model as prompts, and the model outputs a response. This results in a response that includes detailed explanations that meet the user's expectations. 【0548】 Step 5: 【0549】 The server utilizes an emotion analysis engine to analyze the user's emotional state. It receives user input text and voice data, analyzes voice features and emotion-based keywords in the text, and outputs the user's emotional state. This analysis result is used to customize the tone of the response. 【0550】 Step 6: 【0551】 Based on the analysis results, the server adjusts the generated response, customizing it to match the user's emotions in terms of tone and content. This data processing involves restructuring the text and adjusting the tone. The adjusted response data is then prepared and output. 【0552】 Step 7: 【0553】 The server sends the adjusted response data to the terminal. The terminal receives this data and displays it in the user interface. This allows the user to visually confirm information that matches their emotions. 【0554】 Step 8: 【0555】 The user receives information displayed through the device, deepening their understanding and asking new questions. This information reception acts as new input for the next interaction. The user's responses and actions are used as new input information in the next interaction. 【0556】 (Application Example 2) 【0557】 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." 【0558】 When users access target information and optimize their learning experience through interaction with that information, it is necessary to dynamically adjust the content of the dialogue while considering the user's emotional state. However, existing systems lack the means to grasp user emotions and generate appropriate responses based on them, resulting in a limited quality of user experience. This invention aims to solve these problems and provide a more meaningful and user-friendly learning environment. 【0559】 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. 【0560】 In this invention, the server includes an input / output device for the user to input information about a target and select a target of interest, a data processing device via a natural language processing system for generating a response to the user based on the selected target, and an emotion analysis device for analyzing the user's voice and input to obtain emotion information. This makes it possible to recognize the user's emotional state in real time and generate a response accordingly. 【0561】 An "input / output device" is a device that receives user selection information and instructions and provides content to display to the user. 【0562】 A "natural language processing system" is a system that provides data processing technology to understand user questions and requests and generate appropriate responses. 【0563】 A "data processing device" is a device that processes necessary information based on a selected object and provides it to the user. 【0564】 An "emotion analysis device" is a device that uses technology to analyze a user's voice or input and detect the user's emotional state. 【0565】 A "language conversion device" is a device that translates dialogue into different languages, enabling users to obtain information in multiple languages. 【0566】 A "data storage device" is a device that saves data generated during a conversation and uses it for future conversations. 【0567】 This invention is a system that enables interactive dialogue with users in a physical store environment. Users input information about objects and select objects of interest using a smartphone or smart glasses. The terminal sends the user's selection information to a server, which retrieves the necessary data. 【0568】 The server generates responses based on selected subjects via a natural language processing system. Specifically, it uses software such as Google Cloud's Natural Language API and IBM's Watson Tone Analyzer to analyze the emotional state of voice and text received from the user. This allows the emotion analysis device to detect the user's emotions in real time and provide appropriate responses accordingly. 【0569】 Furthermore, it utilizes OpenAI's GPT model, a generative AI model, to generate natural language responses that match the user's questions. The generated responses are visually displayed to the user on the device, allowing the user to have a more meaningful conversational experience that is appropriate to their emotional state. 【0570】 This system is also used to generate learning profiles for specific users. By analyzing data accumulated through interactions with the user and applying it to subsequent interactions, it efficiently provides users with customized information. 【0571】 As a concrete example, when using a museum exhibit, it is possible to use the app to ask a question such as, "Could you tell me more about the background of this exhibit?" The system will respond in a way that is tailored to the user's interests and emotions, such as, "This exhibit was created in XX year, and it has the following background from that era. Is there anything else you would like to know?" 【0572】 An example of a prompt for a generative AI model might be something like, "Please tell me about the historical background of the exhibit. The questioner is interested and is seeking detailed information." 【0573】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0574】 Step 1: 【0575】 The user uses a smartphone or smart glasses to select a historical subject of interest. The device receives the user's selection information as input and sends this information to the server. This prepares the server to begin retrieving the necessary data. 【0576】 Step 2: 【0577】 The server retrieves relevant data based on the target information sent by the user. Here, it extracts detailed information related to the target from the database and performs preprocessing for natural language processing. The output of this process becomes the input data for the natural language processing engine. 【0578】 Step 3: 【0579】 On the server, the natural language processing system uses a natural language processing engine to generate responses to questions posed by the user. Here, a generative AI model is utilized to output natural language responses that match the questions, and these responses are then sent to the next processing step. 【0580】 Step 4: 【0581】 The emotion analyzer acquires emotion data from the user's voice or text input and adjusts the response based on that emotion. In this step, the user's input is analyzed and the tone and content are converted to suit the emotion. The input to this process is the user's emotion information, and the output is the adjusted response. 【0582】 Step 5: 【0583】 The server sends a coordinated response to the terminal, which then displays the response to the user. The output device visually presents the response to the user, providing a means for the user to verify the information. This allows the user to obtain information about historical subjects of interest in a form that is optimally tailored to their emotions. 【0584】 Step 6: 【0585】 The server stores data obtained through user interaction in a data storage device and uses it to inform future interactions and learning profiles. This allows for more customized information to be provided to the user in subsequent interactions. The input is the interaction data, and the output is the updated learning profile. 【0586】 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. 【0587】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0588】 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. 【0589】 [Fourth Embodiment] 【0590】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0591】 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. 【0592】 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). 【0593】 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. 【0594】 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. 【0595】 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). 【0596】 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. 【0597】 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. 【0598】 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. 【0599】 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. 【0600】 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. 【0601】 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. 【0602】 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". 【0603】 This invention provides a system for users to interactively acquire information related to historical objects. The system consists of a server, a terminal, and a user, and each component performs the following functions. 【0604】 First, the user accesses the system using a terminal and selects a historical subject of interest. The terminal receives input information from the user and sends it to the server. 【0605】 The server generates a data model of the selected historical subject based on the information it receives. This data model incorporates a natural language processing engine, which processes user input and generates appropriate responses. The responses are tailored based on the historical context and the characteristics of the selected person. 【0606】 The generated response is sent by the server to the terminal and displayed to the user. Along with the display, annotations and additional information about the relevant historical context are also provided. This is an important element for enhancing the educational effect. 【0607】 The system supports multilingual interaction depending on the user's settings. If the user changes the language, the server translates the response into the specified language and sends it to the terminal in the appropriate format. 【0608】 Furthermore, the terminal sends data from the user's interaction session to the server, which then uses this data to create a user learning profile. This profile is then used when the user revisits the system to provide a personalized learning experience. 【0609】 For example, if a user wants to retrieve information related to "19th-century scientists" from their device, the server will suggest the most relevant scientists and topics for discussion. If the user selects a specific scientist and asks a question, a response based on that scientist's perspective will be generated and displayed to the user through their device. This makes it possible to learn about historical conflicts and scientific discoveries from a practical perspective. 【0610】 In this way, the system aims to make history learning more interactive and meaningful, and to sustainably engage users' interest. 【0611】 The following describes the processing flow. 【0612】 Step 1: 【0613】 The user logs into the terminal and interacts with the interface to select historical subjects of interest. The terminal then sends the selected information to the server. 【0614】 Step 2: 【0615】 The server retrieves a data model of the selected historical object based on the information received from the user. This model includes known information about the object and its relevant historical context. 【0616】 Step 3: 【0617】 The server uses a natural language processing engine to generate responses to user questions and requests. This engine analyzes information within the data model and forms answers in the appropriate context. 【0618】 Step 4: 【0619】 The server attaches annotations and additional historical explanations related to the generated response and sends it to the terminal. This allows the user to gain a deeper understanding. 【0620】 Step 5: 【0621】 The terminal visually displays the responses and annotations sent from the server to the user. The user can then use this information to ask further questions or select other historical subjects. 【0622】 Step 6: 【0623】 When a user utilizes multilingual support, the device notifies the server of the selected language setting. The server translates the response into the specified language and resends it to the device. 【0624】 Step 7: 【0625】 When a user wishes to end a conversation, a command to end the conversation is sent from the terminal to the server. The server saves the conversation history and incorporates it into the user's learning profile. 【0626】 Step 8: 【0627】 Based on the saved learning profile, the server prepares to suggest the most relevant historical subjects and information for the next time the user logs in. 【0628】 (Example 1) 【0629】 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". 【0630】 Traditional history learning systems have made it difficult for users to search for specific historical information and efficiently obtain related detailed information. Furthermore, the availability of multilingual information and the provision of customized learning experiences tailored to individual users have been limited. Additionally, interactive systems have struggled to accumulate and utilize individual user learning profiles. 【0631】 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. 【0632】 In this invention, the server includes display means for the user to input information related to a specific historical context and select items of interest; analysis means using natural language processing technology to generate a response for the user based on the selected information; and output means for adjusting and communicating the response and supplementary information displayed to the user. This enables efficient information acquisition, multilingual search capabilities, provision of a learning experience tailored to the user, and utilization of the user's learning history to help with future learning. 【0633】 A "user" is an individual or group that operates the system and acquires information, utilizing the functions provided by the system for learning or information retrieval. 【0634】 "Historical context" refers to the social, cultural, and political environment and conditions of a particular period, and forms the basis for understanding the context of historical information. 【0635】 A "display means" is an interface provided for users to visually confirm and select information, and it assists in communication between the system and the user. 【0636】 "Natural language processing technology" is a technique for understanding human language and generating responses, and it is a method used by computers to analyze text data and imitate human speech. 【0637】 An "analysis tool" is a function designed to generate an appropriate response based on received information, and is a process for facilitating data analysis and understanding. 【0638】 "Output means" refers to a method of providing analyzed information or responses to users, and is a mechanism for transmitting messages through visual, auditory, or other means. 【0639】 A "knowledge engineering model" is a theoretical framework for solving concrete problems by utilizing abstract knowledge, and is a technology that is effectively applied in the generation and processing of information. 【0640】 A "learning profile" is personalized learning data built based on a user's past interactions and learning history, and is used to improve their next learning experience. 【0641】 This invention provides a system for users to interactively acquire historical information, and consists of three components: a server, a terminal, and a user. 【0642】 First, the user accesses the system using a terminal. This terminal is a personal computer or mobile device equipped with a browser, which receives user input through its user interface and allows the user to select historical subjects of interest. The entered request is then sent to the server via the internet connection. 【0643】 The server consists of a computing device equipped with a high-performance processor and sufficient memory. The server receives user requests and analyzes the information using a generative AI model (e.g., a large-scale language model). Using natural language processing techniques, it generates a data model based on the input and constructs an appropriate response. The server performs this processing using open-source natural language processing engines or proprietary software. 【0644】 The generated response is sent from the server to the terminal, which then displays it to the user. The response includes annotations and relevant information that supplement the historical context, allowing the user to gain a deeper understanding. For example, if the user enters the prompt, "Please tell me about 19th-century scientists," the server will identify the relevant scientists and provide detailed information about them. 【0645】 Furthermore, the system features multilingual support, allowing the server to translate responses into the user's specified language according to their settings. When the user uses the system again, the terminal sends past conversation data to the server, which then uses this data to create a learning profile for the user. This makes it possible to provide a learning experience that is individually optimized for each user. 【0646】 As a concrete example of its use, by using the prompt "I want to learn more about Renaissance cultural figures," users can obtain information about relevant cultural figures and proceed with their learning based on that information. In this way, the system is a powerful tool for users to deepen their historical knowledge. 【0647】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0648】 Step 1: 【0649】 The user uses a terminal to enter text-based prompts to identify historical subjects or topics of interest. The terminal receives this input, formats the string data selected by the user, and prepares it for transmission to the server. The prompt text as input data is transferred to the server. 【0650】 Step 2: 【0651】 The server receives a prompt message from the user and begins data analysis using a generative AI model. The server processes the prompt message, searches relevant databases, and extracts appropriate information fragments. During this process, the natural language processing engine analyzes the meaning of the text and prepares to construct a highly relevant response. The output is the determination of the contextual data necessary for response generation. 【0652】 Step 3: 【0653】 The server generates a specific natural language response based on contextual data created by the generative AI model. During this process, data processing is performed to improve the quality of the response by referring to historical data and supplementary information. The generated response is adjusted according to the user's settings. The generated response is then provided as the final output. 【0654】 Step 4: 【0655】 The server sends the generated response to the terminal. The terminal visually displays the received data to the user, also providing corresponding historical context and annotation information on the screen. Specifically, the terminal renders the received data in an appropriate format, such as HTML, to make the information easy for the user to understand. In this process, the output response and annotated data are presented on the user's screen. 【0656】 Step 5: 【0657】 After the interaction ends, the device sends user interaction and selection data as feedback to the server. The server receives the transmitted data and manages it to update the user's unique learning profile. The saved learning data is used to provide personalized services in the next interaction. As output, the latest learning profile is generated, enabling an optimized experience for the user. 【0658】 (Application Example 1) 【0659】 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". 【0660】 Traditional history education often relies on static materials, making it difficult to maintain user interest. Furthermore, providing history education in multiple languages ​​and individually optimized learning experiences has been challenging, highlighting the need to address diverse learning needs. 【0661】 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. 【0662】 In this invention, the server includes a user interface means for the user to input historical information and select items of interest, a natural language processing processor means for generating responses to the user based on the selected historical items, and means for providing historical information via voice in an educational artifact and facilitating learning through dialogue. This allows the user to have an interactive and engaging learning experience and enables history learning that meets diverse learning needs. 【0663】 A "user interface means" is an interface used by a user to input historical information and select items of interest. 【0664】 A "natural language processing processor" is a device that generates a response to the user based on selected historical items. 【0665】 An "educational artifact" is a device that provides historical information through audio and facilitates learning through dialogue. 【0666】 An "information output means" is a device for adjusting and transmitting responses and annotations displayed to the user. 【0667】 "Multilingual translation" refers to a translation function that enables the acquisition of information in multiple languages. 【0668】 A "data profile" is a user-specific learning profile generated based on conversational data saved for individual learning. 【0669】 To implement this invention, the system is constructed based on the following configuration: The server receives information from the user and generates a response based on selected historical items using a natural language processing engine. The server sends the generated response to the terminal and provides it to the user. The server can also utilize a multilingual translation function to generate a response in the language selected by the user. 【0670】 The specific hardware for this system will be an educational artifact equipped with a microphone for voice input and a speaker for output. The software will be developed using Python, controlled by the Robotics Operating System (ROS). The API will utilize Google Cloud's Speech-to-Text to convert speech to text and Dialogflow for natural language processing. A generative AI model will be used to generate responses. The server will then convert the generated responses into speech using Google Cloud Text-to-Speech and provide them to the user via a terminal. 【0671】 For example, if a user asks the device, "Tell me about 19th-century scientists," the server performs natural language processing and generates a response such as, "Representative scientists who were active in the 19th century include A, among others. Which scientist would you like to learn about?" 【0672】 An example of a prompt would be: "Please provide information about 19th-century scientists. Please include specific examples and the impact of those scientists." 【0673】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0674】 Step 1: 【0675】 The user enters a question via voice into the educational artifact. This voice input is then sent to the server. 【0676】 Step 2: 【0677】 The server uses the Google Cloud Speech-to-Text API to convert received audio data into text. The input is audio data, and the output is text data. 【0678】 Step 3: 【0679】 The server sends the converted text data to Dialogflow, where it interprets the user's intent. The input is text data, and the output is data about the user's intent and requests. 【0680】 Step 4: 【0681】 The server generates an appropriate response using a generative AI model based on the interpreted intent data. The input is the user's intent data, and the output is a response in natural language. 【0682】 Step 5: 【0683】 The server converts the generated response into speech data using Google Cloud Text-to-Speech. The input is text response data, and the output is speech data. 【0684】 Step 6: 【0685】 The server sends audio data to the terminal and provides it to the user as an audio response. The terminal plays the audio response, thereby conveying the information to the user. 【0686】 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. 【0687】 This invention provides a system that, when a user interacts with a historical object, combines an emotion engine to recognize the user's emotional state and optimize the content of the interaction. 【0688】 This system consists primarily of a server, a terminal, and a user. The user logs into the terminal and selects a historical subject of interest. The terminal sends the user's selection information to the server, retrieving the necessary data from it. The server invokes a data model related to the selected historical subject and uses a natural language processing engine to generate a response based on the user's question. 【0689】 The server is equipped with an emotion engine that analyzes features such as the user's voice tone, input speed, and selected phrases to recognize the user's emotional state in real time. This emotion information influences the generated response, creating a customized response with a tone and content that matches the user's emotions. 【0690】 For example, if a user is engaging in a conversation related to a historical war, and feelings of anger or sadness are detected, the server will generate a correspondingly careful and empathetic response. On the other hand, if the user is expressing excitement or joy, the response will be more lively and positive. 【0691】 The generated response and its annotations are sent to the terminal and displayed visually to the user. Sentimental information may also be provided as visual feedback, allowing the user to understand how the system is responding based on their own emotional state. 【0692】 Furthermore, this system creates a learning profile that reflects the user's emotional state and uses that profile in subsequent interactions to provide the user with the most meaningful and stress-free learning experience. In this way, users can enjoy a rich learning environment optimized for their own emotional state, going beyond mere information acquisition. 【0693】 The following describes the processing flow. 【0694】 Step 1: 【0695】 The user accesses the terminal and logs into the system. After logging in, they select a historical subject of interest from the interface on the terminal. Based on this, the terminal sends the selection information to the server. 【0696】 Step 2: 【0697】 Based on the information received, the server loads data on the selected historical subject. This data includes background information related to the subject and response patterns to common questions. 【0698】 Step 3: 【0699】 The user enters a question about a historical subject from their terminal. The terminal sends this question to the server, which uses a natural language processing engine to generate an appropriate response. 【0700】 Step 4: 【0701】 Simultaneously, if the device receives voice input from the user, it sends that voice data to the server. The server uses an emotion engine to analyze the user's emotions based on factors such as the tone and speed of their voice. 【0702】 Step 5: 【0703】 The server adjusts the tone and content of the responses it generates based on the user's emotional state. For example, if the emotion engine detects anger in the user, the server will select a response with a calm and empathetic tone. 【0704】 Step 6: 【0705】 The server sends a reconciled response, along with annotations and additional information corresponding to the analyzed sentiment, to the terminal. The terminal then displays these to the user. 【0706】 Step 7: 【0707】 If the user continues the conversation, the server records the user's sentiment history to help analyze new questions. If the conversation ends, the data from the entire conversation is reflected in the user's learning profile, which can be used to improve future conversations. 【0708】 Step 8: 【0709】 The next time you log in, the server will be prepared to recommend individually optimized topics and relevant information to your device based on your past sentiment history and learning profile. 【0710】 (Example 2) 【0711】 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". 【0712】 The problem that this invention aims to solve is to provide a system that can recognize a user's emotions and generate responses adapted to their emotional state in interactions with historical or other subject areas. Current dialogue systems lack the functionality to effectively customize responses according to the user's emotions, which can result in an unsatisfactory user experience. Furthermore, there is the problem that it is not easy to generate individualized learning profiles based on the user's past interactions. 【0713】 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. 【0714】 In this invention, the server includes means for providing a dialogue interface for the user to input information about a target area and select an area of ​​interest; information processing means via natural language processing technology for generating a response to the user based on the selected target area; and means including an emotion analysis engine for acquiring and analyzing the user's emotional state. This enables the generation of adaptive responses in accordance with the user's emotions and the provision of a learning experience optimized for each user. 【0715】 A "user" refers to an individual or group that interacts with a system. 【0716】 A "target area" refers to a historical or other topic that the user is interested in and wishes to learn about through interaction with the system. 【0717】 A "conversational interface" refers to a user interface that a user uses to input information and select items of interest. 【0718】 "Natural language processing technology" refers to a technique used to generate appropriate responses based on user input, and it describes the computer's ability to understand and process human language. 【0719】 "Information processing means" refers to methods and means for processing information related to a selected target area and generating a response in accordance with the user's request. 【0720】 A "sentiment analysis engine" refers to the technology and devices used to acquire and analyze a user's emotional state. 【0721】 "Response" refers to the provision of information or instructions that a system issues based on user input. 【0722】 A "personalized learning profile" refers to a user-specific information profile created based on the user's past conversation data and emotional information, designed to provide a personalized experience in subsequent interactions. 【0723】 The system in this invention consists primarily of a user, a terminal, and a server, which work together to optimize interaction with the user. Specific methods for carrying out the invention include the following: 【0724】 The terminal provides an interface for users to log into the system and select a target area. When a user selects a topic of interest, such as "Medieval European Culture," that information is sent from the terminal to the server. The terminal can be implemented as a web browser or a dedicated application, making it easy to connect with users. 【0725】 The server operates to retrieve necessary information from relevant databases based on information received from the terminal. Natural language processing techniques are used for information processing. Specifically, natural language processing libraries are used to implement generative AI models. For example, a generative AI model is used to generate responses based on user questions. In addition, a sentiment analysis engine analyzes user input and voice data to identify emotional states. For this analysis, acoustic analysis software to extract voice features and natural language processing tools to analyze the sentiment of text are used. 【0726】 The responses generated based on the analysis are adjusted to match the user's emotions in terms of tone and content. For example, if the emotion analysis engine determines that the user is excited by a question about medieval European culture, the response will be customized to a lively and engaging tone. 【0727】 Ultimately, the server sends a refined response back to the terminal, which then displays it in the user interface. This process allows the user to receive information tailored to their emotions and gain new learning opportunities. 【0728】 An example of a prompt might be, "Please tell me more about medieval European culture. I'd also like to hear about any episodes that particularly excited you." In this way, the system continuously analyzes the user's emotional state, making the learning experience more personalized and meaningful. 【0729】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0730】 Step 1: 【0731】 The user logs into the device. The user enters login information through an interface accessible on the device. The device receives this information, queries the database to authenticate the user, and outputs the authentication result. 【0732】 Step 2: 【0733】 The user selects a subject area of ​​interest, such as "Medieval European Culture," through the terminal's interface. The selected information is treated as input data, and the terminal sends it to the server. After transmission, the server receives this selection information and uses it as a basis for retrieving related data. 【0734】 Step 3: 【0735】 Based on the selection information received by the server, it retrieves relevant historical data from the database. The server then queries the database, processes the necessary information, and outputs it. This processing includes filtering and organizing the data. 【0736】 Step 4: 【0737】 The server analyzes the acquired data using a generative AI model. Specific questions and requests from the user are input to the generative AI model as prompts, and the model outputs a response. This results in a response that includes detailed explanations that meet the user's expectations. 【0738】 Step 5: 【0739】 The server utilizes an emotion analysis engine to analyze the user's emotional state. It receives user input text and voice data, analyzes voice features and emotion-based keywords in the text, and outputs the user's emotional state. This analysis result is used to customize the tone of the response. 【0740】 Step 6: 【0741】 Based on the analysis results, the server adjusts the generated response, customizing it to match the user's emotions in terms of tone and content. This data processing involves restructuring the text and adjusting the tone. The adjusted response data is then prepared and output. 【0742】 Step 7: 【0743】 The server sends the adjusted response data to the terminal. The terminal receives this data and displays it in the user interface. This allows the user to visually confirm information that matches their emotions. 【0744】 Step 8: 【0745】 The user receives information displayed through the device, deepening their understanding and asking new questions. This information reception acts as new input for the next interaction. The user's responses and actions are used as new input information in the next interaction. 【0746】 (Application Example 2) 【0747】 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". 【0748】 When users access target information and optimize their learning experience through interaction with that information, it is necessary to dynamically adjust the content of the dialogue while considering the user's emotional state. However, existing systems lack the means to grasp user emotions and generate appropriate responses based on them, resulting in a limited quality of user experience. This invention aims to solve these problems and provide a more meaningful and user-friendly learning environment. 【0749】 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. 【0750】 In this invention, the server includes an input / output device for the user to input information about a target and select a target of interest, a data processing device via a natural language processing system for generating a response to the user based on the selected target, and an emotion analysis device for analyzing the user's voice and input to obtain emotion information. This makes it possible to recognize the user's emotional state in real time and generate a response accordingly. 【0751】 An "input / output device" is a device that receives user selection information and instructions and provides content to display to the user. 【0752】 A "natural language processing system" is a system that provides data processing technology to understand user questions and requests and generate appropriate responses. 【0753】 A "data processing device" is a device that processes necessary information based on a selected object and provides it to the user. 【0754】 An "emotion analysis device" is a device that uses technology to analyze a user's voice or input and detect the user's emotional state. 【0755】 A "language conversion device" is a device that translates dialogue into different languages, enabling users to obtain information in multiple languages. 【0756】 A "data storage device" is a device that saves data generated during a conversation and uses it for future conversations. 【0757】 This invention is a system that enables interactive dialogue with users in a physical store environment. Users input information about objects and select objects of interest using a smartphone or smart glasses. The terminal sends the user's selection information to a server, which retrieves the necessary data. 【0758】 The server generates responses based on selected subjects via a natural language processing system. Specifically, it uses software such as Google Cloud's Natural Language API and IBM's Watson Tone Analyzer to analyze the emotional state of voice and text received from the user. This allows the emotion analysis device to detect the user's emotions in real time and provide appropriate responses accordingly. 【0759】 Furthermore, it utilizes OpenAI's GPT model, a generative AI model, to generate natural language responses that match the user's questions. The generated responses are visually displayed to the user on the device, allowing the user to have a more meaningful conversational experience that is appropriate to their emotional state. 【0760】 This system is also used to generate learning profiles for specific users. By analyzing data accumulated through interactions with the user and applying it to subsequent interactions, it efficiently provides users with customized information. 【0761】 As a concrete example, when using a museum exhibit, it is possible to use the app to ask a question such as, "Could you tell me more about the background of this exhibit?" The system will respond in a way that is tailored to the user's interests and emotions, such as, "This exhibit was created in XX year, and it has the following background from that era. Is there anything else you would like to know?" 【0762】 An example of a prompt for a generative AI model might be something like, "Please tell me about the historical background of the exhibit. The questioner is interested and is seeking detailed information." 【0763】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0764】 Step 1: 【0765】 The user uses a smartphone or smart glasses to select a historical subject of interest. The device receives the user's selection information as input and sends this information to the server. This prepares the server to begin retrieving the necessary data. 【0766】 Step 2: 【0767】 The server retrieves relevant data based on the target information sent by the user. Here, it extracts detailed information related to the target from the database and performs preprocessing for natural language processing. The output of this process becomes the input data for the natural language processing engine. 【0768】 Step 3: 【0769】 On the server, the natural language processing system uses a natural language processing engine to generate responses to questions posed by the user. Here, a generative AI model is utilized to output natural language responses that match the questions, and these responses are then sent to the next processing step. 【0770】 Step 4: 【0771】 The emotion analyzer acquires emotion data from the user's voice or text input and adjusts the response based on that emotion. In this step, the user's input is analyzed and the tone and content are converted to suit the emotion. The input to this process is the user's emotion information, and the output is the adjusted response. 【0772】 Step 5: 【0773】 The server sends a coordinated response to the terminal, which then displays the response to the user. The output device visually presents the response to the user, providing a means for the user to verify the information. This allows the user to obtain information about historical subjects of interest in a form that is optimally tailored to their emotions. 【0774】 Step 6: 【0775】 The server stores data obtained through user interaction in a data storage device and uses it to inform future interactions and learning profiles. This allows for more customized information to be provided to the user in subsequent interactions. The input is the interaction data, and the output is the updated learning profile. 【0776】 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. 【0777】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0778】 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. 【0779】 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. 【0780】 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. 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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." 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 The following is further disclosed regarding the embodiments described above. 【0798】 (Claim 1) 【0799】 A means of providing an interface for users to input information about historical subjects and select subjects of interest, 【0800】 Information processing means via a natural language processing engine for generating a response to the user based on selected historical objects, 【0801】 Output means for adjusting and sending responses and annotations to be displayed to the user, 【0802】 A system that includes this. 【0803】 (Claim 2) 【0804】 The system according to claim 1, further comprising means for providing multilingual translation in dialogue with historical objects, enabling users to obtain information in multiple languages. 【0805】 (Claim 3) 【0806】 The system according to claim 1, further comprising data management means for generating an individual learning profile of the user using session data saved during the interaction and for reflecting this in the next interaction. 【0807】 "Example 1" 【0808】 (Claim 1) 【0809】 A display method for users to input information related to a specific historical context and select items of interest, 【0810】 An analysis means using natural language processing technology to generate a response for the user based on selected information, 【0811】 Output means for adjusting and communicating responses and supplementary information displayed to the user, 【0812】 A means of collecting information to receive requests from users and generate data structures, 【0813】 An analytical method that uses knowledge engineering models to create detailed documents based on requirements, 【0814】 A system that includes this. 【0815】 (Claim 2) 【0816】 The system according to claim 1, further comprising means for providing multilingual translation in a dialogue, enabling users to obtain information in different languages. 【0817】 (Claim 3) 【0818】 The system according to claim 1, further comprising a data management means for creating a user learning profile by utilizing the usage history accumulated during the conversation and reflecting it in the next conversation. 【0819】 "Application Example 1" 【0820】 (Claim 1) 【0821】 A user interface means for users to input historical information and select items of interest, 【0822】 A natural language processing processor means that generates a response to the user based on selected historical items, 【0823】 A means of providing historical information through audio in educational artifacts and promoting learning through dialogue, 【0824】 Information output means for adjusting and transmitting responses and annotations displayed to the user, 【0825】 A system that includes this. 【0826】 (Claim 2) 【0827】 The system according to claim 1, further comprising means for providing multilingual translation and enabling users to obtain information in multiple languages. 【0828】 (Claim 3) 【0829】 The system according to claim 1, further comprising data processing means for generating a data profile for individualized learning using stored dialogue data and for use in subsequent dialogues. 【0830】 "Example 2 of combining an emotion engine" 【0831】 (Claim 1) 【0832】 A means of providing a conversational interface for users to input information about a target area and select items of interest, 【0833】 Information processing means via natural language processing technology for generating a response to the user based on the selected target area, 【0834】 A means including an emotion analysis engine for acquiring and analyzing the emotional state of a user, 【0835】 Output means for adjusting and transmitting the generated response and annotation to suit the user's emotional state, 【0836】 A data management system that creates an individual learning profile for the user based on the conversation history and reflects it in the next conversation, 【0837】 A system that includes this. 【0838】 (Claim 2) 【0839】 The system according to claim 1, further comprising means for providing multilingual translation in interaction with a target domain, enabling the user to obtain information in multiple languages. 【0840】 (Claim 3) 【0841】 The system according to claim 1, further comprising a data management means for generating an individualized learning profile based on user emotion analysis using emotion information and session data acquired during the dialogue, and for utilizing it in the next dialogue. 【0842】 "Application example 2 when combining with an emotional engine" 【0843】 (Claim 1) 【0844】 An input / output device for the user to input information about the target and select the target of interest, 【0845】 A data processing device via a natural language processing system for generating a response to the user based on the selected object, 【0846】 An emotion analysis device for obtaining emotional information by analyzing the user's voice and input, 【0847】 An output device for adjusting and transmitting responses and annotations displayed to the user, 【0848】 A system that includes this. 【0849】 (Claim 2) 【0850】 The system according to claim 1, further comprising a language conversion device that provides multilingual translation in interaction with a target, enabling the user to obtain information in multiple languages. 【0851】 (Claim 3) 【0852】 The system according to claim 1, further comprising a data storage device for generating individual learning attributes of the user using data saved during the interaction and for reflecting these attributes in the next interaction. [Explanation of symbols] 【0853】 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

[Claim 1] A means of providing an interface for users to input information about historical subjects and select subjects of interest, Information processing means via a natural language processing engine for generating a response to the user based on selected historical objects, Output means for adjusting and sending responses and annotations to be displayed to the user, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for providing multilingual translation in dialogue with historical objects, enabling users to obtain information in multiple languages. [Claim 3] The system according to claim 1, further comprising data management means for generating an individual learning profile of the user using session data saved during the interaction and for reflecting this in the next interaction.

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