system

Abstract

We provide the system. [Solution] To improve information acquisition and experiences in museums, A means for extracting digital data from data storage and structuring text and image data, A means for constructing a generative artificial intelligence model that responds to user questions using natural language processing technology, A means of generating answers to user questions in real time using a constructed artificial intelligence model, A system including means for displaying the answer and related visual information on a user terminal.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Although museums possess a vast amount of digital data, it is not being fully utilized. In addition, in response to the increasing demand for interactive and experiential services in recent years, they have not been able to effectively respond due to technical constraints and budget limitations. As a result, there is a problem that the educational value and the quality of the experience for visitors have not been sufficiently improved.

Means for Solving the Problems

[0005] This invention provides a system for efficiently structuring digital data and constructing a generative artificial intelligence model that responds to user questions using natural language processing technology, in order to improve information acquisition and the experience within a museum. The artificial intelligence model also includes means for generating answers to user questions in real time and displaying the answers and related visual information on the user's terminal. Furthermore, by using three-dimensional models and virtual reality technology, exhibits can be visualized in an immersive way, and a function for converting questions using voice input into text is also provided, enabling access to a wider range of information.

[0006] "Digital data" refers to a collection of information stored or processed by electronic means, including in the form of text, images, and audio.

[0007] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and process human language, and is used when interpreting user input.

[0008] A "generative artificial intelligence model" is a type of AI that has the ability to generate new data based on given data, and has the function of creating answers to user questions.

[0009] A "three-dimensional model" refers to a data format that can reproduce an object or scene in three-dimensional space, and is a structure generated in a way that allows for visual observation.

[0010] "Virtual reality technology" is a technology that makes it possible to visually experience a virtual environment created using a computer, providing users with an immersive experience as if they were actually there.

[0011] A "user terminal" refers to hardware equipment such as computers or mobile devices that are directly operated by the user, and functions as an interface between the system and the user. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that utilizes museum digital data to provide visitors with an interactive experience. This system consists of three main components: a server, terminals, and users.

[0034] First, the server aggregates and efficiently manages digital data within the museum. Here, diverse data such as scans of ancient documents, photographs, and audio recordings are stored in a digital database. The server analyzes this data using natural language processing techniques and builds a generative artificial intelligence model. This model is trained to generate appropriate answers to questions from visitors.

[0035] Next, the user terminal provides a user-friendly interface that allows visitors to access a wealth of information within the museum. For example, users can input questions about exhibits of interest through the terminal. These questions are sent from the terminal to the server. The terminal visually displays AI-generated answers through its screen, and can simultaneously display related 3D models and virtual reality content. This allows users to gain a deeper understanding of the exhibits.

[0036] Users can utilize this system within the museum and directly interact with terminals to enjoy a wealth of information and engaging interactive experiences. For example, if a user asks, "How was this sculpture made?", the system can provide information about its historical background and production process, and display a 3D animation recreating the creation process.

[0037] Thus, the present invention aims to enhance educational value by providing visitors with in-depth information and visually and tactile experiences that could not be offered by conventional static exhibits.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server collects text and image data from the museum's digital data storage and extracts them in electronic format. This converts various materials into structured digital data.

[0041] Step 2:

[0042] The server applies natural language processing techniques to the extracted digital data, analyzing and classifying relevant keywords and contextual information. This makes the information efficiently searchable and processable.

[0043] Step 3:

[0044] The server begins building an AI model using organized data to train a generative artificial intelligence model. This model is specialized to generate appropriate answers to user questions.

[0045] Step 4:

[0046] When a user uses a terminal and enters a question about an exhibit, the terminal sends that question data to the server in real time. At this point, the user's input is accessible to the entire system.

[0047] Step 5:

[0048] The server inputs the user's submitted question into a generative artificial intelligence model. This model then generates an appropriate answer by referring to a pre-trained database.

[0049] Step 6:

[0050] The server sends the generated response to the terminal and provides it to the user. The terminal displays the received response, and the user can obtain an explanation in natural language.

[0051] Step 7:

[0052] The device also displays related content, such as 3D models and virtual reality-based visual information. This allows users to gain a deeper visual understanding of the exhibits.

[0053] (Example 1)

[0054] 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."

[0055] Traditional information facilities lacked the means to quickly and accurately answer visitors' questions about the exhibits. Furthermore, providing additional visual and experiential information to deepen understanding of the exhibits was difficult, highlighting the need to maximize the visitor experience.

[0056] 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.

[0057] In this invention, the server includes means for collecting digital content and organizing text and still image data; means for constructing a generative intelligence model that responds to user inquiries using natural language processing technology; and means for generating responses to user inquiries in real time using the constructed intelligence model. This enables the rapid and accurate provision of information to visitors and the sharing of visually rich experiences.

[0058] An "information facility" is a place where the general public can obtain information and knowledge, and includes museums and libraries.

[0059] "Digital content" refers to electronically stored data such as text, images, audio, and video, and includes exhibits and explanatory information provided at information facilities.

[0060] "Methods of systematization" refer to methods of organizing digital content based on categories and tags, making it easier to search and use.

[0061] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used in question answering systems.

[0062] A "generative intelligence model" refers to an intelligent system that uses pre-trained data to generate responses to user questions.

[0063] "Real-time" refers to the ability to provide immediate responses and processing results in response to user input.

[0064] "Visual materials" refer to supplementary images and illustrations used to convey information visually, and in some cases, include 3D models and animations.

[0065] "User equipment" refers to terminals and devices that visitors can access and operate within an information facility.

[0066] This invention is a system in which servers, terminals, and users work together to improve the visitor experience in information facilities.

[0067] The server is responsible for aggregating and managing digital content within the information facility. Specifically, it stores various media such as text, images, and audio related to exhibits and explanations in a digital database. The server applies natural language processing techniques to this data to build a generative intelligence model. This model is trained using a large-scale question-and-answer dataset and has the ability to respond appropriately to questions from visitors.

[0068] Users can interact with the system using terminals installed within the information facility. Users input questions through the terminal's intuitive interface. For example, they can enter specific prompts such as, "Who is the artist of this painting?" The terminal then sends the entered question to the server.

[0069] The server generates answers in real time using a generative AI model based on the received questions. The generated answers are sent to the terminal and presented visually to the user. Furthermore, if there are visual materials related to the answers, the terminal provides them on the display screen. For example, 3D models or animations recreating the production process can be displayed.

[0070] This allows users to receive quick and accurate answers to their questions within the information facility, deepening their understanding of the exhibits. The system aims to provide visitors with a rich experience and educational value.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server collects digital content from within the information facility. Inputs include text descriptions of exhibits, image data, and audio files. Data processing within the server involves organizing and storing this diverse media data into a digital database. Thus, a searchable database is constructed as output.

[0074] Step 2:

[0075] The server applies natural language processing techniques to stored digital data. Text data is processed as input, undergoing syntactic and semantic analysis. As a data computation, a generative AI model is constructed based on the analysis results. This model is trained on a large-scale question-answer dataset, and the output is an AI model ready to appropriately respond to questions from visitors.

[0076] Step 3:

[0077] The user enters a question about an exhibit of interest through the terminal. The input is a prompt, such as "Who is the artist of this painting?". The terminal's specific function is to send the entered question to the server. The output is the question data arriving at the server.

[0078] Step 4:

[0079] The server processes the received questions. It receives user question data as input and generates answers in real time using a generative AI model. Here, response generation by the model is performed as data computation, and the output is a specific and appropriate answer to the question.

[0080] Step 5:

[0081] The terminal visually displays the responses sent from the server. It receives response data from the server as input. Its specific actions include displaying the responses on the screen and providing related visual materials and 3D models. As output, all information presented to the user is visually displayed, allowing the user to deepen their understanding of the exhibits.

[0082] (Application Example 1)

[0083] 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."

[0084] In today's world, museums and physical stores, which serve as centers for information exchange, are required to provide visitors with richer and more immersive experiences. However, conventional technologies have limited ways of effectively collecting and interactively providing information about exhibits and products. As a result, visitors face the challenge of not being able to quickly and accurately obtain the information they need.

[0085] 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.

[0086] In this invention, the server includes means for extracting information signals from a data storage device and structuring symbolic and visual signals; means for constructing a generative artificial intelligence model that responds to user inquiries using natural language processing technology; and means for acquiring product information using product identification codes and presenting it to the user. This makes it possible to quickly and interactively acquire detailed information about exhibits and products of interest to visitors and customers, and to provide them with a rich experience.

[0087] An "information exchange space" is a physical or digital space where users can acquire information about knowledge and products and interact with each other.

[0088] A "data storage device" is a device that stores information signals and makes them accessible as needed.

[0089] An "information signal" is electrical or digital data used to represent information.

[0090] "Means for structuring symbols and visual signals" refers to methods aimed at converting information signals into a form that is easily understandable to users and providing them visually.

[0091] "Natural language processing technology" is a field of information technology that analyzes and processes human language.

[0092] "Users" refers to individuals or groups who visit an information exchange forum and utilize the information and services provided.

[0093] A "generative artificial intelligence model" is a program or algorithm that automatically generates new information or answers based on input data.

[0094] A "product identification code" is a code used to uniquely identify a specific product.

[0095] "Means of acquiring and presenting product information to users" refers to methods for collecting and displaying detailed information related to products and services.

[0096] The system implementing this invention comprises a data storage device, a user terminal, and a server. The server is responsible for recording and structuring information signals. Voice questions from the user are transmitted to the server via the terminal. The server analyzes the questions using natural language processing technology and generates appropriate answers using a generative artificial intelligence model.

[0097] The server is equipped with a database management system (DBMS), natural language processing frameworks (e.g., NLTK and spaCy), and a platform for using generative AI models (e.g., OpenAI®'s GPT) to store digital data, particularly as information signals, and to retrieve detailed product information from product identification codes. This information is structured and transmitted to the user terminal in an easy-to-use format.

[0098] On the user's device, the generated answers are presented to the user in a visual format. This may involve using a mobile application development framework (e.g., Flutter® or React Native). The device also displays relevant information based on product identification codes, providing a means for users to interactively learn more about products and exhibits.

[0099] As a concrete example, a user enters a question at a physical store, such as, "How do I use this product?" The server uses a generative AI model to retrieve information on how to use the product based on its product identification code, and sends the answer back to the user's terminal. Through this, the user can deepen their understanding of the product by reviewing the visually explained user guide and related 3D models.

[0100] An example of a prompt message might be: "Please provide detailed information about this product based on the <product's QR code (registered trademark) information>. In particular, I would like to know about the ingredients, usage instructions, and development background." In this way, it becomes possible to create a system where visitors and customers can have a rich and valuable experience even in information exchange settings.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user uses a terminal to scan the identification code of a product or exhibit and enters a question. The input consists of the user's voice question and the product identification code. The terminal converts this voice question into text data and sends it to the server along with the identification code.

[0104] Step 2:

[0105] The server analyzes the text data and identification code received from the terminal. The input consists of text data and an identification code. The server uses natural language processing techniques to understand the intent of the query and prepares to retrieve relevant information.

[0106] Step 3:

[0107] The server uses a generative AI model to generate corresponding answers based on text data. The input is parsed text data. The server extracts relevant product information from a database and creates answers based on that information.

[0108] Step 4:

[0109] The server constructs visual information based on the generated responses. The input consists of the generated responses and related product information. The server then formats this information into user-friendly data.

[0110] Step 5:

[0111] The server sends the generated visual information to the terminal. The output is formatted data as visual information. The terminal receives this information and prepares to display it to the user.

[0112] Step 6:

[0113] The device displays the received visual information on its screen and presents it to the user. The final output allows the user to view the answers to their questions and related visual information. This enables the user to intuitively understand product details and usage.

[0114] 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.

[0115] This invention is a system that combines an emotion engine that recognizes user emotions in order to further improve information acquisition and experience in museums. This system allows for more personalized interaction with exhibits in museums and enables the provision of information that is adapted to the user's emotions.

[0116] First, the server extracts text and image data from the museum's digital data storage. This data is classified and organized using natural language processing techniques and used as training data for a generative artificial intelligence model. This model has the ability to generate responses to user questions.

[0117] Next, the user terminal acquires voice and facial expression data from the user, and the emotion engine analyzes this data to recognize the user's emotional state. For example, if a child looks at an exhibit and makes an interested expression, the emotion engine evaluates this as "interest." This evaluation is sent to the server and used to adjust the content of the information provided.

[0118] As a concrete example, consider a scenario where a user asks, "What is the history of this armor?" The server generates an answer to this question through a generative artificial intelligence model. Simultaneously, if the emotion engine recognizes the user's emotion as "excitement," the server adjusts the answer to be presented in a more entertaining format and sends relevant visual content to the device.

[0119] Furthermore, the device utilizes 3D models and virtual reality technology to recreate exhibits in a highly immersive way. This allows users to gain a more enriching experience based on emotional feedback.

[0120] This system will personalize the museum experience more than ever before, enabling educational and interactive exhibits that resonate with users' emotions.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The server extracts text and image data from the museum's data storage and classifies and organizes them using natural language processing techniques. This process creates a dataset in a format usable by AI models.

[0124] Step 2:

[0125] The server uses the extracted and organized data to train a generative artificial intelligence model. This model is designed to respond to user questions and generate appropriate answers.

[0126] Step 3:

[0127] The user enters a question about the exhibit into a terminal. The terminal sends this question data to the server in real time, initiating the process of generating the answer.

[0128] Step 4:

[0129] The device collects the user's voice and facial expression data and inputs it into the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state.

[0130] Step 5:

[0131] The server uses a generative artificial intelligence model to generate answers to user questions, and then adjusts the content and format of the answers, taking into account the sentiment data recognized by the sentiment engine.

[0132] Step 6:

[0133] The server sends the adjusted response along with associated visual information, such as a 3D model or virtual reality content, to the terminal.

[0134] Step 7:

[0135] The terminal displays the user the answers and visual information received from the server. Based on the displayed information, the user can gain a deeper understanding of the museum exhibits.

[0136] Step 8:

[0137] Users can experience the provided interactive content, receive feedback from the system, and ask further questions or explore further.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] The challenge lies in improving the quality of information acquisition and experiences in museums, while also providing visitors with personalized and emotionally resonant interactive experiences. Traditional methods have made it difficult to provide information based on visitors' emotions, resulting in limited improvements in visitor satisfaction.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for extracting digital information from a data storage device and structuring document and image information; means for constructing a response generation device that responds to user inquiries using natural language processing technology; and means for acquiring user voice and facial expression information and determining the emotional state with an analysis device. This makes it possible to adjust the content of responses based on the visitor's emotions, providing a more personalized museum experience.

[0143] "Digital information" refers to data that is stored and processed electronically, and includes formats such as documents and images.

[0144] "Structuring methods" refer to methods of organizing digital information and systematically arranging it so that it can be easily analyzed and used.

[0145] "Natural language processing technology" refers to the technology that enables computers to understand and process the language that humans use on a daily basis.

[0146] A "response generation device" refers to a system that uses natural language processing technology to generate appropriate responses to user inquiries.

[0147] "User device" refers to a terminal device used by users to input or receive information.

[0148] An "analysis device" refers to a device that analyzes acquired data to identify the user's state and patterns.

[0149] "Emotional state" refers to the emotional situation or reaction inferred from the user's voice and facial expressions.

[0150] This invention is a system that improves the quality of the user experience in museums by personalizing the experience and providing information that is tailored to the user's emotions. The system consists of three elements: a server, a terminal, and the user.

[0151] The server extracts digital information from data storage devices and structures it as document and image information. Specifically, it retrieves data from a database management system using SQL and organizes it using programming languages ​​such as Python and natural language processing libraries (e.g., NLTK and spaCy). This organized data is used as training data for a generative AI model. The generative AI model is built using a deep learning framework (e.g., TENSORFLOW® and PyTorch) and has the ability to generate appropriate responses to natural language inquiries from users.

[0152] The device uses a microphone and camera to collect user voice and facial expression data. Voice is converted to text using the Google® Speech-to-Text API, and facial expression data is analyzed using facial recognition technologies such as OpenCV and Dlib. This information is then analyzed by an emotion engine to identify the user's emotional state. The results are sent to a server and used to adjust the content of the information provided.

[0153] When a user asks a question, such as "What is the history of this armor?", the generative AI model generates a corresponding answer on the server. Simultaneously, if the emotion engine identifies the user's emotion as "excitement," the server generates visual content and animations to provide more entertaining related information and sends them to the device. This allows the device to recreate museum exhibits using 3D models and virtual reality technology, promoting an immersive experience for the user.

[0154] An example of a prompt message could be an instruction such as, "When the user shows interest, provide relevant information with an added element of entertainment."

[0155] Through the implementation described above, this invention makes it possible to provide an interactive and educational museum experience that takes into account the emotions of the users.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server extracts digital information from the data storage device. Specifically, it queries the database to retrieve documents and image data. This data is provided as input and then structured and classified. Using a Python program and natural language processing libraries (NLTK and spaCy), the data is organized by document type. As output, a dataset is generated that is appropriately formatted for use in model training.

[0159] Step 2:

[0160] The server trains a generative AI model based on the data organized in Step 1. Pre-structured document and image information is provided as input data, and the model is built using a deep learning framework (TensorFlow or PyTorch). The output is an AI model that responds to user inquiries.

[0161] Step 3:

[0162] Users input voice and facial expressions using a device. The device is equipped with a microphone and camera, which collect user data. Voice data is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using OpenCV, Dlib, etc. Inputs include voice signals and video, and output is text data and emotion labels.

[0163] Step 4:

[0164] The emotion labels and text data obtained from the device are sent to the server. The server analyzes these inputs and uses an emotion engine to determine the emotional state. For example, if the server determines that the user is "excited," this information is reflected in the response format. A refined response is output, and a generative AI model is utilized.

[0165] Step 5:

[0166] The server adjusts its response to be more entertaining based on the emotion labels it receives. It instructs the model using a prompt message that says, "Provide relevant information with added entertainment value when the user shows interest." The generated response and visual content are sent to the device. The output is a personalized response and related media data.

[0167] Step 6:

[0168] The terminal receives content sent from the server and displays it to the user. It virtually recreates exhibits using 3D models and virtual reality technology. The output is visually displayed content, which allows the user to have a more enriching experience.

[0169] (Application Example 2)

[0170] 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".

[0171] In museums, when visitors obtain information about exhibits, common methods of information provision do not take into account individual interests and emotions, resulting in a uniform quality of experience and a lack of emotionally resonant information. Furthermore, there are limited interactive means to deepen visitors' interest in the exhibits. A system is needed to improve this and enable personalized information provision and experiences that respond to emotions.

[0172] 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.

[0173] In this invention, the server includes means for extracting digital data from a data storage device and structuring text and image data; means for constructing a generative intelligence model that responds to user questions using natural language processing technology; and means for acquiring gaze and facial expression information and estimating emotional states. This enables the provision of appropriate information in real time according to the user's emotions and the dynamic adjustment of visual and auditory content to create a personalized and enriching museum experience.

[0174] A "museum" is an institution that displays historical, artistic, and scientific exhibits with the aim of promoting education and culture.

[0175] "Information acquisition" is the process of collecting information that users need by utilizing digital data and real-time data.

[0176] "Enhancing the experience" refers to increasing understanding of and interest in exhibits through interactive content and information tailored to the user's emotions and interests.

[0177] A "data storage device" is a device that records and stores digital information and makes it accessible as needed.

[0178] "Text data" refers to text information written in natural language that is stored or transmitted in digital format.

[0179] "Image data" refers to visual information represented in digital format, and is an element that constitutes visual content.

[0180] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0181] A "generative intelligence model" is an algorithm that uses artificial intelligence to generate new text or information based on input data.

[0182] "Emotion analysis methods" refer to technologies that recognize emotions from a user's facial expressions and voice, and interpret them as data.

[0183] "Visual and auditory content" refers to interactive information composed of audio and video, provided to attract the user's interest.

[0184] To implement this invention, the server first extracts digital data from a data storage device and structures it as text and image data. The server responds to user questions in real time using a generative intelligence model that utilizes natural language processing technology. It is preferable to use a library such as Hugging Face Transformers for this intelligence model.

[0185] The user terminal is equipped with the necessary hardware to acquire gaze and facial expression information in real time. Specifically, it utilizes the camera and microphone of smart glasses and implements facial expression analysis libraries such as OpenCV. This identifies the user's emotional state and transmits it to the server. Based on this emotional data, the server dynamically adjusts the visual and auditory content to provide more personalized information. Interactive content can enhance the sense of presence of exhibits by utilizing 3D models and virtual reality technologies.

[0186] For example, if a user shows interest in a particular painting, a generative intelligence model can provide in-depth information about the painting's history and background. A possible prompt might be, "Please explain the background of this painting in detail. Please also display any visual aids." By providing appropriate information in response to the user's emotions, the museum experience can be enriched.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The user moves around the museum and begins viewing the exhibits. The camera and microphone of the user's smart glasses capture the user's gaze and facial expressions in real time. Camera video and audio data are received as input, and the user's emotional state is estimated using facial expression analysis libraries such as OpenCV. The output is the recognized emotion data.

[0190] Step 2:

[0191] When a user views an exhibit and expresses an emotion, the device sends that emotion data to a server. The server receives the emotion data and performs data analysis. The input is emotion data, and the output includes instructions for generating appropriate content based on that emotion. Specifically, if the emotion is excitement, the system prepares to provide more detailed information.

[0192] Step 3:

[0193] When a user expresses a question or interest, the audio is converted to text on the user's device. This is done using speech recognition technology. The input is audio data, and the output is converted text data. This text data is sent to the server. Once the transcribed question or interest arrives at the server, a generative AI model (e.g., ChatGPT®) prepares to generate an appropriate response based on this input.

[0194] Step 4:

[0195] The server generates responses based on the received text data using a generative AI model. The input consists of transcribed question data and sentiment data, while the output is the response information for the user along with associated visual and auditory content. The generative AI model uses appropriate prompts, such as "Please describe the background of this painting in detail. Please also display any visual materials," to generate accurate information.

[0196] Step 5:

[0197] The response information and content sent from the server are displayed on the user's terminal. The terminal utilizes 3D models and virtual reality technology to generate visual content in real time, providing the user with an immersive experience. The input is the information and content generated by the server, while the output is a reproduction of the interactive exhibits experienced by the user on the display. As a result, the user experience is enriched.

[0198] 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.

[0199] 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 (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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention is a system that utilizes museum digital data to provide visitors with an interactive experience. This system consists of three main components: a server, terminals, and users.

[0215] First, the server aggregates and efficiently manages digital data within the museum. Here, diverse data such as scans of ancient documents, photographs, and audio recordings are stored in a digital database. The server analyzes this data using natural language processing techniques and builds a generative artificial intelligence model. This model is trained to generate appropriate answers to questions from visitors.

[0216] Next, the user terminal provides a user-friendly interface that allows visitors to access a wealth of information within the museum. For example, users can input questions about exhibits of interest through the terminal. These questions are sent from the terminal to the server. The terminal visually displays AI-generated answers through its screen, and can simultaneously display related 3D models and virtual reality content. This allows users to gain a deeper understanding of the exhibits.

[0217] Users can utilize this system within the museum and directly interact with terminals to enjoy a wealth of information and engaging interactive experiences. For example, if a user asks, "How was this sculpture made?", the system can provide information about its historical background and production process, and display a 3D animation recreating the creation process.

[0218] Thus, the present invention aims to enhance educational value by providing visitors with in-depth information and visually and tactile experiences that could not be offered by conventional static exhibits.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server collects text and image data from the museum's digital data storage and extracts them in electronic format. This converts various materials into structured digital data.

[0222] Step 2:

[0223] The server applies natural language processing techniques to the extracted digital data, analyzing and classifying relevant keywords and contextual information. This makes the information efficiently searchable and processable.

[0224] Step 3:

[0225] The server begins building an AI model using organized data to train a generative artificial intelligence model. This model is specialized to generate appropriate answers to user questions.

[0226] Step 4:

[0227] When a user uses a terminal and enters a question about an exhibit, the terminal sends that question data to the server in real time. At this point, the user's input is accessible to the entire system.

[0228] Step 5:

[0229] The server inputs the user's submitted question into a generative artificial intelligence model. This model then generates an appropriate answer by referring to a pre-trained database.

[0230] Step 6:

[0231] The server sends the generated response to the terminal and provides it to the user. The terminal displays the received response, and the user can obtain an explanation in natural language.

[0232] Step 7:

[0233] The device also displays related content, such as 3D models and virtual reality-based visual information. This allows users to gain a deeper visual understanding of the exhibits.

[0234] (Example 1)

[0235] 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."

[0236] Traditional information facilities lacked the means to quickly and accurately answer visitors' questions about the exhibits. Furthermore, providing additional visual and experiential information to deepen understanding of the exhibits was difficult, highlighting the need to maximize the visitor experience.

[0237] 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.

[0238] In this invention, the server includes means for collecting digital content and organizing text and still image data; means for constructing a generative intelligence model that responds to user inquiries using natural language processing technology; and means for generating responses to user inquiries in real time using the constructed intelligence model. This enables the rapid and accurate provision of information to visitors and the sharing of visually rich experiences.

[0239] An "information facility" is a place where the general public can obtain information and knowledge, and includes museums and libraries.

[0240] "Digital content" refers to electronically stored data such as text, images, audio, and video, and includes exhibits and explanatory information provided at information facilities.

[0241] "Methods of systematization" refer to methods of organizing digital content based on categories and tags, making it easier to search and use.

[0242] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used in question answering systems.

[0243] A "generative intelligence model" refers to an intelligent system that uses pre-trained data to generate responses to user questions.

[0244] "Real-time" refers to the ability to provide immediate responses and processing results in response to user input.

[0245] "Visual materials" refer to supplementary images and illustrations used to convey information visually, and in some cases, include 3D models and animations.

[0246] "User equipment" refers to terminals and devices that visitors can access and operate within an information facility.

[0247] This invention is a system in which servers, terminals, and users work together to improve the visitor experience in information facilities.

[0248] The server is responsible for aggregating and managing digital content within the information facility. Specifically, it stores various media such as text, images, and audio related to exhibits and explanations in a digital database. The server applies natural language processing techniques to this data to build a generative intelligence model. This model is trained using a large-scale question-and-answer dataset and has the ability to respond appropriately to questions from visitors.

[0249] Users can interact with the system using terminals installed within the information facility. Users input questions through the terminal's intuitive interface. For example, they can enter specific prompts such as, "Who is the artist of this painting?" The terminal then sends the entered question to the server.

[0250] The server generates answers in real time using a generative AI model based on the received questions. The generated answers are sent to the terminal and presented visually to the user. Furthermore, if there are visual materials related to the answers, the terminal provides them on the display screen. For example, 3D models or animations recreating the production process can be displayed.

[0251] This allows users to receive quick and accurate answers to their questions within the information facility, deepening their understanding of the exhibits. The system aims to provide visitors with a rich experience and educational value.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The server collects digital content from within the information facility. Inputs include text descriptions of exhibits, image data, and audio files. Data processing within the server involves organizing and storing this diverse media data into a digital database. Thus, a searchable database is constructed as output.

[0255] Step 2:

[0256] The server applies natural language processing techniques to stored digital data. Text data is processed as input, undergoing syntactic and semantic analysis. As a data computation, a generative AI model is constructed based on the analysis results. This model is trained on a large-scale question-answer dataset, and the output is an AI model ready to appropriately respond to questions from visitors.

[0257] Step 3:

[0258] The user enters a question about an exhibit of interest through the terminal. The input is a prompt, such as "Who is the artist of this painting?". The terminal's specific function is to send the entered question to the server. The output is the question data arriving at the server.

[0259] Step 4:

[0260] The server processes the received questions. It receives user question data as input and generates answers in real time using a generative AI model. Here, response generation by the model is performed as data computation, and the output is a specific and appropriate answer to the question.

[0261] Step 5:

[0262] The terminal visually displays the responses sent from the server. It receives response data from the server as input. Its specific actions include displaying the responses on the screen and providing related visual materials and 3D models. As output, all information presented to the user is visually displayed, allowing the user to deepen their understanding of the exhibits.

[0263] (Application Example 1)

[0264] 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."

[0265] In today's world, museums and physical stores, which serve as centers for information exchange, are required to provide visitors with richer and more immersive experiences. However, conventional technologies have limited ways of effectively collecting and interactively providing information about exhibits and products. As a result, visitors face the challenge of not being able to quickly and accurately obtain the information they need.

[0266] 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.

[0267] In this invention, the server includes means for extracting information signals from a data storage device and structuring symbolic and visual signals; means for constructing a generative artificial intelligence model that responds to user inquiries using natural language processing technology; and means for acquiring product information using product identification codes and presenting it to the user. This makes it possible to quickly and interactively acquire detailed information about exhibits and products of interest to visitors and customers, and to provide them with a rich experience.

[0268] An "information exchange space" is a physical or digital space where users can acquire information about knowledge and products and interact with each other.

[0269] A "data storage device" is a device that stores information signals and makes them accessible as needed.

[0270] An "information signal" is electrical or digital data used to represent information.

[0271] "Means for structuring symbols and visual signals" refers to methods aimed at converting information signals into a form that is easily understandable to users and providing them visually.

[0272] "Natural language processing technology" is a field of information technology that analyzes and processes human language.

[0273] "Users" refers to individuals or groups who visit an information exchange forum and utilize the information and services provided.

[0274] A "generative artificial intelligence model" is a program or algorithm that automatically generates new information or answers based on input data.

[0275] A "product identification code" is a code used to uniquely identify a specific product.

[0276] "Means of acquiring and presenting product information to users" refers to methods for collecting and displaying detailed information related to products and services.

[0277] The system implementing this invention comprises a data storage device, a user terminal, and a server. The server is responsible for recording and structuring information signals. Voice questions from the user are transmitted to the server via the terminal. The server analyzes the questions using natural language processing technology and generates appropriate answers using a generative artificial intelligence model.

[0278] The server is equipped with a database management system (DBMS), natural language processing frameworks (e.g., NLTK and spaCy), and a platform for using generative AI models (e.g., OpenAI's GPT) to store digital data, particularly as information signals, and to retrieve detailed product information from product identification codes. This information is structured and transmitted to the user terminal in an easily usable format.

[0279] On the user's device, the generated answers are presented to the user in a visual format. This could be done using a mobile application development framework (e.g., Flutter or React Native). The device also displays relevant information based on product identification codes, providing a means for users to interactively learn more about products and exhibits.

[0280] As a concrete example, a user enters a question at a physical store, such as, "How do I use this product?" The server uses a generative AI model to retrieve information on how to use the product based on its product identification code, and sends the answer back to the user's terminal. Through this, the user can deepen their understanding of the product by reviewing the visually explained user guide and related 3D models.

[0281] Examples of prompt texts include content such as "Based on the <product's QR code information>, please provide detailed information about this product. In particular, explanations about ingredients, usage methods, and development background are desired." In this way, it becomes possible to realize a system in which visitors and customers can have a rich and valuable experience even in an information exchange venue.

[0282] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0283] Step 1:

[0284] The user uses the terminal to scan the identification code of the product or exhibit and enter a question. As inputs, the user's voice question and the product identification code are obtained. The terminal converts this voice question into text data and transmits it to the server together with the identification code.

[0285] Step 2:

[0286] The server analyzes the text data and identification code received from the terminal. The input is the text data and the identification code. The server uses natural language processing technology to understand the intention of the inquiry content and prepares to obtain relevant information.

[0287] Step 3:

[0288] The server uses the generative AI model to generate a corresponding answer based on the text data. The input is the analyzed text data. The server extracts relevant product information from the database and creates an answer based on that information.

[0289] Step 4:

[0290] The server constructs visual information based on the generated answer.The inputs are the generated answer and the relevant product information. The server formats these into data in an easy-to-view format for the user.

[0291] Step 5:

[0292] The server sends the generated visual information to the terminal. The output is formatted data as visual information. The terminal receives this information and prepares to display it to the user.

[0293] Step 6:

[0294] The device displays the received visual information on its screen and presents it to the user. The final output allows the user to view the answers to their questions and related visual information. This enables the user to intuitively understand product details and usage.

[0295] 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.

[0296] This invention is a system that combines an emotion engine that recognizes user emotions in order to further improve information acquisition and experience in museums. This system allows for more personalized interaction with exhibits in museums and enables the provision of information that is adapted to the user's emotions.

[0297] First, the server extracts text and image data from the museum's digital data storage. This data is classified and organized using natural language processing techniques and used as training data for a generative artificial intelligence model. This model has the ability to generate responses to user questions.

[0298] Next, the user terminal acquires voice and facial expression data from the user, and the emotion engine analyzes this data to recognize the user's emotional state. For example, if a child looks at an exhibit and makes an interested expression, the emotion engine evaluates this as "interest." This evaluation is sent to the server and used to adjust the content of the information provided.

[0299] As a specific example, consider the case where a user asks, "What is the history of this armor?" The server creates an answer to this question through a generative artificial intelligence model. At the same time, if the emotion engine recognizes the user's emotion as "excitement", the server adjusts to provide the answer in a more entertaining format and transmits relevant visual content to the terminal.

[0300] In addition, the terminal uses a three-dimensional model or virtual reality technology to reproduce the exhibit in a vivid manner. As a result, the user can obtain a more fulfilling experience based on emotional feedback.

[0301] With this system, the museum experience is more personalized than ever, enabling an educational and interactive exhibition that adapts to the user's emotions.

[0302] The processing flow will be described below.

[0303] [[ID=十七]]Step 1:

[0304] [[ID=二十]] The server extracts text and image data from the museum's data storage and classifies and organizes them using natural language processing technology. Through this process, a dataset in a format that can be used by the AI model is constructed.

[0305] Step 2:

[0306] 十九]] The server trains a generative artificial intelligence model using the extracted and organized data. This model is for responding to questions from users and generating appropriate answers. 日]]

[0307] Step 3:

[0308] The user inputs a question about the exhibit into the terminal. The terminal transmits the question data to the server in real time and starts the answer generation process.

[0309] <Step 4:

[0310] The device collects the user's voice and facial expression data and inputs it into the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state.

[0311] Step 5:

[0312] The server uses a generative artificial intelligence model to generate answers to user questions, and then adjusts the content and format of the answers, taking into account the sentiment data recognized by the sentiment engine.

[0313] Step 6:

[0314] The server sends the adjusted response along with associated visual information, such as a 3D model or virtual reality content, to the terminal.

[0315] Step 7:

[0316] The terminal displays the user the answers and visual information received from the server. Based on the displayed information, the user can gain a deeper understanding of the museum exhibits.

[0317] Step 8:

[0318] Users can experience the provided interactive content, receive feedback from the system, and ask further questions or explore further.

[0319] (Example 2)

[0320] 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".

[0321] The challenge lies in improving the quality of information acquisition and experiences in museums, while also providing visitors with personalized and emotionally resonant interactive experiences. Traditional methods have made it difficult to provide information based on visitors' emotions, resulting in limited improvements in visitor satisfaction.

[0322] 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.

[0323] In this invention, the server includes means for extracting digital information from a data storage device and structuring document and image information; means for constructing a response generation device that responds to user inquiries using natural language processing technology; and means for acquiring user voice and facial expression information and determining the emotional state with an analysis device. This makes it possible to adjust the content of responses based on the visitor's emotions, providing a more personalized museum experience.

[0324] "Digital information" refers to data that is stored and processed electronically, and includes formats such as documents and images.

[0325] "Structuring methods" refer to methods of organizing digital information and systematically arranging it so that it can be easily analyzed and used.

[0326] "Natural language processing technology" refers to the technology that enables computers to understand and process the language that humans use on a daily basis.

[0327] A "response generation device" refers to a system that uses natural language processing technology to generate appropriate responses to user inquiries.

[0328] "User device" refers to a terminal device used by users to input or receive information.

[0329] An "analysis device" refers to a device that analyzes acquired data to identify the user's state and patterns.

[0330] "Emotional state" refers to the emotional situation or reaction inferred from the user's voice and facial expressions.

[0331] This invention is a system that improves the quality of the user experience in museums by personalizing the experience and providing information that is tailored to the user's emotions. The system consists of three elements: a server, a terminal, and the user.

[0332] The server extracts digital information from data storage devices and structures it as document and image information. Specifically, it retrieves data from a database management system using SQL and organizes it using programming languages ​​such as Python and natural language processing libraries (e.g., NLTK and spaCy). This organized data is used as training data for a generative AI model. The generative AI model is built using a deep learning framework (e.g., TensorFlow and PyTorch) and has the ability to generate appropriate responses to natural language inquiries from users.

[0333] The device uses a microphone and camera to collect user voice and facial expression data. Voice is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using facial recognition technologies such as OpenCV and Dlib. This information is then analyzed by an emotion engine to identify the user's emotional state. The results are sent to a server and used to tailor the information provided.

[0334] When a user asks a question, such as "What is the history of this armor?", the generative AI model generates a corresponding answer on the server. Simultaneously, if the emotion engine identifies the user's emotion as "excitement," the server generates visual content and animations to provide more entertaining related information and sends them to the device. This allows the device to recreate museum exhibits using 3D models and virtual reality technology, promoting an immersive experience for the user.

[0335] An example of a prompt message could be an instruction such as, "When the user shows interest, provide relevant information with an added element of entertainment."

[0336] Through the implementation described above, this invention makes it possible to provide an interactive and educational museum experience that takes into account the emotions of the users.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The server extracts digital information from the data storage device. Specifically, it queries the database to retrieve documents and image data. This data is provided as input and then structured and classified. Using a Python program and natural language processing libraries (NLTK and spaCy), the data is organized by document type. As output, a dataset is generated that is appropriately formatted for use in model training.

[0340] Step 2:

[0341] The server trains a generative AI model based on the data organized in Step 1. Pre-structured document and image information is provided as input data, and the model is built using a deep learning framework (TensorFlow or PyTorch). The output is an AI model that responds to user inquiries.

[0342] Step 3:

[0343] Users input voice and facial expressions using a device. The device is equipped with a microphone and camera, which collect user data. Voice data is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using OpenCV, Dlib, etc. Inputs include voice signals and video, and output is text data and emotion labels.

[0344] Step 4:

[0345] The emotion labels and text data obtained from the device are sent to the server. The server analyzes these inputs and uses an emotion engine to determine the emotional state. For example, if the server determines that the user is "excited," this information is reflected in the response format. A refined response is output, and a generative AI model is utilized.

[0346] Step 5:

[0347] The server adjusts its response to be more entertaining based on the emotion labels it receives. It instructs the model using a prompt message that says, "Provide relevant information with added entertainment value when the user shows interest." The generated response and visual content are sent to the device. The output is a personalized response and related media data.

[0348] Step 6:

[0349] The terminal receives content sent from the server and displays it to the user. It virtually recreates exhibits using 3D models and virtual reality technology. The output is visually displayed content, which allows the user to have a more enriching experience.

[0350] (Application Example 2)

[0351] 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."

[0352] In museums, when visitors obtain information about exhibits, common methods of information provision do not take into account individual interests and emotions, resulting in a uniform quality of experience and a lack of emotionally resonant information. Furthermore, there are limited interactive means to deepen visitors' interest in the exhibits. A system is needed to improve this and enable personalized information provision and experiences that respond to emotions.

[0353] 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.

[0354] In this invention, the server includes means for extracting digital data from a data storage device and structuring text and image data; means for constructing a generative intelligence model that responds to user questions using natural language processing technology; and means for acquiring gaze and facial expression information and estimating emotional states. This enables the provision of appropriate information in real time according to the user's emotions and the dynamic adjustment of visual and auditory content to create a personalized and enriching museum experience.

[0355] A "museum" is an institution that displays historical, artistic, and scientific exhibits with the aim of promoting education and culture.

[0356] "Information acquisition" is the process of collecting information that users need by utilizing digital data and real-time data.

[0357] "Enhancing the experience" refers to increasing understanding of and interest in exhibits through interactive content and information tailored to the user's emotions and interests.

[0358] A "data storage device" is a device that records and stores digital information and makes it accessible as needed.

[0359] "Text data" refers to text information written in natural language that is stored or transmitted in digital format.

[0360] "Image data" refers to visual information represented in digital format, and is an element that constitutes visual content.

[0361] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0362] A "generative intelligence model" is an algorithm that uses artificial intelligence to generate new text or information based on input data.

[0363] "Emotion analysis methods" refer to technologies that recognize emotions from a user's facial expressions and voice, and interpret them as data.

[0364] "Visual and auditory content" refers to interactive information composed of audio and video, provided to attract the user's interest.

[0365] To implement this invention, the server first extracts digital data from a data storage device and structures it as text and image data. The server responds to user questions in real time using a generative intelligence model that utilizes natural language processing technology. It is preferable to use a library such as Hugging Face Transformers for this intelligence model.

[0366] The user terminal is equipped with the necessary hardware to acquire gaze and facial expression information in real time. Specifically, it utilizes the camera and microphone of smart glasses and implements facial expression analysis libraries such as OpenCV. This identifies the user's emotional state and transmits it to the server. Based on this emotional data, the server dynamically adjusts the visual and auditory content to provide more personalized information. Interactive content can enhance the sense of presence of exhibits by utilizing 3D models and virtual reality technologies.

[0367] For example, if a user shows interest in a particular painting, a generative intelligence model can provide in-depth information about the painting's history and background. A possible prompt might be, "Please explain the background of this painting in detail. Please also display any visual aids." By providing appropriate information in response to the user's emotions, the museum experience can be enriched.

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The user moves around the museum and begins viewing the exhibits. The camera and microphone of the user's smart glasses capture the user's gaze and facial expressions in real time. Camera video and audio data are received as input, and the user's emotional state is estimated using facial expression analysis libraries such as OpenCV. The output is the recognized emotion data.

[0371] Step 2:

[0372] When a user views an exhibit and expresses an emotion, the device sends that emotion data to a server. The server receives the emotion data and performs data analysis. The input is emotion data, and the output includes instructions for generating appropriate content based on that emotion. Specifically, if the emotion is excitement, the system prepares to provide more detailed information.

[0373] Step 3:

[0374] When a user expresses a question or interest, the audio is converted to text on the user's device. This is done using speech recognition technology. The input is audio data, and the output is converted text data. This text data is sent to the server. Once the transcribed question or interest arrives at the server, a generative AI model (e.g., ChatGPT) prepares to generate an appropriate response based on this input.

[0375] Step 4:

[0376] The server generates responses based on the received text data using a generative AI model. The input consists of transcribed question data and sentiment data, while the output is the response information for the user along with associated visual and auditory content. The generative AI model uses appropriate prompts, such as "Please describe the background of this painting in detail. Please also display any visual materials," to generate accurate information.

[0377] Step 5:

[0378] The response information and content sent from the server are displayed on the user's terminal. The terminal utilizes 3D models and virtual reality technology to generate visual content in real time, providing the user with an immersive experience. The input is the information and content generated by the server, while the output is a reproduction of the interactive exhibits experienced by the user on the display. As a result, the user experience is enriched.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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".

[0395] This invention is a system that utilizes museum digital data to provide visitors with an interactive experience. This system consists of three main components: a server, terminals, and users.

[0396] First, the server aggregates and efficiently manages digital data within the museum. Here, diverse data such as scans of ancient documents, photographs, and audio recordings are stored in a digital database. The server analyzes this data using natural language processing techniques and builds a generative artificial intelligence model. This model is trained to generate appropriate answers to questions from visitors.

[0397] Next, the user terminal provides a user-friendly interface that allows visitors to access a wealth of information within the museum. For example, users can input questions about exhibits of interest through the terminal. These questions are sent from the terminal to the server. The terminal visually displays AI-generated answers through its screen, and can simultaneously display related 3D models and virtual reality content. This allows users to gain a deeper understanding of the exhibits.

[0398] Users can utilize this system within the museum and directly interact with terminals to enjoy a wealth of information and engaging interactive experiences. For example, if a user asks, "How was this sculpture made?", the system can provide information about its historical background and production process, and display a 3D animation recreating the creation process.

[0399] Thus, the present invention aims to enhance educational value by providing visitors with in-depth information and visually and tactile experiences that could not be offered by conventional static exhibits.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The server collects text and image data from the museum's digital data storage and extracts them in electronic format. This converts various materials into structured digital data.

[0403] Step 2:

[0404] The server applies natural language processing techniques to the extracted digital data, analyzing and classifying relevant keywords and contextual information. This makes the information efficiently searchable and processable.

[0405] Step 3:

[0406] The server begins building an AI model using organized data to train a generative artificial intelligence model. This model is specialized to generate appropriate answers to user questions.

[0407] Step 4:

[0408] When a user uses a terminal and enters a question about an exhibit, the terminal sends that question data to the server in real time. At this point, the user's input is accessible to the entire system.

[0409] Step 5:

[0410] The server inputs the user's submitted question into a generative artificial intelligence model. This model then generates an appropriate answer by referring to a pre-trained database.

[0411] Step 6:

[0412] The server sends the generated response to the terminal and provides it to the user. The terminal displays the received response, and the user can obtain an explanation in natural language.

[0413] Step 7:

[0414] The device also displays related content, such as 3D models and virtual reality-based visual information. This allows users to gain a deeper visual understanding of the exhibits.

[0415] (Example 1)

[0416] 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."

[0417] Traditional information facilities lacked the means to quickly and accurately answer visitors' questions about the exhibits. Furthermore, providing additional visual and experiential information to deepen understanding of the exhibits was difficult, highlighting the need to maximize the visitor experience.

[0418] 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.

[0419] In this invention, the server includes means for collecting digital content and organizing text and still image data; means for constructing a generative intelligence model that responds to user inquiries using natural language processing technology; and means for generating responses to user inquiries in real time using the constructed intelligence model. This enables the rapid and accurate provision of information to visitors and the sharing of visually rich experiences.

[0420] An "information facility" is a place where the general public can obtain information and knowledge, and includes museums and libraries.

[0421] "Digital content" refers to electronically stored data such as text, images, audio, and video, and includes exhibits and explanatory information provided at information facilities.

[0422] "Methods of systematization" refer to methods of organizing digital content based on categories and tags, making it easier to search and use.

[0423] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used in question answering systems.

[0424] A "generative intelligence model" refers to an intelligent system that uses pre-trained data to generate responses to user questions.

[0425] "Real-time" refers to the ability to provide immediate responses and processing results in response to user input.

[0426] "Visual materials" refer to supplementary images and illustrations used to convey information visually, and in some cases, include 3D models and animations.

[0427] "User equipment" refers to terminals and devices that visitors can access and operate within an information facility.

[0428] This invention is a system in which servers, terminals, and users work together to improve the visitor experience in information facilities.

[0429] The server is responsible for aggregating and managing digital content within the information facility. Specifically, it stores various media such as text, images, and audio related to exhibits and explanations in a digital database. The server applies natural language processing techniques to this data to build a generative intelligence model. This model is trained using a large-scale question-and-answer dataset and has the ability to respond appropriately to questions from visitors.

[0430] Users can interact with the system using terminals installed within the information facility. Users input questions through the terminal's intuitive interface. For example, they can enter specific prompts such as, "Who is the artist of this painting?" The terminal then sends the entered question to the server.

[0431] The server generates answers in real time using a generative AI model based on the received questions. The generated answers are sent to the terminal and presented visually to the user. Furthermore, if there are visual materials related to the answers, the terminal provides them on the display screen. For example, 3D models or animations recreating the production process can be displayed.

[0432] This allows users to receive quick and accurate answers to their questions within the information facility, deepening their understanding of the exhibits. The system aims to provide visitors with a rich experience and educational value.

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The server collects digital content from within the information facility. Inputs include text descriptions of exhibits, image data, and audio files. Data processing within the server involves organizing and storing this diverse media data into a digital database. Thus, a searchable database is constructed as output.

[0436] Step 2:

[0437] The server applies natural language processing techniques to stored digital data. Text data is processed as input, undergoing syntactic and semantic analysis. As a data computation, a generative AI model is constructed based on the analysis results. This model is trained on a large-scale question-answer dataset, and the output is an AI model ready to appropriately respond to questions from visitors.

[0438] Step 3:

[0439] The user enters a question about an exhibit of interest through the terminal. The input is a prompt, such as "Who is the artist of this painting?". The terminal's specific function is to send the entered question to the server. The output is the question data arriving at the server.

[0440] Step 4:

[0441] The server processes the received questions. It receives user question data as input and generates answers in real time using a generative AI model. Here, response generation by the model is performed as data computation, and the output is a specific and appropriate answer to the question.

[0442] Step 5:

[0443] The terminal visually displays the responses sent from the server. It receives response data from the server as input. Its specific actions include displaying the responses on the screen and providing related visual materials and 3D models. As output, all information presented to the user is visually displayed, allowing the user to deepen their understanding of the exhibits.

[0444] (Application Example 1)

[0445] 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."

[0446] In today's world, museums and physical stores, which serve as centers for information exchange, are required to provide visitors with richer and more immersive experiences. However, conventional technologies have limited ways of effectively collecting and interactively providing information about exhibits and products. As a result, visitors face the challenge of not being able to quickly and accurately obtain the information they need.

[0447] 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.

[0448] In this invention, the server includes means for extracting information signals from a data storage device and structuring symbolic and visual signals; means for constructing a generative artificial intelligence model that responds to user inquiries using natural language processing technology; and means for acquiring product information using product identification codes and presenting it to the user. This makes it possible to quickly and interactively acquire detailed information about exhibits and products of interest to visitors and customers, and to provide them with a rich experience.

[0449] An "information exchange space" is a physical or digital space where users can acquire information about knowledge and products and interact with each other.

[0450] A "data storage device" is a device that stores information signals and makes them accessible as needed.

[0451] An "information signal" is electrical or digital data used to represent information.

[0452] "Means for structuring symbols and visual signals" refers to methods aimed at converting information signals into a form that is easily understandable to users and providing them visually.

[0453] "Natural language processing technology" is a field of information technology that analyzes and processes human language.

[0454] "Users" refers to individuals or groups who visit an information exchange forum and utilize the information and services provided.

[0455] A "generative artificial intelligence model" is a program or algorithm that automatically generates new information or answers based on input data.

[0456] A "product identification code" is a code used to uniquely identify a specific product.

[0457] "Means of acquiring and presenting product information to users" refers to methods for collecting and displaying detailed information related to products and services.

[0458] The system implementing this invention comprises a data storage device, a user terminal, and a server. The server is responsible for recording and structuring information signals. Voice questions from the user are transmitted to the server via the terminal. The server analyzes the questions using natural language processing technology and generates appropriate answers using a generative artificial intelligence model.

[0459] The server is equipped with a database management system (DBMS), natural language processing frameworks (e.g., NLTK and spaCy), and a platform for using generative AI models (e.g., OpenAI's GPT) to store digital data, particularly as information signals, and to retrieve detailed product information from product identification codes. This information is structured and transmitted to the user terminal in an easily usable format.

[0460] On the user's device, the generated answers are presented to the user in a visual format. This could be done using a mobile application development framework (e.g., Flutter or React Native). The device also displays relevant information based on product identification codes, providing a means for users to interactively learn more about products and exhibits.

[0461] As a concrete example, a user enters a question at a physical store, such as, "How do I use this product?" The server uses a generative AI model to retrieve information on how to use the product based on its product identification code, and sends the answer back to the user's terminal. Through this, the user can deepen their understanding of the product by reviewing the visually explained user guide and related 3D models.

[0462] An example of a prompt message might be, "Please provide detailed information about this product based on the <product's QR code information>. In particular, I would like to know about the ingredients, usage instructions, and development background." In this way, it becomes possible to create a system where visitors and shoppers can have a rich and valuable experience even in information exchange settings.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The user uses a terminal to scan the identification code of a product or exhibit and enters a question. The input consists of the user's voice question and the product identification code. The terminal converts this voice question into text data and sends it to the server along with the identification code.

[0466] Step 2:

[0467] The server analyzes the text data and identification code received from the terminal. The input consists of text data and an identification code. The server uses natural language processing techniques to understand the intent of the query and prepares to retrieve relevant information.

[0468] Step 3:

[0469] The server uses a generative AI model to generate corresponding answers based on text data. The input is parsed text data. The server extracts relevant product information from a database and creates answers based on that information.

[0470] Step 4:

[0471] The server constructs visual information based on the generated responses. The input consists of the generated responses and related product information. The server then formats this information into user-friendly data.

[0472] Step 5:

[0473] The server sends the generated visual information to the terminal. The output is formatted data as visual information. The terminal receives this information and prepares to display it to the user.

[0474] Step 6:

[0475] The device displays the received visual information on its screen and presents it to the user. The final output allows the user to view the answers to their questions and related visual information. This enables the user to intuitively understand product details and usage.

[0476] 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.

[0477] This invention is a system that combines an emotion engine that recognizes user emotions in order to further improve information acquisition and experience in museums. This system allows for more personalized interaction with exhibits in museums and enables the provision of information that is adapted to the user's emotions.

[0478] First, the server extracts text and image data from the museum's digital data storage. This data is classified and organized using natural language processing techniques and used as training data for a generative artificial intelligence model. This model has the ability to generate responses to user questions.

[0479] Next, the user terminal acquires voice and facial expression data from the user, and the emotion engine analyzes this data to recognize the user's emotional state. For example, if a child looks at an exhibit and makes an interested expression, the emotion engine evaluates this as "interest." This evaluation is sent to the server and used to adjust the content of the information provided.

[0480] As a concrete example, consider a scenario where a user asks, "What is the history of this armor?" The server generates an answer to this question through a generative artificial intelligence model. Simultaneously, if the emotion engine recognizes the user's emotion as "excitement," the server adjusts the answer to be presented in a more entertaining format and sends relevant visual content to the device.

[0481] Furthermore, the device utilizes 3D models and virtual reality technology to recreate exhibits in a highly immersive way. This allows users to gain a more enriching experience based on emotional feedback.

[0482] This system will personalize the museum experience more than ever before, enabling educational and interactive exhibits that resonate with users' emotions.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The server extracts text and image data from the museum's data storage and classifies and organizes them using natural language processing techniques. This process creates a dataset in a format usable by AI models.

[0486] Step 2:

[0487] The server uses the extracted and organized data to train a generative artificial intelligence model. This model is designed to respond to user questions and generate appropriate answers.

[0488] Step 3:

[0489] The user enters a question about the exhibit into a terminal. The terminal sends this question data to the server in real time, initiating the process of generating the answer.

[0490] Step 4:

[0491] The device collects the user's voice and facial expression data and inputs it into the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state.

[0492] Step 5:

[0493] The server uses a generative artificial intelligence model to generate answers to user questions, and then adjusts the content and format of the answers, taking into account the sentiment data recognized by the sentiment engine.

[0494] Step 6:

[0495] The server sends the adjusted response along with associated visual information, such as a 3D model or virtual reality content, to the terminal.

[0496] Step 7:

[0497] The terminal displays the user the answers and visual information received from the server. Based on the displayed information, the user can gain a deeper understanding of the museum exhibits.

[0498] Step 8:

[0499] Users can experience the provided interactive content, receive feedback from the system, and ask further questions or explore further.

[0500] (Example 2)

[0501] 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."

[0502] The challenge lies in improving the quality of information acquisition and experiences in museums, while also providing visitors with personalized and emotionally resonant interactive experiences. Traditional methods have made it difficult to provide information based on visitors' emotions, resulting in limited improvements in visitor satisfaction.

[0503] 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.

[0504] In this invention, the server includes means for extracting digital information from a data storage device and structuring document and image information; means for constructing a response generation device that responds to user inquiries using natural language processing technology; and means for acquiring user voice and facial expression information and determining the emotional state with an analysis device. This makes it possible to adjust the content of responses based on the visitor's emotions, providing a more personalized museum experience.

[0505] "Digital information" refers to data that is stored and processed electronically, and includes formats such as documents and images.

[0506] "Structuring methods" refer to methods of organizing digital information and systematically arranging it so that it can be easily analyzed and used.

[0507] "Natural language processing technology" refers to the technology that enables computers to understand and process the language that humans use on a daily basis.

[0508] A "response generation device" refers to a system that uses natural language processing technology to generate appropriate responses to user inquiries.

[0509] "User device" refers to a terminal device used by users to input or receive information.

[0510] An "analysis device" refers to a device that analyzes acquired data to identify the user's state and patterns.

[0511] "Emotional state" refers to the emotional situation or reaction inferred from the user's voice and facial expressions.

[0512] This invention is a system that improves the quality of the user experience in museums by personalizing the experience and providing information that is tailored to the user's emotions. The system consists of three elements: a server, a terminal, and the user.

[0513] The server extracts digital information from data storage devices and structures it as document and image information. Specifically, it retrieves data from a database management system using SQL and organizes it using programming languages ​​such as Python and natural language processing libraries (e.g., NLTK and spaCy). This organized data is used as training data for a generative AI model. The generative AI model is built using a deep learning framework (e.g., TensorFlow and PyTorch) and has the ability to generate appropriate responses to natural language inquiries from users.

[0514] The device uses a microphone and camera to collect user voice and facial expression data. Voice is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using facial recognition technologies such as OpenCV and Dlib. This information is then analyzed by an emotion engine to identify the user's emotional state. The results are sent to a server and used to tailor the information provided.

[0515] When a user asks a question, such as "What is the history of this armor?", the generative AI model generates a corresponding answer on the server. Simultaneously, if the emotion engine identifies the user's emotion as "excitement," the server generates visual content and animations to provide more entertaining related information and sends them to the device. This allows the device to recreate museum exhibits using 3D models and virtual reality technology, promoting an immersive experience for the user.

[0516] An example of a prompt message could be an instruction such as, "When the user shows interest, provide relevant information with an added element of entertainment."

[0517] Through the implementation described above, this invention makes it possible to provide an interactive and educational museum experience that takes into account the emotions of the users.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The server extracts digital information from the data storage device. Specifically, it queries the database to retrieve documents and image data. This data is provided as input and then structured and classified. Using a Python program and natural language processing libraries (NLTK and spaCy), the data is organized by document type. As output, a dataset is generated that is appropriately formatted for use in model training.

[0521] Step 2:

[0522] The server trains a generative AI model based on the data organized in Step 1. Pre-structured document and image information is provided as input data, and the model is built using a deep learning framework (TensorFlow or PyTorch). The output is an AI model that responds to user inquiries.

[0523] Step 3:

[0524] Users input voice and facial expressions using a device. The device is equipped with a microphone and camera, which collect user data. Voice data is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using OpenCV, Dlib, etc. Inputs include voice signals and video, and output is text data and emotion labels.

[0525] Step 4:

[0526] The emotion labels and text data obtained from the device are sent to the server. The server analyzes these inputs and uses an emotion engine to determine the emotional state. For example, if the server determines that the user is "excited," this information is reflected in the response format. A refined response is output, and a generative AI model is utilized.

[0527] Step 5:

[0528] The server adjusts its response to be more entertaining based on the emotion labels it receives. It instructs the model using a prompt message that says, "Provide relevant information with added entertainment value when the user shows interest." The generated response and visual content are sent to the device. The output is a personalized response and related media data.

[0529] Step 6:

[0530] The terminal receives content sent from the server and displays it to the user. It virtually recreates exhibits using 3D models and virtual reality technology. The output is visually displayed content, which allows the user to have a more enriching experience.

[0531] (Application Example 2)

[0532] 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."

[0533] In museums, when visitors obtain information about exhibits, common methods of information provision do not take into account individual interests and emotions, resulting in a uniform quality of experience and a lack of emotionally resonant information. Furthermore, there are limited interactive means to deepen visitors' interest in the exhibits. A system is needed to improve this and enable personalized information provision and experiences that respond to emotions.

[0534] 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.

[0535] In this invention, the server includes means for extracting digital data from a data storage device and structuring text and image data; means for constructing a generative intelligence model that responds to user questions using natural language processing technology; and means for acquiring gaze and facial expression information and estimating emotional states. This enables the provision of appropriate information in real time according to the user's emotions and the dynamic adjustment of visual and auditory content to create a personalized and enriching museum experience.

[0536] A "museum" is an institution that displays historical, artistic, and scientific exhibits with the aim of promoting education and culture.

[0537] "Information acquisition" is the process of collecting information that users need by utilizing digital data and real-time data.

[0538] "Enhancing the experience" refers to increasing understanding of and interest in exhibits through interactive content and information tailored to the user's emotions and interests.

[0539] A "data storage device" is a device that records and stores digital information and makes it accessible as needed.

[0540] "Text data" refers to text information written in natural language that is stored or transmitted in digital format.

[0541] "Image data" refers to visual information represented in digital format, and is an element that constitutes visual content.

[0542] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0543] A "generative intelligence model" is an algorithm that uses artificial intelligence to generate new text or information based on input data.

[0544] "Emotion analysis methods" refer to technologies that recognize emotions from a user's facial expressions and voice, and interpret them as data.

[0545] "Visual and auditory content" refers to interactive information composed of audio and video, provided to attract the user's interest.

[0546] To implement this invention, the server first extracts digital data from a data storage device and structures it as text and image data. The server responds to user questions in real time using a generative intelligence model that utilizes natural language processing technology. It is preferable to use a library such as Hugging Face Transformers for this intelligence model.

[0547] The user terminal is equipped with the necessary hardware to acquire gaze and facial expression information in real time. Specifically, it utilizes the camera and microphone of smart glasses and implements facial expression analysis libraries such as OpenCV. This identifies the user's emotional state and transmits it to the server. Based on this emotional data, the server dynamically adjusts the visual and auditory content to provide more personalized information. Interactive content can enhance the sense of presence of exhibits by utilizing 3D models and virtual reality technologies.

[0548] For example, if a user shows interest in a particular painting, a generative intelligence model can provide in-depth information about the painting's history and background. A possible prompt might be, "Please explain the background of this painting in detail. Please also display any visual aids." By providing appropriate information in response to the user's emotions, the museum experience can be enriched.

[0549] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0550] Step 1:

[0551] The user moves around the museum and begins viewing the exhibits. The camera and microphone of the user's smart glasses capture the user's gaze and facial expressions in real time. Camera video and audio data are received as input, and the user's emotional state is estimated using facial expression analysis libraries such as OpenCV. The output is the recognized emotion data.

[0552] Step 2:

[0553] When a user views an exhibit and expresses an emotion, the device sends that emotion data to a server. The server receives the emotion data and performs data analysis. The input is emotion data, and the output includes instructions for generating appropriate content based on that emotion. Specifically, if the emotion is excitement, the system prepares to provide more detailed information.

[0554] Step 3:

[0555] When a user expresses a question or interest, the audio is converted to text on the user's device. This is done using speech recognition technology. The input is audio data, and the output is converted text data. This text data is sent to the server. Once the transcribed question or interest arrives at the server, a generative AI model (e.g., ChatGPT) prepares to generate an appropriate response based on this input.

[0556] Step 4:

[0557] The server generates responses based on the received text data using a generative AI model. The input consists of transcribed question data and sentiment data, while the output is the response information for the user along with associated visual and auditory content. The generative AI model uses appropriate prompts, such as "Please describe the background of this painting in detail. Please also display any visual materials," to generate accurate information.

[0558] Step 5:

[0559] The response information and content sent from the server are displayed on the user's terminal. The terminal utilizes 3D models and virtual reality technology to generate visual content in real time, providing the user with an immersive experience. The input is the information and content generated by the server, while the output is a reproduction of the interactive exhibits experienced by the user on the display. As a result, the user experience is enriched.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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".

[0577] This invention is a system that utilizes museum digital data to provide visitors with an interactive experience. This system consists of three main components: a server, terminals, and users.

[0578] First, the server aggregates and efficiently manages digital data within the museum. Here, diverse data such as scans of ancient documents, photographs, and audio recordings are stored in a digital database. The server analyzes this data using natural language processing techniques and builds a generative artificial intelligence model. This model is trained to generate appropriate answers to questions from visitors.

[0579] Next, the user terminal provides a user-friendly interface that allows visitors to access a wealth of information within the museum. For example, users can input questions about exhibits of interest through the terminal. These questions are sent from the terminal to the server. The terminal visually displays AI-generated answers through its screen, and can simultaneously display related 3D models and virtual reality content. This allows users to gain a deeper understanding of the exhibits.

[0580] Users can utilize this system within the museum and directly interact with terminals to enjoy a wealth of information and engaging interactive experiences. For example, if a user asks, "How was this sculpture made?", the system can provide information about its historical background and production process, and display a 3D animation recreating the creation process.

[0581] Thus, the present invention aims to enhance educational value by providing visitors with in-depth information and visually and tactile experiences that could not be offered by conventional static exhibits.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The server collects text and image data from the museum's digital data storage and extracts them in electronic format. This converts various materials into structured digital data.

[0585] Step 2:

[0586] The server applies natural language processing techniques to the extracted digital data, analyzing and classifying relevant keywords and contextual information. This makes the information efficiently searchable and processable.

[0587] Step 3:

[0588] The server begins building an AI model using organized data to train a generative artificial intelligence model. This model is specialized to generate appropriate answers to user questions.

[0589] Step 4:

[0590] When a user uses a terminal and enters a question about an exhibit, the terminal sends that question data to the server in real time. At this point, the user's input is accessible to the entire system.

[0591] Step 5:

[0592] The server inputs the user's submitted question into a generative artificial intelligence model. This model then generates an appropriate answer by referring to a pre-trained database.

[0593] Step 6:

[0594] The server sends the generated response to the terminal and provides it to the user. The terminal displays the received response, and the user can obtain an explanation in natural language.

[0595] Step 7:

[0596] The device also displays related content, such as 3D models and virtual reality-based visual information. This allows users to gain a deeper visual understanding of the exhibits.

[0597] (Example 1)

[0598] 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".

[0599] Traditional information facilities lacked the means to quickly and accurately answer visitors' questions about the exhibits. Furthermore, providing additional visual and experiential information to deepen understanding of the exhibits was difficult, highlighting the need to maximize the visitor experience.

[0600] 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.

[0601] In this invention, the server includes means for collecting digital content and organizing text and still image data; means for constructing a generative intelligence model that responds to user inquiries using natural language processing technology; and means for generating responses to user inquiries in real time using the constructed intelligence model. This enables the rapid and accurate provision of information to visitors and the sharing of visually rich experiences.

[0602] An "information facility" is a place where the general public can obtain information and knowledge, and includes museums and libraries.

[0603] "Digital content" refers to electronically stored data such as text, images, audio, and video, and includes exhibits and explanatory information provided at information facilities.

[0604] "Methods of systematization" refer to methods of organizing digital content based on categories and tags, making it easier to search and use.

[0605] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language, and is used in question answering systems.

[0606] A "generative intelligence model" refers to an intelligent system that uses pre-trained data to generate responses to user questions.

[0607] "Real-time" refers to the ability to provide immediate responses and processing results in response to user input.

[0608] "Visual materials" refer to supplementary images and illustrations used to convey information visually, and in some cases, include 3D models and animations.

[0609] "User equipment" refers to terminals and devices that visitors can access and operate within an information facility.

[0610] This invention is a system in which servers, terminals, and users work together to improve the visitor experience in information facilities.

[0611] The server is responsible for aggregating and managing digital content within the information facility. Specifically, it stores various media such as text, images, and audio related to exhibits and explanations in a digital database. The server applies natural language processing techniques to this data to build a generative intelligence model. This model is trained using a large-scale question-and-answer dataset and has the ability to respond appropriately to questions from visitors.

[0612] Users can interact with the system using terminals installed within the information facility. Users input questions through the terminal's intuitive interface. For example, they can enter specific prompts such as, "Who is the artist of this painting?" The terminal then sends the entered question to the server.

[0613] The server generates answers in real time using a generative AI model based on the received questions. The generated answers are sent to the terminal and presented visually to the user. Furthermore, if there are visual materials related to the answers, the terminal provides them on the display screen. For example, 3D models or animations recreating the production process can be displayed.

[0614] This allows users to receive quick and accurate answers to their questions within the information facility, deepening their understanding of the exhibits. The system aims to provide visitors with a rich experience and educational value.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] The server collects digital content from within the information facility. Inputs include text descriptions of exhibits, image data, and audio files. Data processing within the server involves organizing and storing this diverse media data into a digital database. Thus, a searchable database is constructed as output.

[0618] Step 2:

[0619] The server applies natural language processing techniques to stored digital data. Text data is processed as input, undergoing syntactic and semantic analysis. As a data computation, a generative AI model is constructed based on the analysis results. This model is trained on a large-scale question-answer dataset, and the output is an AI model ready to appropriately respond to questions from visitors.

[0620] Step 3:

[0621] The user enters a question about an exhibit of interest through the terminal. The input is a prompt, such as "Who is the artist of this painting?". The terminal's specific function is to send the entered question to the server. The output is the question data arriving at the server.

[0622] Step 4:

[0623] The server processes the received questions. It receives user question data as input and generates answers in real time using a generative AI model. Here, response generation by the model is performed as data computation, and the output is a specific and appropriate answer to the question.

[0624] Step 5:

[0625] The terminal visually displays the responses sent from the server. It receives response data from the server as input. Its specific actions include displaying the responses on the screen and providing related visual materials and 3D models. As output, all information presented to the user is visually displayed, allowing the user to deepen their understanding of the exhibits.

[0626] (Application Example 1)

[0627] 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".

[0628] In today's world, museums and physical stores, which serve as centers for information exchange, are required to provide visitors with richer and more immersive experiences. However, conventional technologies have limited ways of effectively collecting and interactively providing information about exhibits and products. As a result, visitors face the challenge of not being able to quickly and accurately obtain the information they need.

[0629] 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.

[0630] In this invention, the server includes means for extracting information signals from a data storage device and structuring symbolic and visual signals; means for constructing a generative artificial intelligence model that responds to user inquiries using natural language processing technology; and means for acquiring product information using product identification codes and presenting it to the user. This makes it possible to quickly and interactively acquire detailed information about exhibits and products of interest to visitors and customers, and to provide them with a rich experience.

[0631] An "information exchange space" is a physical or digital space where users can acquire information about knowledge and products and interact with each other.

[0632] A "data storage device" is a device that stores information signals and makes them accessible as needed.

[0633] An "information signal" is electrical or digital data used to represent information.

[0634] "Means for structuring symbols and visual signals" refers to methods aimed at converting information signals into a form that is easily understandable to users and providing them visually.

[0635] "Natural language processing technology" is a field of information technology that analyzes and processes human language.

[0636] "Users" refers to individuals or groups who visit an information exchange forum and utilize the information and services provided.

[0637] A "generative artificial intelligence model" is a program or algorithm that automatically generates new information or answers based on input data.

[0638] A "product identification code" is a code used to uniquely identify a specific product.

[0639] "Means of acquiring and presenting product information to users" refers to methods for collecting and displaying detailed information related to products and services.

[0640] The system implementing this invention comprises a data storage device, a user terminal, and a server. The server is responsible for recording and structuring information signals. Voice questions from the user are transmitted to the server via the terminal. The server analyzes the questions using natural language processing technology and generates appropriate answers using a generative artificial intelligence model.

[0641] The server is equipped with a database management system (DBMS), natural language processing frameworks (e.g., NLTK and spaCy), and a platform for using generative AI models (e.g., OpenAI's GPT) to store digital data, particularly as information signals, and to retrieve detailed product information from product identification codes. This information is structured and transmitted to the user terminal in an easily usable format.

[0642] On the user's device, the generated answers are presented to the user in a visual format. This could be done using a mobile application development framework (e.g., Flutter or React Native). The device also displays relevant information based on product identification codes, providing a means for users to interactively learn more about products and exhibits.

[0643] As a concrete example, a user enters a question at a physical store, such as, "How do I use this product?" The server uses a generative AI model to retrieve information on how to use the product based on its product identification code, and sends the answer back to the user's terminal. Through this, the user can deepen their understanding of the product by reviewing the visually explained user guide and related 3D models.

[0644] An example of a prompt message might be, "Please provide detailed information about this product based on the <product's QR code information>. In particular, I would like to know about the ingredients, usage instructions, and development background." In this way, it becomes possible to create a system where visitors and shoppers can have a rich and valuable experience even in information exchange settings.

[0645] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0646] Step 1:

[0647] The user uses a terminal to scan the identification code of a product or exhibit and enters a question. The input consists of the user's voice question and the product identification code. The terminal converts this voice question into text data and sends it to the server along with the identification code.

[0648] Step 2:

[0649] The server analyzes the text data and identification code received from the terminal. The input consists of text data and an identification code. The server uses natural language processing techniques to understand the intent of the query and prepares to retrieve relevant information.

[0650] Step 3:

[0651] The server uses a generative AI model to generate corresponding answers based on text data. The input is parsed text data. The server extracts relevant product information from a database and creates answers based on that information.

[0652] Step 4:

[0653] The server constructs visual information based on the generated responses. The input consists of the generated responses and related product information. The server then formats this information into user-friendly data.

[0654] Step 5:

[0655] The server sends the generated visual information to the terminal. The output is formatted data as visual information. The terminal receives this information and prepares to display it to the user.

[0656] Step 6:

[0657] The device displays the received visual information on its screen and presents it to the user. The final output allows the user to view the answers to their questions and related visual information. This enables the user to intuitively understand product details and usage.

[0658] 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.

[0659] This invention is a system that combines an emotion engine that recognizes user emotions in order to further improve information acquisition and experience in museums. This system allows for more personalized interaction with exhibits in museums and enables the provision of information that is adapted to the user's emotions.

[0660] First, the server extracts text and image data from the museum's digital data storage. This data is classified and organized using natural language processing techniques and used as training data for a generative artificial intelligence model. This model has the ability to generate responses to user questions.

[0661] Next, the user terminal acquires voice and facial expression data from the user, and the emotion engine analyzes this data to recognize the user's emotional state. For example, if a child looks at an exhibit and makes an interested expression, the emotion engine evaluates this as "interest." This evaluation is sent to the server and used to adjust the content of the information provided.

[0662] As a concrete example, consider a scenario where a user asks, "What is the history of this armor?" The server generates an answer to this question through a generative artificial intelligence model. Simultaneously, if the emotion engine recognizes the user's emotion as "excitement," the server adjusts the answer to be presented in a more entertaining format and sends relevant visual content to the device.

[0663] Furthermore, the device utilizes 3D models and virtual reality technology to recreate exhibits in a highly immersive way. This allows users to gain a more enriching experience based on emotional feedback.

[0664] This system will personalize the museum experience more than ever before, enabling educational and interactive exhibits that resonate with users' emotions.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The server extracts text and image data from the museum's data storage and classifies and organizes them using natural language processing techniques. This process creates a dataset in a format usable by AI models.

[0668] Step 2:

[0669] The server uses the extracted and organized data to train a generative artificial intelligence model. This model is designed to respond to user questions and generate appropriate answers.

[0670] Step 3:

[0671] The user enters a question about the exhibit into a terminal. The terminal sends this question data to the server in real time, initiating the process of generating the answer.

[0672] Step 4:

[0673] The device collects the user's voice and facial expression data and inputs it into the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state.

[0674] Step 5:

[0675] The server uses a generative artificial intelligence model to generate answers to user questions, and then adjusts the content and format of the answers, taking into account the sentiment data recognized by the sentiment engine.

[0676] Step 6:

[0677] The server sends the adjusted response along with associated visual information, such as a 3D model or virtual reality content, to the terminal.

[0678] Step 7:

[0679] The terminal displays the user the answers and visual information received from the server. Based on the displayed information, the user can gain a deeper understanding of the museum exhibits.

[0680] Step 8:

[0681] Users can experience the provided interactive content, receive feedback from the system, and ask further questions or explore further.

[0682] (Example 2)

[0683] 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".

[0684] The challenge lies in improving the quality of information acquisition and experiences in museums, while also providing visitors with personalized and emotionally resonant interactive experiences. Traditional methods have made it difficult to provide information based on visitors' emotions, resulting in limited improvements in visitor satisfaction.

[0685] 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.

[0686] In this invention, the server includes means for extracting digital information from a data storage device and structuring document and image information; means for constructing a response generation device that responds to user inquiries using natural language processing technology; and means for acquiring user voice and facial expression information and determining the emotional state with an analysis device. This makes it possible to adjust the content of responses based on the visitor's emotions, providing a more personalized museum experience.

[0687] "Digital information" refers to data that is stored and processed electronically, and includes formats such as documents and images.

[0688] "Structuring methods" refer to methods of organizing digital information and systematically arranging it so that it can be easily analyzed and used.

[0689] "Natural language processing technology" refers to the technology that enables computers to understand and process the language that humans use on a daily basis.

[0690] A "response generation device" refers to a system that uses natural language processing technology to generate appropriate responses to user inquiries.

[0691] "User device" refers to a terminal device used by users to input or receive information.

[0692] An "analysis device" refers to a device that analyzes acquired data to identify the user's state and patterns.

[0693] "Emotional state" refers to the emotional situation or reaction inferred from the user's voice and facial expressions.

[0694] This invention is a system that improves the quality of the user experience in museums by personalizing the experience and providing information that is tailored to the user's emotions. The system consists of three elements: a server, a terminal, and the user.

[0695] The server extracts digital information from data storage devices and structures it as document and image information. Specifically, it retrieves data from a database management system using SQL and organizes it using programming languages ​​such as Python and natural language processing libraries (e.g., NLTK and spaCy). This organized data is used as training data for a generative AI model. The generative AI model is built using a deep learning framework (e.g., TensorFlow and PyTorch) and has the ability to generate appropriate responses to natural language inquiries from users.

[0696] The device uses a microphone and camera to collect user voice and facial expression data. Voice is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using facial recognition technologies such as OpenCV and Dlib. This information is then analyzed by an emotion engine to identify the user's emotional state. The results are sent to a server and used to tailor the information provided.

[0697] When a user asks a question, such as "What is the history of this armor?", the generative AI model generates a corresponding answer on the server. Simultaneously, if the emotion engine identifies the user's emotion as "excitement," the server generates visual content and animations to provide more entertaining related information and sends them to the device. This allows the device to recreate museum exhibits using 3D models and virtual reality technology, promoting an immersive experience for the user.

[0698] An example of a prompt message could be an instruction such as, "When the user shows interest, provide relevant information with an added element of entertainment."

[0699] Through the implementation described above, this invention makes it possible to provide an interactive and educational museum experience that takes into account the emotions of the users.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The server extracts digital information from the data storage device. Specifically, it queries the database to retrieve documents and image data. This data is provided as input and then structured and classified. Using a Python program and natural language processing libraries (NLTK and spaCy), the data is organized by document type. As output, a dataset is generated that is appropriately formatted for use in model training.

[0703] Step 2:

[0704] The server trains a generative AI model based on the data organized in Step 1. Pre-structured document and image information is provided as input data, and the model is built using a deep learning framework (TensorFlow or PyTorch). The output is an AI model that responds to user inquiries.

[0705] Step 3:

[0706] Users input voice and facial expressions using a device. The device is equipped with a microphone and camera, which collect user data. Voice data is converted to text using the Google Speech-to-Text API, and facial expression data is analyzed using OpenCV, Dlib, etc. Inputs include voice signals and video, and output is text data and emotion labels.

[0707] Step 4:

[0708] The emotion labels and text data obtained from the device are sent to the server. The server analyzes these inputs and uses an emotion engine to determine the emotional state. For example, if the server determines that the user is "excited," this information is reflected in the response format. A refined response is output, and a generative AI model is utilized.

[0709] Step 5:

[0710] The server adjusts its response to be more entertaining based on the emotion labels it receives. It instructs the model using a prompt message that says, "Provide relevant information with added entertainment value when the user shows interest." The generated response and visual content are sent to the device. The output is a personalized response and related media data.

[0711] Step 6:

[0712] The terminal receives content sent from the server and displays it to the user. It virtually recreates exhibits using 3D models and virtual reality technology. The output is visually displayed content, which allows the user to have a more enriching experience.

[0713] (Application Example 2)

[0714] 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".

[0715] In museums, when visitors obtain information about exhibits, common methods of information provision do not take into account individual interests and emotions, resulting in a uniform quality of experience and a lack of emotionally resonant information. Furthermore, there are limited interactive means to deepen visitors' interest in the exhibits. A system is needed to improve this and enable personalized information provision and experiences that respond to emotions.

[0716] 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.

[0717] In this invention, the server includes means for extracting digital data from a data storage device and structuring text and image data; means for constructing a generative intelligence model that responds to user questions using natural language processing technology; and means for acquiring gaze and facial expression information and estimating emotional states. This enables the provision of appropriate information in real time according to the user's emotions and the dynamic adjustment of visual and auditory content to create a personalized and enriching museum experience.

[0718] A "museum" is an institution that displays historical, artistic, and scientific exhibits with the aim of promoting education and culture.

[0719] "Information acquisition" is the process of collecting information that users need by utilizing digital data and real-time data.

[0720] "Enhancing the experience" refers to increasing understanding of and interest in exhibits through interactive content and information tailored to the user's emotions and interests.

[0721] A "data storage device" is a device that records and stores digital information and makes it accessible as needed.

[0722] "Text data" refers to text information written in natural language that is stored or transmitted in digital format.

[0723] "Image data" refers to visual information represented in digital format, and is an element that constitutes visual content.

[0724] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0725] A "generative intelligence model" is an algorithm that uses artificial intelligence to generate new text or information based on input data.

[0726] "Emotion analysis methods" refer to technologies that recognize emotions from a user's facial expressions and voice, and interpret them as data.

[0727] "Visual and auditory content" refers to interactive information composed of audio and video, provided to attract the user's interest.

[0728] To implement this invention, the server first extracts digital data from a data storage device and structures it as text and image data. The server responds to user questions in real time using a generative intelligence model that utilizes natural language processing technology. It is preferable to use a library such as Hugging Face Transformers for this intelligence model.

[0729] The user terminal is equipped with the necessary hardware to acquire gaze and facial expression information in real time. Specifically, it utilizes the camera and microphone of smart glasses and implements facial expression analysis libraries such as OpenCV. This identifies the user's emotional state and transmits it to the server. Based on this emotional data, the server dynamically adjusts the visual and auditory content to provide more personalized information. Interactive content can enhance the sense of presence of exhibits by utilizing 3D models and virtual reality technologies.

[0730] For example, if a user shows interest in a particular painting, a generative intelligence model can provide in-depth information about the painting's history and background. A possible prompt might be, "Please explain the background of this painting in detail. Please also display any visual aids." By providing appropriate information in response to the user's emotions, the museum experience can be enriched.

[0731] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0732] Step 1:

[0733] The user moves around the museum and begins viewing the exhibits. The camera and microphone of the user's smart glasses capture the user's gaze and facial expressions in real time. Camera video and audio data are received as input, and the user's emotional state is estimated using facial expression analysis libraries such as OpenCV. The output is the recognized emotion data.

[0734] Step 2:

[0735] When a user views an exhibit and expresses an emotion, the device sends that emotion data to a server. The server receives the emotion data and performs data analysis. The input is emotion data, and the output includes instructions for generating appropriate content based on that emotion. Specifically, if the emotion is excitement, the system prepares to provide more detailed information.

[0736] Step 3:

[0737] When a user expresses a question or interest, the audio is converted to text on the user's device. This is done using speech recognition technology. The input is audio data, and the output is converted text data. This text data is sent to the server. Once the transcribed question or interest arrives at the server, a generative AI model (e.g., ChatGPT) prepares to generate an appropriate response based on this input.

[0738] Step 4:

[0739] The server generates responses based on the received text data using a generative AI model. The input consists of transcribed question data and sentiment data, while the output is the response information for the user along with associated visual and auditory content. The generative AI model uses appropriate prompts, such as "Please describe the background of this painting in detail. Please also display any visual materials," to generate accurate information.

[0740] Step 5:

[0741] The response information and content sent from the server are displayed on the user's terminal. The terminal utilizes 3D models and virtual reality technology to generate visual content in real time, providing the user with an immersive experience. The input is the information and content generated by the server, while the output is a reproduction of the interactive exhibits experienced by the user on the display. As a result, the user experience is enriched.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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."

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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 as being incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] To improve information acquisition and experiences in museums,

[0766] A means for extracting digital data from data storage and structuring text and image data,

[0767] A means for constructing a generative artificial intelligence model that responds to user questions using natural language processing technology,

[0768] A means of generating answers to user questions in real time using a constructed artificial intelligence model,

[0769] A system including means for displaying the answer and related visual information on a user terminal.

[0770] (Claim 2)

[0771] The system according to claim 1, characterized in that it reproduces exhibits using three-dimensional models and virtual reality technology as visualization means, and provides users with an immersive experience.

[0772] (Claim 3)

[0773] The system according to claim 1, characterized in that it includes means for converting voice questions entered via a user terminal into text data and transmitting the text data to a server.

[0774] "Example 1"

[0775] (Claim 1)

[0776] To improve the user experience in information facilities,

[0777] A means for collecting digital content from a data storage unit and organizing text and still image data,

[0778] A means for constructing a generative intelligence model that responds to user questions using natural language processing technology,

[0779] A means for generating responses to user questions in real time using a constructed intelligent model,

[0780] A system including means for displaying the response and associated visual materials on a user device.

[0781] (Claim 2)

[0782] The system according to claim 1, characterized in that it reproduces exhibits using three-dimensional structures and virtual reality technology as means of three-dimensional representation, and provides users with an immersive experience.

[0783] (Claim 3)

[0784] The system according to claim 1, characterized in that it includes means for converting voice questions entered via a user device into text data and transmitting the text data to a central device.

[0785] "Application Example 1"

[0786] (Claim 1)

[0787] To improve information acquisition and experience in information exchange settings,

[0788] A means for extracting information signals from a data storage device and structuring symbols and visual signals,

[0789] A means for constructing a generative artificial intelligence model that responds to user questions using natural language processing technology,

[0790] A means of generating answers to user questions in real time using a constructed artificial intelligence model,

[0791] Means for displaying the answer and related visual information on a user device,

[0792] A system that includes means for obtaining product information using a product identification code and presenting it to the user.

[0793] (Claim 2)

[0794] The system according to claim 1, characterized in that it reproduces an object using three-dimensional reproduction means and virtual space technology, and provides users with an immersive experience.

[0795] (Claim 3)

[0796] The system according to claim 1, characterized in that it includes means for converting voice questions input via a user device into symbolic data and transmitting the symbolic data to a computer.

[0797] "Example 2 of combining an emotion engine"

[0798] (Claim 1)

[0799] A means for extracting digital information from a data storage device and structuring document and image information,

[0800] A means for constructing a response generation device that responds to user inquiries using natural language processing technology,

[0801] A means for generating responses to user inquiries in real time using a constructed response generation device,

[0802] Means for displaying responses and related visual information on the user device,

[0803] A means for acquiring the user's voice and facial expression information and determining their emotional state using an analysis device,

[0804] A means of adjusting the content of the response based on the identified emotional state,

[0805] ...

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, characterized in that it reproduces exhibits using three-dimensional models and virtual reality technology as a visualization device, providing users with an immersive experience.

[0809] (Claim 3)

[0810] The system according to claim 1, characterized in that it includes means for converting voice inquiries input via a user device into document information and transmitting the document information to a central device.

[0811] "Application example 2 when combining with an emotional engine"

[0812] (Claim 1)

[0813] To improve information acquisition and experiences in museums,

[0814] A means for extracting digital data from a data storage device and structuring text and image data,

[0815] A means for constructing a generative intelligence model that responds to user questions using natural language processing technology,

[0816] A means for generating answers to user questions in real time using a constructed intelligent model,

[0817] Means for displaying the answer and related visual information on the user's terminal,

[0818] An emotion analysis means for acquiring gaze and facial expression information and estimating emotional state,

[0819] A means for adjusting the information provided based on emotional state and dynamically generating visual and auditory content,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, characterized in that it reproduces exhibits using three-dimensional models and virtual reality technology as visualization means, and provides users with an immersive experience.

[0823] (Claim 3)

[0824] The system according to claim 1, characterized in that it includes means for converting voice questions entered via a user terminal into text data and transmitting the text data to a server. [Explanation of symbols]

[0825] 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. To improve information acquisition and experiences in museums, A means for extracting digital data from data storage and structuring text and image data, A means for constructing a generative artificial intelligence model that responds to user questions using natural language processing technology, A means of generating answers to user questions in real time using a constructed artificial intelligence model, A system including means for displaying the answer and related visual information on a user terminal.

2. The system according to claim 1, characterized in that it reproduces exhibits using three-dimensional models and virtual reality technology as visualization means, and provides users with an immersive experience.

3. The system according to claim 1, characterized in that it includes means for converting voice questions entered via a user terminal into text data and transmitting the text data to a server.

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