system

Abstract

We provide the system. [Solution] A means of retrieving data from data storage, A data analysis method for analyzing acquired data, A difference calculation method that compares the analyzed data and calculates the monthly difference, A data visualization method for visualizing the results of difference calculations, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 There is a problem that manual acquisition, analysis, and reporting of budget information from data storage require a great deal of time and effort. In particular, manually aggregating budget data and performing differential analysis between months is a factor that reduces work efficiency. The present invention aims to eliminate such a decrease in efficiency due to manual work. 【Means for Solving the Problems】 【0005】 The present invention solves the above problems by providing a system that includes means for automatically acquiring data from data storage, means for analyzing the acquired data, means for calculating monthly differences based on the analyzed data, and means for visualizing the results of this difference calculation. This system significantly reduces manual data collection and analysis work, enabling efficient data management and reporting. 【0006】 "Data storage" refers to a physical or virtual area used to store and make digital data accessible. 【0007】 "Data acquisition means" refers to an element that has the function of selectively extracting necessary data from data storage and transferring it to another process or system. 【0008】 "Data analysis methods" refer to algorithms and processes used to interpret acquired data and extract meaningful information. 【0009】 A "difference calculation means" refers to a process or device that has the function of quantitatively evaluating the differences between two or more datasets and calculating changes or differences. 【0010】 "Data visualization means" refers to elements that have the function of generating shapes, graphs, and charts to display data and its analysis results in a format that is easy for humans to understand. [Brief explanation of the drawing] 【0011】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0012】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0013】 First, let's explain the terminology used in the following explanation. 【0014】 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. 【0015】 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. 【0016】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0017】 In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0018】 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." 【0019】 [First Embodiment] 【0020】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0021】 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. 【0022】 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). 【0023】 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. 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0028】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0029】 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. 【0030】 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. 【0031】 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". 【0032】 The present invention provides an integrated management system that automatically retrieves data from data storage, performs analysis and difference calculations, and visualizes the results. To implement this system, a program operating as follows will be developed. 【0033】 First, the server periodically accesses data storage to retrieve the necessary data. For example, online spreadsheet software might be used for data storage, containing budget information for each project. The server then uses an API to extract a specific range of data and proceed to the next processing step. 【0034】 Next, the server analyzes the acquired data. During this process, natural language processing is used to effectively extract numerical and textual information from the spreadsheet. For example, data on monthly budgets and their increases and decreases for each project is aggregated and then organized using data analysis tools. 【0035】 Subsequently, the server uses the analyzed data to calculate the difference from the previous month. This difference calculation method allows for the analysis of budget increases and decreases for each month and comparisons between multiple projects. This process enables quantitative evaluation of data changes and facilitates understanding of the financial situation. 【0036】 The aggregated results are sent to the terminal and displayed in an intuitively understandable format using data visualization tools. The terminal uses visual elements such as bar graphs and pie charts to allow users to easily recognize data trends and anomalies. 【0037】 Ultimately, users make financial reporting and budget management decisions based on this visualized information. The system provides an interactive interface, allowing users to filter and perform detailed analysis. This significantly improves operational efficiency and reduces manual work. 【0038】 This system supports reliable financial management through regular updates and real-time data processing. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 The server periodically connects to the data storage and uses the API to retrieve the latest spreadsheet data. At this time, the server specifies a particular range of the spreadsheet and extracts the necessary budget data. 【0042】 Step 2: 【0043】 The server organizes the acquired data and begins analysis using the data analysis tools of the generating AI. Here, the server identifies budget figures and related date information within the data and stores them as structured data. 【0044】 Step 3: 【0045】 Based on the analyzed data, the server activates a difference calculation mechanism to calculate month-on-month changes. This detects budget fluctuations for each month and calculates increases or decreases for each project. Projects with large differences undergo particularly detailed analysis. 【0046】 Step 4: 【0047】 The terminal receives differential calculation results from the server and generates graphs and charts based on them. The terminal uses visualization tools to visually represent the budget status for each month and each project in a way that is easy for the user to understand. 【0048】 Step 5: 【0049】 Users can view the dashboard provided on their device and filter data for specific periods or projects as needed. Through the dashboard's interactive features, users can perform detailed budget analyses and extract data for use in reports. 【0050】 (Example 1) 【0051】 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." 【0052】 In businesses and organizations, project-based budget management and financial reporting are essential, but the manual processes of data acquisition, analysis, difference calculation, and visualization require considerable effort and time. Furthermore, since decision-making requires visually understandable data presentation, a system that efficiently processes data and allows for interactive use is necessary. 【0053】 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. 【0054】 In this invention, the server includes means for acquiring information from a data storage device, means for analyzing the acquired information, and means for comparing the analyzed information and calculating periodic differences. This makes it possible to efficiently acquire and process information and present it in a format that can be intuitively understood through visualization. 【0055】 A "data storage device" refers to a device or platform that electronically stores information and provides it in a form that can be reused later. 【0056】 "Means of acquiring information" refers to the methods and processes for selecting necessary information from a data storage device and converting it into a format that can be used within the system. 【0057】 "Information analysis methods" refer to methods and tools for organizing acquired information according to a specific purpose and obtaining valuable insights. 【0058】 A "difference calculation method" refers to a method or process for comparing information at different points in time and quantitatively evaluating its changes or increases / decreases. 【0059】 "Information visualization means" refers to methods and techniques for visually representing analyzed and calculated data so that it can be intuitively understood. 【0060】 An "interactive interface" refers to a user interface designed to allow users to dynamically interact with the system, filter information, and perform additional analysis. 【0061】 To implement this invention, the server first accesses an online spreadsheet program, which serves as a data storage device, to retrieve information. In this process, an API is used to automatically extract the necessary range of data from the spreadsheet. A widely used online spreadsheet platform could be used as the specific software. This allows for the real-time acquisition of project-specific financial data and budget information. 【0062】 Next, the server employs information analysis tools to analyze the acquired information. Natural language processing techniques are used to systematically organize and extract numerical and text data from spreadsheets. For specific analysis, libraries such as Python's NLTK or spaCy might be used. This analysis allows for an understanding of monthly income and expenditure trends for each project. 【0063】 The server then compares the analyzed data and calculates the difference over a specific period. To calculate this difference, it uses the Pandas data processing library to determine the difference from historical data. This allows for a quantitative evaluation of budget fluctuations for each project and enables the rapid identification of problems. 【0064】 The terminal then visualizes the results of the difference calculation obtained from the server. Specifically, data visualization libraries such as Matplotlib and Plotly are used to display the information as bar graphs and pie charts. This visualization allows users to understand the increase or decrease and trends in the data at a glance. 【0065】 Ultimately, users make decisions based on visualized information, optimizing project budget management and reporting. Through an interactive interface, users can obtain further details and filter information based on criteria, enabling effective decision-making. 【0066】 As a concrete example, consider a situation where a company manages the monthly income and expenses of various projects. This system visualizes the fluctuations in income and expenses for each project, making it easy to identify budget overruns and underruns, thereby enabling the company to quickly take appropriate financial action. 【0067】 A concrete example of a prompt is: "Develop a program that uses data stored in online spreadsheet software to analyze monthly budget increases and decreases and visualize them in a graph." This prompt serves as a foundation for designing an appropriate program using a generative AI model. 【0068】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0069】 Step 1: 【0070】 The server accesses an online spreadsheet program as a data storage device to retrieve information. The input consists of API authentication credentials and a specified data range. The server uses this information to extract the data within the specified range in bulk and format it into an analyzable format. The output is the extracted spreadsheet data, which is then passed on to the next processing step. 【0071】 Step 2: 【0072】 The server analyzes the acquired data. Formatted spreadsheet data is provided as input. The server utilizes natural language processing tools (e.g., Python's NLTK) to identify numerical data and related text data. Specifically, it analyzes monthly income and expenses for each project, and generates aggregated income and expense data for each project as output. This data is used for further analysis and diff calculation. 【0073】 Step 3: 【0074】 The server uses the analyzed data to calculate the difference. The input is the project-specific revenue and expenditure summary data obtained in the previous step. The server uses a data processing library (e.g., Pandas) to create a data frame and calculate the difference in data for each month. In this process, the data fluctuations are quantified, and the output is the monthly difference results. 【0075】 Step 4: 【0076】 The terminal visualizes the differential calculation results. Differential data from the server is supplied as input. The terminal uses a data visualization tool (e.g., Matplotlib) to display the information as bar graphs or pie charts. This allows the user to easily grasp the data trends. The output is a graphical visual representation that the user can directly review. 【0077】 Step 5: 【0078】 Users make decisions based on visualized information. Input is data provided as graphs and diagrams on the device. Through an interactive interface, users filter and perform additional analysis to formulate optimal solutions and action plans. Output includes actions based on user decisions and newly established business strategies. This input-output cycle promotes the effective use of the system. 【0079】 (Application Example 1) 【0080】 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." 【0081】 In data center management and operation, it is challenging to grasp budget and resource usage in real time and to respond immediately when anomalies occur. Traditional methods involve manually collecting and analyzing information, and the time it takes to visually grasp the situation reduces management efficiency. 【0082】 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. 【0083】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, means for calculating a difference by comparing the analyzed information and calculating a periodic difference, means for displaying the results of the difference calculation, and means for notifying the administrator based on the results using a portable device. This enables data center administrators to check information in real time using a portable device and respond to anomalies immediately. 【0084】 An "information storage device" is a device that has the function of accumulating data and storing it in a state that allows it to be retrieved later. 【0085】 "Information analysis means" refers to the technology or process of classifying and organizing acquired information, and extracting and analyzing the necessary data. 【0086】 A "difference calculation method" is a calculation method that compares data at different points in time and determines the amount of change. 【0087】 "Information display means" refers to a device or technology that visually provides analyzed and calculated data. 【0088】 A "portable device" is an electronic device that is easily portable and capable of transmitting, receiving, and displaying information. 【0089】 "Means of notifying administrators" refers to processes or technologies for communicating anomalies or significant changes in information to human users. 【0090】 The system implementing this invention consists mainly of a server and a portable device. The server automatically acquires data from an information storage device and analyzes it using an information analysis means. Specifically, it uses the Python pandas library to organize and classify the data and extract the necessary information. The analyzed data is then analyzed using a difference calculation means to calculate data fluctuations between different timestamps. This calculation clearly shows changes in the data center's budget and resource usage. 【0091】 The server sends the obtained results to an information display device, which then visually represents the data using D3.js or Chart.js. This display is also possible on portable devices, which use applications built with the React Native framework. This application allows administrators to monitor the data center's status in real time. 【0092】 The administrator, as a user, receives information via a portable device and is immediately notified if an anomaly occurs. This enables rapid problem resolution. For example, by detecting a sudden surge in power costs at a data center, the administrator can immediately analyze the cause and take appropriate measures. An example of a prompt message is: "I want to design an app that analyzes the data center's budget usage on a monthly basis and notifies of anomalies. What method of data visualization would be most useful for the administrator?" 【0093】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0094】 Step 1: 【0095】 The server periodically accesses the data storage device to retrieve necessary data. Input data typically comes from an online spreadsheet application, usually accessed via an API. The output is raw data, which includes project-specific budget information. 【0096】 Step 2: 【0097】 The server processes the acquired data using information analysis tools. It receives the raw data acquired in step 1 as input, organizes the data using the Python pandas library, and extracts specific information. The output is a dataset organized by category. Specifically, the data is sorted chronologically and aggregated as monthly budget data. 【0098】 Step 3: 【0099】 The server performs difference calculations using the organized data. The input includes the organized data generated in step 2. To calculate the changes in the data, it is compared with the previous month's data. This calculation determines the increase or decrease in each item, and the results are output. The comparison operation involves a process that references the same dataset from the previous month. 【0100】 Step 4: 【0101】 The server uses information display tools to visualize the results of difference calculations. Input data is graphically transformed using D3.js or Chart.js and output as bar graphs or pie charts. This visual information helps to intuitively understand data fluctuations. 【0102】 Step 5: 【0103】 The visualized data is sent to the terminal, allowing the user to view it in real time on a portable device. The input is the visual data from step 4, and the output is displayed on the screen of the portable app. The specific actions involve data communication and the display process within the application. A user-friendly UI is used on the screen to enable the user to quickly detect data anomalies. 【0104】 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. 【0105】 This invention is a system that integrates automatic acquisition, analysis, and visualization of budget data with an emotion engine that recognizes user emotions. This system is designed to enable users to manage data effectively and comfortably. The following are details of embodiments for carrying out the invention. 【0106】 First, the server periodically retrieves budget data from online spreadsheet software. The server accesses data storage using an API and automatically extracts the necessary data. This data includes monthly budgets and related text information for each project. 【0107】 Next, the server utilizes data analysis tools to analyze the acquired data using natural language processing. During the analysis process, data consistency checks and budget discrepancies for each project are identified. This process allows users to gain detailed insights that can aid in data-driven decision-making. 【0108】 Subsequently, the server evaluates data fluctuations using a differential calculation method based on the analyzed data. It calculates monthly budget increases and decreases and identifies points that are important to the user. This information is then supplied to the subsequent data visualization process. 【0109】 Once the data is ready, the device uses visualization algorithms to support intuitive data display in graph and dashboard formats. At this point, the emotion engine kicks in, recognizing emotions from the user's responses and actions. For example, if the device determines the user is stressed, it reduces user burden by presenting calmer colors and a simpler layout. 【0110】 Users can provide feedback and make additional requests regarding the data through the interface provided by the device. The emotion engine uses this interaction to perform more accurate emotion recognition and utilizes this information to adjust the interface in the future. In this way, the system adapts individually to the user, providing a more personalized experience. 【0111】 This invention aims to achieve both efficient data management and a comfortable user experience, and is expected to play an important role in future financial and project management. 【0112】 The following describes the processing flow. 【0113】 Step 1: 【0114】 The server accesses the API of an online spreadsheet software at scheduled intervals to retrieve the necessary budget data. The server reads the specified sheet and range and saves the data to the server's internal database. 【0115】 Step 2: 【0116】 The server activates data analysis tools and analyzes the acquired data. This analysis includes natural language processing, extracting budget figures and related text comments for each project and converting them into structured data. The server also performs anomaly detection and generates alerts if inconsistencies are found. 【0117】 Step 3: 【0118】 The server uses a differential calculation method to calculate the month-on-month change in the analyzed data. It aggregates the budget increases and decreases for each project and identifies areas with significant fluctuations. The server prepares these results for the next processing step. 【0119】 Step 4: 【0120】 The terminal receives differential calculation results sent from the server and generates a dashboard using data visualization tools. The terminal activates an emotion engine, estimates emotions from user input and actions, and dynamically adjusts the user interface. 【0121】 Step 5: 【0122】 Users review the dashboard displayed on their device and utilize the interactive feedback features provided by the sentiment engine. Based on the visualized data, users analyze specific projects or time periods in detail and filter the data as needed. 【0123】 Step 6: 【0124】 The emotion engine continuously analyzes user actions and feedback to accurately recognize the user's current emotional state. Based on this, it adjusts the device's display methods and data presentation formats to optimize the user experience. 【0125】 (Example 2) 【0126】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0127】 In data management, the ability to efficiently acquire, analyze, and visualize vast amounts of information is essential. Furthermore, providing a more comfortable and effective data usage environment by individually adjusting the interface to consider the user's emotions is a significant challenge. In addition, flexible information presentation tailored to the user's intentions and state is required. 【0128】 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. 【0129】 In this invention, the server includes means for acquiring information from a data set, means for analyzing the acquired information, means for comparing the analyzed information and calculating periodic differences, and means for visualization and emotion recognition. This enables efficient information management and the provision of a personalized interface that responds to the user's emotions. 【0130】 A "data set" refers to a structure or device that aggregates information, and includes online computing software and databases. 【0131】 "Information" refers to numerical or textual data obtained from a data set. 【0132】 "Means of acquisition" refers to mechanisms and methods for collecting information from a data set, including the use of APIs. 【0133】 "Information analysis means" refers to technologies and methods for analyzing acquired information and extracting useful insights, and includes natural language processing technology. 【0134】 A "difference calculation method" refers to a technique or mechanism that calculates fluctuations over a specific period by comparing the results of information analysis. 【0135】 "Information visualization means" refers to methods and technologies for visually representing and presenting analyzed information to the user, and includes the generation of graphs and dashboards. 【0136】 "Emotion recognition means" refers to methods and technologies for detecting a user's emotions and individually adjusting the interface accordingly. 【0137】 This system is an advanced data management solution that efficiently retrieves, analyzes, and visualizes information from data sets, and adapts based on the user's emotions. 【0138】 The server first retrieves data from online computing software, such as a spreadsheet application, via an API. This API typically collects information periodically at specific points in time and receives it in JSON format. The server then analyzes the retrieved data using natural language processing (NLP). NLP libraries such as "NLTK" and "spaCy" can be used. This allows for consistency checks of text information and identification of budget discrepancies in numerical data. The analysis process is automated, enabling rapid identification of outliers and trends. 【0139】 Subsequently, the server uses variance calculation methods to analyze periodic budget fluctuations. This makes it possible to understand the budget situation for each project more accurately. At this stage, statistical methods are also used to calculate month-on-month changes and cumulative variances. 【0140】 The terminal receives analysis results sent from the server and presents the information to the user using a visualization algorithm. For visualization, JavaScript libraries such as "D3.js" and "Chart.js" are used to generate graphs and dashboards. This allows the user to intuitively understand the information. 【0141】 Furthermore, the device uses a built-in emotion engine to recognize emotions from the user's facial expressions and actions. This is done by analyzing camera and user behavior data. For example, if the user is feeling stressed, the device will change the screen's color tone to alleviate the user's burden. 【0142】 As a concrete example, when a user wants to check past budget data, they can enter a prompt into the system saying, "Retrieve last month's budget data for Project A and analyze the budget variance." This prompt will prompt the system to retrieve and analyze the data, generate a visualized report as a result, and provide it to the user. This enables the user to make data-driven decisions quickly. 【0143】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0144】 Step 1: 【0145】 The server periodically retrieves information from the data set using the API of online computing software. At this stage, the server sends an HTTP request to the API endpoint and receives the spreadsheet-formatted data in JSON format. The input is the URL or identifier of the spreadsheet, and the output is the data in JSON format. 【0146】 Step 2: 【0147】 The server analyzes the acquired JSON data using natural language processing (NLP) techniques. Specifically, it tokenizes the data using an NLP library and extracts important keywords and numerical values. The input is JSON data, and the output generates data that has been verified for consistency, as well as information identifying budget discrepancies for each project. 【0148】 Step 3: 【0149】 Based on the information analyzed in the previous step, the server uses a variance calculation method to calculate monthly budget fluctuations. Statistical methods are then used to calculate month-on-month changes and list important fluctuation points. The input is the analyzed numerical data, and the output is comparative data indicating budget increases / decreases and anomalies. 【0150】 Step 4: 【0151】 The terminal receives analysis results sent from the server and displays the information in graph format using a visualization algorithm. This process generates bar graphs and pie charts using D3.js or Chart.js. The input is comparative data based on the analysis results, and the output is a visualized graphical dashboard. 【0152】 Step 5: 【0153】 The device uses an emotion engine to recognize the user's emotions when displaying visualized data. Specifically, it analyzes camera footage and mouse click patterns to determine whether the user is experiencing stress or fatigue. The input is user behavior data, and the output is the user's emotional state. 【0154】 Step 6: 【0155】 Users can send feedback on data and request additional analysis through an interface provided via their device. Based on this, the sentiment engine adjusts the interface and collects data to improve the next user experience. The input is user feedback and requests, and the output is data for future interface adjustments. 【0156】 (Application Example 2) 【0157】 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". 【0158】 Traditional data management systems focus on information acquisition, analysis, and visualization, but they fail to adequately improve usability based on user emotions. In particular, there is a lack of technology to adjust visual information presentation methods when users experience stress or burden. There is a need for a system that can adapt to the emotions of operators, reduce their burden, and manage data efficiently. 【0159】 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. 【0160】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, and means for recognizing the emotions of the person manipulating the information and adjusting the information display. This enables automatic adjustment of the method of presenting visual information according to the emotional state of the operator, reducing the burden and facilitating efficient data management. 【0161】 A "data storage device" is a system or platform that has the function of securely storing information and allowing that information to be retrieved as needed. 【0162】 "Information analysis means" refers to a method or apparatus for processing acquired data and deriving data-based insights. 【0163】 A "change calculation means" is a method or apparatus that includes a function to measure changes over a specific period from compared data and calculate the increase or decrease. 【0164】 "Information visualization means" refers to a method or apparatus for visually representing data and converting it into an easily understandable form using graphs and charts. 【0165】 "Emotion recognition means" refers to a method or device that determines a user's emotions from their facial expressions, voice, etc., and uses that information to adaptively adjust the behavior of equipment or engines. 【0166】 The system realizing this invention is designed to effectively manage data and present information while considering user sentiment. The server periodically retrieves relevant information from an information storage device using an API. This information includes periodic reporting data on budgets and projects, and data retrieval is performed via an online spreadsheet program. The retrieved information is processed by an information analysis tool. This process uses a Python®-based natural language processing library (e.g., NLTK) to identify project-specific variations while verifying data integrity. 【0167】 Next, the server uses emotion recognition means, such as the Face++ API, to analyze the user's emotional data from the camera and microphone. The results of this analysis are then adjusted by data visualization means. For example, if it is determined that the operator is experiencing stress, the displayed colors and layout are changed to reduce the visual burden. 【0168】 As a concrete example, if a data center operator wearing smart glasses experiences stress while checking the budget status, the display screen will change, providing data in a visually easy-to-understand format. This system adapts to the individual user's state, providing a personalized experience. 【0169】 The generating AI model will consider prompts such as, "Please tell me how to check fluctuations in the data center's monthly budget, analyze the operators' sentiment, and take appropriate measures." 【0170】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0171】 Step 1: 【0172】 The server periodically retrieves budget data from the data storage device using an API. In this step, relevant budget information is input from the data storage, and the retrieved data is output. Specifically, the server accesses an online spreadsheet program to download the latest budget information. 【0173】 Step 2: 【0174】 The server analyzes the acquired data using a Python-based natural language processing library. The input is acquired budget data, and the output is the analysis results, with data integrity verified. In detail, this step involves the server calculating budget variances for each project and evaluating for inconsistencies. 【0175】 Step 3: 【0176】 The server uses the Face++ API, etc., to recognize the user's emotions. For this recognition, it uses the camera and microphone of smart glasses or a smartphone. The input is the user's real-time video and audio data, and the output is the result of an analysis of the user's emotional state. In this step, the server identifies the emotions the user is expressing (e.g., stress). 【0177】 Step 4: 【0178】 The server adjusts the data display using information visualization tools based on the sentiment analysis results obtained in the previous step. The input is the sentiment analysis results and analysis data, and the output is a user-optimized visualization. Specifically, it changes the colors and data arrangement according to the operator's emotions to present the information in an easy-to-understand manner. 【0179】 Step 5: 【0180】 The user reviews the data visualization results and provides various feedback through an interface provided by the server. This step may involve further data refinement or requests based on user actions. Based on the output, the user decides on their next action. 【0181】 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. 【0182】 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. 【0183】 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. 【0184】 [Second Embodiment] 【0185】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0186】 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. 【0187】 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). 【0188】 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. 【0189】 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. 【0190】 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). 【0191】 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. 【0192】 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. 【0193】 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. 【0194】 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. 【0195】 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. 【0196】 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". 【0197】 The present invention provides an integrated management system that automatically retrieves data from data storage, performs analysis and difference calculations, and visualizes the results. To implement this system, a program operating as follows will be developed. 【0198】 First, the server periodically accesses data storage to retrieve the necessary data. For example, online spreadsheet software might be used for data storage, containing budget information for each project. The server then uses an API to extract a specific range of data and proceed to the next processing step. 【0199】 Next, the server analyzes the acquired data. During this process, natural language processing is used to effectively extract numerical and textual information from the spreadsheet. For example, data on monthly budgets and their increases and decreases for each project is aggregated and then organized using data analysis tools. 【0200】 Subsequently, the server uses the analyzed data to calculate the difference from the previous month. This difference calculation method allows for the analysis of budget increases and decreases for each month and comparisons between multiple projects. This process enables quantitative evaluation of data changes and facilitates understanding of the financial situation. 【0201】 The aggregated results are sent to the terminal and displayed in an intuitively understandable format using data visualization tools. The terminal uses visual elements such as bar graphs and pie charts to allow users to easily recognize data trends and anomalies. 【0202】 Ultimately, users make financial reporting and budget management decisions based on this visualized information. The system provides an interactive interface, allowing users to filter and perform detailed analysis. This significantly improves operational efficiency and reduces manual work. 【0203】 This system supports reliable financial management through regular updates and real-time data processing. 【0204】 The following describes the processing flow. 【0205】 Step 1: 【0206】 The server periodically connects to the data storage and uses the API to retrieve the latest spreadsheet data. At this time, the server specifies a particular range of the spreadsheet and extracts the necessary budget data. 【0207】 Step 2: 【0208】 The server organizes the acquired data and begins analysis using the data analysis tools of the generating AI. Here, the server identifies budget figures and related date information within the data and stores them as structured data. 【0209】 Step 3: 【0210】 Based on the analyzed data, the server activates a difference calculation mechanism to calculate month-on-month changes. This detects budget fluctuations for each month and calculates increases or decreases for each project. Projects with large differences undergo particularly detailed analysis. 【0211】 Step 4: 【0212】 The terminal receives differential calculation results from the server and generates graphs and charts based on them. The terminal uses visualization tools to visually represent the budget status for each month and each project in a way that is easy for the user to understand. 【0213】 Step 5: 【0214】 Users can view the dashboard provided on their device and filter data for specific periods or projects as needed. Through the dashboard's interactive features, users can perform detailed budget analyses and extract data for use in reports. 【0215】 (Example 1) 【0216】 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." 【0217】 In businesses and organizations, project-based budget management and financial reporting are essential, but the manual processes of data acquisition, analysis, difference calculation, and visualization require considerable effort and time. Furthermore, since decision-making requires visually understandable data presentation, a system that efficiently processes data and allows for interactive use is necessary. 【0218】 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. 【0219】 In this invention, the server includes means for acquiring information from a data storage device, means for analyzing the acquired information, and means for comparing the analyzed information and calculating periodic differences. This makes it possible to efficiently acquire and process information and present it in a format that can be intuitively understood through visualization. 【0220】 A "data storage device" refers to a device or platform that electronically stores information and provides it in a form that can be reused later. 【0221】 "Means of acquiring information" refers to the methods and processes for selecting necessary information from a data storage device and converting it into a format that can be used within the system. 【0222】 "Information analysis methods" refer to methods and tools for organizing acquired information according to a specific purpose and obtaining valuable insights. 【0223】 A "difference calculation method" refers to a method or process for comparing information at different points in time and quantitatively evaluating its changes or increases / decreases. 【0224】 "Information visualization means" refers to methods and techniques for visually representing analyzed and calculated data so that it can be intuitively understood. 【0225】 An "interactive interface" refers to a user interface designed to allow users to dynamically interact with the system, filter information, and perform additional analysis. 【0226】 To implement this invention, the server first accesses an online spreadsheet program, which serves as a data storage device, to retrieve information. In this process, an API is used to automatically extract the necessary range of data from the spreadsheet. A widely used online spreadsheet platform could be used as the specific software. This allows for the real-time acquisition of project-specific financial data and budget information. 【0227】 Next, the server employs information analysis tools to analyze the acquired information. Natural language processing techniques are used to systematically organize and extract numerical and text data from spreadsheets. For specific analysis, libraries such as Python's NLTK or spaCy might be used. This analysis allows for an understanding of monthly income and expenditure trends for each project. 【0228】 The server then compares the analyzed data and calculates the difference over a specific period. To calculate this difference, it uses the Pandas data processing library to determine the difference from historical data. This allows for a quantitative evaluation of budget fluctuations for each project and enables the rapid identification of problems. 【0229】 The terminal then visualizes the results of the difference calculation obtained from the server. Specifically, data visualization libraries such as Matplotlib and Plotly are used to display the information as bar graphs and pie charts. This visualization allows users to understand the increase or decrease and trends in the data at a glance. 【0230】 Ultimately, users make decisions based on visualized information, optimizing project budget management and reporting. Through an interactive interface, users can obtain further details and filter information based on criteria, enabling effective decision-making. 【0231】 As a concrete example, consider a situation where a company manages the monthly income and expenses of various projects. This system visualizes the fluctuations in income and expenses for each project, making it easy to identify budget overruns and underruns, thereby enabling the company to quickly take appropriate financial action. 【0232】 A concrete example of a prompt is: "Develop a program that uses data stored in online spreadsheet software to analyze monthly budget increases and decreases and visualize them in a graph." This prompt serves as a foundation for designing an appropriate program using a generative AI model. 【0233】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0234】 Step 1: 【0235】 The server accesses an online spreadsheet program as a data storage device to retrieve information. The input consists of API authentication credentials and a specified data range. The server uses this information to extract the data within the specified range in bulk and format it into an analyzable format. The output is the extracted spreadsheet data, which is then passed on to the next processing step. 【0236】 Step 2: 【0237】 The server analyzes the acquired data. Formatted spreadsheet data is provided as input. The server utilizes natural language processing tools (e.g., Python's NLTK) to identify numerical data and related text data. Specifically, it analyzes monthly income and expenses for each project, and generates aggregated income and expense data for each project as output. This data is used for further analysis and diff calculation. 【0238】 Step 3: 【0239】 The server uses the analyzed data to calculate the difference. The input is the project-specific revenue and expenditure summary data obtained in the previous step. The server uses a data processing library (e.g., Pandas) to create a data frame and calculate the difference in data for each month. In this process, the data fluctuations are quantified, and the output is the monthly difference results. 【0240】 Step 4: 【0241】 The terminal visualizes the differential calculation results. Differential data from the server is supplied as input. The terminal uses a data visualization tool (e.g., Matplotlib) to display the information as bar graphs or pie charts. This allows the user to easily grasp the data trends. The output is a graphical visual representation that the user can directly review. 【0242】 Step 5: 【0243】 Users make decisions based on visualized information. Input is data provided as graphs and diagrams on the device. Through an interactive interface, users filter and perform additional analysis to formulate optimal solutions and action plans. Output includes actions based on user decisions and newly established business strategies. This input-output cycle promotes the effective use of the system. 【0244】 (Application Example 1) 【0245】 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." 【0246】 In data center management and operation, it is challenging to grasp budget and resource usage in real time and to respond immediately when anomalies occur. Traditional methods involve manually collecting and analyzing information, and the time it takes to visually grasp the situation reduces management efficiency. 【0247】 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. 【0248】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, means for calculating a difference by comparing the analyzed information and calculating a periodic difference, means for displaying the results of the difference calculation, and means for notifying the administrator based on the results using a portable device. This enables data center administrators to check information in real time using a portable device and respond to anomalies immediately. 【0249】 An "information storage device" is a device that has the function of accumulating data and storing it in a state that allows it to be retrieved later. 【0250】 "Information analysis means" refers to the technology or process of classifying and organizing acquired information, and extracting and analyzing the necessary data. 【0251】 A "difference calculation method" is a calculation method that compares data at different points in time and determines the amount of change. 【0252】 "Information display means" refers to a device or technology that visually provides analyzed and calculated data. 【0253】 A "portable device" is an electronic device that is easily portable and capable of transmitting, receiving, and displaying information. 【0254】 "Means of notifying administrators" refers to processes or technologies for communicating anomalies or significant changes in information to human users. 【0255】 The system implementing this invention consists mainly of a server and a portable device. The server automatically acquires data from an information storage device and analyzes it using an information analysis means. Specifically, it uses the Python pandas library to organize and classify the data and extract the necessary information. The analyzed data is then analyzed using a difference calculation means to calculate data fluctuations between different timestamps. This calculation clearly shows changes in the data center's budget and resource usage. 【0256】 The server sends the obtained results to an information display device, which then visually represents the data using D3.js or Chart.js. This display is also possible on portable devices, which use applications built with the React Native framework. This application allows administrators to monitor the data center's status in real time. 【0257】 The administrator, as a user, receives information via a portable device and is immediately notified if an anomaly occurs. This enables rapid problem resolution. For example, by detecting a sudden surge in power costs at a data center, the administrator can immediately analyze the cause and take appropriate measures. An example of a prompt message is: "I want to design an app that analyzes the data center's budget usage on a monthly basis and notifies of anomalies. What method of data visualization would be most useful for the administrator?" 【0258】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0259】 Step 1: 【0260】 The server periodically accesses the data storage device to retrieve necessary data. Input data typically comes from an online spreadsheet application, usually accessed via an API. The output is raw data, which includes project-specific budget information. 【0261】 Step 2: 【0262】 The server processes the acquired data using information analysis tools. It receives the raw data acquired in step 1 as input, organizes the data using the Python pandas library, and extracts specific information. The output is a dataset organized by category. Specifically, the data is sorted chronologically and aggregated as monthly budget data. 【0263】 Step 3: 【0264】 The server performs difference calculations using the organized data. The input includes the organized data generated in step 2. To calculate the changes in the data, it is compared with the previous month's data. This calculation determines the increase or decrease in each item, and the results are output. The comparison operation involves a process that references the same dataset from the previous month. 【0265】 Step 4: 【0266】 The server uses information display tools to visualize the results of difference calculations. Input data is graphically transformed using D3.js or Chart.js and output as bar graphs or pie charts. This visual information helps to intuitively understand data fluctuations. 【0267】 Step 5: 【0268】 The visualized data is sent to the terminal, allowing the user to view it in real time on a portable device. The input is the visual data from step 4, and the output is displayed on the screen of the portable app. The specific actions involve data communication and the display process within the application. A user-friendly UI is used on the screen to enable the user to quickly detect data anomalies. 【0269】 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. 【0270】 This invention is a system that integrates automatic acquisition, analysis, and visualization of budget data with an emotion engine that recognizes user emotions. This system is designed to enable users to manage data effectively and comfortably. The following are details of embodiments for carrying out the invention. 【0271】 First, the server periodically retrieves budget data from online spreadsheet software. The server accesses data storage using an API and automatically extracts the necessary data. This data includes monthly budgets and related text information for each project. 【0272】 Next, the server utilizes data analysis tools to analyze the acquired data using natural language processing. During the analysis process, data consistency checks and budget discrepancies for each project are identified. This process allows users to gain detailed insights that can aid in data-driven decision-making. 【0273】 Subsequently, the server evaluates data fluctuations using a differential calculation method based on the analyzed data. It calculates monthly budget increases and decreases and identifies points that are important to the user. This information is then supplied to the subsequent data visualization process. 【0274】 Once the data is ready, the device uses visualization algorithms to support intuitive data display in graph and dashboard formats. At this point, the emotion engine kicks in, recognizing emotions from the user's responses and actions. For example, if the device determines the user is stressed, it reduces user burden by presenting calmer colors and a simpler layout. 【0275】 The user can provide feedback on the data and additional requests through the interface provided by the terminal. The emotion engine performs more accurate emotion recognition through this interaction and utilizes it for future interface adjustments. In this way, the system adapts to the user individually and provides a more personalized experience. 【0276】 The present invention aims to achieve both the efficiency of data management and a comfortable operation experience for the user, and is expected to play an important role in future financial and project management. 【0277】 The following describes the process flow. 【0278】 Step 1: 【0279】 The server accesses the API of the online spreadsheet software at scheduled intervals to obtain the necessary budget data. The server reads the specified sheet and range and saves the data in the internal database of the server. 【0280】 Step 2: 【0281】 The server activates the data analysis means and analyzes the acquired data. This analysis includes natural language processing, extracts the budget numerical values and related text comments for each project, and converts them into structured data. The server also performs outlier detection and generates an alert if there is an inconsistency. 【0282】 Step 3: 【0283】 The server uses the difference calculation means to calculate the month-on-month comparison of the analyzed data. Aggregate the budget increases and decreases for each project and identify the parts with significant fluctuations. The server prepares these results for the next processing step. 【0284】 [[ID=·36]]Step 4: 【0285】 The terminal receives the differential calculation results sent from the server and generates a dashboard using data visualization means. The terminal activates the emotion engine, estimates the emotion from the user's input and operations, and dynamically adjusts the user interface. 【0286】 Step 5: 【0287】 The user checks the dashboard displayed on the terminal and uses the interactive feedback function provided by the emotion engine. Based on the visualized data, the user analyzes a specific project or period in detail and filters the data as needed. 【0288】 Step 6: 【0289】 The emotion engine continuously analyzes the user's operations and feedback to accurately recognize the user's current emotional state. Based on this, the terminal adjusts the display method and data presentation format to optimize the user experience. 【0290】 (Example 2) 【0291】 Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0292】 In data management, there is a need to efficiently acquire, analyze, and visualize a large amount of information. In addition, it is a challenge to provide a more comfortable and effective data usage environment by individually adjusting the interface considering the user's emotion. In addition, flexible information presentation according to the user's intention and state is required. 【0293】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0294】 In this invention, the server includes means for acquiring information from a data set, means for analyzing the acquired information, means for comparing the analyzed information and calculating periodic differences, and means for visualization and emotion recognition. This enables efficient information management and the provision of a personalized interface that responds to the user's emotions. 【0295】 A "data set" refers to a structure or device that aggregates information, and includes online computing software and databases. 【0296】 "Information" refers to numerical or textual data obtained from a data set. 【0297】 "Means of acquisition" refers to mechanisms and methods for collecting information from a data set, including the use of APIs. 【0298】 "Information analysis means" refers to technologies and methods for analyzing acquired information and extracting useful insights, and includes natural language processing technology. 【0299】 A "difference calculation method" refers to a technique or mechanism that calculates fluctuations over a specific period by comparing the results of information analysis. 【0300】 "Information visualization means" refers to methods and technologies for visually representing and presenting analyzed information to the user, and includes the generation of graphs and dashboards. 【0301】 "Emotion recognition means" refers to methods and technologies for detecting a user's emotions and individually adjusting the interface accordingly. 【0302】 This system is an advanced data management solution that efficiently retrieves, analyzes, and visualizes information from data sets, and adapts based on the user's emotions. 【0303】 The server first obtains data from online calculation software, such as a spreadsheet application, through an API. This API generally collects information periodically at specific times and receives it in JSON format. The server analyzes the obtained data using natural language processing (NLP). Libraries such as "NLTK" and "spaCy" can be used for NLP technology. This enables checking the consistency of text information and identifying budget differences in numerical data. The analysis process is automated, and outliers and trends can be quickly identified. 【0304】 After that, the server uses difference calculation means to analyze the periodic budget fluctuations. This makes it possible to more accurately grasp the budget situation for each project. At this stage, statistical methods are used to calculate month-on-month ratios and cumulative differences, etc. 【0305】 The terminal receives the analysis results sent from the server and presents the information to the user using a visualization algorithm. For visualization, JavaScript libraries such as "D3.js" and "Chart.js" are used to generate graphs and dashboards. This enables the user to intuitively understand the information. 【0306】 Furthermore, the terminal uses a built-in emotion engine to recognize emotions from the user's expressions and actions. This is done by analyzing camera and user behavior data. For example, when the user is feeling stressed, the terminal reduces the user's burden by changing the screen color. 【0307】 As a specific example, when the user wants to check past budget data, the user inputs "Please obtain the budget data for last month of Project A and analyze the budget difference" as a prompt sentence into the system. This prompt encourages the system to obtain and analyze the data, and as a result, generates a visualized report and provides it to the user. This enables the user to quickly make data-driven decisions. 【0308】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0309】 Step 1: 【0310】 The server periodically retrieves information from the data set using the API of online computing software. At this stage, the server sends an HTTP request to the API endpoint and receives the spreadsheet-formatted data in JSON format. The input is the URL or identifier of the spreadsheet, and the output is the data in JSON format. 【0311】 Step 2: 【0312】 The server analyzes the acquired JSON data using natural language processing (NLP) techniques. Specifically, it tokenizes the data using an NLP library and extracts important keywords and numerical values. The input is JSON data, and the output generates data that has been verified for consistency, as well as information identifying budget discrepancies for each project. 【0313】 Step 3: 【0314】 Based on the information analyzed in the previous step, the server uses a variance calculation method to calculate monthly budget fluctuations. Statistical methods are then used to calculate month-on-month changes and list important fluctuation points. The input is the analyzed numerical data, and the output is comparative data indicating budget increases / decreases and anomalies. 【0315】 Step 4: 【0316】 The terminal receives analysis results sent from the server and displays the information in graph format using a visualization algorithm. This process generates bar graphs and pie charts using D3.js or Chart.js. The input is comparative data based on the analysis results, and the output is a visualized graphical dashboard. 【0317】 Step 5: 【0318】 The device uses an emotion engine to recognize the user's emotions when displaying visualized data. Specifically, it analyzes camera footage and mouse click patterns to determine whether the user is experiencing stress or fatigue. The input is user behavior data, and the output is the user's emotional state. 【0319】 Step 6: 【0320】 Users can send feedback on data and request additional analysis through an interface provided via their device. Based on this, the sentiment engine adjusts the interface and collects data to improve the next user experience. The input is user feedback and requests, and the output is data for future interface adjustments. 【0321】 (Application Example 2) 【0322】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0323】 Traditional data management systems focus on information acquisition, analysis, and visualization, but they fail to adequately improve usability based on user emotions. In particular, there is a lack of technology to adjust visual information presentation methods when users experience stress or burden. There is a need for a system that can adapt to the emotions of operators, reduce their burden, and manage data efficiently. 【0324】 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. 【0325】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, and means for recognizing the emotions of the person manipulating the information and adjusting the information display. This enables automatic adjustment of the method of presenting visual information according to the emotional state of the operator, reducing the burden and facilitating efficient data management. 【0326】 A "data storage device" is a system or platform that has the function of securely storing information and allowing that information to be retrieved as needed. 【0327】 "Information analysis means" refers to a method or apparatus for processing acquired data and deriving data-based insights. 【0328】 A "change calculation means" is a method or apparatus that includes a function to measure changes over a specific period from compared data and calculate the increase or decrease. 【0329】 "Information visualization means" refers to a method or apparatus for visually representing data and converting it into an easily understandable form using graphs and charts. 【0330】 "Emotion recognition means" refers to a method or device that determines a user's emotions from their facial expressions, voice, etc., and uses that information to adaptively adjust the behavior of equipment or engines. 【0331】 The system realizing this invention is designed to effectively manage data and present information while considering user sentiment. The server periodically retrieves relevant information from an information storage device using an API. This information includes periodic reporting data on budgets and projects, and data retrieval is performed via an online spreadsheet program. The retrieved information is processed by an information analysis tool. This process uses a Python-based natural language processing library (e.g., NLTK) to identify project-specific variations while verifying data integrity. 【0332】 Next, the server uses emotion recognition means, such as the Face++ API, to analyze the user's emotional data from the camera and microphone. The results of this analysis are then adjusted by data visualization means. For example, if it is determined that the operator is experiencing stress, the displayed colors and layout are changed to reduce the visual burden. 【0333】 As a concrete example, if a data center operator wearing smart glasses experiences stress while checking the budget status, the display screen will change, providing data in a visually easy-to-understand format. This system adapts to the individual user's state, providing a personalized experience. 【0334】 The generating AI model will consider prompts such as, "Please tell me how to check fluctuations in the data center's monthly budget, analyze the operators' sentiment, and take appropriate measures." 【0335】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0336】 Step 1: 【0337】 The server periodically retrieves budget data from the data storage device using an API. In this step, relevant budget information is input from the data storage, and the retrieved data is output. Specifically, the server accesses an online spreadsheet program to download the latest budget information. 【0338】 Step 2: 【0339】 The server analyzes the acquired data using a Python-based natural language processing library. The input is acquired budget data, and the output is the analysis results, with data integrity verified. In detail, this step involves the server calculating budget variances for each project and evaluating for inconsistencies. 【0340】 Step 3: 【0341】 The server uses the Face++ API, etc., to recognize the user's emotions. For this recognition, it uses the camera and microphone of smart glasses or a smartphone. The input is the user's real-time video and audio data, and the output is the result of an analysis of the user's emotional state. In this step, the server identifies the emotions the user is expressing (e.g., stress). 【0342】 Step 4: 【0343】 The server adjusts the data display using information visualization tools based on the sentiment analysis results obtained in the previous step. The input is the sentiment analysis results and analysis data, and the output is a user-optimized visualization. Specifically, it changes the colors and data arrangement according to the operator's emotions to present the information in an easy-to-understand manner. 【0344】 Step 5: 【0345】 The user reviews the data visualization results and provides various feedback through an interface provided by the server. This step may involve further data refinement or requests based on user actions. Based on the output, the user decides on their next action. 【0346】 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. 【0347】 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. 【0348】 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. 【0349】 [Third Embodiment] 【0350】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0351】 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. 【0352】 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). 【0353】 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. 【0354】 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. 【0355】 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). 【0356】 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. 【0357】 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. 【0358】 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. 【0359】 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. 【0360】 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. 【0361】 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". 【0362】 The present invention provides an integrated management system that automatically retrieves data from data storage, performs analysis and difference calculations, and visualizes the results. To implement this system, a program operating as follows will be developed. 【0363】 First, the server periodically accesses data storage to retrieve the necessary data. For example, online spreadsheet software might be used for data storage, containing budget information for each project. The server then uses an API to extract a specific range of data and proceed to the next processing step. 【0364】 Next, the server analyzes the acquired data. During this process, natural language processing is used to effectively extract numerical and textual information from the spreadsheet. For example, data on monthly budgets and their increases and decreases for each project is aggregated and then organized using data analysis tools. 【0365】 Subsequently, the server uses the analyzed data to calculate the difference from the previous month. This difference calculation method allows for the analysis of budget increases and decreases for each month and comparisons between multiple projects. This process enables quantitative evaluation of data changes and facilitates understanding of the financial situation. 【0366】 The aggregated results are sent to the terminal and displayed in an intuitively understandable format using data visualization tools. The terminal uses visual elements such as bar graphs and pie charts to allow users to easily recognize data trends and anomalies. 【0367】 Ultimately, users make financial reporting and budget management decisions based on this visualized information. The system provides an interactive interface, allowing users to filter and perform detailed analysis. This significantly improves operational efficiency and reduces manual work. 【0368】 This system supports reliable financial management through regular updates and real-time data processing. 【0369】 The following describes the processing flow. 【0370】 Step 1: 【0371】 The server periodically connects to the data storage and uses the API to retrieve the latest spreadsheet data. At this time, the server specifies a particular range of the spreadsheet and extracts the necessary budget data. 【0372】 Step 2: 【0373】 The server organizes the acquired data and begins analysis using the data analysis tools of the generating AI. Here, the server identifies budget figures and related date information within the data and stores them as structured data. 【0374】 Step 3: 【0375】 Based on the analyzed data, the server activates a difference calculation mechanism to calculate month-on-month changes. This detects budget fluctuations for each month and calculates increases or decreases for each project. Projects with large differences undergo particularly detailed analysis. 【0376】 Step 4: 【0377】 The terminal receives differential calculation results from the server and generates graphs and charts based on them. The terminal uses visualization tools to visually represent the budget status for each month and each project in a way that is easy for the user to understand. 【0378】 Step 5: 【0379】 Users can view the dashboard provided on their device and filter data for specific periods or projects as needed. Through the dashboard's interactive features, users can perform detailed budget analyses and extract data for use in reports. 【0380】 (Example 1) 【0381】 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." 【0382】 In businesses and organizations, project-based budget management and financial reporting are essential, but the manual processes of data acquisition, analysis, difference calculation, and visualization require considerable effort and time. Furthermore, since decision-making requires visually understandable data presentation, a system that efficiently processes data and allows for interactive use is necessary. 【0383】 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. 【0384】 In this invention, the server includes means for acquiring information from a data storage device, means for analyzing the acquired information, and means for comparing the analyzed information and calculating periodic differences. This makes it possible to efficiently acquire and process information and present it in a format that can be intuitively understood through visualization. 【0385】 A "data storage device" refers to a device or platform that electronically stores information and provides it in a form that can be reused later. 【0386】 "Means of acquiring information" refers to the methods and processes for selecting necessary information from a data storage device and converting it into a format that can be used within the system. 【0387】 "Information analysis methods" refer to methods and tools for organizing acquired information according to a specific purpose and obtaining valuable insights. 【0388】 A "difference calculation method" refers to a method or process for comparing information at different points in time and quantitatively evaluating its changes or increases / decreases. 【0389】 "Information visualization means" refers to methods and techniques for visually representing analyzed and calculated data so that it can be intuitively understood. 【0390】 An "interactive interface" refers to a user interface designed to allow users to dynamically interact with the system, filter information, and perform additional analysis. 【0391】 To implement this invention, the server first accesses an online spreadsheet program, which serves as a data storage device, to retrieve information. In this process, an API is used to automatically extract the necessary range of data from the spreadsheet. A widely used online spreadsheet platform could be used as the specific software. This allows for the real-time acquisition of project-specific financial data and budget information. 【0392】 Next, the server employs information analysis tools to analyze the acquired information. Natural language processing techniques are used to systematically organize and extract numerical and text data from spreadsheets. For specific analysis, libraries such as Python's NLTK or spaCy might be used. This analysis allows for an understanding of monthly income and expenditure trends for each project. 【0393】 The server then compares the analyzed data and calculates the difference over a specific period. To calculate this difference, it uses the Pandas data processing library to determine the difference from historical data. This allows for a quantitative evaluation of budget fluctuations for each project and enables the rapid identification of problems. 【0394】 The terminal then visualizes the results of the difference calculation obtained from the server. Specifically, data visualization libraries such as Matplotlib and Plotly are used to display the information as bar graphs and pie charts. This visualization allows users to understand the increase or decrease and trends in the data at a glance. 【0395】 Ultimately, users make decisions based on visualized information, optimizing project budget management and reporting. Through an interactive interface, users can obtain further details and filter information based on criteria, enabling effective decision-making. 【0396】 As a concrete example, consider a situation where a company manages the monthly income and expenses of various projects. This system visualizes the fluctuations in income and expenses for each project, making it easy to identify budget overruns and underruns, thereby enabling the company to quickly take appropriate financial action. 【0397】 A concrete example of a prompt is: "Develop a program that uses data stored in online spreadsheet software to analyze monthly budget increases and decreases and visualize them in a graph." This prompt serves as a foundation for designing an appropriate program using a generative AI model. 【0398】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0399】 Step 1: 【0400】 The server accesses an online spreadsheet program as a data storage device to retrieve information. The input consists of API authentication credentials and a specified data range. The server uses this information to extract the data within the specified range in bulk and format it into an analyzable format. The output is the extracted spreadsheet data, which is then passed on to the next processing step. 【0401】 Step 2: 【0402】 The server analyzes the acquired data. Formatted spreadsheet data is provided as input. The server utilizes natural language processing tools (e.g., Python's NLTK) to identify numerical data and related text data. Specifically, it analyzes monthly income and expenses for each project, and generates aggregated income and expense data for each project as output. This data is used for further analysis and diff calculation. 【0403】 Step 3: 【0404】 The server uses the analyzed data to calculate the difference. The input is the project-specific revenue and expenditure summary data obtained in the previous step. The server uses a data processing library (e.g., Pandas) to create a data frame and calculate the difference in data for each month. In this process, the data fluctuations are quantified, and the output is the monthly difference results. 【0405】 Step 4: 【0406】 The terminal visualizes the differential calculation results. Differential data from the server is supplied as input. The terminal uses a data visualization tool (e.g., Matplotlib) to display the information as bar graphs or pie charts. This allows the user to easily grasp the data trends. The output is a graphical visual representation that the user can directly review. 【0407】 Step 5: 【0408】 Users make decisions based on visualized information. Input is data provided as graphs and diagrams on the device. Through an interactive interface, users filter and perform additional analysis to formulate optimal solutions and action plans. Output includes actions based on user decisions and newly established business strategies. This input-output cycle promotes the effective use of the system. 【0409】 (Application Example 1) 【0410】 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." 【0411】 In data center management and operation, it is challenging to grasp budget and resource usage in real time and to respond immediately when anomalies occur. Traditional methods involve manually collecting and analyzing information, and the time it takes to visually grasp the situation reduces management efficiency. 【0412】 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. 【0413】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, means for calculating a difference by comparing the analyzed information and calculating a periodic difference, means for displaying the results of the difference calculation, and means for notifying the administrator based on the results using a portable device. This enables data center administrators to check information in real time using a portable device and respond to anomalies immediately. 【0414】 An "information storage device" is a device that has the function of accumulating data and storing it in a state that allows it to be retrieved later. 【0415】 "Information analysis means" refers to the technology or process of classifying and organizing acquired information, and extracting and analyzing the necessary data. 【0416】 A "difference calculation method" is a calculation method that compares data at different points in time and determines the amount of change. 【0417】 "Information display means" refers to a device or technology that visually provides analyzed and calculated data. 【0418】 A "portable device" is an electronic device that is easily portable and capable of transmitting, receiving, and displaying information. 【0419】 "Means of notifying administrators" refers to processes or technologies for communicating anomalies or significant changes in information to human users. 【0420】 The system implementing this invention consists mainly of a server and a portable device. The server automatically acquires data from an information storage device and analyzes it using an information analysis means. Specifically, it uses the Python pandas library to organize and classify the data and extract the necessary information. The analyzed data is then analyzed using a difference calculation means to calculate data fluctuations between different timestamps. This calculation clearly shows changes in the data center's budget and resource usage. 【0421】 The server sends the obtained results to an information display device, which then visually represents the data using D3.js or Chart.js. This display is also possible on portable devices, which use applications built with the React Native framework. This application allows administrators to monitor the data center's status in real time. 【0422】 The administrator, as a user, receives information via a portable device and is immediately notified if an anomaly occurs. This enables rapid problem resolution. For example, by detecting a sudden surge in power costs at a data center, the administrator can immediately analyze the cause and take appropriate measures. An example of a prompt message is: "I want to design an app that analyzes the data center's budget usage on a monthly basis and notifies of anomalies. What method of data visualization would be most useful for the administrator?" 【0423】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0424】 Step 1: 【0425】 The server periodically accesses the data storage device to retrieve necessary data. Input data typically comes from an online spreadsheet application, usually accessed via an API. The output is raw data, which includes project-specific budget information. 【0426】 Step 2: 【0427】 The server processes the acquired data using information analysis tools. It receives the raw data acquired in step 1 as input, organizes the data using the Python pandas library, and extracts specific information. The output is a dataset organized by category. Specifically, the data is sorted chronologically and aggregated as monthly budget data. 【0428】 Step 3: 【0429】 The server performs difference calculations using the organized data. The input includes the organized data generated in step 2. To calculate the changes in the data, it is compared with the previous month's data. This calculation determines the increase or decrease in each item, and the results are output. The comparison operation involves a process that references the same dataset from the previous month. 【0430】 Step 4: 【0431】 The server uses information display tools to visualize the results of difference calculations. Input data is graphically transformed using D3.js or Chart.js and output as bar graphs or pie charts. This visual information helps to intuitively understand data fluctuations. 【0432】 Step 5: 【0433】 The visualized data is sent to the terminal, allowing the user to view it in real time on a portable device. The input is the visual data from step 4, and the output is displayed on the screen of the portable app. The specific actions involve data communication and the display process within the application. A user-friendly UI is used on the screen to enable the user to quickly detect data anomalies. 【0434】 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. 【0435】 This invention is a system that integrates automatic acquisition, analysis, and visualization of budget data with an emotion engine that recognizes user emotions. This system is designed to enable users to manage data effectively and comfortably. The following are details of embodiments for carrying out the invention. 【0436】 First, the server periodically retrieves budget data from online spreadsheet software. The server accesses data storage using an API and automatically extracts the necessary data. This data includes monthly budgets and related text information for each project. 【0437】 Next, the server utilizes data analysis tools to analyze the acquired data using natural language processing. During the analysis process, data consistency checks and budget discrepancies for each project are identified. This process allows users to gain detailed insights that can aid in data-driven decision-making. 【0438】 Subsequently, the server evaluates data fluctuations using a differential calculation method based on the analyzed data. It calculates monthly budget increases and decreases and identifies points that are important to the user. This information is then supplied to the subsequent data visualization process. 【0439】 Once the data is ready, the device uses visualization algorithms to support intuitive data display in graph and dashboard formats. At this point, the emotion engine kicks in, recognizing emotions from the user's responses and actions. For example, if the device determines the user is stressed, it reduces user burden by presenting calmer colors and a simpler layout. 【0440】 Users can provide feedback and make additional requests regarding the data through the interface provided by the device. The emotion engine uses this interaction to perform more accurate emotion recognition and utilizes this information to adjust the interface in the future. In this way, the system adapts individually to the user, providing a more personalized experience. 【0441】 This invention aims to achieve both efficient data management and a comfortable user experience, and is expected to play an important role in future financial and project management. 【0442】 The following describes the processing flow. 【0443】 Step 1: 【0444】 The server accesses the API of an online spreadsheet software at scheduled intervals to retrieve the necessary budget data. The server reads the specified sheet and range and saves the data to the server's internal database. 【0445】 Step 2: 【0446】 The server activates data analysis tools and analyzes the acquired data. This analysis includes natural language processing, extracting budget figures and related text comments for each project and converting them into structured data. The server also performs anomaly detection and generates alerts if inconsistencies are found. 【0447】 Step 3: 【0448】 The server uses a differential calculation method to calculate the month-on-month change in the analyzed data. It aggregates the budget increases and decreases for each project and identifies areas with significant fluctuations. The server prepares these results for the next processing step. 【0449】 Step 4: 【0450】 The terminal receives differential calculation results sent from the server and generates a dashboard using data visualization tools. The terminal activates an emotion engine, estimates emotions from user input and actions, and dynamically adjusts the user interface. 【0451】 Step 5: 【0452】 Users review the dashboard displayed on their device and utilize the interactive feedback features provided by the sentiment engine. Based on the visualized data, users analyze specific projects or time periods in detail and filter the data as needed. 【0453】 Step 6: 【0454】 The emotion engine continuously analyzes user actions and feedback to accurately recognize the user's current emotional state. Based on this, it adjusts the device's display methods and data presentation formats to optimize the user experience. 【0455】 (Example 2) 【0456】 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." 【0457】 In data management, the ability to efficiently acquire, analyze, and visualize vast amounts of information is essential. Furthermore, providing a more comfortable and effective data usage environment by individually adjusting the interface to consider the user's emotions is a significant challenge. In addition, flexible information presentation tailored to the user's intentions and state is required. 【0458】 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. 【0459】 In this invention, the server includes means for acquiring information from a data set, means for analyzing the acquired information, means for comparing the analyzed information and calculating periodic differences, and means for visualization and emotion recognition. This enables efficient information management and the provision of a personalized interface that responds to the user's emotions. 【0460】 A "data set" refers to a structure or device that aggregates information, and includes online computing software and databases. 【0461】 "Information" refers to numerical or textual data obtained from a data set. 【0462】 "Means of acquisition" refers to mechanisms and methods for collecting information from a data set, including the use of APIs. 【0463】 "Information analysis means" refers to technologies and methods for analyzing acquired information and extracting useful insights, and includes natural language processing technology. 【0464】 A "difference calculation method" refers to a technique or mechanism that calculates fluctuations over a specific period by comparing the results of information analysis. 【0465】 "Information visualization means" refers to methods and technologies for visually representing and presenting analyzed information to the user, and includes the generation of graphs and dashboards. 【0466】 "Emotion recognition means" refers to methods and technologies for detecting a user's emotions and individually adjusting the interface accordingly. 【0467】 This system is an advanced data management solution that efficiently retrieves, analyzes, and visualizes information from data sets, and adapts based on the user's emotions. 【0468】 The server first retrieves data from online computing software, such as a spreadsheet application, via an API. This API typically collects information periodically at specific points in time and receives it in JSON format. The server then analyzes the retrieved data using natural language processing (NLP). NLP libraries such as "NLTK" and "spaCy" can be used. This allows for consistency checks of text information and identification of budget discrepancies in numerical data. The analysis process is automated, enabling rapid identification of outliers and trends. 【0469】 Subsequently, the server uses variance calculation methods to analyze periodic budget fluctuations. This makes it possible to understand the budget situation for each project more accurately. At this stage, statistical methods are also used to calculate month-on-month changes and cumulative variances. 【0470】 The terminal receives analysis results sent from the server and presents the information to the user using a visualization algorithm. For visualization, JavaScript libraries such as "D3.js" and "Chart.js" are used to generate graphs and dashboards. This allows the user to intuitively understand the information. 【0471】 Furthermore, the device uses a built-in emotion engine to recognize emotions from the user's facial expressions and actions. This is done by analyzing camera and user behavior data. For example, if the user is feeling stressed, the device will change the screen's color tone to alleviate the user's burden. 【0472】 As a concrete example, when a user wants to check past budget data, they can enter a prompt into the system saying, "Retrieve last month's budget data for Project A and analyze the budget variance." This prompt will prompt the system to retrieve and analyze the data, generate a visualized report as a result, and provide it to the user. This enables the user to make data-driven decisions quickly. 【0473】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0474】 Step 1: 【0475】 The server periodically retrieves information from the data set using the API of online computing software. At this stage, the server sends an HTTP request to the API endpoint and receives the spreadsheet-formatted data in JSON format. The input is the URL or identifier of the spreadsheet, and the output is the data in JSON format. 【0476】 Step 2: 【0477】 The server analyzes the acquired JSON data using natural language processing (NLP) techniques. Specifically, it tokenizes the data using an NLP library and extracts important keywords and numerical values. The input is JSON data, and the output generates data that has been verified for consistency, as well as information identifying budget discrepancies for each project. 【0478】 Step 3: 【0479】 Based on the information analyzed in the previous step, the server uses a variance calculation method to calculate monthly budget fluctuations. Statistical methods are then used to calculate month-on-month changes and list important fluctuation points. The input is the analyzed numerical data, and the output is comparative data indicating budget increases / decreases and anomalies. 【0480】 Step 4: 【0481】 The terminal receives analysis results sent from the server and displays the information in graph format using a visualization algorithm. This process generates bar graphs and pie charts using D3.js or Chart.js. The input is comparative data based on the analysis results, and the output is a visualized graphical dashboard. 【0482】 Step 5: 【0483】 The device uses an emotion engine to recognize the user's emotions when displaying visualized data. Specifically, it analyzes camera footage and mouse click patterns to determine whether the user is experiencing stress or fatigue. The input is user behavior data, and the output is the user's emotional state. 【0484】 Step 6: 【0485】 Users can send feedback on data and request additional analysis through an interface provided via their device. Based on this, the sentiment engine adjusts the interface and collects data to improve the next user experience. The input is user feedback and requests, and the output is data for future interface adjustments. 【0486】 (Application Example 2) 【0487】 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." 【0488】 Traditional data management systems focus on information acquisition, analysis, and visualization, but they fail to adequately improve usability based on user emotions. In particular, there is a lack of technology to adjust visual information presentation methods when users experience stress or burden. There is a need for a system that can adapt to the emotions of operators, reduce their burden, and manage data efficiently. 【0489】 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. 【0490】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, and means for recognizing the emotions of the person manipulating the information and adjusting the information display. This enables automatic adjustment of the method of presenting visual information according to the emotional state of the operator, reducing the burden and facilitating efficient data management. 【0491】 A "data storage device" is a system or platform that has the function of securely storing information and allowing that information to be retrieved as needed. 【0492】 "Information analysis means" refers to a method or apparatus for processing acquired data and deriving data-based insights. 【0493】 A "change calculation means" is a method or apparatus that includes a function to measure changes over a specific period from compared data and calculate the increase or decrease. 【0494】 "Information visualization means" refers to a method or apparatus for visually representing data and converting it into an easily understandable form using graphs and charts. 【0495】 "Emotion recognition means" refers to a method or device that determines a user's emotions from their facial expressions, voice, etc., and uses that information to adaptively adjust the behavior of equipment or engines. 【0496】 The system realizing this invention is designed to effectively manage data and present information while considering user sentiment. The server periodically retrieves relevant information from an information storage device using an API. This information includes periodic reporting data on budgets and projects, and data retrieval is performed via an online spreadsheet program. The retrieved information is processed by an information analysis tool. This process uses a Python-based natural language processing library (e.g., NLTK) to identify project-specific variations while verifying data integrity. 【0497】 Next, the server uses emotion recognition means, such as the Face++ API, to analyze the user's emotional data from the camera and microphone. The results of this analysis are then adjusted by data visualization means. For example, if it is determined that the operator is experiencing stress, the displayed colors and layout are changed to reduce the visual burden. 【0498】 As a concrete example, if a data center operator wearing smart glasses experiences stress while checking the budget status, the display screen will change, providing data in a visually easy-to-understand format. This system adapts to the individual user's state, providing a personalized experience. 【0499】 The generating AI model will consider prompts such as, "Please tell me how to check fluctuations in the data center's monthly budget, analyze the operators' sentiment, and take appropriate measures." 【0500】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0501】 Step 1: 【0502】 The server periodically retrieves budget data from the data storage device using an API. In this step, relevant budget information is input from the data storage, and the retrieved data is output. Specifically, the server accesses an online spreadsheet program to download the latest budget information. 【0503】 Step 2: 【0504】 The server analyzes the acquired data using a Python-based natural language processing library. The input is acquired budget data, and the output is the analysis results, with data integrity verified. In detail, this step involves the server calculating budget variances for each project and evaluating for inconsistencies. 【0505】 Step 3: 【0506】 The server uses the Face++ API, etc., to recognize the user's emotions. For this recognition, it uses the camera and microphone of smart glasses or a smartphone. The input is the user's real-time video and audio data, and the output is the result of an analysis of the user's emotional state. In this step, the server identifies the emotions the user is expressing (e.g., stress). 【0507】 Step 4: 【0508】 The server adjusts the data display using information visualization tools based on the sentiment analysis results obtained in the previous step. The input is the sentiment analysis results and analysis data, and the output is a user-optimized visualization. Specifically, it changes the colors and data arrangement according to the operator's emotions to present the information in an easy-to-understand manner. 【0509】 Step 5: 【0510】 The user reviews the data visualization results and provides various feedback through an interface provided by the server. This step may involve further data refinement or requests based on user actions. Based on the output, the user decides on their next action. 【0511】 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. 【0512】 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. 【0513】 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. 【0514】 [Fourth Embodiment] 【0515】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0516】 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. 【0517】 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). 【0518】 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. 【0519】 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. 【0520】 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). 【0521】 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. 【0522】 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. 【0523】 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. 【0524】 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. 【0525】 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. 【0526】 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. 【0527】 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". 【0528】 The present invention provides an integrated management system that automatically retrieves data from data storage, performs analysis and difference calculations, and visualizes the results. To implement this system, a program operating as follows will be developed. 【0529】 First, the server periodically accesses data storage to retrieve the necessary data. For example, online spreadsheet software might be used for data storage, containing budget information for each project. The server then uses an API to extract a specific range of data and proceed to the next processing step. 【0530】 Next, the server analyzes the acquired data. During this process, natural language processing is used to effectively extract numerical and textual information from the spreadsheet. For example, data on monthly budgets and their increases and decreases for each project is aggregated and then organized using data analysis tools. 【0531】 Subsequently, the server uses the analyzed data to calculate the difference from the previous month. This difference calculation method allows for the analysis of budget increases and decreases for each month and comparisons between multiple projects. This process enables quantitative evaluation of data changes and facilitates understanding of the financial situation. 【0532】 The aggregated results are sent to the terminal and displayed in an intuitively understandable format using data visualization tools. The terminal uses visual elements such as bar graphs and pie charts to allow users to easily recognize data trends and anomalies. 【0533】 Ultimately, users make financial reporting and budget management decisions based on this visualized information. The system provides an interactive interface, allowing users to filter and perform detailed analysis. This significantly improves operational efficiency and reduces manual work. 【0534】 This system supports reliable financial management through regular updates and real-time data processing. 【0535】 The following describes the processing flow. 【0536】 Step 1: 【0537】 The server periodically connects to the data storage and uses the API to retrieve the latest spreadsheet data. At this time, the server specifies a particular range of the spreadsheet and extracts the necessary budget data. 【0538】 Step 2: 【0539】 The server organizes the acquired data and begins analysis using the data analysis tools of the generating AI. Here, the server identifies budget figures and related date information within the data and stores them as structured data. 【0540】 Step 3: 【0541】 Based on the analyzed data, the server activates a difference calculation mechanism to calculate month-on-month changes. This detects budget fluctuations for each month and calculates increases or decreases for each project. Projects with large differences undergo particularly detailed analysis. 【0542】 Step 4: 【0543】 The terminal receives differential calculation results from the server and generates graphs and charts based on them. The terminal uses visualization tools to visually represent the budget status for each month and each project in a way that is easy for the user to understand. 【0544】 Step 5: 【0545】 Users can view the dashboard provided on their device and filter data for specific periods or projects as needed. Through the dashboard's interactive features, users can perform detailed budget analyses and extract data for use in reports. 【0546】 (Example 1) 【0547】 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". 【0548】 In businesses and organizations, project-based budget management and financial reporting are essential, but the manual processes of data acquisition, analysis, difference calculation, and visualization require considerable effort and time. Furthermore, since decision-making requires visually understandable data presentation, a system that efficiently processes data and allows for interactive use is necessary. 【0549】 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. 【0550】 In this invention, the server includes means for acquiring information from a data storage device, means for analyzing the acquired information, and means for comparing the analyzed information and calculating periodic differences. This makes it possible to efficiently acquire and process information and present it in a format that can be intuitively understood through visualization. 【0551】 A "data storage device" refers to a device or platform that electronically stores information and provides it in a form that can be reused later. 【0552】 "Means of acquiring information" refers to the methods and processes for selecting necessary information from a data storage device and converting it into a format that can be used within the system. 【0553】 "Information analysis methods" refer to methods and tools for organizing acquired information according to a specific purpose and obtaining valuable insights. 【0554】 A "difference calculation method" refers to a method or process for comparing information at different points in time and quantitatively evaluating its changes or increases / decreases. 【0555】 "Information visualization means" refers to methods and techniques for visually representing analyzed and calculated data so that it can be intuitively understood. 【0556】 An "interactive interface" refers to a user interface designed to allow users to dynamically interact with the system, filter information, and perform additional analysis. 【0557】 To implement this invention, the server first accesses an online spreadsheet program, which serves as a data storage device, to retrieve information. In this process, an API is used to automatically extract the necessary range of data from the spreadsheet. A widely used online spreadsheet platform could be used as the specific software. This allows for the real-time acquisition of project-specific financial data and budget information. 【0558】 Next, the server employs information analysis tools to analyze the acquired information. Natural language processing techniques are used to systematically organize and extract numerical and text data from spreadsheets. For specific analysis, libraries such as Python's NLTK or spaCy might be used. This analysis allows for an understanding of monthly income and expenditure trends for each project. 【0559】 The server then compares the analyzed data and calculates the difference over a specific period. To calculate this difference, it uses the Pandas data processing library to determine the difference from historical data. This allows for a quantitative evaluation of budget fluctuations for each project and enables the rapid identification of problems. 【0560】 The terminal then visualizes the results of the difference calculation obtained from the server. Specifically, data visualization libraries such as Matplotlib and Plotly are used to display the information as bar graphs and pie charts. This visualization allows users to understand the increase or decrease and trends in the data at a glance. 【0561】 Ultimately, users make decisions based on visualized information, optimizing project budget management and reporting. Through an interactive interface, users can obtain further details and filter information based on criteria, enabling effective decision-making. 【0562】 As a concrete example, consider a situation where a company manages the monthly income and expenses of various projects. This system visualizes the fluctuations in income and expenses for each project, making it easy to identify budget overruns and underruns, thereby enabling the company to quickly take appropriate financial action. 【0563】 A concrete example of a prompt is: "Develop a program that uses data stored in online spreadsheet software to analyze monthly budget increases and decreases and visualize them in a graph." This prompt serves as a foundation for designing an appropriate program using a generative AI model. 【0564】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0565】 Step 1: 【0566】 The server accesses an online spreadsheet program as a data storage device to retrieve information. The input consists of API authentication credentials and a specified data range. The server uses this information to extract the data within the specified range in bulk and format it into an analyzable format. The output is the extracted spreadsheet data, which is then passed on to the next processing step. 【0567】 Step 2: 【0568】 The server analyzes the acquired data. Formatted spreadsheet data is provided as input. The server utilizes natural language processing tools (e.g., Python's NLTK) to identify numerical data and related text data. Specifically, it analyzes monthly income and expenses for each project, and generates aggregated income and expense data for each project as output. This data is used for further analysis and diff calculation. 【0569】 Step 3: 【0570】 The server uses the analyzed data to calculate the difference. The input is the project-specific revenue and expenditure summary data obtained in the previous step. The server uses a data processing library (e.g., Pandas) to create a data frame and calculate the difference in data for each month. In this process, the data fluctuations are quantified, and the output is the monthly difference results. 【0571】 Step 4: 【0572】 The terminal visualizes the differential calculation results. Differential data from the server is supplied as input. The terminal uses a data visualization tool (e.g., Matplotlib) to display the information as bar graphs or pie charts. This allows the user to easily grasp the data trends. The output is a graphical visual representation that the user can directly review. 【0573】 Step 5: 【0574】 Users make decisions based on visualized information. Input is data provided as graphs and diagrams on the device. Through an interactive interface, users filter and perform additional analysis to formulate optimal solutions and action plans. Output includes actions based on user decisions and newly established business strategies. This input-output cycle promotes the effective use of the system. 【0575】 (Application Example 1) 【0576】 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". 【0577】 In data center management and operation, it is challenging to grasp budget and resource usage in real time and to respond immediately when anomalies occur. Traditional methods involve manually collecting and analyzing information, and the time it takes to visually grasp the situation reduces management efficiency. 【0578】 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. 【0579】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, means for calculating a difference by comparing the analyzed information and calculating a periodic difference, means for displaying the results of the difference calculation, and means for notifying the administrator based on the results using a portable device. This enables data center administrators to check information in real time using a portable device and respond to anomalies immediately. 【0580】 An "information storage device" is a device that has the function of accumulating data and storing it in a state that allows it to be retrieved later. 【0581】 "Information analysis means" refers to the technology or process of classifying and organizing acquired information, and extracting and analyzing the necessary data. 【0582】 A "difference calculation method" is a calculation method that compares data at different points in time and determines the amount of change. 【0583】 "Information display means" refers to a device or technology that visually provides analyzed and calculated data. 【0584】 A "portable device" is an electronic device that is easily portable and capable of transmitting, receiving, and displaying information. 【0585】 "Means of notifying administrators" refers to processes or technologies for communicating anomalies or significant changes in information to human users. 【0586】 The system implementing this invention consists mainly of a server and a portable device. The server automatically acquires data from an information storage device and analyzes it using an information analysis means. Specifically, it uses the Python pandas library to organize and classify the data and extract the necessary information. The analyzed data is then analyzed using a difference calculation means to calculate data fluctuations between different timestamps. This calculation clearly shows changes in the data center's budget and resource usage. 【0587】 The server sends the obtained results to an information display device, which then visually represents the data using D3.js or Chart.js. This display is also possible on portable devices, which use applications built with the React Native framework. This application allows administrators to monitor the data center's status in real time. 【0588】 The administrator, as a user, receives information via a portable device and is immediately notified if an anomaly occurs. This enables rapid problem resolution. For example, by detecting a sudden surge in power costs at a data center, the administrator can immediately analyze the cause and take appropriate measures. An example of a prompt message is: "I want to design an app that analyzes the data center's budget usage on a monthly basis and notifies of anomalies. What method of data visualization would be most useful for the administrator?" 【0589】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0590】 Step 1: 【0591】 The server periodically accesses the data storage device to retrieve necessary data. Input data typically comes from an online spreadsheet application, usually accessed via an API. The output is raw data, which includes project-specific budget information. 【0592】 Step 2: 【0593】 The server processes the acquired data using information analysis tools. It receives the raw data acquired in step 1 as input, organizes the data using the Python pandas library, and extracts specific information. The output is a dataset organized by category. Specifically, the data is sorted chronologically and aggregated as monthly budget data. 【0594】 Step 3: 【0595】 The server performs difference calculations using the organized data. The input includes the organized data generated in step 2. To calculate the changes in the data, it is compared with the previous month's data. This calculation determines the increase or decrease in each item, and the results are output. The comparison operation involves a process that references the same dataset from the previous month. 【0596】 Step 4: 【0597】 The server uses information display tools to visualize the results of difference calculations. Input data is graphically transformed using D3.js or Chart.js and output as bar graphs or pie charts. This visual information helps to intuitively understand data fluctuations. 【0598】 Step 5: 【0599】 The visualized data is sent to the terminal, allowing the user to view it in real time on a portable device. The input is the visual data from step 4, and the output is displayed on the screen of the portable app. The specific actions involve data communication and the display process within the application. A user-friendly UI is used on the screen to enable the user to quickly detect data anomalies. 【0600】 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. 【0601】 This invention is a system that integrates automatic acquisition, analysis, and visualization of budget data with an emotion engine that recognizes user emotions. This system is designed to enable users to manage data effectively and comfortably. The following are details of embodiments for carrying out the invention. 【0602】 First, the server periodically retrieves budget data from online spreadsheet software. The server accesses data storage using an API and automatically extracts the necessary data. This data includes monthly budgets and related text information for each project. 【0603】 Next, the server utilizes data analysis tools to analyze the acquired data using natural language processing. During the analysis process, data consistency checks and budget discrepancies for each project are identified. This process allows users to gain detailed insights that can aid in data-driven decision-making. 【0604】 Subsequently, the server evaluates data fluctuations using a differential calculation method based on the analyzed data. It calculates monthly budget increases and decreases and identifies points that are important to the user. This information is then supplied to the subsequent data visualization process. 【0605】 Once the data is ready, the device uses visualization algorithms to support intuitive data display in graph and dashboard formats. At this point, the emotion engine kicks in, recognizing emotions from the user's responses and actions. For example, if the device determines the user is stressed, it reduces user burden by presenting calmer colors and a simpler layout. 【0606】 Users can provide feedback and make additional requests regarding the data through the interface provided by the device. The emotion engine uses this interaction to perform more accurate emotion recognition and utilizes this information to adjust the interface in the future. In this way, the system adapts individually to the user, providing a more personalized experience. 【0607】 This invention aims to achieve both efficient data management and a comfortable user experience, and is expected to play an important role in future financial and project management. 【0608】 The following describes the processing flow. 【0609】 Step 1: 【0610】 The server accesses the API of an online spreadsheet software at scheduled intervals to retrieve the necessary budget data. The server reads the specified sheet and range and saves the data to the server's internal database. 【0611】 Step 2: 【0612】 The server activates data analysis tools and analyzes the acquired data. This analysis includes natural language processing, extracting budget figures and related text comments for each project and converting them into structured data. The server also performs anomaly detection and generates alerts if inconsistencies are found. 【0613】 Step 3: 【0614】 The server uses a differential calculation method to calculate the month-on-month change in the analyzed data. It aggregates the budget increases and decreases for each project and identifies areas with significant fluctuations. The server prepares these results for the next processing step. 【0615】 Step 4: 【0616】 The terminal receives differential calculation results sent from the server and generates a dashboard using data visualization tools. The terminal activates an emotion engine, estimates emotions from user input and actions, and dynamically adjusts the user interface. 【0617】 Step 5: 【0618】 Users review the dashboard displayed on their device and utilize the interactive feedback features provided by the sentiment engine. Based on the visualized data, users analyze specific projects or time periods in detail and filter the data as needed. 【0619】 Step 6: 【0620】 The emotion engine continuously analyzes user actions and feedback to accurately recognize the user's current emotional state. Based on this, it adjusts the device's display methods and data presentation formats to optimize the user experience. 【0621】 (Example 2) 【0622】 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". 【0623】 In data management, the ability to efficiently acquire, analyze, and visualize vast amounts of information is essential. Furthermore, providing a more comfortable and effective data usage environment by individually adjusting the interface to consider the user's emotions is a significant challenge. In addition, flexible information presentation tailored to the user's intentions and state is required. 【0624】 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. 【0625】 In this invention, the server includes means for acquiring information from a data set, means for analyzing the acquired information, means for comparing the analyzed information and calculating periodic differences, and means for visualization and emotion recognition. This enables efficient information management and the provision of a personalized interface that responds to the user's emotions. 【0626】 A "data set" refers to a structure or device that aggregates information, and includes online computing software and databases. 【0627】 "Information" refers to numerical or textual data obtained from a data set. 【0628】 "Means of acquisition" refers to mechanisms and methods for collecting information from a data set, including the use of APIs. 【0629】 "Information analysis means" refers to technologies and methods for analyzing acquired information and extracting useful insights, and includes natural language processing technology. 【0630】 A "difference calculation method" refers to a technique or mechanism that calculates fluctuations over a specific period by comparing the results of information analysis. 【0631】 "Information visualization means" refers to methods and technologies for visually representing and presenting analyzed information to the user, and includes the generation of graphs and dashboards. 【0632】 "Emotion recognition means" refers to methods and technologies for detecting a user's emotions and individually adjusting the interface accordingly. 【0633】 This system is an advanced data management solution that efficiently retrieves, analyzes, and visualizes information from data sets, and adapts based on the user's emotions. 【0634】 The server first retrieves data from online computing software, such as a spreadsheet application, via an API. This API typically collects information periodically at specific points in time and receives it in JSON format. The server then analyzes the retrieved data using natural language processing (NLP). NLP libraries such as "NLTK" and "spaCy" can be used. This allows for consistency checks of text information and identification of budget discrepancies in numerical data. The analysis process is automated, enabling rapid identification of outliers and trends. 【0635】 Subsequently, the server uses variance calculation methods to analyze periodic budget fluctuations. This makes it possible to understand the budget situation for each project more accurately. At this stage, statistical methods are also used to calculate month-on-month changes and cumulative variances. 【0636】 The terminal receives analysis results sent from the server and presents the information to the user using a visualization algorithm. For visualization, JavaScript libraries such as "D3.js" and "Chart.js" are used to generate graphs and dashboards. This allows the user to intuitively understand the information. 【0637】 Furthermore, the device uses a built-in emotion engine to recognize emotions from the user's facial expressions and actions. This is done by analyzing camera and user behavior data. For example, if the user is feeling stressed, the device will change the screen's color tone to alleviate the user's burden. 【0638】 As a concrete example, when a user wants to check past budget data, they can enter a prompt into the system saying, "Retrieve last month's budget data for Project A and analyze the budget variance." This prompt will prompt the system to retrieve and analyze the data, generate a visualized report as a result, and provide it to the user. This enables the user to make data-driven decisions quickly. 【0639】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0640】 Step 1: 【0641】 The server periodically retrieves information from the data set using the API of online computing software. At this stage, the server sends an HTTP request to the API endpoint and receives the spreadsheet-formatted data in JSON format. The input is the URL or identifier of the spreadsheet, and the output is the data in JSON format. 【0642】 Step 2: 【0643】 The server analyzes the acquired JSON data using natural language processing (NLP) techniques. Specifically, it tokenizes the data using an NLP library and extracts important keywords and numerical values. The input is JSON data, and the output generates data that has been verified for consistency, as well as information identifying budget discrepancies for each project. 【0644】 Step 3: 【0645】 Based on the information analyzed in the previous step, the server uses a variance calculation method to calculate monthly budget fluctuations. Statistical methods are then used to calculate month-on-month changes and list important fluctuation points. The input is the analyzed numerical data, and the output is comparative data indicating budget increases / decreases and anomalies. 【0646】 Step 4: 【0647】 The terminal receives analysis results sent from the server and displays the information in graph format using a visualization algorithm. This process generates bar graphs and pie charts using D3.js or Chart.js. The input is comparative data based on the analysis results, and the output is a visualized graphical dashboard. 【0648】 Step 5: 【0649】 The device uses an emotion engine to recognize the user's emotions when displaying visualized data. Specifically, it analyzes camera footage and mouse click patterns to determine whether the user is experiencing stress or fatigue. The input is user behavior data, and the output is the user's emotional state. 【0650】 Step 6: 【0651】 Users can send feedback on data and request additional analysis through an interface provided via their device. Based on this, the sentiment engine adjusts the interface and collects data to improve the next user experience. The input is user feedback and requests, and the output is data for future interface adjustments. 【0652】 (Application Example 2) 【0653】 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". 【0654】 Traditional data management systems focus on information acquisition, analysis, and visualization, but they fail to adequately improve usability based on user emotions. In particular, there is a lack of technology to adjust visual information presentation methods when users experience stress or burden. There is a need for a system that can adapt to the emotions of operators, reduce their burden, and manage data efficiently. 【0655】 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. 【0656】 In this invention, the server includes means for acquiring information from an information storage device, means for analyzing the acquired information, and means for recognizing the emotions of the person manipulating the information and adjusting the information display. This enables automatic adjustment of the method of presenting visual information according to the emotional state of the operator, reducing the burden and facilitating efficient data management. 【0657】 A "data storage device" is a system or platform that has the function of securely storing information and allowing that information to be retrieved as needed. 【0658】 "Information analysis means" refers to a method or apparatus for processing acquired data and deriving data-based insights. 【0659】 A "change calculation means" is a method or apparatus that includes a function to measure changes over a specific period from compared data and calculate the increase or decrease. 【0660】 "Information visualization means" refers to a method or apparatus for visually representing data and converting it into an easily understandable form using graphs and charts. 【0661】 "Emotion recognition means" refers to a method or device that determines a user's emotions from their facial expressions, voice, etc., and uses that information to adaptively adjust the behavior of equipment or engines. 【0662】 The system realizing this invention is designed to effectively manage data and present information while considering user sentiment. The server periodically retrieves relevant information from an information storage device using an API. This information includes periodic reporting data on budgets and projects, and data retrieval is performed via an online spreadsheet program. The retrieved information is processed by an information analysis tool. This process uses a Python-based natural language processing library (e.g., NLTK) to identify project-specific variations while verifying data integrity. 【0663】 Next, the server uses emotion recognition means, such as the Face++ API, to analyze the user's emotional data from the camera and microphone. The results of this analysis are then adjusted by data visualization means. For example, if it is determined that the operator is experiencing stress, the displayed colors and layout are changed to reduce the visual burden. 【0664】 As a concrete example, if a data center operator wearing smart glasses experiences stress while checking the budget status, the display screen will change, providing data in a visually easy-to-understand format. This system adapts to the individual user's state, providing a personalized experience. 【0665】 The generating AI model will consider prompts such as, "Please tell me how to check fluctuations in the data center's monthly budget, analyze the operators' sentiment, and take appropriate measures." 【0666】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0667】 Step 1: 【0668】 The server periodically retrieves budget data from the data storage device using an API. In this step, relevant budget information is input from the data storage, and the retrieved data is output. Specifically, the server accesses an online spreadsheet program to download the latest budget information. 【0669】 Step 2: 【0670】 The server analyzes the acquired data using a Python-based natural language processing library. The input is acquired budget data, and the output is the analysis results, with data integrity verified. In detail, this step involves the server calculating budget variances for each project and evaluating for inconsistencies. 【0671】 Step 3: 【0672】 The server uses the Face++ API, etc., to recognize the user's emotions. For this recognition, it uses the camera and microphone of smart glasses or a smartphone. The input is the user's real-time video and audio data, and the output is the result of an analysis of the user's emotional state. In this step, the server identifies the emotions the user is expressing (e.g., stress). 【0673】 Step 4: 【0674】 The server adjusts the data display using information visualization tools based on the sentiment analysis results obtained in the previous step. The input is the sentiment analysis results and analysis data, and the output is a user-optimized visualization. Specifically, it changes the colors and data arrangement according to the operator's emotions to present the information in an easy-to-understand manner. 【0675】 Step 5: 【0676】 The user reviews the data visualization results and provides various feedback through an interface provided by the server. This step may involve further data refinement or requests based on user actions. Based on the output, the user decides on their next action. 【0677】 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. 【0678】 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. 【0679】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0680】 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. 【0681】 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. 【0682】 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. 【0683】 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. 【0684】 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. 【0685】 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." 【0686】 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. 【0687】 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. 【0688】 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. 【0689】 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. 【0690】 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. 【0691】 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. 【0692】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0693】 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. 【0694】 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. 【0695】 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. 【0696】 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. 【0697】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0698】 The following is further disclosed regarding the embodiments described above. 【0699】 (Claim 1) 【0700】 A means of retrieving data from data storage, 【0701】 A data analysis method for analyzing acquired data, 【0702】 A difference calculation method that compares the analyzed data and calculates the monthly difference, 【0703】 A data visualization method for visualizing the results of difference calculations, 【0704】 A system that includes this. 【0705】 (Claim 2) 【0706】 The system according to claim 1, wherein the data storage is online spreadsheet software. 【0707】 (Claim 3) 【0708】 The system according to claim 1, wherein the data analysis is performed using natural language processing. 【0709】 "Example 1" 【0710】 (Claim 1) 【0711】 A means of obtaining information from a data storage device, 【0712】 Information analysis tools for analyzing acquired information, 【0713】 A difference calculation method that compares the analyzed information and calculates the periodic difference, 【0714】 Information visualization means for visualizing the results of difference calculation, 【0715】 An interactive interface that supports decision-making based on visualized results, 【0716】 A system that includes this. 【0717】 (Claim 2) 【0718】 The system according to claim 1, wherein the data storage device is an online spreadsheet program. 【0719】 (Claim 3) 【0720】 The system according to claim 1, wherein the aforementioned information analysis is performed using natural language processing. 【0721】 "Application Example 1" 【0722】 (Claim 1) 【0723】 Means for acquiring information from an information storage device, 【0724】 Information analysis means for analyzing acquired information, 【0725】 A difference calculation means that compares the analyzed information and calculates the periodic difference, 【0726】 Information display means for displaying the results of difference calculation, 【0727】 A means for notifying the administrator based on the results in a portable device, 【0728】 A system that includes this. 【0729】 (Claim 2) 【0730】 The system according to claim 1, wherein the information storage device is an online spreadsheet application. 【0731】 (Claim 3) 【0732】 The system according to claim 1, wherein the aforementioned information analysis is performed using natural language processing technology. 【0733】 "Example 2 of combining an emotion engine" 【0734】 (Claim 1) 【0735】 Means for obtaining information from a data set, 【0736】 Information analysis means for analyzing acquired information, 【0737】 A difference calculation means that compares the analyzed information and calculates the periodic difference, 【0738】 Information visualization means for visualizing the results of difference calculation, 【0739】 An emotion recognition means for recognizing the user's emotions and adapting to them individually, 【0740】 A system that includes this. 【0741】 (Claim 2) 【0742】 The system according to claim 1, wherein the data set is online computing software. 【0743】 (Claim 3) 【0744】 The system according to claim 1, wherein the aforementioned information analysis is performed using natural language processing. 【0745】 "Application example 2 when combining with an emotional engine" 【0746】 (Claim 1) 【0747】 Means for obtaining information from a data storage device, 【0748】 Information analysis tools for analyzing acquired information, 【0749】 A change calculation method that compares the analyzed information and calculates monthly changes, 【0750】 Information visualization means for visualizing the results of change calculations, 【0751】 A means of recognizing emotions to recognize the emotions of those who manipulate information and to adjust the display of that information, 【0752】 A system that includes this. 【0753】 (Claim 2) 【0754】 The system according to claim 1, wherein the information storage device is an online spreadsheet program. 【0755】 (Claim 3) 【0756】 The system according to claim 1, wherein the aforementioned information analysis is performed using natural language processing. [Explanation of symbols] 【0757】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of retrieving data from data storage, A data analysis method for analyzing acquired data, A difference calculation method that compares the analyzed data and calculates the monthly difference, A data visualization method for visualizing the results of difference calculations, A system that includes this. [Claim 2] The system according to claim 1, wherein the data storage is online spreadsheet software. [Claim 3] The system according to claim 1, wherein the data analysis is performed using natural language processing.

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