system

Abstract

To provide an information processing system that can deliver highly specialized information to general users in an easily understandable way, thereby stimulating their intellectual curiosity. [Solution] A data collection means for acquiring information about space and the deep sea; a preprocessing means for converting the collected data into a unified format and filtering out irrelevant information; a data analysis means for executing a machine learning algorithm using the organized data and extracting important patterns and insights; a visualization preparation means for preparing a dataset for visualizing the analysis results and generating information necessary for visual representation; an information visualization means for displaying information in a visual and interactive form based on the prepared data; a user interface providing means for providing an interface that makes it easy for users to search for and view specific information; and a means for automatically generating reports and explanations based on the visualized data and conveying knowledge to the user.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Information about the universe and the deep sea is difficult for general users to understand and access due to its specificity and vastness, and access to the latest research results is also highly specialized, thus there is a problem of inhibiting the curiosity and understanding of the general knowledge layer. The purpose of the present invention is to provide such highly specialized information to general users in an easy-to-understand manner and arouse intellectual curiosity. 【Means for Solving the Problems】 【0005】 The information processing system of the present invention collects data on space and the deep sea using data collection means, organizes the data using preprocessing means, and then extracts patterns using machine learning algorithms with data analysis means. By visualizing the data using visualization preparation means and information visualization means, and providing intuitive access to the user with user interface provision means, insight presentation and education means enable the information to be presented to the user in an easy-to-understand format. 【0006】 "Data collection methods" refer to means of obtaining information about space and the deep sea from open data repositories and specialized organizations. 【0007】 "Preprocessing means" refers to methods for converting collected data into a unified format and filtering out irrelevant information to organize it. 【0008】 "Data analysis methods" refer to means of using organized data to execute machine learning algorithms and extract important patterns and insights. 【0009】 "Visualization preparation means" refers to means of preparing a dataset for visualizing analysis results and generating the information necessary for visual representation. 【0010】 An "information visualization method" is a means of displaying information in a visual and interactive form based on prepared data. 【0011】 "User interface provision means" refers to means of providing an interface that makes it easier for users to search for and view specific information. 【0012】 "Knowledge presentation and educational tools" refer to methods for automatically generating reports and explanations based on visualized data and transmitting knowledge to users. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, 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] 【0014】 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. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 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). 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 The information processing system of the present invention is configured as follows: The data collection means uses a server to efficiently collect information on space and the deep sea from open data repositories and specialized institutions. The server accesses the database in real time and continuously acquires new data. 【0035】 Subsequently, in the preprocessing stage, the server converts the collected data into a consistent format and filters out unnecessary data. This makes it possible to prepare the data in a state that is most suitable for data analysis. 【0036】 In data analysis methods, the device uses machine learning algorithms to analyze the data and extract significant patterns and results. This process allows for the discovery of important insights from complex datasets. 【0037】 In the visualization preparation mechanism, the terminal prepares a dataset for visualizing the analysis results and sends it to the information visualization mechanism. Here, the information is presented to the user in a visually clear and easy-to-understand manner. 【0038】 In information visualization, the terminal generates interactive graphs, 3D models, and simulations, allowing users to intuitively manipulate information. In user interface provisioning, the terminal provides users with an easy-to-use interface, supporting quick access to specific information. 【0039】 Ultimately, through insight presentation and educational tools, the server automatically generates reports and explanations based on visualized data, providing information to the user. This allows users to access specialized knowledge and gain further advanced insights. 【0040】 For example, if a user wants to learn about new planetary exploration, the server collects relevant astronomical data in real time, the terminal analyzes that data, and extracts characteristic patterns. By visualizing these results, the planet's topography and weather data are displayed as a 3D model, which the user can freely explore. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The server accesses open data repositories and APIs from specialized organizations to collect data about space and the deep sea. The server periodically or on demand sends queries to retrieve the latest information and downloads the necessary data from the database. 【0044】 Step 2: 【0045】 The server preprocesses the collected data. This involves converting the data to CSV or JSON format to standardize the data format, filtering out unnecessary and duplicate data, and filling in any missing data. This preprocessing lays the foundation for analysis. 【0046】 Step 3: 【0047】 The device analyzes pre-processed data. Machine learning algorithms are used to detect patterns and anomalies within the data. Techniques such as clustering and regression analysis are applied to extract new insights. This reveals important features within the dataset. 【0048】 Step 4: 【0049】 The terminal prepares a dataset for visualization based on the analysis results. It calculates coordinates, color coding information, and other necessary data for visualization, thereby creating the data required to generate graphs and 3D models. 【0050】 Step 5: 【0051】 The device performs visualization. It visually displays information in an interactive format, allowing users to intuitively understand and interact with it. At this stage, 3D models and graphical interfaces are rendered and provided to the user. 【0052】 Step 6: 【0053】 Users explore and view specific information using the device's interface. The device, through the user interface, helps users reconstruct and interactively manipulate the information they select. 【0054】 Step 7: 【0055】 The server automatically generates reports and explanations based on the visualized information. During this knowledge presentation and education phase, the server uses natural language processing to summarize the information clearly and present it to the user. As a result, users can access advanced information. 【0056】 (Example 1) 【0057】 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." 【0058】 Existing information processing systems have a problem in that they struggle to efficiently collect, analyze, and intuitively provide information from specialized fields such as space and the deep sea to users. As a result, there is a challenge in that general users who are not experts cannot fully utilize this information. 【0059】 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. 【0060】 In this invention, the server includes a device for collecting data, a device for formatting the data into a consistent format, and a device for analyzing the data and extracting patterns. This makes it possible to quickly analyze information in a specialized field and present it in a visually easy-to-understand manner. 【0061】 A "data collection device" is a device that has the function of obtaining necessary information from an information source and storing it in a format that can be used within the system. 【0062】 A "data formatting device" is a device that unifies collected data in different formats into a specific format, making it easier to analyze. 【0063】 A "data analysis and pattern extraction device" is a device that performs analysis on formatted data to find useful information and hidden patterns. 【0064】 A "device for preparing data for visualization" is a device that constructs the dataset necessary to visually represent the analyzed data. 【0065】 A "device for visually displaying analysis results" is a device that can present data analysis results to the user as interactive graphics or models. 【0066】 A "device that provides information in an easy-to-manipulate manner to users" is a device that provides an interface that allows users to easily manipulate and view information. 【0067】 A "device that provides insights based on visualized data" is a device that generates reports and explanations based on visualized information, thereby providing users with meaningful insights. 【0068】 This information processing system is primarily composed of servers and terminals. The servers handle data collection, data formatting, and insight provision, while the terminals are responsible for data analysis, visualization, and the user interface. 【0069】 The server first accesses open data repositories and APIs from specialized organizations to automatically retrieve data related to space and the deep sea. The server's programs utilize programming languages ​​such as Python and Java to optimize data collection and storage. For example, it performs scheduled crawling and real-time API calls. MySQL and MongoDB are used for the database to ensure data consistency and high-speed retrieval. 【0070】 The server formats the collected data into a unified format. This process removes unnecessary information while arranging the data into the required fields. Typically, ETL (Extract, Transform, Load) tools or automated scripts are used for this. The formatted data is then sent to the terminal, ready for analysis. 【0071】 The terminal receives the pre-processed data and performs analysis using machine learning algorithms. Specifically, it often uses libraries such as Python's Scikit-learn and TENSORFLOW®. The features and patterns extracted through the analysis can be used to visualize the information. 【0072】 Next, the device prepares a dataset for visualization and uses libraries such as Three.js and D3.js to draw interactive graphs and 3D models. This process allows users to deepen their visual understanding of the data. 【0073】 Users interact with the provided interface using their device, quickly accessing the information they need. This interface is often built using front-end frameworks such as React or Vue.js. 【0074】 Finally, the server automatically generates reports based on the visualized data and its analysis results, providing users with information in the most optimal format. This gives users a foundation for competitive decision-making and advancing academic research. For example, if a user enters a prompt such as, "Display the topography and weather data of a new planet in a 3D model, and include detailed analysis results," the corresponding astronomical data will be analyzed and visualized in real time. 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 The server retrieves data from open data repositories and specialized organizations. Specifically, it uses APIs to collect data related to space and the deep sea. The input is the specified data source, and the output is the collected raw data. This raw data is stored in a database and made accessible for subsequent processing. 【0078】 Step 2: 【0079】 The server converts the collected data into a consistent format and filters out unnecessary information. For example, it converts data written in different units to standard units and eliminates unnecessary columns and missing values. The input is raw data, and the output is formatted data. Through this process, the formatted data is prepared for analysis. 【0080】 Step 3: 【0081】 The device analyzes the formatted data and extracts important patterns and trends. This involves applying analytical methods using machine learning algorithms. The input is the formatted data, and the output is the features and patterns obtained from the analysis. Specifically, it divides the data into meaningful groups using clustering and classification methods. 【0082】 Step 4: 【0083】 The terminal prepares a dataset for visualizing the analysis results. It selects the necessary fields for visualization and adjusts the format. The input is the analysis results, and the output is a visualizeable dataset. For example, it might perform actions such as gathering the coordinate data necessary to generate a 3D model. 【0084】 Step 5: 【0085】 The device displays data through a visual interface. Specifically, it uses Three.js and D3.js to generate interactive 3D models and graphs. The input is a dataset for visualization, and the output is a user-operable graphical interface. Users can intuitively explore the data. 【0086】 Step 6: 【0087】 The server automatically generates user-facing reports and explanations based on visualized data and analysis results. The input is visualized information and analysis results, and the output is the reports provided to the user. This allows users to gain advanced data-driven insights using the generated AI model. 【0088】 (Application Example 1) 【0089】 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." 【0090】 In today's retail industry, efficient inventory management and optimal product placement based on customer preferences are crucial for brick-and-mortar stores. However, systems that analyze this data in real time and provide visually clear placement plans are still not fully developed. As a result, store operators face many challenges in product display and inventory management. 【0091】 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. 【0092】 In this invention, the server includes data collection means, preprocessing means, and data analysis means. This makes it possible to analyze store inventory information and customer purchasing patterns in real time and provide a visual and intuitive product placement plan. 【0093】 "Data collection means" refers to functions for obtaining necessary information from databases and external information sources. 【0094】 "Preprocessing means" refers to a function that formats the collected data into an analyzable format and removes unnecessary information. 【0095】 "Data analysis means" refers to the function of executing machine learning algorithms to extract significant patterns and insights from data used for analysis. 【0096】 "Visualization preparation means" refers to a function that creates a basic dataset for visualizing the analyzed data. 【0097】 "Information visualization methods" are functions that visually represent data in a way that is easy for users to understand. 【0098】 "User interface provision means" refers to a function that provides an intuitive interface for users to operate and supports their access to information. 【0099】 "Insight presentation and educational tools" refers to a function that automatically generates reports and explanations based on visualized information and provides them to users. 【0100】 "Support tools for inventory management and product placement optimization" refers to functions that manage the inventory status of products and propose efficient display and placement. 【0101】 "Means for generating visual plans based on analysis results" refers to a function that creates visual product placement proposals and display plans based on results obtained from data analysis. 【0102】 The system for implementing this invention consists of a central server and terminal devices. The server first uses data collection means to acquire information in real time from store inventory databases and sales information systems. Using libraries such as Python's Pandas, the data is preprocessed to remove noise and then converted into an analyzable format. 【0103】 Next, the server executes machine learning algorithms as a data analysis tool. Using tools such as Scikit-learn, it gains insights based on customer purchasing patterns and inventory status. Then, the terminal uses visualization preparation tools to convert the analysis results into a base dataset for visualization. This data is then represented as 3D models or interactive graphs using information visualization tools such as Tableau or Matplotlib. 【0104】 Furthermore, the user interface provisioning mechanism allows the device to provide users with intuitive operation through a UI built with React Native. This enables store operators to easily understand and implement visual product placement optimization suggestions based on analysis results. For example, when the stock of a popular product decreases, it may be possible to receive suggestions to move that product to a more prominent location. 【0105】 This system significantly improves the operational efficiency of physical stores, particularly through optimizing product inventory management and display. An example of a prompt that utilizes the generative AI model is, "Based on customer movement and purchase data within the store, suggest effective ways to place products that are running low on stock." 【0106】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0107】 Step 1: 【0108】 The server uses data collection tools to retrieve real-time data from store inventory databases and sales information systems. In this step, the server accesses the information sources via APIs and retrieves the generated datasets as input data. The data output is provided in a structured format (e.g., CSV or JSON). 【0109】 Step 2: 【0110】 The server uses preprocessing tools to transform the collected data into an analyzable format. It uses Pandas to filter out unnecessary data and perform consistent data cleaning, such as imputing missing values. As a result, a clean and formatted dataset is output and passed on to the next analysis step. 【0111】 Step 3: 【0112】 The server uses data analysis tools to execute machine learning algorithms. At this point, the server uses Scikit-learn to perform pattern recognition and predict customer purchasing behavior and inventory supply and demand. The output obtained from this analysis will be numerical data and categorical information as analysis results. 【0113】 Step 4: 【0114】 The terminal uses visualization preparation tools to convert the analysis results into a format suitable for graphing or 3D modeling. The prepared dataset is provided as output data in a format intended for processing with visualization software. 【0115】 Step 5: 【0116】 The terminal uses information visualization tools to generate interactive graphs and 3D models based on the prepared data. This step utilizes Tableau and Matplotlib to visualize the analysis results, allowing users to intuitively understand the data insights. 【0117】 Step 6: 【0118】 Through the user interface, users interact with an intuitive UI and consider store display optimization plans based on the visualized information. In this step, a user-friendly operating environment is provided using a UI based on React Native. 【0119】 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. 【0120】 The information processing system of the present invention begins with a data collection means and incorporates an emotion engine that presents information while considering the user's emotions. The server collects data on space and the deep sea from open data repositories and specialized institutions, and a preprocessing means organizes the data into a consistent format. This makes it easier for the data analysis means to extract significant patterns using machine learning algorithms. 【0121】 The analyzed data is prepared for visualization by the terminal. Here, the visualization preparation means builds the necessary dataset and transfers it to the information visualization means. This prepares the data to be visualized as interactive 3D models and graphs and presented to the user. 【0122】 This invention enables users to intuitively access information through a user interface and further analyzes the user's emotional data using an emotion engine. This emotion engine is built into the terminal and can recognize emotions in real time by analyzing the user's facial expressions and voice. The recognized emotional information is sent to a server and used to dynamically adjust the content of the visualized data. For example, if the user is emotionally agitated, the terminal will provide more detailed information and prioritize topics that are likely to be of interest. 【0123】 The visualized information is tailored to highlight changes and importance in the data and presented in a way that is emotionally relatable to the user. The server also compiles the information into reports using insightful presentations and educational tools, making it easy for the user to understand. At this stage, the user is more receptive to absorbing new discoveries and knowledge. 【0124】 As a concrete example, suppose a user is researching the possibility of life on a new planet. The server collects relevant research data, and the terminal analyzes and visualizes it. Simultaneously, the emotion engine measures the user's level of excitement and interest, and adjusts the level of detail in the visualization accordingly. This adjustment allows the user to be more drawn to interesting information and actively participate in the exploration. 【0125】 The following describes the processing flow. 【0126】 Step 1: 【0127】 The server collects information on space and the deep sea through open data repositories and APIs from specialized organizations. The server periodically sends queries and retrieves the latest data in real time, providing the necessary foundational data for the entire system. 【0128】 Step 2: 【0129】 The server preprocesses the acquired data. This includes standardizing the data format, imputing missing values ​​as needed, and filtering out unnecessary data. At this stage, a clean dataset suitable for analysis is prepared. 【0130】 Step 3: 【0131】 The terminal receives pre-processed data and executes data analysis methods. The terminal uses machine learning algorithms to identify patterns, trends, and outliers present in the data and extract new insights. 【0132】 Step 4: 【0133】 The terminal prepares a dataset for visualization based on the analysis results. Here, it calculates coordinate data and color schemes to determine how the information should be displayed. 【0134】 Step 5: 【0135】 The device visualizes the information. Using visual elements generated from the analyzed data, it prepares to create interactive dashboards, 3D models, or animations for presentation to the user. 【0136】 Step 6: 【0137】 Users access visualized data through the device's user interface. At this stage, users can interact with the interface and select or customize information of interest. 【0138】 Step 7: 【0139】 The emotion engine recognizes the user's emotional state in real time. The device analyzes the user's facial expressions and voice through the camera and microphone to analyze their emotional responses. 【0140】 Step 8: 【0141】 The server receives emotional data from the emotion engine and feeds it back into the information visualization system. The presentation method and level of detail of the data are dynamically adjusted according to the user's emotions. 【0142】 Step 9: 【0143】 The server automatically generates reports and explanations for users using insight presentation and educational tools. These reports are refined by an emotion engine, ensuring they are easy to read and engaging for the user. 【0144】 (Example 2) 【0145】 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". 【0146】 There are challenges in improving the user experience by effectively and dynamically processing information obtained from large amounts of data and optimizing information presentation based on the user's emotional state. Furthermore, providing users with quick access to the information they need in an easily understandable format is also a crucial requirement. 【0147】 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. 【0148】 In this invention, the server includes data acquisition means, data formatting means, and data analysis means. This makes it possible to recognize the user's emotional state and dynamically adjust the information presented, providing the user with the information they seek in an easy-to-understand manner. 【0149】 "Data acquisition methods" refer to technical means for collecting necessary information from public data repositories or specialized organizations. 【0150】 "Data formatting methods" are technical means that convert collected information into a consistent format and perform preprocessing such as imputing missing values ​​and removing outliers. 【0151】 "Data analysis methods" are technical means of extracting regularities and trends from information that has been formatted using learning algorithms. 【0152】 "Visualization preparation means" refers to technical means for constructing datasets and making the necessary preparations for visually displaying information. 【0153】 "Information visualization means" are technical methods that present information in visual formats such as interactive graphs and 3D models, based on a prepared dataset. 【0154】 "Operation screen provision means" refers to a technical means that provides a user interface that allows users to intuitively access information. 【0155】 A "recognition engine" is a technical means of analyzing a user's facial expressions and voice data to determine their emotional state in real time. 【0156】 "Knowledge presentation and educational tools" are technical means that provide users with easily understandable insights derived from visualization and analysis results, enabling them to efficiently learn new information. 【0157】 The information processing system of the present invention encompasses the functions of data acquisition, formatting, analysis, visualization, emotion recognition, dynamic adjustment, and knowledge presentation, thereby effectively providing high-level information to users. 【0158】 The server collects necessary information from public data repositories and specialized organizations using data acquisition methods. This data is then transformed into a consistent format using formatting methods, and missing or outlier values ​​are handled. Subsequently, the server extracts regularities and trends from the data using learning algorithms through data analysis methods. The software used in this process includes common data analysis platforms and libraries (e.g., TensorFlow, SciKit-Learn). 【0159】 Next, the terminal uses visualization preparation tools to prepare the analyzed data for visual display. The prepared dataset is passed to the information visualization tools and visualized as a 3D model or interactive graph. Data visualization tools (e.g., D3.js, Three.js) are commonly used for this visualization. 【0160】 Furthermore, the recognition engine built into the device analyzes the user's facial expressions and voice to determine their emotions in real time. It analyzes the user's emotional state and sends this information to a server. The server uses this emotional information to dynamically adjust the content of the information presented, highlighting the information that is most of the user's interest. This process significantly improves the user experience. 【0161】 Ultimately, the server provides reports generated based on visualized information, using knowledge presentation and educational tools. Through these reports, users can efficiently learn practical knowledge. 【0162】 For example, when a user investigates the possibility of life on a new planet, the server collects relevant research data, which the terminal then analyzes and visualizes. The emotion engine then measures the user's level of excitement and interest, adjusting the level of detail displayed accordingly. This allows the user to deepen their interest in the exploration and participate more actively. An example of input to the generative AI model would be, "Adjust the level of visualization of planetary exploration data based on the user's emotions." 【0163】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0164】 Step 1: 【0165】 The server collects information using data acquisition methods. It receives API endpoints and request parameters from public data repositories and specialized organizations as input, and retrieves data based on these. As output, it generates the retrieved raw dataset and passes it to the next step. The server operates to retrieve the latest data by making requests at a specified update frequency. 【0166】 Step 2: 【0167】 The server performs preprocessing using data formatting tools. It receives raw datasets as input and performs data formatting standardization, missing value imputation, and outlier removal to transform them into a consistent format. It generates a formatted dataset as output, which is then passed to the analysis step. 【0168】 Step 3: 【0169】 The server analyzes the data using data analysis tools. It receives a formatted dataset as input and applies machine learning algorithms to extract regularities and patterns. As output, it generates a dataset containing the analysis results and sends it to the visualization preparation step. The server then selects and executes the desired analysis model (e.g., clustering, regression analysis). 【0170】 Step 4: 【0171】 The terminal prepares for visualization using visualization preparation means. It receives analysis results as input and organizes them into data structures (e.g., graphs, 3D models) for visual display. As output, it generates a visualizeable dataset and passes it to the information visualization means. The terminal selects an appropriate visualization format according to the characteristics of the data. 【0172】 Step 5: 【0173】 The device visualizes data using information visualization tools. It receives a visualizeable dataset as input and displays it as interactive graphs or 3D models. It generates user-accessible interface screens as output. The device optimizes the interactivity and usability of the visualization during the display process. 【0174】 Step 6: 【0175】 The recognition engine built into the device analyzes the user's emotions in real time. It receives the user's facial expressions and voice data as input, analyzes them, and determines their emotional state. As output, it generates user emotion information and sends it to the server. The device uses an emotion analysis algorithm to make accurate judgments. 【0176】 Step 7: 【0177】 The server dynamically adjusts the displayed information based on emotional information. It receives user emotional information as input and adjusts the level of detail and priority of the visualized data. As output, it generates and provides the adjusted information display to the user. The server optimizes the displayed content based on the user's interests and concerns. 【0178】 Step 8: 【0179】 The server provides information using knowledge presentation and educational tools. It receives a formatted information display as input, organizes it in an easily understandable way, and generates a learnable report. As output, it provides an information report that is easily comprehensible to the user. The server constructs explanations while considering the user's level of understanding. 【0180】 (Application Example 2) 【0181】 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". 【0182】 Conventional information processing systems have difficulty providing flexible content that responds to changes in users' emotions, and have been insufficient in presenting information that reflects users' instantaneous preferences and interests. Furthermore, there is room for improvement in the effective delivery of visualized information, and there is a need to enhance the user experience. 【0183】 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. 【0184】 In this invention, the server includes data collection means, emotion recognition means, and content recommendation means. This makes it possible to analyze the user's emotional state in real time and dynamically provide appropriate content. 【0185】 "Data collection methods" refer to means of obtaining information from open data repositories and specialized institutions, and detecting the user's emotional state through personal analysis devices. 【0186】 "Preprocessing means" refers to means of organizing collected data into a standardized format and preparing it to facilitate analysis. 【0187】 "Data analysis methods" refer to techniques that use machine learning algorithms to extract significant patterns from data and present information in a way that responds to the user's emotions. 【0188】 "Visualization preparation means" refers to methods for building the necessary datasets for visualization, thereby enabling the smooth visualization of information. 【0189】 "Information visualization methods" refer to means of visualizing analyzed data as interactive 3D models or graphs and providing them to users. 【0190】 "User interface provision means" refers to means of providing an interface that enables users to intuitively access information. 【0191】 An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to recognize their emotions in real time. 【0192】 A "content recommendation method" is a means of selecting and recommending appropriate content based on the user's emotional state. 【0193】 "Knowledge presentation and educational methods" refer to means of providing information to users in an easy-to-understand manner, enabling them to gain new insights and learn. 【0194】 This invention realizes a system that dynamically provides appropriate content based on the user's emotional state. The server acquires relevant data from open data repositories and specialized institutions using data collection means. Furthermore, it collects emotional data in real time from the user's facial expressions and voice via personal analysis devices such as smartphones and smart glasses. 【0195】 The collected data is organized into a consistent format by preprocessing and then analyzed using machine learning algorithms by data analysis tools. This allows for the extraction of patterns related to user emotions, which can then be used to dynamically present information. Specifically, sentiment analysis is performed using machine learning libraries such as TensorFlow and PyTorch, and based on the results, a content recommendation tool selects the most suitable content. 【0196】 The terminal uses visualization preparation means to construct a dataset for visualization and uses information visualization means to visualize the data as interactive 3D models and graphs. This allows information to be intuitively provided to the user through user interface provisioning means. 【0197】 For example, if a user says, "I want to relax today," the system will evaluate their emotional state and recommend relaxing music or nature videos. In this way, it is possible to dynamically provide content that responds to the user's preferences and momentary emotions. 【0198】 Examples of prompt statements include the following: 【0199】 Create a program that dynamically suggests streaming content based on the user's emotional state, selecting from the following options. 【0200】 Action movie 【0201】 Comedy show 【0202】 Relaxation music 【0203】 Nature documentary 【0204】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0205】 Step 1: 【0206】 The server uses data collection methods to acquire data on space and the deep sea from open data repositories and specialized organizations. Input is database access information from repositories and organizations, and output is a standardized dataset. This data collection process involves executing necessary API calls and database queries to obtain relevant information. 【0207】 Step 2: 【0208】 The server standardizes the format of the collected data through preprocessing. Specifically, it formats the input data into a form that can be analyzed all at once. This makes it easier to maintain data consistency and improves the efficiency of subsequent analysis. For example, the data may be formatted into time-series or categorical data. 【0209】 Step 3: 【0210】 The device collects the user's emotional state in real time using facial recognition and voice analysis software. Input is emotion-related data from the camera and microphone, and output is an emotion category (e.g., excited, relaxed). In this phase, emotions are inferred from facial expressions and tone of voice using OpenCV and voice analysis algorithms. 【0211】 Step 4: 【0212】 The server uses machine learning algorithms as a data analysis tool to extract significant patterns from collected user data and deep-sea space data. The input is a pre-processed dataset and user sentiment data, and the output is an information presentation strategy based on user sentiment tendencies. For example, TensorFlow is used to analyze real-time sentiment trends. 【0213】 Step 5: 【0214】 The device prepares and executes visualizations that respond to the user's emotional state. Specifically, it generates interactive 3D models and graphs based on the input emotional and pattern data. The output is a visualized data model. At this stage, visualization libraries such as Three.js can be used. 【0215】 Step 6: 【0216】 Users access and experience dynamically generated information through the provided user interface. Here, content tailored to the user's emotions and interests (e.g., relaxing music or action videos) is recommended. Input is user feedback and interaction, while output is customized information that enhances user engagement. 【0217】 Step 7: 【0218】 The server generates reports that provide information to users in an easy-to-understand manner, using insight presentation and educational tools. The input consists of analyzed data and the user's desired insights, while the output is detailed and easy-to-understand report content. This allows users to easily absorb new discoveries and knowledge. 【0219】 This series of processes significantly improves the user experience and enables the delivery of personalized information. 【0220】 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. 【0221】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0222】 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. 【0223】 [Second Embodiment] 【0224】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0225】 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. 【0226】 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). 【0227】 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. 【0228】 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. 【0229】 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). 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 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". 【0236】 The information processing system of the present invention is configured as follows: The data collection means uses a server to efficiently collect information on space and the deep sea from open data repositories and specialized institutions. The server accesses the database in real time and continuously acquires new data. 【0237】 Subsequently, in the preprocessing stage, the server converts the collected data into a consistent format and filters out unnecessary data. This makes it possible to prepare the data in a state that is most suitable for data analysis. 【0238】 In data analysis methods, the device uses machine learning algorithms to analyze the data and extract significant patterns and results. This process allows for the discovery of important insights from complex datasets. 【0239】 In the visualization preparation mechanism, the terminal prepares a dataset for visualizing the analysis results and sends it to the information visualization mechanism. Here, the information is presented to the user in a visually clear and easy-to-understand manner. 【0240】 In information visualization, the terminal generates interactive graphs, 3D models, and simulations, allowing users to intuitively manipulate information. In user interface provisioning, the terminal provides users with an easy-to-use interface, supporting quick access to specific information. 【0241】 Ultimately, through insight presentation and educational tools, the server automatically generates reports and explanations based on visualized data, providing information to the user. This allows users to access specialized knowledge and gain further advanced insights. 【0242】 For example, if a user wants to learn about new planetary exploration, the server collects relevant astronomical data in real time, the terminal analyzes that data, and extracts characteristic patterns. By visualizing these results, the planet's topography and weather data are displayed as a 3D model, which the user can freely explore. 【0243】 The following describes the processing flow. 【0244】 Step 1: 【0245】 The server accesses open data repositories and APIs from specialized organizations to collect data about space and the deep sea. The server periodically or on demand sends queries to retrieve the latest information and downloads the necessary data from the database. 【0246】 Step 2: 【0247】 The server preprocesses the collected data. This involves converting the data to CSV or JSON format to standardize the data format, filtering out unnecessary and duplicate data, and filling in any missing data. This preprocessing lays the foundation for analysis. 【0248】 Step 3: 【0249】 The device analyzes pre-processed data. Machine learning algorithms are used to detect patterns and anomalies within the data. Techniques such as clustering and regression analysis are applied to extract new insights. This reveals important features within the dataset. 【0250】 Step 4: 【0251】 The terminal prepares a dataset for visualization based on the analysis results. It calculates coordinates, color coding information, and other necessary data for visualization, thereby creating the data required to generate graphs and 3D models. 【0252】 Step 5: 【0253】 The device performs visualization. It visually displays information in an interactive format, allowing users to intuitively understand and interact with it. At this stage, 3D models and graphical interfaces are rendered and provided to the user. 【0254】 Step 6: 【0255】 Users explore and view specific information using the device's interface. The device, through the user interface, helps users reconstruct and interactively manipulate the information they select. 【0256】 Step 7: 【0257】 The server automatically generates reports and explanations based on the visualized information. During this knowledge presentation and education phase, the server uses natural language processing to summarize the information clearly and present it to the user. As a result, users can access advanced information. 【0258】 (Example 1) 【0259】 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". 【0260】 Existing information processing systems have a problem in that they struggle to efficiently collect, analyze, and intuitively provide information from specialized fields such as space and the deep sea to users. As a result, there is a challenge in that general users who are not experts cannot fully utilize this information. 【0261】 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. 【0262】 In this invention, the server includes a device for collecting data, a device for formatting the data into a consistent format, and a device for analyzing the data and extracting patterns. This makes it possible to quickly analyze information in a specialized field and present it in a visually easy-to-understand manner. 【0263】 A "data collection device" is a device that has the function of obtaining necessary information from an information source and storing it in a format that can be used within the system. 【0264】 A "data formatting device" is a device that unifies collected data in different formats into a specific format, making it easier to analyze. 【0265】 A "data analysis and pattern extraction device" is a device that performs analysis on formatted data to find useful information and hidden patterns. 【0266】 A "device for preparing data for visualization" is a device that constructs the dataset necessary to visually represent the analyzed data. 【0267】 A "device for visually displaying analysis results" is a device that can present data analysis results to the user as interactive graphics or models. 【0268】 A "device that provides information in an easy-to-manipulate manner to users" is a device that provides an interface that allows users to easily manipulate and view information. 【0269】 A "device that provides insights based on visualized data" is a device that generates reports and explanations based on visualized information, thereby providing users with meaningful insights. 【0270】 This information processing system is primarily composed of servers and terminals. The servers handle data collection, data formatting, and insight provision, while the terminals are responsible for data analysis, visualization, and the user interface. 【0271】 The server first accesses open data repositories and APIs from specialized organizations to automatically retrieve data related to space and the deep sea. The server's programs utilize programming languages ​​such as Python and Java to optimize data collection and storage. For example, it performs scheduled crawling and real-time API calls. MySQL and MongoDB are used as databases to ensure data consistency and high-speed retrieval. 【0272】 The server formats the collected data into a unified format. This process removes unnecessary information while arranging the data into the required fields. Typically, ETL (Extract, Transform, Load) tools or automated scripts are used for this. The formatted data is then sent to the terminal, ready for analysis. 【0273】 The terminal receives the pre-processed data and performs analysis using machine learning algorithms. Specifically, it often uses libraries such as Python's Scikit-learn and TensorFlow. The features and patterns extracted through the analysis can be used to visualize the information. 【0274】 Next, the device prepares a dataset for visualization and uses libraries such as Three.js and D3.js to draw interactive graphs and 3D models. This process allows users to deepen their visual understanding of the data. 【0275】 Users interact with the provided interface using their device, quickly accessing the information they need. This interface is often built using front-end frameworks such as React or Vue.js. 【0276】 Finally, the server automatically generates reports based on the visualized data and its analysis results, providing users with information in the most optimal format. This gives users a foundation for competitive decision-making and advancing academic research. For example, if a user enters a prompt such as, "Display the topography and weather data of a new planet in a 3D model, and include detailed analysis results," the corresponding astronomical data will be analyzed and visualized in real time. 【0277】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0278】 Step 1: 【0279】 The server obtains data from open data repositories and specialized institutions. Specifically, it collects data related to the universe and the deep sea using APIs. The input is the specified data source, and the output is the raw data collected. This raw data is stored in a database and becomes accessible for subsequent processing. 【0280】 Step 2: 【0281】 The server converts the collected data into a consistent format and filters out unnecessary information. For example, it converts data described in different units into standard units and excludes unnecessary columns and missing values. The input is the raw data, and the output is the formatted data. Through this process, the formatted data is made suitable for analysis. 【0282】 Step 3: 【0283】 The terminal analyzes the formatted data and extracts important patterns and trends. For this, it applies analysis methods using machine learning algorithms. The input is the formatted data, and the output is the features and patterns obtained from the analysis. As a specific operation, it uses clustering and classification methods to divide the data into meaningful groups. 【0284】 Step 4: 【0285】 The terminal prepares a dataset for visualizing the analysis results. It selects the necessary fields for visualization and adjusts the format. The input is the analysis result, and the output is a dataset that can be visualized. For example, it performs an operation to align the coordinate data required for generating a 3D model. 【0286】 Step 5: 【0287】 The device displays data through a visual interface. Specifically, it uses Three.js and D3.js to generate interactive 3D models and graphs. The input is a dataset for visualization, and the output is a user-operable graphical interface. Users can intuitively explore the data. 【0288】 Step 6: 【0289】 The server automatically generates user-facing reports and explanations based on visualized data and analysis results. The input is visualized information and analysis results, and the output is the reports provided to the user. This allows users to gain advanced data-driven insights using the generated AI model. 【0290】 (Application Example 1) 【0291】 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." 【0292】 In today's retail industry, efficient inventory management and optimal product placement based on customer preferences are crucial for brick-and-mortar stores. However, systems that analyze this data in real time and provide visually clear placement plans are still not fully developed. As a result, store operators face many challenges in product display and inventory management. 【0293】 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. 【0294】 In this invention, the server includes data collection means, preprocessing means, and data analysis means. This makes it possible to analyze store inventory information and customer purchasing patterns in real time and provide a visual and intuitive product placement plan. 【0295】 "Data collection means" refers to functions for obtaining necessary information from databases and external information sources. 【0296】 "Preprocessing means" refers to a function that formats the collected data into an analyzable format and removes unnecessary information. 【0297】 "Data analysis means" refers to the function of executing machine learning algorithms to extract significant patterns and insights from data used for analysis. 【0298】 "Visualization preparation means" refers to a function that creates a basic dataset for visualizing the analyzed data. 【0299】 "Information visualization methods" are functions that visually represent data in a way that is easy for users to understand. 【0300】 "User interface provision means" refers to a function that provides an intuitive interface for users to operate and supports their access to information. 【0301】 "Insight presentation and educational tools" refers to a function that automatically generates reports and explanations based on visualized information and provides them to users. 【0302】 "Support tools for inventory management and product placement optimization" refers to functions that manage the inventory status of products and propose efficient display and placement. 【0303】 "Means for generating visual plans based on analysis results" refers to a function that creates visual product placement proposals and display plans based on results obtained from data analysis. 【0304】 The system for implementing this invention consists of a central server and terminal devices. First, the server uses data collection means to obtain information in real-time from the store's inventory database and sales information system. After preprocessing the data and removing noise using libraries such as Python's Pandas, it is converted into an analyzable format. 【0305】 Next, the server executes a machine learning algorithm as data analysis means. Using tools such as Scikit-learn, insights based on customer purchase patterns and inventory status are obtained. Then, the terminal uses visualization preparation means to convert the analysis results into a basic dataset for visualization. This data is represented as 3D models or interactive graphs using information visualization means by software such as Tableau or Matplotlib. 【0306】 Furthermore, the terminal provides intuitive operations to the user through a UI composed of React Native by the user interface providing means. As a result, store operators can easily understand and implement a visual merchandise placement optimization plan based on the analysis results. As a specific example, when the inventory of a popular product decreases, it is possible to receive a proposal to move the product to a more prominent location. 【0307】 This system significantly improves the operational efficiency of physical stores, particularly through inventory management and optimization of product displays. As an example of a prompt using a generative AI model, an instruction such as "Please propose a method to effectively arrange products with approaching inventory based on customer movement lines and purchase data within the store." is possible. 【0308】 The flow of the specific process in Application Example 1 will be described using Figure 12. 【0309】 Step 1: 【0310】 The server uses data collection tools to retrieve real-time data from store inventory databases and sales information systems. In this step, the server accesses the information sources via APIs and retrieves the generated datasets as input data. The data output is provided in a structured format (e.g., CSV or JSON). 【0311】 Step 2: 【0312】 The server uses preprocessing tools to transform the collected data into an analyzable format. It uses Pandas to filter out unnecessary data and perform consistent data cleaning, such as imputing missing values. As a result, a clean and formatted dataset is output and passed on to the next analysis step. 【0313】 Step 3: 【0314】 The server uses data analysis tools to execute machine learning algorithms. At this point, the server uses Scikit-learn to perform pattern recognition and predict customer purchasing behavior and inventory supply and demand. The output obtained from this analysis will be numerical data and categorical information as analysis results. 【0315】 Step 4: 【0316】 The terminal uses visualization preparation tools to convert the analysis results into a format suitable for graphing or 3D modeling. The prepared dataset is provided as output data in a format intended for processing with visualization software. 【0317】 Step 5: 【0318】 The terminal uses information visualization tools to generate interactive graphs and 3D models based on the prepared data. This step utilizes Tableau and Matplotlib to visualize the analysis results, allowing users to intuitively understand the data insights. 【0319】 Step 6: 【0320】 Through the user interface, users interact with an intuitive UI and consider store display optimization plans based on the visualized information. In this step, a user-friendly operating environment is provided using a UI based on React Native. 【0321】 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. 【0322】 The information processing system of the present invention begins with a data collection means and incorporates an emotion engine that presents information while considering the user's emotions. The server collects data on space and the deep sea from open data repositories and specialized institutions, and a preprocessing means organizes the data into a consistent format. This makes it easier for the data analysis means to extract significant patterns using machine learning algorithms. 【0323】 The analyzed data is prepared for visualization by the terminal. Here, the visualization preparation means builds the necessary dataset and transfers it to the information visualization means. This prepares the data to be visualized as interactive 3D models and graphs and presented to the user. 【0324】 This invention enables users to intuitively access information through a user interface and further analyzes the user's emotional data using an emotion engine. This emotion engine is built into the terminal and can recognize emotions in real time by analyzing the user's facial expressions and voice. The recognized emotional information is sent to a server and used to dynamically adjust the content of the visualized data. For example, if the user is emotionally agitated, the terminal will provide more detailed information and prioritize topics that are likely to be of interest. 【0325】 The visualized information is tailored to highlight changes and importance in the data and presented in a way that is emotionally relatable to the user. The server also compiles the information into reports using insightful presentations and educational tools, making it easy for the user to understand. At this stage, the user is more receptive to absorbing new discoveries and knowledge. 【0326】 As a concrete example, suppose a user is researching the possibility of life on a new planet. The server collects relevant research data, and the terminal analyzes and visualizes it. Simultaneously, the emotion engine measures the user's level of excitement and interest, and adjusts the level of detail in the visualization accordingly. This adjustment allows the user to be more drawn to interesting information and actively participate in the exploration. 【0327】 The following describes the processing flow. 【0328】 Step 1: 【0329】 The server collects information on space and the deep sea through open data repositories and APIs from specialized organizations. The server periodically sends queries and retrieves the latest data in real time, providing the necessary foundational data for the entire system. 【0330】 Step 2: 【0331】 The server preprocesses the acquired data. This includes standardizing the data format, imputing missing values ​​as needed, and filtering out unnecessary data. At this stage, a clean dataset suitable for analysis is prepared. 【0332】 Step 3: 【0333】 The terminal receives pre-processed data and executes data analysis methods. The terminal uses machine learning algorithms to identify patterns, trends, and outliers present in the data and extract new insights. 【0334】 Step 4: 【0335】 The terminal prepares a dataset for visualization based on the analysis results. Here, it calculates coordinate data and color schemes to determine how the information should be displayed. 【0336】 Step 5: 【0337】 The device visualizes the information. Using visual elements generated from the analyzed data, it prepares to create interactive dashboards, 3D models, or animations for presentation to the user. 【0338】 Step 6: 【0339】 Users access visualized data through the device's user interface. At this stage, users can interact with the interface and select or customize information of interest. 【0340】 Step 7: 【0341】 The emotion engine recognizes the user's emotional state in real time. The device analyzes the user's facial expressions and voice through the camera and microphone to analyze their emotional responses. 【0342】 Step 8: 【0343】 The server receives emotional data from the emotion engine and feeds it back into the information visualization system. The presentation method and level of detail of the data are dynamically adjusted according to the user's emotions. 【0344】 Step 9: 【0345】 The server automatically generates reports and explanations for users using insight presentation and educational tools. These reports are refined by an emotion engine, ensuring they are easy to read and engaging for the user. 【0346】 (Example 2) 【0347】 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". 【0348】 There are challenges in improving the user experience by effectively and dynamically processing information obtained from large amounts of data and optimizing information presentation based on the user's emotional state. Furthermore, providing users with quick access to the information they need in an easily understandable format is also a crucial requirement. 【0349】 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. 【0350】 In this invention, the server includes data acquisition means, data formatting means, and data analysis means. This makes it possible to recognize the user's emotional state and dynamically adjust the information presented, providing the user with the information they seek in an easy-to-understand manner. 【0351】 "Data acquisition methods" refer to technical means for collecting necessary information from public data repositories or specialized organizations. 【0352】 "Data formatting methods" are technical means that convert collected information into a consistent format and perform preprocessing such as imputing missing values ​​and removing outliers. 【0353】 "Data analysis methods" are technical means of extracting regularities and trends from information that has been formatted using learning algorithms. 【0354】 "Visualization preparation means" refers to technical means for constructing datasets and making the necessary preparations for visually displaying information. 【0355】 "Information visualization means" are technical methods that present information in visual formats such as interactive graphs and 3D models, based on a prepared dataset. 【0356】 "Operation screen provision means" refers to a technical means that provides a user interface that allows users to intuitively access information. 【0357】 A "recognition engine" is a technical means of analyzing a user's facial expressions and voice data to determine their emotional state in real time. 【0358】 "Knowledge presentation and educational tools" are technical means that provide users with easily understandable insights derived from visualization and analysis results, enabling them to efficiently learn new information. 【0359】 The information processing system of the present invention encompasses the functions of data acquisition, formatting, analysis, visualization, emotion recognition, dynamic adjustment, and knowledge presentation, thereby effectively providing high-level information to users. 【0360】 The server collects necessary information from public data repositories and specialized organizations using data acquisition methods. This data is then transformed into a consistent format using formatting methods, and missing or outlier values ​​are handled. Subsequently, the server extracts regularities and trends from the data using learning algorithms through data analysis methods. The software used in this process includes common data analysis platforms and libraries (e.g., TensorFlow, SciKit-Learn). 【0361】 Next, the terminal uses visualization preparation tools to prepare the analyzed data for visual display. The prepared dataset is passed to the information visualization tools and visualized as a 3D model or interactive graph. Data visualization tools (e.g., D3.js, Three.js) are commonly used for this visualization. 【0362】 Furthermore, the recognition engine built into the device analyzes the user's facial expressions and voice to determine their emotions in real time. It analyzes the user's emotional state and sends this information to a server. The server uses this emotional information to dynamically adjust the content of the information presented, highlighting the information that is most of the user's interest. This process significantly improves the user experience. 【0363】 Ultimately, the server provides reports generated based on visualized information, using knowledge presentation and educational tools. Through these reports, users can efficiently learn practical knowledge. 【0364】 For example, when a user investigates the possibility of life on a new planet, the server collects relevant research data, which the terminal then analyzes and visualizes. The emotion engine then measures the user's level of excitement and interest, adjusting the level of detail displayed accordingly. This allows the user to deepen their interest in the exploration and participate more actively. An example of input to the generative AI model would be, "Adjust the level of visualization of planetary exploration data based on the user's emotions." 【0365】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0366】 Step 1: 【0367】 The server collects information using data acquisition methods. It receives API endpoints and request parameters from public data repositories and specialized organizations as input, and retrieves data based on these. As output, it generates the retrieved raw dataset and passes it to the next step. The server operates to retrieve the latest data by making requests at a specified update frequency. 【0368】 Step 2: 【0369】 The server performs preprocessing using data formatting tools. It receives raw datasets as input and performs data formatting standardization, missing value imputation, and outlier removal to transform them into a consistent format. It generates a formatted dataset as output, which is then passed to the analysis step. 【0370】 Step 3: 【0371】 The server analyzes the data using data analysis tools. It receives a formatted dataset as input and applies machine learning algorithms to extract regularities and patterns. As output, it generates a dataset containing the analysis results and sends it to the visualization preparation step. The server then selects and executes the desired analysis model (e.g., clustering, regression analysis). 【0372】 Step 4: 【0373】 The terminal prepares for visualization using visualization preparation means. It receives analysis results as input and organizes them into data structures (e.g., graphs, 3D models) for visual display. As output, it generates a visualizeable dataset and passes it to the information visualization means. The terminal selects an appropriate visualization format according to the characteristics of the data. 【0374】 Step 5: 【0375】 The device visualizes data using information visualization tools. It receives a visualizeable dataset as input and displays it as interactive graphs or 3D models. It generates user-accessible interface screens as output. The device optimizes the interactivity and usability of the visualization during the display process. 【0376】 Step 6: 【0377】 The recognition engine built into the device analyzes the user's emotions in real time. It receives the user's facial expressions and voice data as input, analyzes them, and determines their emotional state. As output, it generates user emotion information and sends it to the server. The device uses an emotion analysis algorithm to make accurate judgments. 【0378】 Step 7: 【0379】 The server dynamically adjusts the displayed information based on emotional information. It receives user emotional information as input and adjusts the level of detail and priority of the visualized data. As output, it generates and provides the adjusted information display to the user. The server optimizes the displayed content based on the user's interests and concerns. 【0380】 Step 8: 【0381】 The server provides information using knowledge presentation and educational tools. It receives a formatted information display as input, organizes it in an easily understandable way, and generates a learnable report. As output, it provides an information report that is easily comprehensible to the user. The server constructs explanations while considering the user's level of understanding. 【0382】 (Application Example 2) 【0383】 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 as the "terminal". 【0384】 Conventional information processing systems have difficulty providing flexible content that responds to changes in users' emotions, and have been insufficient in presenting information that reflects users' instantaneous preferences and interests. Furthermore, there is room for improvement in the effective delivery of visualized information, and there is a need to enhance the user experience. 【0385】 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. 【0386】 In this invention, the server includes data collection means, emotion recognition means, and content recommendation means. This makes it possible to analyze the user's emotional state in real time and dynamically provide appropriate content. 【0387】 "Data collection methods" refer to means of obtaining information from open data repositories and specialized institutions, and detecting the user's emotional state through personal analysis devices. 【0388】 "Preprocessing means" refers to means of organizing collected data into a standardized format and preparing it to facilitate analysis. 【0389】 "Data analysis methods" refer to techniques that use machine learning algorithms to extract significant patterns from data and present information in a way that responds to the user's emotions. 【0390】 "Visualization preparation means" refers to methods for building the necessary datasets for visualization, thereby enabling the smooth visualization of information. 【0391】 "Information visualization methods" refer to means of visualizing analyzed data as interactive 3D models or graphs and providing them to users. 【0392】 "User interface provision means" refers to means of providing an interface that enables users to intuitively access information. 【0393】 An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to recognize their emotions in real time. 【0394】 A "content recommendation method" is a means of selecting and recommending appropriate content based on the user's emotional state. 【0395】 "Knowledge presentation and educational methods" refer to means of providing information to users in an easy-to-understand manner, enabling them to gain new insights and learn. 【0396】 This invention realizes a system that dynamically provides appropriate content based on the user's emotional state. The server acquires relevant data from open data repositories and specialized institutions using data collection means. Furthermore, it collects emotional data in real time from the user's facial expressions and voice via personal analysis devices such as smartphones and smart glasses. 【0397】 The collected data is organized into a consistent format by preprocessing and then analyzed using machine learning algorithms by data analysis tools. This allows for the extraction of patterns related to user emotions, which can then be used to dynamically present information. Specifically, sentiment analysis is performed using machine learning libraries such as TensorFlow and PyTorch, and based on the results, a content recommendation tool selects the most suitable content. 【0398】 The terminal uses visualization preparation means to construct a dataset for visualization and uses information visualization means to visualize the data as interactive 3D models and graphs. This allows information to be intuitively provided to the user through user interface provisioning means. 【0399】 For example, if a user says, "I want to relax today," the system will evaluate their emotional state and recommend relaxing music or nature videos. In this way, it is possible to dynamically provide content that responds to the user's preferences and momentary emotions. 【0400】 Examples of prompt statements include the following: 【0401】 Create a program that dynamically suggests streaming content based on the user's emotional state, selecting from the following options. 【0402】 Action movie 【0403】 Comedy show 【0404】 Relaxation music 【0405】 Nature documentary 【0406】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0407】 Step 1: 【0408】 The server uses data collection methods to acquire data on space and the deep sea from open data repositories and specialized organizations. Input is database access information from repositories and organizations, and output is a standardized dataset. This data collection process involves executing necessary API calls and database queries to obtain relevant information. 【0409】 Step 2: 【0410】 The server standardizes the format of the collected data through preprocessing. Specifically, it formats the input data into a form that can be analyzed all at once. This makes it easier to maintain data consistency and improves the efficiency of subsequent analysis. For example, the data may be formatted into time-series or categorical data. 【0411】 Step 3: 【0412】 The device collects the user's emotional state in real time using facial recognition and voice analysis software. Input is emotion-related data from the camera and microphone, and output is an emotion category (e.g., excited, relaxed). In this phase, emotions are inferred from facial expressions and tone of voice using OpenCV and voice analysis algorithms. 【0413】 Step 4: 【0414】 The server uses machine learning algorithms as a data analysis tool to extract significant patterns from collected user data and deep-sea space data. The input is a pre-processed dataset and user sentiment data, and the output is an information presentation strategy based on user sentiment tendencies. For example, TensorFlow is used to analyze real-time sentiment trends. 【0415】 Step 5: 【0416】 The device prepares and executes visualizations that respond to the user's emotional state. Specifically, it generates interactive 3D models and graphs based on the input emotional and pattern data. The output is a visualized data model. At this stage, visualization libraries such as Three.js can be used. 【0417】 Step 6: 【0418】 Users access and experience dynamically generated information through the provided user interface. Here, content tailored to the user's emotions and interests (e.g., relaxing music or action videos) is recommended. Input is user feedback and interaction, while output is customized information that enhances user engagement. 【0419】 Step 7: 【0420】 The server generates reports that provide information to users in an easy-to-understand manner, using insight presentation and educational tools. The input consists of analyzed data and the user's desired insights, while the output is detailed and easy-to-understand report content. This allows users to easily absorb new discoveries and knowledge. 【0421】 This series of processes significantly improves the user experience and enables the delivery of personalized information. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 [Third Embodiment] 【0426】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0427】 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. 【0428】 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). 【0429】 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. 【0430】 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. 【0431】 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). 【0432】 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. 【0433】 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. 【0434】 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. 【0435】 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. 【0436】 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. 【0437】 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". 【0438】 The information processing system of the present invention is configured as follows: The data collection means uses a server to efficiently collect information on space and the deep sea from open data repositories and specialized institutions. The server accesses the database in real time and continuously acquires new data. 【0439】 Subsequently, in the preprocessing stage, the server converts the collected data into a consistent format and filters out unnecessary data. This makes it possible to prepare the data in a state that is most suitable for data analysis. 【0440】 In data analysis methods, the device uses machine learning algorithms to analyze the data and extract significant patterns and results. This process allows for the discovery of important insights from complex datasets. 【0441】 In the visualization preparation mechanism, the terminal prepares a dataset for visualizing the analysis results and sends it to the information visualization mechanism. Here, the information is presented to the user in a visually clear and easy-to-understand manner. 【0442】 In information visualization, the terminal generates interactive graphs, 3D models, and simulations, allowing users to intuitively manipulate information. In user interface provisioning, the terminal provides users with an easy-to-use interface, supporting quick access to specific information. 【0443】 Ultimately, through insight presentation and educational tools, the server automatically generates reports and explanations based on visualized data, providing information to the user. This allows users to access specialized knowledge and gain further advanced insights. 【0444】 For example, if a user wants to learn about new planetary exploration, the server collects relevant astronomical data in real time, the terminal analyzes that data, and extracts characteristic patterns. By visualizing these results, the planet's topography and weather data are displayed as a 3D model, which the user can freely explore. 【0445】 The following describes the processing flow. 【0446】 Step 1: 【0447】 The server accesses open data repositories and APIs from specialized organizations to collect data about space and the deep sea. The server periodically or on demand sends queries to retrieve the latest information and downloads the necessary data from the database. 【0448】 Step 2: 【0449】 The server preprocesses the collected data. This involves converting the data to CSV or JSON format to standardize the data format, filtering out unnecessary and duplicate data, and filling in any missing data. This preprocessing lays the foundation for analysis. 【0450】 Step 3: 【0451】 The device analyzes pre-processed data. Machine learning algorithms are used to detect patterns and anomalies within the data. Techniques such as clustering and regression analysis are applied to extract new insights. This reveals important features within the dataset. 【0452】 Step 4: 【0453】 The terminal prepares a dataset for visualization based on the analysis results. It calculates coordinates, color coding information, and other necessary data for visualization, thereby creating the data required to generate graphs and 3D models. 【0454】 Step 5: 【0455】 The device performs visualization. It visually displays information in an interactive format, allowing users to intuitively understand and interact with it. At this stage, 3D models and graphical interfaces are rendered and provided to the user. 【0456】 Step 6: 【0457】 Users explore and view specific information using the device's interface. The device, through the user interface, helps users reconstruct and interactively manipulate the information they select. 【0458】 Step 7: 【0459】 The server automatically generates reports and explanations based on the visualized information. During this knowledge presentation and education phase, the server uses natural language processing to summarize the information clearly and present it to the user. As a result, users can access advanced information. 【0460】 (Example 1) 【0461】 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." 【0462】 Existing information processing systems have a problem in that they struggle to efficiently collect, analyze, and intuitively provide information from specialized fields such as space and the deep sea to users. As a result, there is a challenge in that general users who are not experts cannot fully utilize this information. 【0463】 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. 【0464】 In this invention, the server includes a device for collecting data, a device for formatting the data into a consistent format, and a device for analyzing the data and extracting patterns. This makes it possible to quickly analyze information in a specialized field and present it in a visually easy-to-understand manner. 【0465】 A "data collection device" is a device that has the function of obtaining necessary information from an information source and storing it in a format that can be used within the system. 【0466】 A "data formatting device" is a device that unifies collected data in different formats into a specific format, making it easier to analyze. 【0467】 A "data analysis and pattern extraction device" is a device that performs analysis on formatted data to find useful information and hidden patterns. 【0468】 A "device for preparing data for visualization" is a device that constructs the dataset necessary to visually represent the analyzed data. 【0469】 A "device for visually displaying analysis results" is a device that can present data analysis results to the user as interactive graphics or models. 【0470】 A "device that provides information in an easy-to-manipulate manner to users" is a device that provides an interface that allows users to easily manipulate and view information. 【0471】 A "device that provides insights based on visualized data" is a device that generates reports and explanations based on visualized information, thereby providing users with meaningful insights. 【0472】 This information processing system is primarily composed of servers and terminals. The servers handle data collection, data formatting, and insight provision, while the terminals are responsible for data analysis, visualization, and the user interface. 【0473】 The server first accesses open data repositories and APIs from specialized organizations to automatically retrieve data related to space and the deep sea. The server's programs utilize programming languages ​​such as Python and Java to optimize data collection and storage. For example, it performs scheduled crawling and real-time API calls. MySQL and MongoDB are used as databases to ensure data consistency and high-speed retrieval. 【0474】 The server formats the collected data into a unified format. This process removes unnecessary information while arranging the data into the required fields. Typically, ETL (Extract, Transform, Load) tools or automated scripts are used for this. The formatted data is then sent to the terminal, ready for analysis. 【0475】 The terminal receives the pre-processed data and performs analysis using machine learning algorithms. Specifically, it often uses libraries such as Python's Scikit-learn and TensorFlow. The features and patterns extracted through the analysis can be used to visualize the information. 【0476】 Next, the device prepares a dataset for visualization and uses libraries such as Three.js and D3.js to draw interactive graphs and 3D models. This process allows users to deepen their visual understanding of the data. 【0477】 Users interact with the provided interface using their device, quickly accessing the information they need. This interface is often built using front-end frameworks such as React or Vue.js. 【0478】 Finally, the server automatically generates reports based on the visualized data and its analysis results, providing users with information in the most optimal format. This gives users a foundation for competitive decision-making and advancing academic research. For example, if a user enters a prompt such as, "Display the topography and weather data of a new planet in a 3D model, and include detailed analysis results," the corresponding astronomical data will be analyzed and visualized in real time. 【0479】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0480】 Step 1: 【0481】 The server retrieves data from open data repositories and specialized organizations. Specifically, it uses APIs to collect data related to space and the deep sea. The input is the specified data source, and the output is the collected raw data. This raw data is stored in a database and made accessible for subsequent processing. 【0482】 Step 2: 【0483】 The server converts the collected data into a consistent format and filters out unnecessary information. For example, it converts data written in different units to standard units and eliminates unnecessary columns and missing values. The input is raw data, and the output is formatted data. Through this process, the formatted data is prepared for analysis. 【0484】 Step 3: 【0485】 The device analyzes the formatted data and extracts important patterns and trends. This involves applying analytical methods using machine learning algorithms. The input is the formatted data, and the output is the features and patterns obtained from the analysis. Specifically, it divides the data into meaningful groups using clustering and classification methods. 【0486】 Step 4: 【0487】 The terminal prepares a dataset for visualizing the analysis results. It selects the necessary fields for visualization and adjusts the format. The input is the analysis results, and the output is a visualizeable dataset. For example, it might perform actions such as gathering the coordinate data necessary to generate a 3D model. 【0488】 Step 5: 【0489】 The device displays data through a visual interface. Specifically, it uses Three.js and D3.js to generate interactive 3D models and graphs. The input is a dataset for visualization, and the output is a user-operable graphical interface. Users can intuitively explore the data. 【0490】 Step 6: 【0491】 The server automatically generates user-facing reports and explanations based on visualized data and analysis results. The input is visualized information and analysis results, and the output is the reports provided to the user. This allows users to gain advanced data-driven insights using the generated AI model. 【0492】 (Application Example 1) 【0493】 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." 【0494】 In today's retail industry, efficient inventory management and optimal product placement based on customer preferences are crucial for brick-and-mortar stores. However, systems that analyze this data in real time and provide visually clear placement plans are still not fully developed. As a result, store operators face many challenges in product display and inventory management. 【0495】 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. 【0496】 In this invention, the server includes data collection means, preprocessing means, and data analysis means. This makes it possible to analyze store inventory information and customer purchasing patterns in real time and provide a visual and intuitive product placement plan. 【0497】 "Data collection means" refers to functions for obtaining necessary information from databases and external information sources. 【0498】 "Preprocessing means" refers to a function that formats the collected data into an analyzable format and removes unnecessary information. 【0499】 "Data analysis means" refers to the function of executing machine learning algorithms to extract significant patterns and insights from data used for analysis. 【0500】 "Visualization preparation means" refers to a function that creates a basic dataset for visualizing the analyzed data. 【0501】 "Information visualization methods" are functions that visually represent data in a way that is easy for users to understand. 【0502】 "User interface provision means" refers to a function that provides an intuitive interface for users to operate and supports their access to information. 【0503】 "Insight presentation and educational tools" refers to a function that automatically generates reports and explanations based on visualized information and provides them to users. 【0504】 "Support tools for inventory management and product placement optimization" refers to functions that manage the inventory status of products and propose efficient display and placement. 【0505】 "Means for generating visual plans based on analysis results" refers to a function that creates visual product placement proposals and display plans based on results obtained from data analysis. 【0506】 The system for implementing this invention consists of a central server and terminal devices. The server first uses data collection means to acquire information in real time from store inventory databases and sales information systems. Using libraries such as Python's Pandas, the data is preprocessed to remove noise and then converted into an analyzable format. 【0507】 Next, the server executes machine learning algorithms as a data analysis tool. Using tools such as Scikit-learn, it gains insights based on customer purchasing patterns and inventory status. Then, the terminal uses visualization preparation tools to convert the analysis results into a base dataset for visualization. This data is then represented as 3D models or interactive graphs using information visualization tools such as Tableau or Matplotlib. 【0508】 Furthermore, the user interface provisioning mechanism allows the device to provide users with intuitive operation through a UI built with React Native. This enables store operators to easily understand and implement visual product placement optimization suggestions based on analysis results. For example, when the stock of a popular product decreases, it may be possible to receive suggestions to move that product to a more prominent location. 【0509】 This system significantly improves the operational efficiency of physical stores, particularly through optimizing product inventory management and display. An example of a prompt that utilizes the generative AI model is, "Based on customer movement and purchase data within the store, suggest effective ways to place products that are running low on stock." 【0510】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0511】 Step 1: 【0512】 The server uses data collection tools to retrieve real-time data from store inventory databases and sales information systems. In this step, the server accesses the information sources via APIs and retrieves the generated datasets as input data. The data output is provided in a structured format (e.g., CSV or JSON). 【0513】 Step 2: 【0514】 The server uses preprocessing tools to transform the collected data into an analyzable format. It uses Pandas to filter out unnecessary data and perform consistent data cleaning, such as imputing missing values. As a result, a clean and formatted dataset is output and passed on to the next analysis step. 【0515】 Step 3: 【0516】 The server uses data analysis tools to execute machine learning algorithms. At this point, the server uses Scikit-learn to perform pattern recognition and predict customer purchasing behavior and inventory supply and demand. The output obtained from this analysis will be numerical data and categorical information as analysis results. 【0517】 Step 4: 【0518】 The terminal uses visualization preparation tools to convert the analysis results into a format suitable for graphing or 3D modeling. The prepared dataset is provided as output data in a format intended for processing with visualization software. 【0519】 Step 5: 【0520】 The terminal uses information visualization tools to generate interactive graphs and 3D models based on the prepared data. This step utilizes Tableau and Matplotlib to visualize the analysis results, allowing users to intuitively understand the data insights. 【0521】 Step 6: 【0522】 Through the user interface, users interact with an intuitive UI and consider store display optimization plans based on the visualized information. In this step, a user-friendly operating environment is provided using a UI based on React Native. 【0523】 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. 【0524】 The information processing system of the present invention begins with a data collection means and incorporates an emotion engine that presents information while considering the user's emotions. The server collects data on space and the deep sea from open data repositories and specialized institutions, and a preprocessing means organizes the data into a consistent format. This makes it easier for the data analysis means to extract significant patterns using machine learning algorithms. 【0525】 The analyzed data is prepared for visualization by the terminal. Here, the visualization preparation means builds the necessary dataset and transfers it to the information visualization means. This prepares the data to be visualized as interactive 3D models and graphs and presented to the user. 【0526】 This invention enables users to intuitively access information through a user interface and further analyzes the user's emotional data using an emotion engine. This emotion engine is built into the terminal and can recognize emotions in real time by analyzing the user's facial expressions and voice. The recognized emotional information is sent to a server and used to dynamically adjust the content of the visualized data. For example, if the user is emotionally agitated, the terminal will provide more detailed information and prioritize topics that are likely to be of interest. 【0527】 The visualized information is tailored to highlight changes and importance in the data and presented in a way that is emotionally relatable to the user. The server also compiles the information into reports using insightful presentations and educational tools, making it easy for the user to understand. At this stage, the user is more receptive to absorbing new discoveries and knowledge. 【0528】 As a concrete example, suppose a user is researching the possibility of life on a new planet. The server collects relevant research data, and the terminal analyzes and visualizes it. Simultaneously, the emotion engine measures the user's level of excitement and interest, and adjusts the level of detail in the visualization accordingly. This adjustment allows the user to be more drawn to interesting information and actively participate in the exploration. 【0529】 The following describes the processing flow. 【0530】 Step 1: 【0531】 The server collects information on space and the deep sea through open data repositories and APIs from specialized organizations. The server periodically sends queries and retrieves the latest data in real time, providing the necessary foundational data for the entire system. 【0532】 Step 2: 【0533】 The server preprocesses the acquired data. This includes standardizing the data format, imputing missing values ​​as needed, and filtering out unnecessary data. At this stage, a clean dataset suitable for analysis is prepared. 【0534】 Step 3: 【0535】 The terminal receives pre-processed data and executes data analysis methods. The terminal uses machine learning algorithms to identify patterns, trends, and outliers present in the data and extract new insights. 【0536】 Step 4: 【0537】 The terminal prepares a dataset for visualization based on the analysis results. Here, it calculates coordinate data and color schemes to determine how the information should be displayed. 【0538】 Step 5: 【0539】 The device visualizes the information. Using visual elements generated from the analyzed data, it prepares to create interactive dashboards, 3D models, or animations for presentation to the user. 【0540】 Step 6: 【0541】 Users access visualized data through the device's user interface. At this stage, users can interact with the interface and select or customize information of interest. 【0542】 Step 7: 【0543】 The emotion engine recognizes the user's emotional state in real time. The device analyzes the user's facial expressions and voice through the camera and microphone to analyze their emotional responses. 【0544】 Step 8: 【0545】 The server receives emotional data from the emotion engine and feeds it back into the information visualization system. The presentation method and level of detail of the data are dynamically adjusted according to the user's emotions. 【0546】 Step 9: 【0547】 The server automatically generates reports and explanations for users using insight presentation and educational tools. These reports are refined by an emotion engine, ensuring they are easy to read and engaging for the user. 【0548】 (Example 2) 【0549】 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." 【0550】 There are challenges in improving the user experience by effectively and dynamically processing information obtained from large amounts of data and optimizing information presentation based on the user's emotional state. Furthermore, providing users with quick access to the information they need in an easily understandable format is also a crucial requirement. 【0551】 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. 【0552】 In this invention, the server includes data acquisition means, data formatting means, and data analysis means. This makes it possible to recognize the user's emotional state and dynamically adjust the information presented, providing the user with the information they seek in an easy-to-understand manner. 【0553】 "Data acquisition methods" refer to technical means for collecting necessary information from public data repositories or specialized organizations. 【0554】 "Data formatting methods" are technical means that convert collected information into a consistent format and perform preprocessing such as imputing missing values ​​and removing outliers. 【0555】 "Data analysis methods" are technical means of extracting regularities and trends from information that has been formatted using learning algorithms. 【0556】 "Visualization preparation means" refers to technical means for constructing datasets and making the necessary preparations for visually displaying information. 【0557】 "Information visualization means" are technical methods that present information in visual formats such as interactive graphs and 3D models, based on a prepared dataset. 【0558】 "Operation screen provision means" refers to a technical means that provides a user interface that allows users to intuitively access information. 【0559】 A "recognition engine" is a technical means of analyzing a user's facial expressions and voice data to determine their emotional state in real time. 【0560】 "Knowledge presentation and educational tools" are technical means that provide users with easily understandable insights derived from visualization and analysis results, enabling them to efficiently learn new information. 【0561】 The information processing system of the present invention encompasses the functions of data acquisition, formatting, analysis, visualization, emotion recognition, dynamic adjustment, and knowledge presentation, thereby effectively providing high-level information to users. 【0562】 The server collects necessary information from public data repositories and specialized organizations using data acquisition methods. This data is then transformed into a consistent format using formatting methods, and missing or outlier values ​​are handled. Subsequently, the server extracts regularities and trends from the data using learning algorithms through data analysis methods. The software used in this process includes common data analysis platforms and libraries (e.g., TensorFlow, SciKit-Learn). 【0563】 Next, the terminal uses visualization preparation tools to prepare the analyzed data for visual display. The prepared dataset is passed to the information visualization tools and visualized as a 3D model or interactive graph. Data visualization tools (e.g., D3.js, Three.js) are commonly used for this visualization. 【0564】 Furthermore, the recognition engine built into the device analyzes the user's facial expressions and voice to determine their emotions in real time. It analyzes the user's emotional state and sends this information to a server. The server uses this emotional information to dynamically adjust the content of the information presented, highlighting the information that is most of the user's interest. This process significantly improves the user experience. 【0565】 Ultimately, the server provides reports generated based on visualized information, using knowledge presentation and educational tools. Through these reports, users can efficiently learn practical knowledge. 【0566】 For example, when a user investigates the possibility of life on a new planet, the server collects relevant research data, which the terminal then analyzes and visualizes. The emotion engine then measures the user's level of excitement and interest, adjusting the level of detail displayed accordingly. This allows the user to deepen their interest in the exploration and participate more actively. An example of input to the generative AI model would be, "Adjust the level of visualization of planetary exploration data based on the user's emotions." 【0567】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0568】 Step 1: 【0569】 The server collects information using data acquisition methods. It receives API endpoints and request parameters from public data repositories and specialized organizations as input, and retrieves data based on these. As output, it generates the retrieved raw dataset and passes it to the next step. The server operates to retrieve the latest data by making requests at a specified update frequency. 【0570】 Step 2: 【0571】 The server performs preprocessing using data formatting tools. It receives raw datasets as input and performs data formatting standardization, missing value imputation, and outlier removal to transform them into a consistent format. It generates a formatted dataset as output, which is then passed to the analysis step. 【0572】 Step 3: 【0573】 The server analyzes the data using data analysis tools. It receives a formatted dataset as input and applies machine learning algorithms to extract regularities and patterns. As output, it generates a dataset containing the analysis results and sends it to the visualization preparation step. The server then selects and executes the desired analysis model (e.g., clustering, regression analysis). 【0574】 Step 4: 【0575】 The terminal prepares for visualization using visualization preparation means. It receives analysis results as input and organizes them into data structures (e.g., graphs, 3D models) for visual display. As output, it generates a visualizeable dataset and passes it to the information visualization means. The terminal selects an appropriate visualization format according to the characteristics of the data. 【0576】 Step 5: 【0577】 The device visualizes data using information visualization tools. It receives a visualizeable dataset as input and displays it as interactive graphs or 3D models. It generates user-accessible interface screens as output. The device optimizes the interactivity and usability of the visualization during the display process. 【0578】 Step 6: 【0579】 The recognition engine built into the device analyzes the user's emotions in real time. It receives the user's facial expressions and voice data as input, analyzes them, and determines their emotional state. As output, it generates user emotion information and sends it to the server. The device uses an emotion analysis algorithm to make accurate judgments. 【0580】 Step 7: 【0581】 The server dynamically adjusts the displayed information based on emotional information. It receives user emotional information as input and adjusts the level of detail and priority of the visualized data. As output, it generates and provides the adjusted information display to the user. The server optimizes the displayed content based on the user's interests and concerns. 【0582】 Step 8: 【0583】 The server provides information using knowledge presentation and educational tools. It receives a formatted information display as input, organizes it in an easily understandable way, and generates a learnable report. As output, it provides an information report that is easily comprehensible to the user. The server constructs explanations while considering the user's level of understanding. 【0584】 (Application Example 2) 【0585】 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." 【0586】 Conventional information processing systems have difficulty providing flexible content that responds to changes in users' emotions, and have been insufficient in presenting information that reflects users' instantaneous preferences and interests. Furthermore, there is room for improvement in the effective delivery of visualized information, and there is a need to enhance the user experience. 【0587】 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. 【0588】 In this invention, the server includes data collection means, emotion recognition means, and content recommendation means. This makes it possible to analyze the user's emotional state in real time and dynamically provide appropriate content. 【0589】 "Data collection methods" refer to means of obtaining information from open data repositories and specialized institutions, and detecting the user's emotional state through personal analysis devices. 【0590】 "Preprocessing means" refers to means of organizing collected data into a standardized format and preparing it to facilitate analysis. 【0591】 "Data analysis methods" refer to techniques that use machine learning algorithms to extract significant patterns from data and present information in a way that responds to the user's emotions. 【0592】 "Visualization preparation means" refers to methods for building the necessary datasets for visualization, thereby enabling the smooth visualization of information. 【0593】 "Information visualization methods" refer to means of visualizing analyzed data as interactive 3D models or graphs and providing them to users. 【0594】 "User interface provision means" refers to means of providing an interface that enables users to intuitively access information. 【0595】 An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to recognize their emotions in real time. 【0596】 A "content recommendation method" is a means of selecting and recommending appropriate content based on the user's emotional state. 【0597】 "Knowledge presentation and educational methods" refer to means of providing information to users in an easy-to-understand manner, enabling them to gain new insights and learn. 【0598】 This invention realizes a system that dynamically provides appropriate content based on the user's emotional state. The server acquires relevant data from open data repositories and specialized institutions using data collection means. Furthermore, it collects emotional data in real time from the user's facial expressions and voice via personal analysis devices such as smartphones and smart glasses. 【0599】 The collected data is organized into a consistent format by preprocessing and then analyzed using machine learning algorithms by data analysis tools. This allows for the extraction of patterns related to user emotions, which can then be used to dynamically present information. Specifically, sentiment analysis is performed using machine learning libraries such as TensorFlow and PyTorch, and based on the results, a content recommendation tool selects the most suitable content. 【0600】 The terminal uses visualization preparation means to construct a dataset for visualization and uses information visualization means to visualize the data as interactive 3D models and graphs. This allows information to be intuitively provided to the user through user interface provisioning means. 【0601】 For example, if a user says, "I want to relax today," the system will evaluate their emotional state and recommend relaxing music or nature videos. In this way, it is possible to dynamically provide content that responds to the user's preferences and momentary emotions. 【0602】 Examples of prompt statements include the following: 【0603】 Create a program that dynamically suggests streaming content based on the user's emotional state, selecting from the following options. 【0604】 Action movie 【0605】 Comedy show 【0606】 Relaxation music 【0607】 Nature documentary 【0608】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0609】 Step 1: 【0610】 The server uses data collection methods to acquire data on space and the deep sea from open data repositories and specialized organizations. Input is database access information from repositories and organizations, and output is a standardized dataset. This data collection process involves executing necessary API calls and database queries to obtain relevant information. 【0611】 Step 2: 【0612】 The server standardizes the format of the collected data through preprocessing. Specifically, it formats the input data into a form that can be analyzed all at once. This makes it easier to maintain data consistency and improves the efficiency of subsequent analysis. For example, the data may be formatted into time-series or categorical data. 【0613】 Step 3: 【0614】 The device collects the user's emotional state in real time using facial recognition and voice analysis software. Input is emotion-related data from the camera and microphone, and output is an emotion category (e.g., excited, relaxed). In this phase, emotions are inferred from facial expressions and tone of voice using OpenCV and voice analysis algorithms. 【0615】 Step 4: 【0616】 The server uses machine learning algorithms as a data analysis tool to extract significant patterns from collected user data and deep-sea space data. The input is a pre-processed dataset and user sentiment data, and the output is an information presentation strategy based on user sentiment tendencies. For example, TensorFlow is used to analyze real-time sentiment trends. 【0617】 Step 5: 【0618】 The device prepares and executes visualizations that respond to the user's emotional state. Specifically, it generates interactive 3D models and graphs based on the input emotional and pattern data. The output is a visualized data model. At this stage, visualization libraries such as Three.js can be used. 【0619】 Step 6: 【0620】 Users access and experience dynamically generated information through the provided user interface. Here, content tailored to the user's emotions and interests (e.g., relaxing music or action videos) is recommended. Input is user feedback and interaction, while output is customized information that enhances user engagement. 【0621】 Step 7: 【0622】 The server generates reports that provide information to users in an easy-to-understand manner, using insight presentation and educational tools. The input consists of analyzed data and the user's desired insights, while the output is detailed and easy-to-understand report content. This allows users to easily absorb new discoveries and knowledge. 【0623】 This series of processes significantly improves the user experience and enables the delivery of personalized information. 【0624】 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. 【0625】 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. 【0626】 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. 【0627】 [Fourth Embodiment] 【0628】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0629】 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. 【0630】 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). 【0631】 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. 【0632】 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. 【0633】 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). 【0634】 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. 【0635】 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. 【0636】 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. 【0637】 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. 【0638】 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. 【0639】 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. 【0640】 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". 【0641】 The information processing system of the present invention is configured as follows: The data collection means uses a server to efficiently collect information on space and the deep sea from open data repositories and specialized institutions. The server accesses the database in real time and continuously acquires new data. 【0642】 Subsequently, in the preprocessing stage, the server converts the collected data into a consistent format and filters out unnecessary data. This makes it possible to prepare the data in a state that is most suitable for data analysis. 【0643】 In data analysis methods, the device uses machine learning algorithms to analyze the data and extract significant patterns and results. This process allows for the discovery of important insights from complex datasets. 【0644】 In the visualization preparation mechanism, the terminal prepares a dataset for visualizing the analysis results and sends it to the information visualization mechanism. Here, the information is presented to the user in a visually clear and easy-to-understand manner. 【0645】 In information visualization, the terminal generates interactive graphs, 3D models, and simulations, allowing users to intuitively manipulate information. In user interface provisioning, the terminal provides users with an easy-to-use interface, supporting quick access to specific information. 【0646】 Ultimately, through insight presentation and educational tools, the server automatically generates reports and explanations based on visualized data, providing information to the user. This allows users to access specialized knowledge and gain further advanced insights. 【0647】 For example, if a user wants to learn about new planetary exploration, the server collects relevant astronomical data in real time, the terminal analyzes that data, and extracts characteristic patterns. By visualizing these results, the planet's topography and weather data are displayed as a 3D model, which the user can freely explore. 【0648】 The following describes the processing flow. 【0649】 Step 1: 【0650】 The server accesses open data repositories and APIs from specialized organizations to collect data about space and the deep sea. The server periodically or on demand sends queries to retrieve the latest information and downloads the necessary data from the database. 【0651】 Step 2: 【0652】 The server preprocesses the collected data. This involves converting the data to CSV or JSON format to standardize the data format, filtering out unnecessary and duplicate data, and filling in any missing data. This preprocessing lays the foundation for analysis. 【0653】 Step 3: 【0654】 The device analyzes pre-processed data. Machine learning algorithms are used to detect patterns and anomalies within the data. Techniques such as clustering and regression analysis are applied to extract new insights. This reveals important features within the dataset. 【0655】 Step 4: 【0656】 The terminal prepares a dataset for visualization based on the analysis results. It calculates coordinates, color coding information, and other necessary data for visualization, thereby creating the data required to generate graphs and 3D models. 【0657】 Step 5: 【0658】 The device performs visualization. It visually displays information in an interactive format, allowing users to intuitively understand and interact with it. At this stage, 3D models and graphical interfaces are rendered and provided to the user. 【0659】 Step 6: 【0660】 Users explore and view specific information using the device's interface. The device, through the user interface, helps users reconstruct and interactively manipulate the information they select. 【0661】 Step 7: 【0662】 The server automatically generates reports and explanations based on the visualized information. During this knowledge presentation and education phase, the server uses natural language processing to summarize the information clearly and present it to the user. As a result, users can access advanced information. 【0663】 (Example 1) 【0664】 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". 【0665】 Existing information processing systems have a problem in that they struggle to efficiently collect, analyze, and intuitively provide information from specialized fields such as space and the deep sea to users. As a result, there is a challenge in that general users who are not experts cannot fully utilize this information. 【0666】 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. 【0667】 In this invention, the server includes a device for collecting data, a device for formatting the data into a consistent format, and a device for analyzing the data and extracting patterns. This makes it possible to quickly analyze information in a specialized field and present it in a visually easy-to-understand manner. 【0668】 A "data collection device" is a device that has the function of obtaining necessary information from an information source and storing it in a format that can be used within the system. 【0669】 A "data formatting device" is a device that unifies collected data in different formats into a specific format, making it easier to analyze. 【0670】 A "data analysis and pattern extraction device" is a device that performs analysis on formatted data to find useful information and hidden patterns. 【0671】 A "device for preparing data for visualization" is a device that constructs the dataset necessary to visually represent the analyzed data. 【0672】 A "device for visually displaying analysis results" is a device that can present data analysis results to the user as interactive graphics or models. 【0673】 A "device that provides information in an easy-to-manipulate manner to users" is a device that provides an interface that allows users to easily manipulate and view information. 【0674】 A "device that provides insights based on visualized data" is a device that generates reports and explanations based on visualized information, thereby providing users with meaningful insights. 【0675】 This information processing system is primarily composed of servers and terminals. The servers handle data collection, data formatting, and insight provision, while the terminals are responsible for data analysis, visualization, and the user interface. 【0676】 The server first accesses open data repositories and APIs from specialized organizations to automatically retrieve data related to space and the deep sea. The server's programs utilize programming languages ​​such as Python and Java to optimize data collection and storage. For example, it performs scheduled crawling and real-time API calls. MySQL and MongoDB are used as databases to ensure data consistency and high-speed retrieval. 【0677】 The server formats the collected data into a unified format. This process removes unnecessary information while arranging the data into the required fields. Typically, ETL (Extract, Transform, Load) tools or automated scripts are used for this. The formatted data is then sent to the terminal, ready for analysis. 【0678】 The terminal receives the pre-processed data and performs analysis using machine learning algorithms. Specifically, it often uses libraries such as Python's Scikit-learn and TensorFlow. The features and patterns extracted through the analysis can be used to visualize the information. 【0679】 Next, the device prepares a dataset for visualization and uses libraries such as Three.js and D3.js to draw interactive graphs and 3D models. This process allows users to deepen their visual understanding of the data. 【0680】 Users interact with the provided interface using their device, quickly accessing the information they need. This interface is often built using front-end frameworks such as React or Vue.js. 【0681】 Finally, the server automatically generates reports based on the visualized data and its analysis results, providing users with information in the most optimal format. This gives users a foundation for competitive decision-making and advancing academic research. For example, if a user enters a prompt such as, "Display the topography and weather data of a new planet in a 3D model, and include detailed analysis results," the corresponding astronomical data will be analyzed and visualized in real time. 【0682】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0683】 Step 1: 【0684】 The server retrieves data from open data repositories and specialized organizations. Specifically, it uses APIs to collect data related to space and the deep sea. The input is the specified data source, and the output is the collected raw data. This raw data is stored in a database and made accessible for subsequent processing. 【0685】 Step 2: 【0686】 The server converts the collected data into a consistent format and filters out unnecessary information. For example, it converts data written in different units to standard units and eliminates unnecessary columns and missing values. The input is raw data, and the output is formatted data. Through this process, the formatted data is prepared for analysis. 【0687】 Step 3: 【0688】 The device analyzes the formatted data and extracts important patterns and trends. This involves applying analytical methods using machine learning algorithms. The input is the formatted data, and the output is the features and patterns obtained from the analysis. Specifically, it divides the data into meaningful groups using clustering and classification methods. 【0689】 Step 4: 【0690】 The terminal prepares a dataset for visualizing the analysis results. It selects the necessary fields for visualization and adjusts the format. The input is the analysis results, and the output is a visualizeable dataset. For example, it might perform actions such as gathering the coordinate data necessary to generate a 3D model. 【0691】 Step 5: 【0692】 The device displays data through a visual interface. Specifically, it uses Three.js and D3.js to generate interactive 3D models and graphs. The input is a dataset for visualization, and the output is a user-operable graphical interface. Users can intuitively explore the data. 【0693】 Step 6: 【0694】 The server automatically generates user-facing reports and explanations based on visualized data and analysis results. The input is visualized information and analysis results, and the output is the reports provided to the user. This allows users to gain advanced data-driven insights using the generated AI model. 【0695】 (Application Example 1) 【0696】 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". 【0697】 In today's retail industry, efficient inventory management and optimal product placement based on customer preferences are crucial for brick-and-mortar stores. However, systems that analyze this data in real time and provide visually clear placement plans are still not fully developed. As a result, store operators face many challenges in product display and inventory management. 【0698】 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. 【0699】 In this invention, the server includes data collection means, preprocessing means, and data analysis means. This makes it possible to analyze store inventory information and customer purchasing patterns in real time and provide a visual and intuitive product placement plan. 【0700】 "Data collection means" refers to functions for obtaining necessary information from databases and external information sources. 【0701】 "Preprocessing means" refers to a function that formats the collected data into an analyzable format and removes unnecessary information. 【0702】 "Data analysis means" refers to the function of executing machine learning algorithms to extract significant patterns and insights from data used for analysis. 【0703】 "Visualization preparation means" refers to a function that creates a basic dataset for visualizing the analyzed data. 【0704】 "Information visualization methods" are functions that visually represent data in a way that is easy for users to understand. 【0705】 "User interface provision means" refers to a function that provides an intuitive interface for users to operate and supports their access to information. 【0706】 "Insight presentation and educational tools" refers to a function that automatically generates reports and explanations based on visualized information and provides them to users. 【0707】 "Support tools for inventory management and product placement optimization" refers to functions that manage the inventory status of products and propose efficient display and placement. 【0708】 "Means for generating visual plans based on analysis results" refers to a function that creates visual product placement proposals and display plans based on results obtained from data analysis. 【0709】 The system for implementing this invention consists of a central server and terminal devices. The server first uses data collection means to acquire information in real time from store inventory databases and sales information systems. Using libraries such as Python's Pandas, the data is preprocessed to remove noise and then converted into an analyzable format. 【0710】 Next, the server executes machine learning algorithms as a data analysis tool. Using tools such as Scikit-learn, it gains insights based on customer purchasing patterns and inventory status. Then, the terminal uses visualization preparation tools to convert the analysis results into a base dataset for visualization. This data is then represented as 3D models or interactive graphs using information visualization tools such as Tableau or Matplotlib. 【0711】 Furthermore, the user interface provisioning mechanism allows the device to provide users with intuitive operation through a UI built with React Native. This enables store operators to easily understand and implement visual product placement optimization suggestions based on analysis results. For example, when the stock of a popular product decreases, it may be possible to receive suggestions to move that product to a more prominent location. 【0712】 This system significantly improves the operational efficiency of physical stores, particularly through optimizing product inventory management and display. An example of a prompt that utilizes the generative AI model is, "Based on customer movement and purchase data within the store, suggest effective ways to place products that are running low on stock." 【0713】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0714】 Step 1: 【0715】 The server uses data collection tools to retrieve real-time data from store inventory databases and sales information systems. In this step, the server accesses the information sources via APIs and retrieves the generated datasets as input data. The data output is provided in a structured format (e.g., CSV or JSON). 【0716】 Step 2: 【0717】 The server uses preprocessing tools to transform the collected data into an analyzable format. It uses Pandas to filter out unnecessary data and perform consistent data cleaning, such as imputing missing values. As a result, a clean and formatted dataset is output and passed on to the next analysis step. 【0718】 Step 3: 【0719】 The server uses data analysis tools to execute machine learning algorithms. At this point, the server uses Scikit-learn to perform pattern recognition and predict customer purchasing behavior and inventory supply and demand. The output obtained from this analysis will be numerical data and categorical information as analysis results. 【0720】 Step 4: 【0721】 The terminal uses visualization preparation tools to convert the analysis results into a format suitable for graphing or 3D modeling. The prepared dataset is provided as output data in a format intended for processing with visualization software. 【0722】 Step 5: 【0723】 The terminal uses information visualization tools to generate interactive graphs and 3D models based on the prepared data. This step utilizes Tableau and Matplotlib to visualize the analysis results, allowing users to intuitively understand the data insights. 【0724】 Step 6: 【0725】 Through the user interface, users interact with an intuitive UI and consider store display optimization plans based on the visualized information. In this step, a user-friendly operating environment is provided using a UI based on React Native. 【0726】 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. 【0727】 The information processing system of the present invention begins with a data collection means and incorporates an emotion engine that presents information while considering the user's emotions. The server collects data on space and the deep sea from open data repositories and specialized institutions, and a preprocessing means organizes the data into a consistent format. This makes it easier for the data analysis means to extract significant patterns using machine learning algorithms. 【0728】 The analyzed data is prepared for visualization by the terminal. Here, the visualization preparation means builds the necessary dataset and transfers it to the information visualization means. This prepares the data to be visualized as interactive 3D models and graphs and presented to the user. 【0729】 This invention enables users to intuitively access information through a user interface and further analyzes the user's emotional data using an emotion engine. This emotion engine is built into the terminal and can recognize emotions in real time by analyzing the user's facial expressions and voice. The recognized emotional information is sent to a server and used to dynamically adjust the content of the visualized data. For example, if the user is emotionally agitated, the terminal will provide more detailed information and prioritize topics that are likely to be of interest. 【0730】 The visualized information is tailored to highlight changes and importance in the data and presented in a way that is emotionally relatable to the user. The server also compiles the information into reports using insightful presentations and educational tools, making it easy for the user to understand. At this stage, the user is more receptive to absorbing new discoveries and knowledge. 【0731】 As a concrete example, suppose a user is researching the possibility of life on a new planet. The server collects relevant research data, and the terminal analyzes and visualizes it. Simultaneously, the emotion engine measures the user's level of excitement and interest, and adjusts the level of detail in the visualization accordingly. This adjustment allows the user to be more drawn to interesting information and actively participate in the exploration. 【0732】 The following describes the processing flow. 【0733】 Step 1: 【0734】 The server collects information on space and the deep sea through open data repositories and APIs from specialized organizations. The server periodically sends queries and retrieves the latest data in real time, providing the necessary foundational data for the entire system. 【0735】 Step 2: 【0736】 The server preprocesses the acquired data. This includes standardizing the data format, imputing missing values ​​as needed, and filtering out unnecessary data. At this stage, a clean dataset suitable for analysis is prepared. 【0737】 Step 3: 【0738】 The terminal receives pre-processed data and executes data analysis methods. The terminal uses machine learning algorithms to identify patterns, trends, and outliers present in the data and extract new insights. 【0739】 Step 4: 【0740】 The terminal prepares a dataset for visualization based on the analysis results. Here, it calculates coordinate data and color schemes to determine how the information should be displayed. 【0741】 Step 5: 【0742】 The device visualizes the information. Using visual elements generated from the analyzed data, it prepares to create interactive dashboards, 3D models, or animations for presentation to the user. 【0743】 Step 6: 【0744】 Users access visualized data through the device's user interface. At this stage, users can interact with the interface and select or customize information of interest. 【0745】 Step 7: 【0746】 The emotion engine recognizes the user's emotional state in real time. The device analyzes the user's facial expressions and voice through the camera and microphone to analyze their emotional responses. 【0747】 Step 8: 【0748】 The server receives emotional data from the emotion engine and feeds it back into the information visualization system. The presentation method and level of detail of the data are dynamically adjusted according to the user's emotions. 【0749】 Step 9: 【0750】 The server automatically generates reports and explanations for users using insight presentation and educational tools. These reports are refined by an emotion engine, ensuring they are easy to read and engaging for the user. 【0751】 (Example 2) 【0752】 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". 【0753】 There are challenges in improving the user experience by effectively and dynamically processing information obtained from large amounts of data and optimizing information presentation based on the user's emotional state. Furthermore, providing users with quick access to the information they need in an easily understandable format is also a crucial requirement. 【0754】 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. 【0755】 In this invention, the server includes data acquisition means, data formatting means, and data analysis means. This makes it possible to recognize the user's emotional state and dynamically adjust the information presented, providing the user with the information they seek in an easy-to-understand manner. 【0756】 "Data acquisition methods" refer to technical means for collecting necessary information from public data repositories or specialized organizations. 【0757】 "Data formatting methods" are technical means that convert collected information into a consistent format and perform preprocessing such as imputing missing values ​​and removing outliers. 【0758】 "Data analysis methods" are technical means of extracting regularities and trends from information that has been formatted using learning algorithms. 【0759】 "Visualization preparation means" refers to technical means for constructing datasets and making the necessary preparations for visually displaying information. 【0760】 "Information visualization means" are technical methods that present information in visual formats such as interactive graphs and 3D models, based on a prepared dataset. 【0761】 "Operation screen provision means" refers to a technical means that provides a user interface that allows users to intuitively access information. 【0762】 A "recognition engine" is a technical means of analyzing a user's facial expressions and voice data to determine their emotional state in real time. 【0763】 "Knowledge presentation and educational tools" are technical means that provide users with easily understandable insights derived from visualization and analysis results, enabling them to efficiently learn new information. 【0764】 The information processing system of the present invention encompasses the functions of data acquisition, formatting, analysis, visualization, emotion recognition, dynamic adjustment, and knowledge presentation, thereby effectively providing high-level information to users. 【0765】 The server collects necessary information from public data repositories and specialized organizations using data acquisition methods. This data is then transformed into a consistent format using formatting methods, and missing or outlier values ​​are handled. Subsequently, the server extracts regularities and trends from the data using learning algorithms through data analysis methods. The software used in this process includes common data analysis platforms and libraries (e.g., TensorFlow, SciKit-Learn). 【0766】 Next, the terminal uses visualization preparation tools to prepare the analyzed data for visual display. The prepared dataset is passed to the information visualization tools and visualized as a 3D model or interactive graph. Data visualization tools (e.g., D3.js, Three.js) are commonly used for this visualization. 【0767】 Furthermore, the recognition engine built into the device analyzes the user's facial expressions and voice to determine their emotions in real time. It analyzes the user's emotional state and sends this information to a server. The server uses this emotional information to dynamically adjust the content of the information presented, highlighting the information that is most of the user's interest. This process significantly improves the user experience. 【0768】 Ultimately, the server provides reports generated based on visualized information, using knowledge presentation and educational tools. Through these reports, users can efficiently learn practical knowledge. 【0769】 For example, when a user investigates the possibility of life on a new planet, the server collects relevant research data, which the terminal then analyzes and visualizes. The emotion engine then measures the user's level of excitement and interest, adjusting the level of detail displayed accordingly. This allows the user to deepen their interest in the exploration and participate more actively. An example of input to the generative AI model would be, "Adjust the level of visualization of planetary exploration data based on the user's emotions." 【0770】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0771】 Step 1: 【0772】 The server collects information using data acquisition methods. It receives API endpoints and request parameters from public data repositories and specialized organizations as input, and retrieves data based on these. As output, it generates the retrieved raw dataset and passes it to the next step. The server operates to retrieve the latest data by making requests at a specified update frequency. 【0773】 Step 2: 【0774】 The server performs preprocessing using data formatting tools. It receives raw datasets as input and performs data formatting standardization, missing value imputation, and outlier removal to transform them into a consistent format. It generates a formatted dataset as output, which is then passed to the analysis step. 【0775】 Step 3: 【0776】 The server analyzes the data using data analysis tools. It receives a formatted dataset as input and applies machine learning algorithms to extract regularities and patterns. As output, it generates a dataset containing the analysis results and sends it to the visualization preparation step. The server then selects and executes the desired analysis model (e.g., clustering, regression analysis). 【0777】 Step 4: 【0778】 The terminal prepares for visualization using visualization preparation means. It receives analysis results as input and organizes them into data structures (e.g., graphs, 3D models) for visual display. As output, it generates a visualizeable dataset and passes it to the information visualization means. The terminal selects an appropriate visualization format according to the characteristics of the data. 【0779】 Step 5: 【0780】 The device visualizes data using information visualization tools. It receives a visualizeable dataset as input and displays it as interactive graphs or 3D models. It generates user-accessible interface screens as output. The device optimizes the interactivity and usability of the visualization during the display process. 【0781】 Step 6: 【0782】 The recognition engine built into the device analyzes the user's emotions in real time. It receives the user's facial expressions and voice data as input, analyzes them, and determines their emotional state. As output, it generates user emotion information and sends it to the server. The device uses an emotion analysis algorithm to make accurate judgments. 【0783】 Step 7: 【0784】 The server dynamically adjusts the displayed information based on emotional information. It receives user emotional information as input and adjusts the level of detail and priority of the visualized data. As output, it generates and provides the adjusted information display to the user. The server optimizes the displayed content based on the user's interests and concerns. 【0785】 Step 8: 【0786】 The server provides information using knowledge presentation and educational tools. It receives a formatted information display as input, organizes it in an easily understandable way, and generates a learnable report. As output, it provides an information report that is easily comprehensible to the user. The server constructs explanations while considering the user's level of understanding. 【0787】 (Application Example 2) 【0788】 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". 【0789】 Conventional information processing systems have difficulty providing flexible content that responds to changes in users' emotions, and have been insufficient in presenting information that reflects users' instantaneous preferences and interests. Furthermore, there is room for improvement in the effective delivery of visualized information, and there is a need to enhance the user experience. 【0790】 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. 【0791】 In this invention, the server includes data collection means, emotion recognition means, and content recommendation means. This makes it possible to analyze the user's emotional state in real time and dynamically provide appropriate content. 【0792】 "Data collection methods" refer to means of obtaining information from open data repositories and specialized institutions, and detecting the user's emotional state through personal analysis devices. 【0793】 "Preprocessing means" refers to means of organizing collected data into a standardized format and preparing it to facilitate analysis. 【0794】 "Data analysis methods" refer to techniques that use machine learning algorithms to extract significant patterns from data and present information in a way that responds to the user's emotions. 【0795】 "Visualization preparation means" refers to methods for building the necessary datasets for visualization, thereby enabling the smooth visualization of information. 【0796】 "Information visualization methods" refer to means of visualizing analyzed data as interactive 3D models or graphs and providing them to users. 【0797】 "User interface provision means" refers to means of providing an interface that enables users to intuitively access information. 【0798】 An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to recognize their emotions in real time. 【0799】 A "content recommendation method" is a means of selecting and recommending appropriate content based on the user's emotional state. 【0800】 "Knowledge presentation and educational methods" refer to means of providing information to users in an easy-to-understand manner, enabling them to gain new insights and learn. 【0801】 This invention realizes a system that dynamically provides appropriate content based on the user's emotional state. The server acquires relevant data from open data repositories and specialized institutions using data collection means. Furthermore, it collects emotional data in real time from the user's facial expressions and voice via personal analysis devices such as smartphones and smart glasses. 【0802】 The collected data is organized into a consistent format by preprocessing and then analyzed using machine learning algorithms by data analysis tools. This allows for the extraction of patterns related to user emotions, which can then be used to dynamically present information. Specifically, sentiment analysis is performed using machine learning libraries such as TensorFlow and PyTorch, and based on the results, a content recommendation tool selects the most suitable content. 【0803】 The terminal uses visualization preparation means to construct a dataset for visualization and uses information visualization means to visualize the data as interactive 3D models and graphs. This allows information to be intuitively provided to the user through user interface provisioning means. 【0804】 For example, if a user says, "I want to relax today," the system will evaluate their emotional state and recommend relaxing music or nature videos. In this way, it is possible to dynamically provide content that responds to the user's preferences and momentary emotions. 【0805】 Examples of prompt statements include the following: 【0806】 Create a program that dynamically suggests streaming content based on the user's emotional state, selecting from the following options. 【0807】 Action movie 【0808】 Comedy show 【0809】 Relaxation music 【0810】 Nature documentary 【0811】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0812】 Step 1: 【0813】 The server uses data collection methods to acquire data on space and the deep sea from open data repositories and specialized organizations. Input is database access information from repositories and organizations, and output is a standardized dataset. This data collection process involves executing necessary API calls and database queries to obtain relevant information. 【0814】 Step 2: 【0815】 The server standardizes the format of the collected data through preprocessing. Specifically, it formats the input data into a form that can be analyzed all at once. This makes it easier to maintain data consistency and improves the efficiency of subsequent analysis. For example, the data may be formatted into time-series or categorical data. 【0816】 Step 3: 【0817】 The device collects the user's emotional state in real time using facial recognition and voice analysis software. Input is emotion-related data from the camera and microphone, and output is an emotion category (e.g., excited, relaxed). In this phase, emotions are inferred from facial expressions and tone of voice using OpenCV and voice analysis algorithms. 【0818】 Step 4: 【0819】 The server uses machine learning algorithms as a data analysis tool to extract significant patterns from collected user data and deep-sea space data. The input is a pre-processed dataset and user sentiment data, and the output is an information presentation strategy based on user sentiment tendencies. For example, TensorFlow is used to analyze real-time sentiment trends. 【0820】 Step 5: 【0821】 The device prepares and executes visualizations that respond to the user's emotional state. Specifically, it generates interactive 3D models and graphs based on the input emotional and pattern data. The output is a visualized data model. At this stage, visualization libraries such as Three.js can be used. 【0822】 Step 6: 【0823】 Users access and experience dynamically generated information through the provided user interface. Here, content tailored to the user's emotions and interests (e.g., relaxing music or action videos) is recommended. Input is user feedback and interaction, while output is customized information that enhances user engagement. 【0824】 Step 7: 【0825】 The server generates reports that provide information to users in an easy-to-understand manner, using insight presentation and educational tools. The input consists of analyzed data and the user's desired insights, while the output is detailed and easy-to-understand report content. This allows users to easily absorb new discoveries and knowledge. 【0826】 This series of processes significantly improves the user experience and enables the delivery of personalized information. 【0827】 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. 【0828】 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. 【0829】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0830】 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. 【0831】 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. 【0832】 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. 【0833】 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. 【0834】 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. 【0835】 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." 【0836】 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. 【0837】 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. 【0838】 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. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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 this memory. 【0843】 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. 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0848】 The following is further disclosed regarding the embodiments described above. 【0849】 (Claim 1) 【0850】 Data collection means, 【0851】 Pre-treatment means, 【0852】 Data analysis means, 【0853】 Visualization preparation methods, 【0854】 Information visualization methods, 【0855】 User interface provisioning means, 【0856】 An information processing system that includes knowledge presentation and educational tools. 【0857】 (Claim 2) 【0858】 The information processing system according to claim 1, wherein the data collection means includes means for acquiring data from open data repositories or specialized institutions. 【0859】 (Claim 3) 【0860】 The information processing system according to claim 1, wherein the data analysis means comprises means for extracting patterns from data using a machine learning algorithm. 【0861】 "Example 1" 【0862】 (Claim 1) 【0863】 A device for collecting data, 【0864】 A device that formats data into a consistent format, 【0865】 A device that analyzes data and extracts patterns, 【0866】 A device for preparing data for visualization, 【0867】 A device that visually displays the analysis results, 【0868】 A device that provides information to users in an easy-to-manipulate manner, 【0869】 A system that includes a device that provides insights based on visualized data. 【0870】 (Claim 2) 【0871】 The data collection device comprises means for obtaining information from an information source, according to claim 1. 【0872】 (Claim 3) 【0873】 The system according to claim 1, wherein the data analysis device is equipped with means for identifying useful information from the information using artificial intelligence. 【0874】 "Application Example 1" 【0875】 (Claim 1) 【0876】 Data collection means, 【0877】 Pre-treatment means, 【0878】 Data analysis means, 【0879】 Visualization preparation methods, 【0880】 Information visualization methods, 【0881】 User interface provisioning means, 【0882】 Presentation of knowledge and educational methods, 【0883】 Support tools for inventory management and optimizing product placement, 【0884】 A means for generating a visual plan based on the analysis results, 【0885】 A system that includes this. 【0886】 (Claim 2) 【0887】 The data collection means includes means for acquiring data from open data repositories and specialized institutions, and the system according to claim 1 also automatically acquires data from in-store inventory databases and sales information systems. 【0888】 (Claim 3) 【0889】 The system according to claim 1, wherein the data analysis means includes means for extracting patterns from data using a machine learning algorithm and proposing an optimization of product placement. 【0890】 "Example 2 of combining an emotion engine" 【0891】 (Claim 1) 【0892】 Data acquisition method, 【0893】 Data formatting methods, 【0894】 Data analysis methods, 【0895】 Visualization preparation means, 【0896】 Information visualization means, 【0897】 Means for providing an operation screen, 【0898】 A means for analyzing emotional states using a recognition engine and dynamically adjusting the information displayed, 【0899】 A system that includes knowledge presentation and educational methods. 【0900】 (Claim 2) 【0901】 The system according to claim 1, wherein the data acquisition means includes means for procuring information from public data repositories or specialized organizations. 【0902】 (Claim 3) 【0903】 The system according to claim 1, wherein the data analysis means comprises means for extracting regularities from information using a learning algorithm. 【0904】 "Application example 2 when combining with an emotional engine" 【0905】 (Claim 1) 【0906】 Data collection means, 【0907】 Pre-treatment means, 【0908】 Data analysis means, 【0909】 Visualization preparation methods, 【0910】 Information visualization methods, 【0911】 User interface provisioning means, 【0912】 Means of recognizing emotions, 【0913】 Content recommendation methods, 【0914】 A system that includes knowledge presentation and educational methods. 【0915】 (Claim 2) 【0916】 The system according to claim 1, wherein the data collection means includes means for acquiring data from open data repositories or specialized institutions and detecting the user's emotional state via a personal analysis device. 【0917】 (Claim 3) 【0918】 The system according to claim 1, wherein the data analysis means includes means for extracting patterns from data using a machine learning algorithm and dynamically adjusting the content of information presentation according to the user's emotions. [Explanation of symbols] 【0919】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Data collection methods for acquiring information about space and the deep sea, A preprocessing means for converting collected data into a unified format and filtering out irrelevant information, A data analysis method that uses organized data to execute machine learning algorithms and extract important patterns and insights, A visualization preparation means for preparing a dataset for visualizing analysis results and generating information necessary for visual representation, An information visualization means that displays information in a visual and interactive form based on prepared data, A user interface provider that provides an interface to facilitate users in searching for and viewing specific information, An information processing system that automatically generates reports and explanations based on visualized data, conveying knowledge to users, and including insight presentation and educational tools. [Claim 2] The information processing system according to claim 1, wherein the data collection means includes means for acquiring data from open data repositories or specialized institutions. [Claim 3] The information processing system according to claim 1, wherein the data analysis means comprises means for extracting patterns from data using a machine learning algorithm.

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