system

Abstract

Provide a system. 【Solution means】 Means for analyzing requests regarding selected cultural events or periods received from the user, Means for obtaining relevant information from the storage device based on the analyzed information, Means for generating an artificial intelligence conversation model using the obtained information and providing a conversation with the user, Means for personalizing the conversation considering the user's attribute information and translating it in multiple languages, Means for transmitting and displaying the generated conversation content to the user's terminal, Means for providing interactive questions and trivia based on the conversation content, Means for generating questions including an evaluation regarding the learning content based on the accessed information, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional history learning mainly relies on textbooks and lectures, and there is a lack of visually and aurally attractive teaching materials, so it is difficult to arouse the interest of students and learners. In addition, since a two-way interactive experience is not provided, learners cannot actively participate, and the opportunity to deepen understanding is limited. Furthermore, since a multilingual learning environment regarding history and culture is not well-established, there are also problems with adaptability to learners with different backgrounds. It is necessary to solve these problems and provide an immersive and personalized history learning experience for learners.

Means for Solving the Problems

[0005] This invention provides an AI learning platform that interactively provides information about historical figures and eras selected by the user. The platform first receives a request from the user and analyzes the inquiry regarding the selected historical figure or era. Based on the analysis, it retrieves relevant information from a database and generates an artificial intelligence dialogue model using the retrieved data. Next, it provides a dialogue with the user based on this model, personalizing the dialogue considering the user's profile information and translating it into multiple languages. Furthermore, it sends the generated dialogue content to the user's device for display and provides real-time feedback by offering interactive quizzes and trivia based on the dialogue. This allows learners to have an engaging and actively participating learning experience, and is also compatible with learners from different cultures and language regions.

[0006] A "user" refers to an individual who uses a system to obtain historical information or experiences.

[0007] A "request" is an inquiry that a user sends to a system seeking information about a specific historical figure or era.

[0008] "Analysis" is the process of using a computer program to analyze a received request in detail and identify the necessary information.

[0009] A "database" is a source of information that organizes and stores information about specific historical figures, events, or eras.

[0010] An "artificial intelligence dialogue model" is a form of computer software that can interact with users based on selected historical figures and eras.

[0011] Personalization refers to individually tailoring the content and experiences provided to each user according to their interests and learning progress.

[0012] Translation is the process of converting text and content between multiple languages ​​to accurately convey information to users who speak different languages.

[0013] "Transmission" refers to delivering generated content to the user's device via communication methods such as the internet.

[0014] An "interactive quiz" is a question-based task in which users can participate in a dialogue, and is used to test their understanding.

[0015] "Trivia" refers to small pieces of information or anecdotes shared during a conversation, used to comprehensively complement the learning process. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This AI learning platform is realized by receiving requests from users and providing information related to selected historical figures and eras. Its specific implementation is described below.

[0038] Users access the system via their devices and make requests to obtain information about specific historical figures or eras. For example, a user might want to learn about medieval European culture.

[0039] The server receives and analyzes this request. This analysis includes identifying the specific person and historical context the user is interested in. Based on the analysis results, the server accesses the system's database and retrieves relevant information that matches the request. Comprehensive data on medieval European culture is retrieved at this stage.

[0040] Next, the server uses an AI dialogue model based on the acquired data to generate an interactive conversation with the user. The generated conversation is provided to the user in real time. For example, if the user asks, "Tell me more about the medieval knighthood," the AI ​​will respond, "The medieval knighthood was an important social structure in Europe, and in many regions, sons of nobles belonged to this system."

[0041] Furthermore, this system can personalize conversations by considering the user's profile information and translate them into multiple languages. This ensures that users who speak different languages ​​receive the necessary information appropriately. The server also sends the generated conversation content to the terminal, which then displays it visually to the user.

[0042] In addition, the system includes features to deepen user understanding through interactive quizzes and trivia. For example, it inserts quizzes such as "Which is a famous medieval architectural style?" into the conversation, and the user's answers facilitate learning. As a result, the server analyzes the user's responses and provides appropriate feedback in real time.

[0043] Through this series of processes, users can enjoy an immersive history learning experience that cannot be obtained through traditional textbook-based learning.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users access the system through their terminals, create and submit requests to obtain information about specific historical figures or periods. For example, they might type, "I want to learn about technological innovations during the Renaissance."

[0047] Step 2:

[0048] The server receives requests sent by users. It analyzes the received requests to identify which historical figures or eras they refer to. This process involves using natural language processing techniques to understand the request's content.

[0049] Step 3:

[0050] The server accesses the database based on the analysis results and retrieves information related to the specified historical figure or era. In this process, it extracts highly relevant information from the large amount of historical data stored in the database.

[0051] Step 4:

[0052] The server uses the acquired information to generate an AI dialogue model. The AI ​​model constructs conversation content in response to user requests and prepares for interactive dialogue with the user.

[0053] Step 5:

[0054] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This step involves customization based on the user's background.

[0055] Step 6:

[0056] The server sends the generated dialogue content to the user's terminal. It optimizes the process to minimize time delays and provide a quick response.

[0057] Step 7:

[0058] The device visually displays the received dialogue content to the user. Through the information displayed on the screen, the user can initiate a dialogue with the AI ​​and exchange information intuitively.

[0059] Step 8:

[0060] Users use the dialogue screen to input additional questions or topics of interest, triggering the next interaction.

[0061] Step 9:

[0062] The server receives the user's additional request, consults the database again, and generates a new response based on the AI ​​dialogue model. This process is interactive and repeatable.

[0063] Step 10:

[0064] The server inserts quizzes and trivia into the conversation to test the user's understanding. Based on the quiz results, it processes information to provide immediate feedback to the user.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] Traditional educational information systems have struggled to flexibly provide information on specific historical contexts according to users' individual interests and levels of understanding. Furthermore, appropriately translating information for users who speak different languages ​​while simultaneously providing an interactive learning experience has been a significant challenge.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for analyzing information requests for historical context received from a user, means for extracting relevant information from information sources based on the analysis results, and means for generating an information processing model using the extracted information and providing the information in an interactive format with the user. This makes it possible to provide a personalized learning experience tailored to the user and realizes multilingual and immediate response interaction.

[0070] A "user" refers to an individual or group that uses an information processing system to acquire specific knowledge or information.

[0071] "Historical context" refers to a specific period or situation related to historical events or culture.

[0072] "Information sources" refer to records and repositories, such as information databases and archives, that store and make accessible the necessary data.

[0073] An "information processing model" refers to an algorithm or system that analyzes data based on received input and generates information that meets the user's requirements.

[0074] "Dialogue format" refers to a method of communication where the user and the system exchange messages and information with each other.

[0075] "Multilingualization" means translating specific information or content into multiple languages ​​and providing it appropriately to users who speak different languages.

[0076] "Real-time response" refers to a system characteristic that allows it to respond quickly to user requests and questions in real time.

[0077] This invention begins with a user making a request to obtain historical information using a terminal. The terminal provides a user interface to accept user input. For example, the user enters a prompt such as, "I want to learn about medieval European culture."

[0078] The server receives requests sent from terminals and analyzes them using a generative AI model. Specifically, a program that performs natural language processing (NLP) is used. Through this process, the server identifies keywords contained in the user's request and extracts information including historical context and related individuals.

[0079] Next, the server communicates with the database based on the analyzed information and collects relevant information. Database query techniques such as SQL are used for this data collection. Subsequently, the server inputs the extracted information into a generating AI model (e.g., GPT-3®) and generates a response in the form of an interactive conversation with the user.

[0080] The generated dialogue is adapted to the user's profile and level of understanding, and is further translated into multiple languages. Software for multilingual support is used, making it compatible with users who speak multiple languages. Finally, the server returns the generated text to the terminal, which then displays it to the user, providing the learning experience.

[0081] This system allows users to enjoy immersive history learning and deepen their understanding through interactive quizzes and trivia. In this way, it provides a personalized and dynamic learning environment that differs from traditional textbook learning.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user accesses the interface using a terminal and enters a prompt. The terminal sends an information request, such as "I want to learn about medieval European culture," as input. The terminal then prepares to send this information to the server.

[0085] Step 2:

[0086] The server receives user input sent from the terminal and begins analyzing the input using natural language processing technology. Specifically, a keyword extraction algorithm is executed to identify important terms such as "medieval Europe" and "culture" from the text. The analyzed keywords are then provided as output.

[0087] Step 3:

[0088] The server uses the analyzed keywords to query the database. As input, queries containing specific keywords are sent to the database. By retrieving information from the database (e.g., materials on medieval European culture), the server obtains relevant data as output.

[0089] Step 4:

[0090] The server generates conversational content using a generative AI model based on the acquired data. Information obtained from the database is passed to the generative AI model as input. The AI ​​analyzes this information and generates output by creating dialogue text.

[0091] Step 5:

[0092] The server performs translation processing to localize the generated dialogue content into multiple languages. The generated text is input to the translation software. The output is the translated text adapted to the user's language.

[0093] Step 6:

[0094] The server sends the translated dialogue to the terminal. The terminal receives this information and displays it visually to the user. This allows the user to have an interactive learning experience.

[0095] Step 7:

[0096] As an additional feature, the server provides interactive quizzes to deepen user understanding during conversations. Interaction and user profile information are used as input. The output consists of generated quiz questions, with immediate feedback provided as the user answers.

[0097] This entire process allows users to learn personalized through the system and gain a wealth of historical knowledge.

[0098] (Application Example 1)

[0099] 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."

[0100] In modern times, learning about history and culture often relies on textbooks and static media, which can lead to learners becoming easily bored. Furthermore, effectively delivering the same content to users in diverse language regions is challenging. In addition, adjusting content to suit individual learners' interests and levels of understanding is not easy. A system is needed to address these challenges.

[0101] 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.

[0102] In this invention, the server includes means for analyzing a request from a user regarding a selected cultural event or time period; means for retrieving relevant information from a storage device based on the analyzed information; and means for generating an artificial intelligence conversation model using the retrieved information and providing a conversation with the user. This makes it possible to provide engaging and personalized cultural learning in real time and reduce learning barriers among multilingual users.

[0103] A "user" is an individual or legal entity that accesses an information system and attempts to obtain information about cultural events or periods.

[0104] "Cultural phenomena" refer to unique lifestyles and social structures created by humankind in specific periods or regions.

[0105] A "period" is a historically defined period, a time interval in which culturally or socially distinctive events occurred.

[0106] A "request" is a request from a user for specific information or knowledge they seek from an information system.

[0107] "Analysis" is the process of examining information received from users in detail and understanding its meaning and purpose.

[0108] "Information" refers to a collection of knowledge that a system holds about cultural events and periods, with the aim of providing it to users.

[0109] A "storage device" is a facility that organizes collected information and stores it so that it can be retrieved upon user request.

[0110] An "artificial intelligence conversation model" is an AI technology used to engage in natural conversations with users based on acquired information.

[0111] "Conversation" refers to the exchange of information or opinions between a user and artificial intelligence.

[0112] "Personalization" refers to individually adjusting the content provided to users based on their attribute information.

[0113] A "multilingual user" is an individual or group that belongs to different linguistic and cultural spheres and wishes to receive information in each of those languages.

[0114] To implement this invention, the following system configuration is necessary. The server analyzes requests from the user regarding cultural events and timings, and retrieves relevant information from storage based on that information. The retrieved information is used to generate a natural conversation with the user using a generative AI model, i.e., an artificial intelligence conversation model.

[0115] The server uses the programming language Python and the web framework Django to receive and analyze user requests. Based on the analyzed requests, it retrieves cultural information from a MySQL® database. The retrieved information is then personalized as an AI conversation model using OpenAI® models and translated into multiple languages.

[0116] Users receive generated conversations using a device such as a smartphone. These conversations are generated based on pre-set prompts to facilitate user learning. A specific example is a question like, "Please tell me about the lifestyle in medieval Europe." Based on this prompt, the AI ​​conversation model provides useful information such as, "In medieval Europe, farmers mainly lived a self-sufficient life..."

[0117] Users receive this information through their devices and can enjoy an immersive learning experience about specific cultures and periods. Furthermore, the system provides users with interactive questions and quizzes to enhance their understanding. In this way, users can gain a deeper understanding of history and culture.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user inputs requests regarding cultural events or periods through a terminal. This input includes prompts such as, "Please tell me about medieval European lifestyles." The terminal then sends this information to the server.

[0121] Step 2:

[0122] The server analyzes the prompt message received from the user. This analysis includes a process that uses natural language processing techniques to extract the intent of the request. Based on the input prompt message, it identifies relevant keywords and topics, and then determines the scope of information to retrieve.

[0123] Step 3:

[0124] The server uses the analysis results to retrieve relevant information from the MySQL database. At this stage, it searches for data related to cultural events and periods that match the identified keywords and collects the relevant information. The retrieved data is used in the next step to generate AI conversations.

[0125] Step 4:

[0126] The server uses OpenAI's generative AI model to generate conversations with the user based on the information it has acquired. This model generates responses in a natural context and designs personalized replies that meet the user's needs. This process is based on the generative AI model's prompt, "Tell me more about medieval European lifestyles."

[0127] Step 5:

[0128] The server sends the generated conversation to the user's terminal. The terminal visually displays the received conversation content, providing information in a format that is easy for the user to understand. Multilingual support is also implemented at this stage, and translated content is displayed as needed.

[0129] Step 6:

[0130] The user reviews the information provided through the device and answers interactive questions to facilitate their understanding. During this stage, the system measures the user's comprehension through quizzes and questions generated by the system and sends their responses to the server.

[0131] Step 7:

[0132] The server analyzes user responses and generates feedback in real time. This analysis uses algorithms that evaluate the accuracy and comprehension of the user's responses, and based on the evaluation results, provides advice and additional information regarding the next learning steps.

[0133] 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.

[0134] This AI learning platform incorporates an emotion engine that analyzes emotional information through interaction with the user and dynamically adjusts the content and tone of the conversation to provide a more personalized learning experience.

[0135] Users access the system via their devices and request information about historical figures and eras. User input is often emotionally charged, sometimes including specific feelings such as, "I want to learn more about Renaissance inventions, but I find it a little difficult."

[0136] Upon receiving a user request, the server analyzes its contents. This is where the emotion engine comes into play, using natural language processing techniques to recognize the emotions contained within the user's text. By analyzing the user's emotional state, the emotion engine determines whether the user is currently excited, anxious, or calm.

[0137] Next, the server generates an AI dialogue model based on the analysis results and emotion data. It adjusts the tone and content of the dialogue, taking into account the emotion information obtained by the emotion engine. For example, if the analysis indicates that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain Renaissance inventions in an easy-to-understand way."

[0138] The server also personalizes and supports multiple languages ​​in the generated conversations based on the user's profile information and language settings. The generated content takes into account the learner's interests and mental state. This enables a more comfortable and engaging learning experience.

[0139] The server then sends the generated dialogue content to the user's device. The device displays the received information to the user, assisting in the interactive dialogue. Based on the information gained from the dialogue, the user can actively learn by asking deeper questions or moving on to new topics.

[0140] Furthermore, the server implements interactive quizzes and trivia within the conversation to test the user's knowledge and understanding. Based on the user's emotional state, the difficulty and content of the quizzes and trivia are adjusted as needed to enhance the learning effect. Through this entire process, users can enjoy a highly personalized and emotionally resonant learning environment.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users use their devices to access the system, create and submit requests seeking information about specific historical figures or periods. For example, they might type, "I want to know about ancient Greek philosophers."

[0144] Step 2:

[0145] The server receives requests from users. It then analyzes the received data and performs a process to identify the relevant historical figures and historical context. This analysis utilizes natural language processing techniques to understand the intent of the input.

[0146] Step 3:

[0147] The server activates an emotion engine to analyze the emotions contained in the user's input. For example, it identifies whether the user is excited, calm, or in some other emotional state based on their description.

[0148] Step 4:

[0149] The server searches the database based on the analysis results and sentiment data to retrieve the necessary historical information. The information retrieved is selected to correspond to the user's requests.

[0150] Step 5:

[0151] The server generates an AI dialogue model using the acquired information and emotion data. This generation process adjusts the tone and content according to the user's emotions, preparing to provide an appropriate dialogue to the user.

[0152] Step 6:

[0153] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This ensures that information is prepared to meet the user's individual needs.

[0154] Step 7:

[0155] The server sends the generated dialogue content to the user's device. The content is delivered to the device immediately and optimized for display.

[0156] Step 8:

[0157] The device displays conversational content received from the server to the user. Through the displayed information, the user can initiate a dialogue with the AI ​​and proceed with a direct learning experience.

[0158] Step 9:

[0159] Users can enter additional questions or requests for new topics based on the information provided. For example, they might ask, "I'd like to know more about the influence of Socrates."

[0160] Step 10:

[0161] The server receives new user requests and, if necessary, looks up the database and regenerates the interaction model. This process can be repeated based on user input.

[0162] Step 11:

[0163] The server inserts interactive quizzes and trivia into the conversation to test the user's knowledge. It also adjusts the content and difficulty of the quizzes based on the user's emotional state, incorporating features to enhance learning motivation.

[0164] These steps enable users to have a deeply personalized history learning experience that is emotionally resonant.

[0165] (Example 2)

[0166] 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 will be referred to as the "terminal."

[0167] Current learning systems often fail to adequately address users' emotional states and individual characteristics, resulting in a tendency to provide uniform information. Furthermore, a lack of multilingual support and interaction can prevent the provision of appropriate learning experiences for users with diverse backgrounds. This invention aims to provide an emotionally resonant, personalized learning experience, enabling responses tailored to the user's interests and level of understanding.

[0168] 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.

[0169] In this invention, the server includes means for analyzing a selected historical event received from a user and recognizing the corresponding emotion; means for obtaining relevant information from a recording medium based on the analyzed information and emotion data; and means for constructing an artificial intelligence dialogue model generated using the obtained information and adjusting the tone and content of the dialogue according to the user's emotional state. This makes it possible to realize a personalized learning experience in multiple languages ​​while responding to the user's individual emotions and needs.

[0170] A "user" is an individual or entity that uses a system and asks questions in search of specific information.

[0171] A "server" is a computer system that receives requests from users and performs data processing such as information analysis and the generation of dialogue models.

[0172] An "inquiry" is a statement or message that a user sends to a system in order to obtain information.

[0173] "Analysis" is the process of information processing that is carried out to interpret received information and understand the user's intentions and emotional state.

[0174] "Emotional data" refers to information that indicates a user's emotional state, and is psychological judgment data extracted from text.

[0175] "Recording medium" is a general term for databases and data storage devices used to hold information.

[0176] A "generated artificial intelligence dialogue model" is a dialogue system designed to provide appropriate responses in response to the user's questions and emotions.

[0177] "User profile information" refers to attribute information about individual users and is data used to provide personalized services.

[0178] "Interactive challenges and knowledge checks" are interactive quizzes and trivia that users can participate in to deepen their learning.

[0179] This invention relates to an advanced interactive system that provides a personalized learning experience while taking user emotions into consideration. The system consists of a user, a terminal, and a server.

[0180] Users access the system via devices such as computers and mobile devices. When users inquire about historical information or eras, they enter prompts that include their feelings, such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult."

[0181] The server activates an emotion engine to analyze the received prompt. This emotion engine uses natural language processing techniques to extract emotions contained in the user's input text and determine the user's psychological state. Text analysis software and emotion analysis algorithms are used for this analysis.

[0182] Furthermore, based on the analysis results, the server constructs a dialogue model using a generative AI model based on the acquired information. This dialogue model is configured to adjust the tone and content according to the user's emotional state, providing an appropriate and gentle conversation for the user. For example, if the user is feeling anxious, it will respond in a gentle tone, such as, "Don't worry, I'll explain the Renaissance inventions in an easy-to-understand way."

[0183] Furthermore, the server personalizes the conversation based on the user's profile information, translates it according to multilingual needs, and accommodates a diverse range of users. For this purpose, a multilingual translation engine and personalization algorithms are utilized.

[0184] The terminal displays generated conversational content sent from the server, providing an environment where users can intuitively interact with it and progress through learning by taking various quizzes and knowledge checks. In this way, the user's learning experience will be enriched.

[0185] These processes allow users to receive emotionally resonant information and learn comfortably. Learning services provided in this way are also suitable for use in educational and cultural institutions.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] The user uses a terminal to enter questions about historical topics. These questions may include prompts such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult." The input is in text format, and the terminal sends this input to the server.

[0189] Step 2:

[0190] The server analyzes user input received from the terminal. It uses natural language processing techniques to analyze the prompt text as input and extract the user's emotions. Specifically, it uses an emotion engine to analyze keywords and context in the text, recognizing emotions such as "anxiety" and "interest." The output generates data indicating the user's emotional state.

[0191] Step 3:

[0192] The server retrieves relevant information from the database based on the analysis results. The input consists of the user's inquiry and sentiment data, which is used to search for relevant historical information. The output provides appropriate information and learning materials in response to the user's question.

[0193] Step 4:

[0194] The server uses generative AI technology to build a dialogue model. This model uses acquired information and emotional data as input to design the tone and content of the dialogue according to the user's current emotional state. Specifically, if the user is feeling anxious, it will generate dialogue content in a gentle tone that provides reassurance. The output is a personalized AI dialogue model.

[0195] Step 5:

[0196] The server matches the dialogue model with the user's profile and prepares a personalized dialogue. If multilingual support is required, it uses a translation engine to translate into the appropriate language. User attribute information and language settings are taken as input, and the output is a dialogue optimized for the user.

[0197] Step 6:

[0198] The terminal displays generated dialogue content sent from the server to the user. Specifically, it uses an interactive user interface to assist the user in progressing through the dialogue or taking quizzes. The input is the dialogue content from the server, and the output is the next step based on the user's actions, i.e., further dialogue or new questions.

[0199] (Application Example 2)

[0200] 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".

[0201] Current interactive learning systems struggle to adequately understand users' emotional states and provide corresponding dialogue. Furthermore, to make the learning experience more personalized, engaging, and user-friendly, there is a need for interactive content that resonates with users' emotions.

[0202] 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.

[0203] In this invention, the server includes means for analyzing a selected time period received from the user, means for obtaining relevant information from a storage device based on the analyzed information, and means for generating an artificial intelligence dialogue model using the obtained information and providing a dialogue based on the user's emotions. This enables a personalized learning experience that takes into account the user's emotional state.

[0204] A "user" refers to an individual who interacts with this system, receives information, and enjoys a learning experience.

[0205] "Inquiries about selected eras" refer to requests from users seeking information about a specific period or historical context.

[0206] "Means of analysis" refers to the technical process of processing information received from the user, clarifying its content, and deriving an appropriate response.

[0207] "Relevant information" refers to the knowledge and data in the database that respond to user inquiries.

[0208] A "storage device" refers to an electronic or digital medium used to store and access data as needed.

[0209] An "artificial intelligence dialogue model" refers to a collection of algorithms and technologies for effectively communicating with users through natural language.

[0210] An "emotion engine" refers to a software component that analyzes and understands the emotional state of a user based on their input.

[0211] "Device" refers to a hardware device that allows a user to visually or audibly confirm information received from a system.

[0212] "Interactive problem-solving and knowledge-based games" refer to activities that encourage user participation through dialogue and enhance learning with enjoyment and interest.

[0213] "Personalizing" refers to tailoring the learning experience to best suit the user based on their profile and preferences.

[0214] The "real-time feedback function" refers to the ability to instantly analyze user input during a conversation and provide a response based on the results.

[0215] This invention begins with a user accessing the system via a device and requesting information about a specific time period. The server utilizes natural language processing techniques to analyze the user's inquiry. This analysis employs an emotion engine to recognize the emotions contained in the user's text. The emotion engine analyzes the user's emotional state and determines whether they are excited, anxious, or calm.

[0216] Based on the analysis results, the server retrieves relevant information from its storage device and generates an artificial intelligence dialogue model. The generated model takes into account the emotional information obtained by the emotion engine and adjusts the tone and content of the dialogue. For example, if the server determines that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain something that will interest you in an easy-to-understand way."

[0217] The server further personalizes these interactions based on the user's profile information and language settings, and also enables multilingual support. The generated dialogue content is transmitted to the user through the device and displayed. This allows the user to ask deeper questions based on the information gained from the dialogue. This includes interactive questions and knowledge games provided by the server to test the user's knowledge and understanding.

[0218] For example, if a child were to tell the server, "I want to learn more about space, but I'm a little worried," the server would provide a calm and encouraging response such as, "Don't worry. I'll start with the basics of space and teach you some interesting facts."

[0219] Examples of prompt messages include the following:

[0220] "User comment: I want to learn more about space, but I feel a little anxious.\nEmotion: Anxious\nResponse: Don't worry. We'll start with the basics of space and teach you some fascinating facts."

[0221] In this way, users can enjoy a learning experience that is tailored to their individual emotions and interests.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The server receives inquiries from users via the device. The input is text data containing specific information and emotions related to a particular era. This text data is analyzed using natural language processing techniques to extract keywords and phrases relevant to specific questions or requests.

[0225] Step 2:

[0226] The server uses an emotion engine to identify the user's emotions based on the analyzed information. The input is the text processed in step 1. The emotion engine analyzes this text to determine whether the user is excited, anxious, or calm. The output of this process is data indicating the user's emotional state.

[0227] Step 3:

[0228] The server retrieves relevant information from its storage based on the user's emotional state and analysis results. Specifically, it uses database queries to search for knowledge directly related to the user's inquiry and extract the necessary information. The input for this step is the user's request and emotional data, and the output is the retrieved relevant information.

[0229] Step 4:

[0230] The server generates an artificial intelligence dialogue model based on acquired information and sentiment data. This model includes prompt sentences intended to respond to the user. Model generation involves setting the tone and content, taking into account both acquired data and sentiment information, to construct dialogue sentences optimized for the user. The generated output is the specific response content within the dialogue model.

[0231] Step 5:

[0232] The server transmits the generated dialogue content to the device and displays it to the user. Here, the dialogue is personalized and multilingual based on the user's profile information and language settings. The transmitted data includes personalized content and is in a format that the user receives visually or audibly.

[0233] Step 6:

[0234] The user participates in interactive questions and knowledge games based on the conversations they have submitted. In this step, the user's knowledge and understanding are evaluated based on the content of the questions generated by the server. The input is the user's response, and feedback is provided as output to enhance the learning effect by evaluating that response.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] [Second Embodiment]

[0239] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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".

[0251] This AI learning platform is realized by receiving requests from users and providing information related to selected historical figures and eras. Its specific implementation is described below.

[0252] Users access the system via their devices and make requests to obtain information about specific historical figures or eras. For example, a user might want to learn about medieval European culture.

[0253] The server receives and analyzes this request. This analysis includes identifying the specific person and historical context the user is interested in. Based on the analysis results, the server accesses the system's database and retrieves relevant information that matches the request. Comprehensive data on medieval European culture is retrieved at this stage.

[0254] Next, the server uses an AI dialogue model based on the acquired data to generate an interactive conversation with the user. The generated conversation is provided to the user in real time. For example, if the user asks, "Tell me more about the medieval knighthood," the AI ​​will respond, "The medieval knighthood was an important social structure in Europe, and in many regions, sons of nobles belonged to this system."

[0255] Furthermore, this system can personalize conversations by considering the user's profile information and translate them into multiple languages. This ensures that users who speak different languages ​​receive the necessary information appropriately. The server also sends the generated conversation content to the terminal, which then displays it visually to the user.

[0256] In addition, the system includes features to deepen user understanding through interactive quizzes and trivia. For example, it inserts quizzes such as "Which is a famous medieval architectural style?" into the conversation, and the user's answers facilitate learning. As a result, the server analyzes the user's responses and provides appropriate feedback in real time.

[0257] Through this series of processes, users can enjoy an immersive history learning experience that cannot be obtained through traditional textbook-based learning.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] Users access the system through their terminals, create and submit requests to obtain information about specific historical figures or periods. For example, they might type, "I want to learn about technological innovations during the Renaissance."

[0261] Step 2:

[0262] The server receives requests sent by users. It analyzes the received requests to identify which historical figures or eras they refer to. This process involves using natural language processing techniques to understand the request's content.

[0263] Step 3:

[0264] The server accesses the database based on the analysis results and retrieves information related to the specified historical figure or era. In this process, it extracts highly relevant information from the large amount of historical data stored in the database.

[0265] Step 4:

[0266] The server uses the acquired information to generate an AI dialogue model. The AI ​​model constructs conversation content in response to user requests and prepares for interactive dialogue with the user.

[0267] Step 5:

[0268] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This step involves customization based on the user's background.

[0269] Step 6:

[0270] The server sends the generated dialogue content to the user's terminal. It optimizes the process to minimize time delays and provide a quick response.

[0271] Step 7:

[0272] The device visually displays the received dialogue content to the user. Through the information displayed on the screen, the user can initiate a dialogue with the AI ​​and exchange information intuitively.

[0273] Step 8:

[0274] Users use the dialogue screen to input additional questions or topics of interest, triggering the next interaction.

[0275] Step 9:

[0276] The server receives the user's additional request, consults the database again, and generates a new response based on the AI ​​dialogue model. This process is interactive and repeatable.

[0277] Step 10:

[0278] The server inserts quizzes and trivia into the conversation to test the user's understanding. Based on the quiz results, it processes information to provide immediate feedback to the user.

[0279] (Example 1)

[0280] 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."

[0281] Traditional educational information systems have struggled to flexibly provide information on specific historical contexts according to users' individual interests and levels of understanding. Furthermore, appropriately translating information for users who speak different languages ​​while simultaneously providing an interactive learning experience has been a significant challenge.

[0282] 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.

[0283] In this invention, the server includes means for analyzing an information request related to the background of the times received from the user, means for extracting relevant information from an information source based on the analysis result, means for generating an information processing model by using the extracted information, and means for providing information in an interactive form with the user. Thereby, it becomes possible to provide an individualized learning experience tailored to the user, and to realize a multilingual and real-time response type interaction.

[0284] The "user" refers to an individual or a group who attempts to acquire specific knowledge or information by using an information processing system.

[0285] The "background of the times" means a specific period related to historical events or cultures and the related situations.

[0286] The "information source" refers to a record or repository such as an information database or an archive that stores necessary data and makes it accessible.

[0287] The "information processing model" means an algorithm or a system for analyzing data based on the received input and generating information according to the user's request.

[0288] The "interactive form" refers to a method in which the user and the system exchange messages with each other and exchange information.

[0289] "Multilingualization" means translating specific information or content into multiple languages and appropriately providing it to users who use different languages.

[0290] The "real-time response type" refers to the characteristic of a system that quickly responds to the user's requests and questions in real time.

[0291] This invention begins with a user making a request to obtain historical information using a terminal. The terminal provides a user interface to accept user input. For example, the user enters a prompt such as, "I want to learn about medieval European culture."

[0292] The server receives requests sent from terminals and analyzes them using a generative AI model. Specifically, a program that performs natural language processing (NLP) is used. Through this process, the server identifies keywords contained in the user's request and extracts information including historical context and related individuals.

[0293] Next, the server communicates with the database based on the analyzed information and collects relevant information. Database query techniques such as SQL are used for this data collection. After that, the server inputs the extracted information into a generating AI model (e.g., GPT-3) and generates a response in the form of an interactive conversation with the user.

[0294] The generated dialogue is adapted to the user's profile and level of understanding, and is further translated into multiple languages. Software for multilingual support is used, making it compatible with users who speak multiple languages. Finally, the server returns the generated text to the terminal, which then displays it to the user, providing the learning experience.

[0295] This system allows users to enjoy immersive history learning and deepen their understanding through interactive quizzes and trivia. In this way, it provides a personalized and dynamic learning environment that differs from traditional textbook learning.

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

[0297] Step 1:

[0298] The user accesses the interface using a terminal and enters a prompt sentence. As input, the user sends an information request such as "want to learn about medieval European culture" to the terminal. The terminal prepares to send this information to the server.

[0299] Step 2:

[0300] The server receives the user input sent from the terminal and starts analyzing the input using natural language processing technology. As a specific operation, a keyword extraction algorithm is executed to identify important terms such as "medieval Europe" and "culture" from the text. As output, the analyzed keywords are prepared.

[0301] Step 3:

[0302] The server queries the database using the analyzed keywords. As input, a query containing specific keywords is sent to the database. By obtaining information from the database (e.g., materials related to medieval European culture), relevant data is obtained as output.

[0303] Step 4:

[0304] Based on the acquired data, the server uses a generative AI model to generate dialogue-style content. As input, the information obtained from the database is passed to the generative AI model. The AI analyzes this and generates output by creating text for dialogue.

[0305] Step 5:

[0306] The server performs a translation process to multilingualize the generated dialogue content. As input, the generated text is input into translation software. The output is the translated text adapted to the user's language.

[0307] Step 6:

[0308] The server sends the translated dialogue to the terminal. The terminal receives this information and displays it visually to the user. This allows the user to have an interactive learning experience.

[0309] Step 7:

[0310] As an additional feature, the server provides interactive quizzes to deepen user understanding during conversations. Interaction and user profile information are used as input. The output consists of generated quiz questions, with immediate feedback provided as the user answers.

[0311] This entire process allows users to learn personalized through the system and gain a wealth of historical knowledge.

[0312] (Application Example 1)

[0313] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0314] In modern times, learning about history and culture often relies on textbooks and static media, which can lead to learners becoming easily bored. Furthermore, effectively delivering the same content to users in diverse language regions is challenging. In addition, adjusting content to suit individual learners' interests and levels of understanding is not easy. A system is needed to address these challenges.

[0315] 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.

[0316] In this invention, the server includes means for analyzing a request from a user regarding a selected cultural event or time period; means for retrieving relevant information from a storage device based on the analyzed information; and means for generating an artificial intelligence conversation model using the retrieved information and providing a conversation with the user. This makes it possible to provide engaging and personalized cultural learning in real time and reduce learning barriers among multilingual users.

[0317] A "user" is an individual or legal entity that accesses an information system and attempts to obtain information about cultural events or periods.

[0318] "Cultural phenomena" refer to unique lifestyles and social structures created by humankind in specific periods or regions.

[0319] A "period" is a historically defined period, a time interval in which culturally or socially distinctive events occurred.

[0320] A "request" is a request from a user for specific information or knowledge they seek from an information system.

[0321] "Analysis" is the process of examining information received from users in detail and understanding its meaning and purpose.

[0322] "Information" refers to a collection of knowledge that a system holds about cultural events and periods, with the aim of providing it to users.

[0323] A "storage device" is a facility that organizes collected information and stores it so that it can be retrieved upon user request.

[0324] An "artificial intelligence conversation model" is an AI technology used to engage in natural conversations with users based on acquired information.

[0325] "Conversation" refers to the exchange of information or opinions between a user and artificial intelligence.

[0326] "Personalization" refers to individually adjusting the content provided to users based on their attribute information.

[0327] A "multilingual user" is an individual or group that belongs to different linguistic and cultural spheres and wishes to receive information in each of those languages.

[0328] To implement this invention, the following system configuration is necessary. The server analyzes requests from the user regarding cultural events and timings, and retrieves relevant information from storage based on that information. The retrieved information is used to generate a natural conversation with the user using a generative AI model, i.e., an artificial intelligence conversation model.

[0329] The server uses the programming language Python and the web framework Django to receive and analyze user requests. Based on the analyzed requests, it retrieves cultural information from a MySQL database. The retrieved information is then personalized as an AI conversation model using OpenAI models and translated into multiple languages.

[0330] Users receive generated conversations using a device such as a smartphone. These conversations are generated based on pre-set prompts to facilitate user learning. A specific example is a question like, "Please tell me about the lifestyle in medieval Europe." Based on this prompt, the AI ​​conversation model provides useful information such as, "In medieval Europe, farmers mainly lived a self-sufficient life..."

[0331] Users receive this information through their devices and can enjoy an immersive learning experience about specific cultures and periods. Furthermore, the system provides users with interactive questions and quizzes to enhance their understanding. In this way, users can gain a deeper understanding of history and culture.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The user inputs requests regarding cultural events or periods through a terminal. This input includes prompts such as, "Please tell me about medieval European lifestyles." The terminal then sends this information to the server.

[0335] Step 2:

[0336] The server analyzes the prompt message received from the user. This analysis includes a process that uses natural language processing techniques to extract the intent of the request. Based on the input prompt message, it identifies relevant keywords and topics, and then determines the scope of information to retrieve.

[0337] Step 3:

[0338] The server uses the analysis results to retrieve relevant information from the MySQL database. At this stage, it searches for data related to cultural events and periods that match the identified keywords and collects the relevant information. The retrieved data is used in the next step to generate AI conversations.

[0339] Step 4:

[0340] The server uses OpenAI's generative AI model to generate conversations with the user based on the information it has acquired. This model generates responses in a natural context and designs personalized replies that meet the user's needs. This process is based on the generative AI model's prompt, "Tell me more about medieval European lifestyles."

[0341] Step 5:

[0342] The server sends the generated conversation to the user's terminal. The terminal visually displays the received conversation content, providing information in a format that is easy for the user to understand. Multilingual support is also implemented at this stage, and translated content is displayed as needed.

[0343] Step 6:

[0344] The user reviews the information provided through the device and answers interactive questions to facilitate their understanding. During this stage, the system measures the user's comprehension through quizzes and questions generated by the system and sends their responses to the server.

[0345] Step 7:

[0346] The server analyzes user responses and generates feedback in real time. This analysis uses algorithms that evaluate the accuracy and comprehension of the user's responses, and based on the evaluation results, provides advice and additional information regarding the next learning steps.

[0347] 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.

[0348] This AI learning platform incorporates an emotion engine that analyzes emotional information through interaction with the user and dynamically adjusts the content and tone of the conversation to provide a more personalized learning experience.

[0349] Users access the system via their devices and request information about historical figures and eras. User input is often emotionally charged, sometimes including specific feelings such as, "I want to learn more about Renaissance inventions, but I find it a little difficult."

[0350] Upon receiving a user request, the server analyzes its contents. This is where the emotion engine comes into play, using natural language processing techniques to recognize the emotions contained within the user's text. By analyzing the user's emotional state, the emotion engine determines whether the user is currently excited, anxious, or calm.

[0351] Next, the server generates an AI dialogue model based on the analysis results and emotion data. It adjusts the tone and content of the dialogue, taking into account the emotion information obtained by the emotion engine. For example, if the analysis indicates that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain Renaissance inventions in an easy-to-understand way."

[0352] The server also personalizes and supports multiple languages ​​in the generated conversations based on the user's profile information and language settings. The generated content takes into account the learner's interests and mental state. This enables a more comfortable and engaging learning experience.

[0353] The server then sends the generated dialogue content to the user's device. The device displays the received information to the user, assisting in the interactive dialogue. Based on the information gained from the dialogue, the user can actively learn by asking deeper questions or moving on to new topics.

[0354] Furthermore, the server implements interactive quizzes and trivia within the conversation to test the user's knowledge and understanding. Based on the user's emotional state, the difficulty and content of the quizzes and trivia are adjusted as needed to enhance the learning effect. Through this entire process, users can enjoy a highly personalized and emotionally resonant learning environment.

[0355] The following describes the processing flow.

[0356] Step 1:

[0357] Users use their devices to access the system, create and submit requests seeking information about specific historical figures or periods. For example, they might type, "I want to know about ancient Greek philosophers."

[0358] Step 2:

[0359] The server receives requests from users. It then analyzes the received data and performs a process to identify the relevant historical figures and historical context. This analysis utilizes natural language processing techniques to understand the intent of the input.

[0360] Step 3:

[0361] The server activates an emotion engine to analyze the emotions contained in the user's input. For example, it identifies whether the user is excited, calm, or in some other emotional state based on their description.

[0362] Step 4:

[0363] The server searches the database based on the analysis results and sentiment data to retrieve the necessary historical information. The information retrieved is selected to correspond to the user's requests.

[0364] Step 5:

[0365] The server generates an AI dialogue model using the acquired information and emotion data. This generation process adjusts the tone and content according to the user's emotions, preparing to provide an appropriate dialogue to the user.

[0366] Step 6:

[0367] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This ensures that information is prepared to meet the user's individual needs.

[0368] Step 7:

[0369] The server sends the generated dialogue content to the user's device. The content is delivered to the device immediately and optimized for display.

[0370] Step 8:

[0371] The device displays conversational content received from the server to the user. Through the displayed information, the user can initiate a dialogue with the AI ​​and proceed with a direct learning experience.

[0372] Step 9:

[0373] Users can enter additional questions or requests for new topics based on the information provided. For example, they might ask, "I'd like to know more about the influence of Socrates."

[0374] Step 10:

[0375] The server receives new user requests and, if necessary, looks up the database and regenerates the interaction model. This process can be repeated based on user input.

[0376] Step 11:

[0377] The server inserts interactive quizzes and trivia into the conversation to test the user's knowledge. It also adjusts the content and difficulty of the quizzes based on the user's emotional state, incorporating features to enhance learning motivation.

[0378] These steps enable users to have a deeply personalized history learning experience that is emotionally resonant.

[0379] (Example 2)

[0380] 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".

[0381] Current learning systems often fail to adequately address users' emotional states and individual characteristics, resulting in a tendency to provide uniform information. Furthermore, a lack of multilingual support and interaction can prevent the provision of appropriate learning experiences for users with diverse backgrounds. This invention aims to provide an emotionally resonant, personalized learning experience, enabling responses tailored to the user's interests and level of understanding.

[0382] 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.

[0383] In this invention, the server includes means for analyzing a selected historical event received from a user and recognizing the corresponding emotion; means for obtaining relevant information from a recording medium based on the analyzed information and emotion data; and means for constructing an artificial intelligence dialogue model generated using the obtained information and adjusting the tone and content of the dialogue according to the user's emotional state. This makes it possible to realize a personalized learning experience in multiple languages ​​while responding to the user's individual emotions and needs.

[0384] A "user" is an individual or entity that uses a system and asks questions in search of specific information.

[0385] A "server" is a computer system that receives requests from users and performs data processing such as information analysis and the generation of dialogue models.

[0386] An "inquiry" is a statement or message that a user sends to a system in order to obtain information.

[0387] "Analysis" is the process of information processing that is carried out to interpret received information and understand the user's intentions and emotional state.

[0388] "Emotional data" refers to information that indicates a user's emotional state, and is psychological judgment data extracted from text.

[0389] "Recording medium" is a general term for databases and data storage devices used to hold information.

[0390] A "generated artificial intelligence dialogue model" is a dialogue system designed to provide appropriate responses in response to the user's questions and emotions.

[0391] "User profile information" refers to attribute information about individual users and is data used to provide personalized services.

[0392] "Interactive challenges and knowledge checks" are interactive quizzes and trivia that users can participate in to deepen their learning.

[0393] This invention relates to an advanced interactive system that provides a personalized learning experience while taking user emotions into consideration. The system consists of a user, a terminal, and a server.

[0394] Users access the system via devices such as computers and mobile devices. When users inquire about historical information or eras, they enter prompts that include their feelings, such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult."

[0395] The server activates an emotion engine to analyze the received prompt. This emotion engine uses natural language processing techniques to extract emotions contained in the user's input text and determine the user's psychological state. Text analysis software and emotion analysis algorithms are used for this analysis.

[0396] Furthermore, based on the analysis results, the server constructs a dialogue model using a generative AI model based on the acquired information. This dialogue model is configured to adjust the tone and content according to the user's emotional state, providing an appropriate and gentle conversation for the user. For example, if the user is feeling anxious, it will respond in a gentle tone, such as, "Don't worry, I'll explain the Renaissance inventions in an easy-to-understand way."

[0397] Furthermore, the server personalizes the conversation based on the user's profile information, translates it according to multilingual needs, and accommodates a diverse range of users. For this purpose, a multilingual translation engine and personalization algorithms are utilized.

[0398] The terminal displays generated conversational content sent from the server, providing an environment where users can intuitively interact with it and progress through learning by taking various quizzes and knowledge checks. In this way, the user's learning experience will be enriched.

[0399] These processes allow users to receive emotionally resonant information and learn comfortably. Learning services provided in this way are also suitable for use in educational and cultural institutions.

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The user uses a terminal to enter questions about historical topics. These questions may include prompts such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult." The input is in text format, and the terminal sends this input to the server.

[0403] Step 2:

[0404] The server analyzes user input received from the terminal. It uses natural language processing techniques to analyze the prompt text as input and extract the user's emotions. Specifically, it uses an emotion engine to analyze keywords and context in the text, recognizing emotions such as "anxiety" and "interest." The output generates data indicating the user's emotional state.

[0405] Step 3:

[0406] The server retrieves relevant information from the database based on the analysis results. The input consists of the user's inquiry and sentiment data, which is used to search for relevant historical information. The output provides appropriate information and learning materials in response to the user's question.

[0407] Step 4:

[0408] The server uses generative AI technology to build a dialogue model. This model uses acquired information and emotional data as input to design the tone and content of the dialogue according to the user's current emotional state. Specifically, if the user is feeling anxious, it will generate dialogue content in a gentle tone that provides reassurance. The output is a personalized AI dialogue model.

[0409] Step 5:

[0410] The server matches the dialogue model with the user's profile and prepares a personalized dialogue. If multilingual support is required, it uses a translation engine to translate into the appropriate language. User attribute information and language settings are taken as input, and the output is a dialogue optimized for the user.

[0411] Step 6:

[0412] The terminal displays generated dialogue content sent from the server to the user. Specifically, it uses an interactive user interface to assist the user in progressing through the dialogue or taking quizzes. The input is the dialogue content from the server, and the output is the next step based on the user's actions, i.e., further dialogue or new questions.

[0413] (Application Example 2)

[0414] 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."

[0415] Current interactive learning systems struggle to adequately understand users' emotional states and provide corresponding dialogue. Furthermore, to make the learning experience more personalized, engaging, and user-friendly, there is a need for interactive content that resonates with users' emotions.

[0416] 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.

[0417] In this invention, the server includes means for analyzing a selected time period received from the user, means for obtaining relevant information from a storage device based on the analyzed information, and means for generating an artificial intelligence dialogue model using the obtained information and providing a dialogue based on the user's emotions. This enables a personalized learning experience that takes into account the user's emotional state.

[0418] A "user" refers to an individual who interacts with this system, receives information, and enjoys a learning experience.

[0419] "Inquiries about selected eras" refer to requests from users seeking information about a specific period or historical context.

[0420] "Means of analysis" refers to the technical process of processing information received from the user, clarifying its content, and deriving an appropriate response.

[0421] "Relevant information" refers to the knowledge and data in the database that respond to user inquiries.

[0422] A "storage device" refers to an electronic or digital medium used to store and access data as needed.

[0423] An "artificial intelligence dialogue model" refers to a collection of algorithms and technologies for effectively communicating with users through natural language.

[0424] An "emotion engine" refers to a software component that analyzes and understands the emotional state of a user based on their input.

[0425] "Device" refers to a hardware device that allows a user to visually or audibly confirm information received from a system.

[0426] "Interactive problem-solving and knowledge-based games" refer to activities that encourage user participation through dialogue and enhance learning with enjoyment and interest.

[0427] "Personalizing" refers to tailoring the learning experience to best suit the user based on their profile and preferences.

[0428] The "real-time feedback function" refers to the ability to instantly analyze user input during a conversation and provide a response based on the results.

[0429] This invention begins with a user accessing the system via a device and requesting information about a specific time period. The server utilizes natural language processing techniques to analyze the user's inquiry. This analysis employs an emotion engine to recognize the emotions contained in the user's text. The emotion engine analyzes the user's emotional state and determines whether they are excited, anxious, or calm.

[0430] Based on the analysis results, the server retrieves relevant information from its storage device and generates an artificial intelligence dialogue model. The generated model takes into account the emotional information obtained by the emotion engine and adjusts the tone and content of the dialogue. For example, if the server determines that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain something that will interest you in an easy-to-understand way."

[0431] The server further personalizes these interactions based on the user's profile information and language settings, and also enables multilingual support. The generated dialogue content is transmitted to the user through the device and displayed. This allows the user to ask deeper questions based on the information gained from the dialogue. This includes interactive questions and knowledge games provided by the server to test the user's knowledge and understanding.

[0432] For example, if a child were to tell the server, "I want to learn more about space, but I'm a little worried," the server would provide a calm and encouraging response such as, "Don't worry. I'll start with the basics of space and teach you some interesting facts."

[0433] Examples of prompt messages include the following:

[0434] "User comment: I want to learn more about space, but I feel a little anxious.\nEmotion: Anxious\nResponse: Don't worry. We'll start with the basics of space and teach you some fascinating facts."

[0435] In this way, users can enjoy a learning experience that is tailored to their individual emotions and interests.

[0436] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0437] Step 1:

[0438] The server receives inquiries from users via the device. The input is text data containing specific information and emotions related to a particular era. This text data is analyzed using natural language processing techniques to extract keywords and phrases relevant to specific questions or requests.

[0439] Step 2:

[0440] The server uses an emotion engine to identify the user's emotions based on the analyzed information. The input is the text processed in step 1. The emotion engine analyzes this text to determine whether the user is excited, anxious, or calm. The output of this process is data indicating the user's emotional state.

[0441] Step 3:

[0442] The server retrieves relevant information from its storage based on the user's emotional state and analysis results. Specifically, it uses database queries to search for knowledge directly related to the user's inquiry and extract the necessary information. The input for this step is the user's request and emotional data, and the output is the retrieved relevant information.

[0443] Step 4:

[0444] The server generates an artificial intelligence dialogue model based on acquired information and sentiment data. This model includes prompt sentences intended to respond to the user. Model generation involves setting the tone and content, taking into account both acquired data and sentiment information, to construct dialogue sentences optimized for the user. The generated output is the specific response content within the dialogue model.

[0445] Step 5:

[0446] The server transmits the generated dialogue content to the device and displays it to the user. Here, the dialogue is personalized and multilingual based on the user's profile information and language settings. The transmitted data includes personalized content and is in a format that the user receives visually or audibly.

[0447] Step 6:

[0448] The user participates in interactive questions and knowledge games based on the conversations they have submitted. In this step, the user's knowledge and understanding are evaluated based on the content of the questions generated by the server. The input is the user's response, and feedback is provided as output to enhance the learning effect by evaluating that response.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] [Third Embodiment]

[0453] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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".

[0465] This AI learning platform is realized by receiving requests from users and providing information related to selected historical figures and eras. Its specific implementation is described below.

[0466] Users access the system via their devices and make requests to obtain information about specific historical figures or eras. For example, a user might want to learn about medieval European culture.

[0467] The server receives and analyzes this request. This analysis includes identifying the specific person and historical context the user is interested in. Based on the analysis results, the server accesses the system's database and retrieves relevant information that matches the request. Comprehensive data on medieval European culture is retrieved at this stage.

[0468] Next, the server uses an AI dialogue model based on the acquired data to generate an interactive conversation with the user. The generated conversation is provided to the user in real time. For example, if the user asks, "Tell me more about the medieval knighthood," the AI ​​will respond, "The medieval knighthood was an important social structure in Europe, and in many regions, sons of nobles belonged to this system."

[0469] Furthermore, this system can personalize conversations by considering the user's profile information and translate them into multiple languages. This ensures that users who speak different languages ​​receive the necessary information appropriately. The server also sends the generated conversation content to the terminal, which then displays it visually to the user.

[0470] In addition, the system includes features to deepen user understanding through interactive quizzes and trivia. For example, it inserts quizzes such as "Which is a famous medieval architectural style?" into the conversation, and the user's answers facilitate learning. As a result, the server analyzes the user's responses and provides appropriate feedback in real time.

[0471] Through this series of processes, users can enjoy an immersive history learning experience that cannot be obtained through traditional textbook-based learning.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] Users access the system through their terminals, create and submit requests to obtain information about specific historical figures or periods. For example, they might type, "I want to learn about technological innovations during the Renaissance."

[0475] Step 2:

[0476] The server receives requests sent by users. It analyzes the received requests to identify which historical figures or eras they refer to. This process involves using natural language processing techniques to understand the request's content.

[0477] Step 3:

[0478] The server accesses the database based on the analysis results and retrieves information related to the specified historical figure or era. In this process, it extracts highly relevant information from the large amount of historical data stored in the database.

[0479] Step 4:

[0480] The server uses the acquired information to generate an AI dialogue model. The AI ​​model constructs conversation content in response to user requests and prepares for interactive dialogue with the user.

[0481] Step 5:

[0482] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This step involves customization based on the user's background.

[0483] Step 6:

[0484] The server sends the generated dialogue content to the user's terminal. It optimizes the process to minimize time delays and provide a quick response.

[0485] Step 7:

[0486] The device visually displays the received dialogue content to the user. Through the information displayed on the screen, the user can initiate a dialogue with the AI ​​and exchange information intuitively.

[0487] Step 8:

[0488] Users use the dialogue screen to input additional questions or topics of interest, triggering the next interaction.

[0489] Step 9:

[0490] The server receives the user's additional request, consults the database again, and generates a new response based on the AI ​​dialogue model. This process is interactive and repeatable.

[0491] Step 10:

[0492] The server inserts quizzes and trivia into the conversation to test the user's understanding. Based on the quiz results, it processes information to provide immediate feedback to the user.

[0493] (Example 1)

[0494] 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."

[0495] Traditional educational information systems have struggled to flexibly provide information on specific historical contexts according to users' individual interests and levels of understanding. Furthermore, appropriately translating information for users who speak different languages ​​while simultaneously providing an interactive learning experience has been a significant challenge.

[0496] 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.

[0497] In this invention, the server includes means for analyzing information requests for historical context received from a user, means for extracting relevant information from information sources based on the analysis results, and means for generating an information processing model using the extracted information and providing the information in an interactive format with the user. This makes it possible to provide a personalized learning experience tailored to the user and realizes multilingual and immediate response interaction.

[0498] A "user" refers to an individual or group that uses an information processing system to acquire specific knowledge or information.

[0499] "Historical context" refers to a specific period or situation related to historical events or culture.

[0500] "Information sources" refer to records and repositories, such as information databases and archives, that store and make accessible the necessary data.

[0501] An "information processing model" refers to an algorithm or system that analyzes data based on received input and generates information that meets the user's requirements.

[0502] "Dialogue format" refers to a method of communication where the user and the system exchange messages and information with each other.

[0503] "Multilingualization" means translating specific information or content into multiple languages ​​and providing it appropriately to users who speak different languages.

[0504] "Real-time response" refers to a system characteristic that allows it to respond quickly to user requests and questions in real time.

[0505] This invention begins with a user making a request to obtain historical information using a terminal. The terminal provides a user interface to accept user input. For example, the user enters a prompt such as, "I want to learn about medieval European culture."

[0506] The server receives requests sent from terminals and analyzes them using a generative AI model. Specifically, a program that performs natural language processing (NLP) is used. Through this process, the server identifies keywords contained in the user's request and extracts information including historical context and related individuals.

[0507] Next, the server communicates with the database based on the analyzed information and collects relevant information. Database query techniques such as SQL are used for this data collection. After that, the server inputs the extracted information into a generating AI model (e.g., GPT-3) and generates a response in the form of an interactive conversation with the user.

[0508] The generated dialogue is adapted to the user's profile and level of understanding, and is further translated into multiple languages. Software for multilingual support is used, making it compatible with users who speak multiple languages. Finally, the server returns the generated text to the terminal, which then displays it to the user, providing the learning experience.

[0509] This system allows users to enjoy immersive history learning and deepen their understanding through interactive quizzes and trivia. In this way, it provides a personalized and dynamic learning environment that differs from traditional textbook learning.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The user accesses the interface using a terminal and enters a prompt. The terminal sends an information request, such as "I want to learn about medieval European culture," as input. The terminal then prepares to send this information to the server.

[0513] Step 2:

[0514] The server receives user input sent from the terminal and begins analyzing the input using natural language processing technology. Specifically, a keyword extraction algorithm is executed to identify important terms such as "medieval Europe" and "culture" from the text. The analyzed keywords are then provided as output.

[0515] Step 3:

[0516] The server uses the analyzed keywords to query the database. As input, queries containing specific keywords are sent to the database. By retrieving information from the database (e.g., materials on medieval European culture), the server obtains relevant data as output.

[0517] Step 4:

[0518] The server generates conversational content using a generative AI model based on the acquired data. Information obtained from the database is passed to the generative AI model as input. The AI ​​analyzes this information and generates output by creating dialogue text.

[0519] Step 5:

[0520] The server performs translation processing to localize the generated dialogue content into multiple languages. The generated text is input to the translation software. The output is the translated text adapted to the user's language.

[0521] Step 6:

[0522] The server sends the translated dialogue to the terminal. The terminal receives this information and displays it visually to the user. This allows the user to have an interactive learning experience.

[0523] Step 7:

[0524] As an additional feature, the server provides interactive quizzes to deepen user understanding during conversations. Interaction and user profile information are used as input. The output consists of generated quiz questions, with immediate feedback provided as the user answers.

[0525] This entire process allows users to learn personalized through the system and gain a wealth of historical knowledge.

[0526] (Application Example 1)

[0527] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0528] In modern times, learning about history and culture often relies on textbooks and static media, which can lead to learners becoming easily bored. Furthermore, effectively delivering the same content to users in diverse language regions is challenging. In addition, adjusting content to suit individual learners' interests and levels of understanding is not easy. A system is needed to address these challenges.

[0529] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0530] In this invention, the server includes means for analyzing a request from a user regarding a selected cultural event or time period; means for retrieving relevant information from a storage device based on the analyzed information; and means for generating an artificial intelligence conversation model using the retrieved information and providing a conversation with the user. This makes it possible to provide engaging and personalized cultural learning in real time and reduce learning barriers among multilingual users.

[0531] A "user" is an individual or legal entity that accesses an information system and attempts to obtain information about cultural events or periods.

[0532] "Cultural phenomena" refer to unique lifestyles and social structures created by humankind in specific periods or regions.

[0533] A "period" is a historically defined period, a time interval in which culturally or socially distinctive events occurred.

[0534] A "request" is a request from a user for specific information or knowledge they seek from an information system.

[0535] "Analysis" is the process of examining information received from users in detail and understanding its meaning and purpose.

[0536] "Information" refers to a collection of knowledge that a system holds about cultural events and periods, with the aim of providing it to users.

[0537] A "storage device" is a facility that organizes collected information and stores it so that it can be retrieved upon user request.

[0538] An "artificial intelligence conversation model" is an AI technology used to engage in natural conversations with users based on acquired information.

[0539] "Conversation" refers to the exchange of information or opinions between a user and artificial intelligence.

[0540] "Personalization" refers to individually adjusting the content provided to users based on their attribute information.

[0541] A "multilingual user" is an individual or group that belongs to different linguistic and cultural spheres and wishes to receive information in each of those languages.

[0542] To implement this invention, the following system configuration is necessary. The server analyzes requests from the user regarding cultural events and timings, and retrieves relevant information from storage based on that information. The retrieved information is used to generate a natural conversation with the user using a generative AI model, i.e., an artificial intelligence conversation model.

[0543] The server uses the programming language Python and the web framework Django to receive and analyze user requests. Based on the analyzed requests, it retrieves cultural information from a MySQL database. The retrieved information is then personalized as an AI conversation model using OpenAI models and translated into multiple languages.

[0544] Users receive generated conversations using a device such as a smartphone. These conversations are generated based on pre-set prompts to facilitate user learning. A specific example is a question like, "Please tell me about the lifestyle in medieval Europe." Based on this prompt, the AI ​​conversation model provides useful information such as, "In medieval Europe, farmers mainly lived a self-sufficient life..."

[0545] Users receive this information through their devices and can enjoy an immersive learning experience about specific cultures and periods. Furthermore, the system provides users with interactive questions and quizzes to enhance their understanding. In this way, users can gain a deeper understanding of history and culture.

[0546] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0547] Step 1:

[0548] The user inputs requests regarding cultural events or periods through a terminal. This input includes prompts such as, "Please tell me about medieval European lifestyles." The terminal then sends this information to the server.

[0549] Step 2:

[0550] The server analyzes the prompt message received from the user. This analysis includes a process that uses natural language processing techniques to extract the intent of the request. Based on the input prompt message, it identifies relevant keywords and topics, and then determines the scope of information to retrieve.

[0551] Step 3:

[0552] The server uses the analysis results to retrieve relevant information from the MySQL database. At this stage, it searches for data related to cultural events and periods that match the identified keywords and collects the relevant information. The retrieved data is used in the next step to generate AI conversations.

[0553] Step 4:

[0554] The server uses OpenAI's generative AI model to generate conversations with the user based on the information it has acquired. This model generates responses in a natural context and designs personalized replies that meet the user's needs. This process is based on the generative AI model's prompt, "Tell me more about medieval European lifestyles."

[0555] Step 5:

[0556] The server sends the generated conversation to the user's terminal. The terminal visually displays the received conversation content, providing information in a format that is easy for the user to understand. Multilingual support is also implemented at this stage, and translated content is displayed as needed.

[0557] Step 6:

[0558] The user reviews the information provided through the device and answers interactive questions to facilitate their understanding. During this stage, the system measures the user's comprehension through quizzes and questions generated by the system and sends their responses to the server.

[0559] Step 7:

[0560] The server analyzes user responses and generates feedback in real time. This analysis uses algorithms that evaluate the accuracy and comprehension of the user's responses, and based on the evaluation results, provides advice and additional information regarding the next learning steps.

[0561] 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.

[0562] This AI learning platform incorporates an emotion engine that analyzes emotional information through interaction with the user and dynamically adjusts the content and tone of the conversation to provide a more personalized learning experience.

[0563] Users access the system via their devices and request information about historical figures and eras. User input is often emotionally charged, sometimes including specific feelings such as, "I want to learn more about Renaissance inventions, but I find it a little difficult."

[0564] Upon receiving a user request, the server analyzes its contents. This is where the emotion engine comes into play, using natural language processing techniques to recognize the emotions contained within the user's text. By analyzing the user's emotional state, the emotion engine determines whether the user is currently excited, anxious, or calm.

[0565] Next, the server generates an AI dialogue model based on the analysis results and emotion data. It adjusts the tone and content of the dialogue, taking into account the emotion information obtained by the emotion engine. For example, if the analysis indicates that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain Renaissance inventions in an easy-to-understand way."

[0566] The server also personalizes and supports multiple languages ​​in the generated conversations based on the user's profile information and language settings. The generated content takes into account the learner's interests and mental state. This enables a more comfortable and engaging learning experience.

[0567] The server then sends the generated dialogue content to the user's device. The device displays the received information to the user, assisting in the interactive dialogue. Based on the information gained from the dialogue, the user can actively learn by asking deeper questions or moving on to new topics.

[0568] Furthermore, the server implements interactive quizzes and trivia within the conversation to test the user's knowledge and understanding. Based on the user's emotional state, the difficulty and content of the quizzes and trivia are adjusted as needed to enhance the learning effect. Through this entire process, users can enjoy a highly personalized and emotionally resonant learning environment.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] Users use their devices to access the system, create and submit requests seeking information about specific historical figures or periods. For example, they might type, "I want to know about ancient Greek philosophers."

[0572] Step 2:

[0573] The server receives requests from users. It then analyzes the received data and performs a process to identify the relevant historical figures and historical context. This analysis utilizes natural language processing techniques to understand the intent of the input.

[0574] Step 3:

[0575] The server activates an emotion engine to analyze the emotions contained in the user's input. For example, it identifies whether the user is excited, calm, or in some other emotional state based on their description.

[0576] Step 4:

[0577] The server searches the database based on the analysis results and sentiment data to retrieve the necessary historical information. The information retrieved is selected to correspond to the user's requests.

[0578] Step 5:

[0579] The server generates an AI dialogue model using the acquired information and emotion data. This generation process adjusts the tone and content according to the user's emotions, preparing to provide an appropriate dialogue to the user.

[0580] Step 6:

[0581] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This ensures that information is prepared to meet the user's individual needs.

[0582] Step 7:

[0583] The server sends the generated dialogue content to the user's device. The content is delivered to the device immediately and optimized for display.

[0584] Step 8:

[0585] The device displays conversational content received from the server to the user. Through the displayed information, the user can initiate a dialogue with the AI ​​and proceed with a direct learning experience.

[0586] Step 9:

[0587] Users can enter additional questions or requests for new topics based on the information provided. For example, they might ask, "I'd like to know more about the influence of Socrates."

[0588] Step 10:

[0589] The server receives new user requests and, if necessary, looks up the database and regenerates the interaction model. This process can be repeated based on user input.

[0590] Step 11:

[0591] The server inserts interactive quizzes and trivia into the conversation to test the user's knowledge. It also adjusts the content and difficulty of the quizzes based on the user's emotional state, incorporating features to enhance learning motivation.

[0592] These steps enable users to have a deeply personalized history learning experience that is emotionally resonant.

[0593] (Example 2)

[0594] 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."

[0595] Current learning systems often fail to adequately address users' emotional states and individual characteristics, resulting in a tendency to provide uniform information. Furthermore, a lack of multilingual support and interaction can prevent the provision of appropriate learning experiences for users with diverse backgrounds. This invention aims to provide an emotionally resonant, personalized learning experience, enabling responses tailored to the user's interests and level of understanding.

[0596] 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.

[0597] In this invention, the server includes means for analyzing a selected historical event received from a user and recognizing the corresponding emotion; means for obtaining relevant information from a recording medium based on the analyzed information and emotion data; and means for constructing an artificial intelligence dialogue model generated using the obtained information and adjusting the tone and content of the dialogue according to the user's emotional state. This makes it possible to realize a personalized learning experience in multiple languages ​​while responding to the user's individual emotions and needs.

[0598] A "user" is an individual or entity that uses a system and asks questions in search of specific information.

[0599] A "server" is a computer system that receives requests from users and performs data processing such as information analysis and the generation of dialogue models.

[0600] An "inquiry" is a statement or message that a user sends to a system in order to obtain information.

[0601] "Analysis" is the process of information processing that is carried out to interpret received information and understand the user's intentions and emotional state.

[0602] "Emotional data" refers to information that indicates a user's emotional state, and is psychological judgment data extracted from text.

[0603] "Recording medium" is a general term for databases and data storage devices used to hold information.

[0604] A "generated artificial intelligence dialogue model" is a dialogue system designed to provide appropriate responses in response to the user's questions and emotions.

[0605] "User profile information" refers to attribute information about individual users and is data used to provide personalized services.

[0606] "Interactive challenges and knowledge checks" are interactive quizzes and trivia that users can participate in to deepen their learning.

[0607] This invention relates to an advanced interactive system that provides a personalized learning experience while taking user emotions into consideration. The system consists of a user, a terminal, and a server.

[0608] Users access the system via devices such as computers and mobile devices. When users inquire about historical information or eras, they enter prompts that include their feelings, such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult."

[0609] The server activates an emotion engine to analyze the received prompt. This emotion engine uses natural language processing techniques to extract emotions contained in the user's input text and determine the user's psychological state. Text analysis software and emotion analysis algorithms are used for this analysis.

[0610] Furthermore, based on the analysis results, the server constructs a dialogue model using a generative AI model based on the acquired information. This dialogue model is configured to adjust the tone and content according to the user's emotional state, providing an appropriate and gentle conversation for the user. For example, if the user is feeling anxious, it will respond in a gentle tone, such as, "Don't worry, I'll explain the Renaissance inventions in an easy-to-understand way."

[0611] Furthermore, the server personalizes the conversation based on the user's profile information, translates it according to multilingual needs, and accommodates a diverse range of users. For this purpose, a multilingual translation engine and personalization algorithms are utilized.

[0612] The terminal displays generated conversational content sent from the server, providing an environment where users can intuitively interact with it and progress through learning by taking various quizzes and knowledge checks. In this way, the user's learning experience will be enriched.

[0613] These processes allow users to receive emotionally resonant information and learn comfortably. Learning services provided in this way are also suitable for use in educational and cultural institutions.

[0614] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0615] Step 1:

[0616] The user uses a terminal to enter questions about historical topics. These questions may include prompts such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult." The input is in text format, and the terminal sends this input to the server.

[0617] Step 2:

[0618] The server analyzes user input received from the terminal. It uses natural language processing techniques to analyze the prompt text as input and extract the user's emotions. Specifically, it uses an emotion engine to analyze keywords and context in the text, recognizing emotions such as "anxiety" and "interest." The output generates data indicating the user's emotional state.

[0619] Step 3:

[0620] The server retrieves relevant information from the database based on the analysis results. The input consists of the user's inquiry and sentiment data, which is used to search for relevant historical information. The output provides appropriate information and learning materials in response to the user's question.

[0621] Step 4:

[0622] The server uses generative AI technology to build a dialogue model. This model uses acquired information and emotional data as input to design the tone and content of the dialogue according to the user's current emotional state. Specifically, if the user is feeling anxious, it will generate dialogue content in a gentle tone that provides reassurance. The output is a personalized AI dialogue model.

[0623] Step 5:

[0624] The server matches the dialogue model with the user's profile and prepares a personalized dialogue. If multilingual support is required, it uses a translation engine to translate into the appropriate language. User attribute information and language settings are taken as input, and the output is a dialogue optimized for the user.

[0625] Step 6:

[0626] The terminal displays generated dialogue content sent from the server to the user. Specifically, it uses an interactive user interface to assist the user in progressing through the dialogue or taking quizzes. The input is the dialogue content from the server, and the output is the next step based on the user's actions, i.e., further dialogue or new questions.

[0627] (Application Example 2)

[0628] 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."

[0629] Current interactive learning systems struggle to adequately understand users' emotional states and provide corresponding dialogue. Furthermore, to make the learning experience more personalized, engaging, and user-friendly, there is a need for interactive content that resonates with users' emotions.

[0630] 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.

[0631] In this invention, the server includes means for analyzing a selected time period received from the user, means for obtaining relevant information from a storage device based on the analyzed information, and means for generating an artificial intelligence dialogue model using the obtained information and providing a dialogue based on the user's emotions. This enables a personalized learning experience that takes into account the user's emotional state.

[0632] A "user" refers to an individual who interacts with this system, receives information, and enjoys a learning experience.

[0633] "Inquiries about selected eras" refer to requests from users seeking information about a specific period or historical context.

[0634] "Means of analysis" refers to the technical process of processing information received from the user, clarifying its content, and deriving an appropriate response.

[0635] "Relevant information" refers to the knowledge and data in the database that respond to user inquiries.

[0636] A "storage device" refers to an electronic or digital medium used to store and access data as needed.

[0637] An "artificial intelligence dialogue model" refers to a collection of algorithms and technologies for effectively communicating with users through natural language.

[0638] An "emotion engine" refers to a software component that analyzes and understands the emotional state of a user based on their input.

[0639] "Device" refers to a hardware device that allows a user to visually or audibly confirm information received from a system.

[0640] "Interactive problem-solving and knowledge-based games" refer to activities that encourage user participation through dialogue and enhance learning with enjoyment and interest.

[0641] "Personalizing" refers to tailoring the learning experience to best suit the user based on their profile and preferences.

[0642] The "real-time feedback function" refers to the ability to instantly analyze user input during a conversation and provide a response based on the results.

[0643] This invention begins with a user accessing the system via a device and requesting information about a specific time period. The server utilizes natural language processing techniques to analyze the user's inquiry. This analysis employs an emotion engine to recognize the emotions contained in the user's text. The emotion engine analyzes the user's emotional state and determines whether they are excited, anxious, or calm.

[0644] Based on the analysis results, the server retrieves relevant information from its storage device and generates an artificial intelligence dialogue model. The generated model takes into account the emotional information obtained by the emotion engine and adjusts the tone and content of the dialogue. For example, if the server determines that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain something that will interest you in an easy-to-understand way."

[0645] The server further personalizes these interactions based on the user's profile information and language settings, and also enables multilingual support. The generated dialogue content is transmitted to the user through the device and displayed. This allows the user to ask deeper questions based on the information gained from the dialogue. This includes interactive questions and knowledge games provided by the server to test the user's knowledge and understanding.

[0646] For example, if a child were to tell the server, "I want to learn more about space, but I'm a little worried," the server would provide a calm and encouraging response such as, "Don't worry. I'll start with the basics of space and teach you some interesting facts."

[0647] Examples of prompt messages include the following:

[0648] "User comment: I want to learn more about space, but I feel a little anxious.\nEmotion: Anxious\nResponse: Don't worry. We'll start with the basics of space and teach you some fascinating facts."

[0649] In this way, users can enjoy a learning experience that is tailored to their individual emotions and interests.

[0650] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0651] Step 1:

[0652] The server receives inquiries from users via the device. The input is text data containing specific information and emotions related to a particular era. This text data is analyzed using natural language processing techniques to extract keywords and phrases relevant to specific questions or requests.

[0653] Step 2:

[0654] The server uses an emotion engine to identify the user's emotions based on the analyzed information. The input is the text processed in step 1. The emotion engine analyzes this text to determine whether the user is excited, anxious, or calm. The output of this process is data indicating the user's emotional state.

[0655] Step 3:

[0656] The server retrieves relevant information from its storage based on the user's emotional state and analysis results. Specifically, it uses database queries to search for knowledge directly related to the user's inquiry and extract the necessary information. The input for this step is the user's request and emotional data, and the output is the retrieved relevant information.

[0657] Step 4:

[0658] The server generates an artificial intelligence dialogue model based on acquired information and sentiment data. This model includes prompt sentences intended to respond to the user. Model generation involves setting the tone and content, taking into account both acquired data and sentiment information, to construct dialogue sentences optimized for the user. The generated output is the specific response content within the dialogue model.

[0659] Step 5:

[0660] The server transmits the generated dialogue content to the device and displays it to the user. Here, the dialogue is personalized and multilingual based on the user's profile information and language settings. The transmitted data includes personalized content and is in a format that the user receives visually or audibly.

[0661] Step 6:

[0662] The user participates in interactive questions and knowledge games based on the conversations they have submitted. In this step, the user's knowledge and understanding are evaluated based on the content of the questions generated by the server. The input is the user's response, and feedback is provided as output to enhance the learning effect by evaluating that response.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] [Fourth Embodiment]

[0667] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0668] 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.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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".

[0680] This AI learning platform is realized by receiving requests from users and providing information related to selected historical figures and eras. Its specific implementation is described below.

[0681] Users access the system via their devices and make requests to obtain information about specific historical figures or eras. For example, a user might want to learn about medieval European culture.

[0682] The server receives and analyzes this request. This analysis includes identifying the specific person and historical context the user is interested in. Based on the analysis results, the server accesses the system's database and retrieves relevant information that matches the request. Comprehensive data on medieval European culture is retrieved at this stage.

[0683] Next, the server uses an AI dialogue model based on the acquired data to generate an interactive conversation with the user. The generated conversation is provided to the user in real time. For example, if the user asks, "Tell me more about the medieval knighthood," the AI ​​will respond, "The medieval knighthood was an important social structure in Europe, and in many regions, sons of nobles belonged to this system."

[0684] Furthermore, this system can personalize conversations by considering the user's profile information and translate them into multiple languages. This ensures that users who speak different languages ​​receive the necessary information appropriately. The server also sends the generated conversation content to the terminal, which then displays it visually to the user.

[0685] In addition, the system includes features to deepen user understanding through interactive quizzes and trivia. For example, it inserts quizzes such as "Which is a famous medieval architectural style?" into the conversation, and the user's answers facilitate learning. As a result, the server analyzes the user's responses and provides appropriate feedback in real time.

[0686] Through this series of processes, users can enjoy an immersive history learning experience that cannot be obtained through traditional textbook-based learning.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] Users access the system through their terminals, create and submit requests to obtain information about specific historical figures or periods. For example, they might type, "I want to learn about technological innovations during the Renaissance."

[0690] Step 2:

[0691] The server receives requests sent by users. It analyzes the received requests to identify which historical figures or eras they refer to. This process involves using natural language processing techniques to understand the request's content.

[0692] Step 3:

[0693] The server accesses the database based on the analysis results and retrieves information related to the specified historical figure or era. In this process, it extracts highly relevant information from the large amount of historical data stored in the database.

[0694] Step 4:

[0695] The server uses the acquired information to generate an AI dialogue model. The AI ​​model constructs conversation content in response to user requests and prepares for interactive dialogue with the user.

[0696] Step 5:

[0697] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This step involves customization based on the user's background.

[0698] Step 6:

[0699] The server sends the generated dialogue content to the user's terminal. It optimizes the process to minimize time delays and provide a quick response.

[0700] Step 7:

[0701] The device visually displays the received dialogue content to the user. Through the information displayed on the screen, the user can initiate a dialogue with the AI ​​and exchange information intuitively.

[0702] Step 8:

[0703] Users use the dialogue screen to input additional questions or topics of interest, triggering the next interaction.

[0704] Step 9:

[0705] The server receives the user's additional request, consults the database again, and generates a new response based on the AI ​​dialogue model. This process is interactive and repeatable.

[0706] Step 10:

[0707] The server inserts quizzes and trivia into the conversation to test the user's understanding. Based on the quiz results, it processes information to provide immediate feedback to the user.

[0708] (Example 1)

[0709] 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".

[0710] Traditional educational information systems have struggled to flexibly provide information on specific historical contexts according to users' individual interests and levels of understanding. Furthermore, appropriately translating information for users who speak different languages ​​while simultaneously providing an interactive learning experience has been a significant challenge.

[0711] 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.

[0712] In this invention, the server includes means for analyzing information requests for historical context received from a user, means for extracting relevant information from information sources based on the analysis results, and means for generating an information processing model using the extracted information and providing the information in an interactive format with the user. This makes it possible to provide a personalized learning experience tailored to the user and realizes multilingual and immediate response interaction.

[0713] A "user" refers to an individual or group that uses an information processing system to acquire specific knowledge or information.

[0714] "Historical context" refers to a specific period or situation related to historical events or culture.

[0715] "Information sources" refer to records and repositories, such as information databases and archives, that store and make accessible the necessary data.

[0716] An "information processing model" refers to an algorithm or system that analyzes data based on received input and generates information that meets the user's requirements.

[0717] "Dialogue format" refers to a method of communication where the user and the system exchange messages and information with each other.

[0718] "Multilingualization" means translating specific information or content into multiple languages ​​and providing it appropriately to users who speak different languages.

[0719] "Real-time response" refers to a system characteristic that allows it to respond quickly to user requests and questions in real time.

[0720] This invention begins with a user making a request to obtain historical information using a terminal. The terminal provides a user interface to accept user input. For example, the user enters a prompt such as, "I want to learn about medieval European culture."

[0721] The server receives requests sent from terminals and analyzes them using a generative AI model. Specifically, a program that performs natural language processing (NLP) is used. Through this process, the server identifies keywords contained in the user's request and extracts information including historical context and related individuals.

[0722] Next, the server communicates with the database based on the analyzed information and collects relevant information. Database query techniques such as SQL are used for this data collection. After that, the server inputs the extracted information into a generating AI model (e.g., GPT-3) and generates a response in the form of an interactive conversation with the user.

[0723] The generated dialogue is adapted to the user's profile and level of understanding, and is further translated into multiple languages. Software for multilingual support is used, making it compatible with users who speak multiple languages. Finally, the server returns the generated text to the terminal, which then displays it to the user, providing the learning experience.

[0724] This system allows users to enjoy immersive history learning and deepen their understanding through interactive quizzes and trivia. In this way, it provides a personalized and dynamic learning environment that differs from traditional textbook learning.

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

[0726] Step 1:

[0727] The user accesses the interface using a terminal and enters a prompt. The terminal sends an information request, such as "I want to learn about medieval European culture," as input. The terminal then prepares to send this information to the server.

[0728] Step 2:

[0729] The server receives user input sent from the terminal and begins analyzing the input using natural language processing technology. Specifically, a keyword extraction algorithm is executed to identify important terms such as "medieval Europe" and "culture" from the text. The analyzed keywords are then provided as output.

[0730] Step 3:

[0731] The server uses the analyzed keywords to query the database. As input, queries containing specific keywords are sent to the database. By retrieving information from the database (e.g., materials on medieval European culture), the server obtains relevant data as output.

[0732] Step 4:

[0733] The server generates conversational content using a generative AI model based on the acquired data. Information obtained from the database is passed to the generative AI model as input. The AI ​​analyzes this information and generates output by creating dialogue text.

[0734] Step 5:

[0735] The server performs translation processing to localize the generated dialogue content into multiple languages. The generated text is input to the translation software. The output is the translated text adapted to the user's language.

[0736] Step 6:

[0737] The server sends the translated dialogue to the terminal. The terminal receives this information and displays it visually to the user. This allows the user to have an interactive learning experience.

[0738] Step 7:

[0739] As an additional feature, the server provides interactive quizzes to deepen user understanding during conversations. Interaction and user profile information are used as input. The output consists of generated quiz questions, with immediate feedback provided as the user answers.

[0740] This entire process allows users to learn personalized through the system and gain a wealth of historical knowledge.

[0741] (Application Example 1)

[0742] 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".

[0743] In modern times, learning about history and culture often relies on textbooks and static media, which can lead to learners becoming easily bored. Furthermore, effectively delivering the same content to users in diverse language regions is challenging. In addition, adjusting content to suit individual learners' interests and levels of understanding is not easy. A system is needed to address these challenges.

[0744] 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.

[0745] In this invention, the server includes means for analyzing a request from a user regarding a selected cultural event or time period; means for retrieving relevant information from a storage device based on the analyzed information; and means for generating an artificial intelligence conversation model using the retrieved information and providing a conversation with the user. This makes it possible to provide engaging and personalized cultural learning in real time and reduce learning barriers among multilingual users.

[0746] A "user" is an individual or legal entity that accesses an information system and attempts to obtain information about cultural events or periods.

[0747] "Cultural phenomena" refer to unique lifestyles and social structures created by humankind in specific periods or regions.

[0748] A "period" is a historically defined period, a time interval in which culturally or socially distinctive events occurred.

[0749] A "request" is a request from a user for specific information or knowledge they seek from an information system.

[0750] "Analysis" is the process of examining information received from users in detail and understanding its meaning and purpose.

[0751] "Information" refers to a collection of knowledge that a system holds about cultural events and periods, with the aim of providing it to users.

[0752] A "storage device" is a facility that organizes collected information and stores it so that it can be retrieved upon user request.

[0753] An "artificial intelligence conversation model" is an AI technology used to engage in natural conversations with users based on acquired information.

[0754] "Conversation" refers to the exchange of information or opinions between a user and artificial intelligence.

[0755] "Personalization" refers to individually adjusting the content provided to users based on their attribute information.

[0756] A "multilingual user" is an individual or group that belongs to different linguistic and cultural spheres and wishes to receive information in each of those languages.

[0757] To implement this invention, the following system configuration is necessary. The server analyzes requests from the user regarding cultural events and timings, and retrieves relevant information from storage based on that information. The retrieved information is used to generate a natural conversation with the user using a generative AI model, i.e., an artificial intelligence conversation model.

[0758] The server uses the programming language Python and the web framework Django to receive and analyze user requests. Based on the analyzed requests, it retrieves cultural information from a MySQL database. The retrieved information is then personalized as an AI conversation model using OpenAI models and translated into multiple languages.

[0759] Users receive generated conversations using a device such as a smartphone. These conversations are generated based on pre-set prompts to facilitate user learning. A specific example is a question like, "Please tell me about the lifestyle in medieval Europe." Based on this prompt, the AI ​​conversation model provides useful information such as, "In medieval Europe, farmers mainly lived a self-sufficient life..."

[0760] Users receive this information through their devices and can enjoy an immersive learning experience about specific cultures and periods. Furthermore, the system provides users with interactive questions and quizzes to enhance their understanding. In this way, users can gain a deeper understanding of history and culture.

[0761] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0762] Step 1:

[0763] The user inputs requests regarding cultural events or periods through a terminal. This input includes prompts such as, "Please tell me about medieval European lifestyles." The terminal then sends this information to the server.

[0764] Step 2:

[0765] The server analyzes the prompt message received from the user. This analysis includes a process that uses natural language processing techniques to extract the intent of the request. Based on the input prompt message, it identifies relevant keywords and topics, and then determines the scope of information to retrieve.

[0766] Step 3:

[0767] The server uses the analysis results to retrieve relevant information from the MySQL database. At this stage, it searches for data related to cultural events and periods that match the identified keywords and collects the relevant information. The retrieved data is used in the next step to generate AI conversations.

[0768] Step 4:

[0769] The server uses OpenAI's generative AI model to generate conversations with the user based on the information it has acquired. This model generates responses in a natural context and designs personalized replies that meet the user's needs. This process is based on the generative AI model's prompt, "Tell me more about medieval European lifestyles."

[0770] Step 5:

[0771] The server sends the generated conversation to the user's terminal. The terminal visually displays the received conversation content, providing information in a format that is easy for the user to understand. Multilingual support is also implemented at this stage, and translated content is displayed as needed.

[0772] Step 6:

[0773] The user reviews the information provided through the device and answers interactive questions to facilitate their understanding. During this stage, the system measures the user's comprehension through quizzes and questions generated by the system and sends their responses to the server.

[0774] Step 7:

[0775] The server analyzes user responses and generates feedback in real time. This analysis uses algorithms that evaluate the accuracy and comprehension of the user's responses, and based on the evaluation results, provides advice and additional information regarding the next learning steps.

[0776] 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.

[0777] This AI learning platform incorporates an emotion engine that analyzes emotional information through interaction with the user and dynamically adjusts the content and tone of the conversation to provide a more personalized learning experience.

[0778] Users access the system via their devices and request information about historical figures and eras. User input is often emotionally charged, sometimes including specific feelings such as, "I want to learn more about Renaissance inventions, but I find it a little difficult."

[0779] Upon receiving a user request, the server analyzes its contents. This is where the emotion engine comes into play, using natural language processing techniques to recognize the emotions contained within the user's text. By analyzing the user's emotional state, the emotion engine determines whether the user is currently excited, anxious, or calm.

[0780] Next, the server generates an AI dialogue model based on the analysis results and emotion data. It adjusts the tone and content of the dialogue, taking into account the emotion information obtained by the emotion engine. For example, if the analysis indicates that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain Renaissance inventions in an easy-to-understand way."

[0781] The server also personalizes and supports multiple languages ​​in the generated conversations based on the user's profile information and language settings. The generated content takes into account the learner's interests and mental state. This enables a more comfortable and engaging learning experience.

[0782] The server then sends the generated dialogue content to the user's device. The device displays the received information to the user, assisting in the interactive dialogue. Based on the information gained from the dialogue, the user can actively learn by asking deeper questions or moving on to new topics.

[0783] Furthermore, the server implements interactive quizzes and trivia within the conversation to test the user's knowledge and understanding. Based on the user's emotional state, the difficulty and content of the quizzes and trivia are adjusted as needed to enhance the learning effect. Through this entire process, users can enjoy a highly personalized and emotionally resonant learning environment.

[0784] The following describes the processing flow.

[0785] Step 1:

[0786] Users use their devices to access the system, create and submit requests seeking information about specific historical figures or periods. For example, they might type, "I want to know about ancient Greek philosophers."

[0787] Step 2:

[0788] The server receives requests from users. It then analyzes the received data and performs a process to identify the relevant historical figures and historical context. This analysis utilizes natural language processing techniques to understand the intent of the input.

[0789] Step 3:

[0790] The server activates an emotion engine to analyze the emotions contained in the user's input. For example, it identifies whether the user is excited, calm, or in some other emotional state based on their description.

[0791] Step 4:

[0792] The server searches the database based on the analysis results and sentiment data to retrieve the necessary historical information. The information retrieved is selected to correspond to the user's requests.

[0793] Step 5:

[0794] The server generates an AI dialogue model using the acquired information and emotion data. This generation process adjusts the tone and content according to the user's emotions, preparing to provide an appropriate dialogue to the user.

[0795] Step 6:

[0796] The server takes into account the user's profile information and language settings to personalize the generated dialogue and translate it into multiple languages ​​as needed. This ensures that information is prepared to meet the user's individual needs.

[0797] Step 7:

[0798] The server sends the generated dialogue content to the user's device. The content is delivered to the device immediately and optimized for display.

[0799] Step 8:

[0800] The device displays conversational content received from the server to the user. Through the displayed information, the user can initiate a dialogue with the AI ​​and proceed with a direct learning experience.

[0801] Step 9:

[0802] Users can enter additional questions or requests for new topics based on the information provided. For example, they might ask, "I'd like to know more about the influence of Socrates."

[0803] Step 10:

[0804] The server receives new user requests and, if necessary, looks up the database and regenerates the interaction model. This process can be repeated based on user input.

[0805] Step 11:

[0806] The server inserts interactive quizzes and trivia into the conversation to test the user's knowledge. It also adjusts the content and difficulty of the quizzes based on the user's emotional state, incorporating features to enhance learning motivation.

[0807] These steps enable users to have a deeply personalized history learning experience that is emotionally resonant.

[0808] (Example 2)

[0809] 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".

[0810] Current learning systems often fail to adequately address users' emotional states and individual characteristics, resulting in a tendency to provide uniform information. Furthermore, a lack of multilingual support and interaction can prevent the provision of appropriate learning experiences for users with diverse backgrounds. This invention aims to provide an emotionally resonant, personalized learning experience, enabling responses tailored to the user's interests and level of understanding.

[0811] 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.

[0812] In this invention, the server includes means for analyzing a selected historical event received from a user and recognizing the corresponding emotion; means for obtaining relevant information from a recording medium based on the analyzed information and emotion data; and means for constructing an artificial intelligence dialogue model generated using the obtained information and adjusting the tone and content of the dialogue according to the user's emotional state. This makes it possible to realize a personalized learning experience in multiple languages ​​while responding to the user's individual emotions and needs.

[0813] A "user" is an individual or entity that uses a system and asks questions in search of specific information.

[0814] A "server" is a computer system that receives requests from users and performs data processing such as information analysis and the generation of dialogue models.

[0815] An "inquiry" is a statement or message that a user sends to a system in order to obtain information.

[0816] "Analysis" is the process of information processing that is carried out to interpret received information and understand the user's intentions and emotional state.

[0817] "Emotional data" refers to information that indicates a user's emotional state, and is psychological judgment data extracted from text.

[0818] "Recording medium" is a general term for databases and data storage devices used to hold information.

[0819] A "generated artificial intelligence dialogue model" is a dialogue system designed to provide appropriate responses in response to the user's questions and emotions.

[0820] "User profile information" refers to attribute information about individual users and is data used to provide personalized services.

[0821] "Interactive challenges and knowledge checks" are interactive quizzes and trivia that users can participate in to deepen their learning.

[0822] This invention relates to an advanced interactive system that provides a personalized learning experience while taking user emotions into consideration. The system consists of a user, a terminal, and a server.

[0823] Users access the system via devices such as computers and mobile devices. When users inquire about historical information or eras, they enter prompts that include their feelings, such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult."

[0824] The server activates an emotion engine to analyze the received prompt. This emotion engine uses natural language processing techniques to extract emotions contained in the user's input text and determine the user's psychological state. Text analysis software and emotion analysis algorithms are used for this analysis.

[0825] Furthermore, based on the analysis results, the server constructs a dialogue model using a generative AI model based on the acquired information. This dialogue model is configured to adjust the tone and content according to the user's emotional state, providing an appropriate and gentle conversation for the user. For example, if the user is feeling anxious, it will respond in a gentle tone, such as, "Don't worry, I'll explain the Renaissance inventions in an easy-to-understand way."

[0826] Furthermore, the server personalizes the conversation based on the user's profile information, translates it according to multilingual needs, and accommodates a diverse range of users. For this purpose, a multilingual translation engine and personalization algorithms are utilized.

[0827] The terminal displays generated conversational content sent from the server, providing an environment where users can intuitively interact with it and progress through learning by taking various quizzes and knowledge checks. In this way, the user's learning experience will be enriched.

[0828] These processes allow users to receive emotionally resonant information and learn comfortably. Learning services provided in this way are also suitable for use in educational and cultural institutions.

[0829] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0830] Step 1:

[0831] The user uses a terminal to enter questions about historical topics. These questions may include prompts such as, "I'd like to learn more about Renaissance inventions, but I find it a little difficult." The input is in text format, and the terminal sends this input to the server.

[0832] Step 2:

[0833] The server analyzes user input received from the terminal. It uses natural language processing techniques to analyze the prompt text as input and extract the user's emotions. Specifically, it uses an emotion engine to analyze keywords and context in the text, recognizing emotions such as "anxiety" and "interest." The output generates data indicating the user's emotional state.

[0834] Step 3:

[0835] The server retrieves relevant information from the database based on the analysis results. The input consists of the user's inquiry and sentiment data, which is used to search for relevant historical information. The output provides appropriate information and learning materials in response to the user's question.

[0836] Step 4:

[0837] The server uses generative AI technology to build a dialogue model. This model uses acquired information and emotional data as input to design the tone and content of the dialogue according to the user's current emotional state. Specifically, if the user is feeling anxious, it will generate dialogue content in a gentle tone that provides reassurance. The output is a personalized AI dialogue model.

[0838] Step 5:

[0839] The server matches the dialogue model with the user's profile and prepares a personalized dialogue. If multilingual support is required, it uses a translation engine to translate into the appropriate language. User attribute information and language settings are taken as input, and the output is a dialogue optimized for the user.

[0840] Step 6:

[0841] The terminal displays generated dialogue content sent from the server to the user. Specifically, it uses an interactive user interface to assist the user in progressing through the dialogue or taking quizzes. The input is the dialogue content from the server, and the output is the next step based on the user's actions, i.e., further dialogue or new questions.

[0842] (Application Example 2)

[0843] 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".

[0844] Current interactive learning systems struggle to adequately understand users' emotional states and provide corresponding dialogue. Furthermore, to make the learning experience more personalized, engaging, and user-friendly, there is a need for interactive content that resonates with users' emotions.

[0845] 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.

[0846] In this invention, the server includes means for analyzing a selected time period received from the user, means for obtaining relevant information from a storage device based on the analyzed information, and means for generating an artificial intelligence dialogue model using the obtained information and providing a dialogue based on the user's emotions. This enables a personalized learning experience that takes into account the user's emotional state.

[0847] A "user" refers to an individual who interacts with this system, receives information, and enjoys a learning experience.

[0848] "Inquiries about selected eras" refer to requests from users seeking information about a specific period or historical context.

[0849] "Means of analysis" refers to the technical process of processing information received from the user, clarifying its content, and deriving an appropriate response.

[0850] "Relevant information" refers to the knowledge and data in the database that respond to user inquiries.

[0851] A "storage device" refers to an electronic or digital medium used to store and access data as needed.

[0852] An "artificial intelligence dialogue model" refers to a collection of algorithms and technologies for effectively communicating with users through natural language.

[0853] An "emotion engine" refers to a software component that analyzes and understands the emotional state of a user based on their input.

[0854] "Device" refers to a hardware device that allows a user to visually or audibly confirm information received from a system.

[0855] "Interactive problem-solving and knowledge-based games" refer to activities that encourage user participation through dialogue and enhance learning with enjoyment and interest.

[0856] "Personalizing" refers to tailoring the learning experience to best suit the user based on their profile and preferences.

[0857] The "real-time feedback function" refers to the ability to instantly analyze user input during a conversation and provide a response based on the results.

[0858] This invention begins with a user accessing the system via a device and requesting information about a specific time period. The server utilizes natural language processing techniques to analyze the user's inquiry. This analysis employs an emotion engine to recognize the emotions contained in the user's text. The emotion engine analyzes the user's emotional state and determines whether they are excited, anxious, or calm.

[0859] Based on the analysis results, the server retrieves relevant information from its storage device and generates an artificial intelligence dialogue model. The generated model takes into account the emotional information obtained by the emotion engine and adjusts the tone and content of the dialogue. For example, if the server determines that the user is feeling anxious, it will create a gentle dialogue such as, "Don't worry, I'll explain something that will interest you in an easy-to-understand way."

[0860] The server further personalizes these interactions based on the user's profile information and language settings, and also enables multilingual support. The generated dialogue content is transmitted to the user through the device and displayed. This allows the user to ask deeper questions based on the information gained from the dialogue. This includes interactive questions and knowledge games provided by the server to test the user's knowledge and understanding.

[0861] For example, if a child were to tell the server, "I want to learn more about space, but I'm a little worried," the server would provide a calm and encouraging response such as, "Don't worry. I'll start with the basics of space and teach you some interesting facts."

[0862] Examples of prompt messages include the following:

[0863] "User comment: I want to learn more about space, but I feel a little anxious.\nEmotion: Anxious\nResponse: Don't worry. We'll start with the basics of space and teach you some fascinating facts."

[0864] In this way, users can enjoy a learning experience that is tailored to their individual emotions and interests.

[0865] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0866] Step 1:

[0867] The server receives inquiries from users via the device. The input is text data containing specific information and emotions related to a particular era. This text data is analyzed using natural language processing techniques to extract keywords and phrases relevant to specific questions or requests.

[0868] Step 2:

[0869] The server uses an emotion engine to identify the user's emotions based on the analyzed information. The input is the text processed in step 1. The emotion engine analyzes this text to determine whether the user is excited, anxious, or calm. The output of this process is data indicating the user's emotional state.

[0870] Step 3:

[0871] The server retrieves relevant information from its storage based on the user's emotional state and analysis results. Specifically, it uses database queries to search for knowledge directly related to the user's inquiry and extract the necessary information. The input for this step is the user's request and emotional data, and the output is the retrieved relevant information.

[0872] Step 4:

[0873] The server generates an artificial intelligence dialogue model based on acquired information and sentiment data. This model includes prompt sentences intended to respond to the user. Model generation involves setting the tone and content, taking into account both acquired data and sentiment information, to construct dialogue sentences optimized for the user. The generated output is the specific response content within the dialogue model.

[0874] Step 5:

[0875] The server transmits the generated dialogue content to the device and displays it to the user. Here, the dialogue is personalized and multilingual based on the user's profile information and language settings. The transmitted data includes personalized content and is in a format that the user receives visually or audibly.

[0876] Step 6:

[0877] The user participates in interactive questions and knowledge games based on the conversations they have submitted. In this step, the user's knowledge and understanding are evaluated based on the content of the questions generated by the server. The input is the user's response, and feedback is provided as output to enhance the learning effect by evaluating that response.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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."

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] The following is further disclosed regarding the embodiments described above.

[0900] (Claim 1)

[0901] A means for analyzing inquiries received from users regarding selected historical figures or eras,

[0902] A means of obtaining relevant data from a database based on the analyzed information,

[0903] A means of generating an artificial intelligence dialogue model using acquired data and providing dialogue with the user,

[0904] A means to personalize conversations by considering user profile information and translate them into multiple languages,

[0905] A means for sending and displaying the generated dialogue content on the user's terminal,

[0906] A means of providing interactive quizzes and trivia based on the content of the conversation,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising the function of analyzing user responses during the course of a dialogue and providing feedback in real time.

[0910] (Claim 3)

[0911] The system according to claim 1, which provides an interactive, user-centered history learning experience suitable for use in educational institutions and museums.

[0912] "Example 1"

[0913] (Claim 1)

[0914] A means of analyzing information requests regarding historical context received from users,

[0915] A means for extracting relevant information from the information source based on the analysis results,

[0916] A means of generating an information processing model using extracted information and providing information in an interactive format with the user,

[0917] A means to optimize conversations by considering user information and to translate information into multiple languages,

[0918] A means for transmitting and visually displaying the generated dialogue content on a user device,

[0919] A means of providing interactive questions and knowledge checks based on dialogue,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, further comprising a function to analyze the user's responses during the dialogue process and provide an immediate response.

[0923] (Claim 3)

[0924] The system according to claim 1, which provides an interactive, learner-oriented educational experience suitable for use in educational and exhibition facilities.

[0925] "Application Example 1"

[0926] (Claim 1)

[0927] A means for analyzing requests from users regarding selected cultural events or periods,

[0928] A means for obtaining relevant information from a storage device based on the analyzed information,

[0929] A means of generating an artificial intelligence conversation model using acquired information and providing conversation with the user,

[0930] A method for personalizing conversations by considering user attribute information and translating them into multiple languages,

[0931] A means of sending the generated conversation content to the user's terminal for display,

[0932] A means of providing interactive questions and trivia based on the conversation content,

[0933] A means for generating questions that include an assessment of the learning content based on accessed information,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, further comprising a function to analyze the user's responses during a conversation and provide instant feedback.

[0937] (Claim 3)

[0938] The system according to claim 1, which provides an interactive, user-centered cultural learning experience by providing historical content using a visualization device.

[0939] "Example 2 of combining an emotion engine"

[0940] (Claim 1)

[0941] A means of analyzing inquiries received from users regarding selected historical events and recognizing the corresponding emotions,

[0942] A means for obtaining relevant information from a recording medium based on analyzed information and sentiment data,

[0943] A means to construct an artificial intelligence dialogue model generated using acquired information, and to adjust the tone and content of the dialogue according to the user's emotional state,

[0944] A means of personalizing conversations by considering user attribute information and translating them into multiple languages,

[0945] A means for transmitting the generated dialogue content to the user's information processing device and displaying it,

[0946] It provides interactive challenges and knowledge checks based on the conversation content, and a means to adjust the difficulty level according to the user's emotional state.

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The system according to claim 1, comprising a function to evaluate the user's response during the analysis process and provide an appropriate response in real time.

[0950] (Claim 3)

[0951] The system according to claim 1, which provides an interactive, user-driven history learning experience for use in educational and cultural facilities.

[0952] "Application example 2 when combining with an emotional engine"

[0953] (Claim 1)

[0954] A means of analyzing inquiries received from users regarding selected time periods,

[0955] A means for obtaining relevant information from a storage device based on the analyzed information,

[0956] A means of generating an artificial intelligence dialogue model using acquired information and providing dialogue with the user,

[0957] A means of personalizing conversations by considering user profile information and translating them into multiple languages,

[0958] A means of analyzing the user's emotions using an emotion engine and adjusting the content of the dialogue,

[0959] A means for transmitting and displaying the generated dialogue content on the user's device,

[0960] A means of providing interactive problems and knowledge games based on the content of the dialogue,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, further comprising the function of analyzing the user's emotion-based responses during the course of a dialogue and providing feedback in real time.

[0964] (Claim 3)

[0965] The system according to claim 1, which provides an emotionally engaging, interactive learning experience suitable for use within the home. [Explanation of Symbols]

[0966] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing requests from users regarding selected cultural events or periods, A means for obtaining relevant information from a storage device based on the analyzed information, A means of generating an artificial intelligence conversation model using acquired information and providing conversation with the user, A method for personalizing conversations by considering user attribute information and translating them into multiple languages, A means of sending the generated conversation content to the user's terminal for display, A means of providing interactive questions and trivia based on the conversation content, A means for generating questions that include an assessment of the learning content based on accessed information, A system that includes this.

2. The system according to claim 1, further comprising a function to analyze the user's responses during the course of a conversation and provide instant feedback.

3. The system according to claim 1, which provides an interactive, user-centered cultural learning experience by providing historical content using a visualization device.

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