system

Abstract

We provide the system. [Solution] A means by which a server retrieves data from multiple data sources, A method for integrating data acquired by the server into a single dataset, A server uses an AI algorithm to analyze data and evaluate the importance and urgency of tasks. A method for prioritizing and listing tasks based on the data evaluated by the server, A means for generating a user interface that displays the server's prioritized tasks, 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 method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Within a company, information from various data sources is in disarray, making it extremely difficult to determine the priority of important tasks. As a result, there is a problem that business efficiency decreases and there is a possibility of overlooking important tasks. Specifically, it is necessary to obtain information from separate data sources such as email services, message platforms, and calendar services, and a lot of time and effort are required to manage these individually. Therefore, there is a demand for unified management of various information and realization of efficient task management. 【Means for Solving the Problems】 【0005】 To solve the above problems, the present invention provides a means for a server to acquire data from multiple data sources. The acquired data is integrated into a single dataset. Furthermore, the server uses an AI algorithm to analyze the integrated data and evaluate the importance and urgency of each task. Based on the evaluation results, tasks are prioritized and listed. The server then generates a user interface for displaying the prioritized tasks, enabling users to manage tasks efficiently. In addition, by using natural language processing to evaluate tasks, more accurate determination of task importance is possible. This enables centralized management of internal company information and effective task management. 【0006】 A "data source" is the origin from which information is obtained, and refers to services such as email services, messaging platforms, and calendar services. 【0007】 A "server" is a central processing unit that acquires, integrates, and analyzes data from data sources, and provides evaluation results to users. 【0008】 A "dataset" is a collection of information that a server has gathered from multiple data sources and put together into one. 【0009】 An "AI algorithm" is a computational method based on artificial intelligence, used to evaluate the importance and urgency of a task. 【0010】 A "user interface" refers to the screen or platform that allows users to visually check and interact with the server's processing results. 【0011】 "Natural language processing" is a technology that uses computers to analyze, understand, and process human language, and is used to analyze and evaluate the content of a task. [Brief explanation of the drawing] 【0012】 [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 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. 【0014】 First, the terms used in the following description will be explained. 【0015】 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. 【0016】 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. 【0017】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0018】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. 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), and the like. 【0019】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0023】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0024】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0025】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0027】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0030】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0031】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0032】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0033】 The system of this invention is designed to centrally manage information from data sources, enabling users to efficiently manage important tasks. First, the server retrieves data from multiple data sources, including email services, messaging platforms, and calendar services. This aggregates all information relevant to the user. 【0034】 Next, the server integrates the acquired information into a single dataset. This dataset includes information such as emails, messages, and calendar events, and is formatted into a consistent format within the server. 【0035】 The server applies an AI algorithm to this integrated dataset. The AI ​​algorithm learns from past processing history and uses natural language processing techniques to analyze the importance and urgency of tasks. This calculates a score to be assigned to each task. 【0036】 Based on the calculated score, the server automatically prioritizes and lists tasks. This allows users to instantly see the most important tasks, improving work efficiency. 【0037】 Furthermore, the server generates a user interface and provides the functionality to display prioritized tasks on the user's terminal. The user interface is designed to be visually intuitive, and each task has a link to detailed information. Users can access this interface on their terminal and easily manage and review the contents of their tasks. 【0038】 As a concrete example, when a user opens their terminal at the start of the workday, the server retrieves recent emails, messages, and calendar events and evaluates the tasks. Tasks are then displayed in order of priority, allowing the user to view the list and ensure each task is completed. This process prevents users from overlooking information and provides an environment where they can focus on important tasks. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 The server uses a scheduler to periodically connect to the APIs of each data source (email service, messaging platform, calendar service) to collect new data. The data is stored on the server in a structured format, including subject, body, sender, and time information. 【0042】 Step 2: 【0043】 The server integrates all acquired data into a single dataset. The data is standardized and stored in a consistent format, enabling subsequent analysis. 【0044】 Step 3: 【0045】 The server analyzes text data from the integrated dataset using natural language processing techniques. It extracts important keywords from messages and email content to understand the meaning and context of tasks. Based on the analysis results, the server calculates a score for each task. This score is used to assess importance and urgency. 【0046】 Step 4: 【0047】 The server prioritizes tasks based on the calculated score. Tasks are sorted by importance and urgency and saved in a list format. This makes it clear which tasks should be handled first. 【0048】 Step 5: 【0049】 The server generates a user interface to present the user with an organized task list. This interface is visually structured to make each task easy to understand intuitively. 【0050】 Step 6: 【0051】 The device displays a generated interface to the user, allowing them to check the status of tasks in real time. The user can select any task from the displayed task list to view detailed information and take action as needed. 【0052】 (Example 1) 【0053】 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." 【0054】 In both professional and personal work, it is essential to properly manage information from various sources and efficiently prioritize tasks. However, centrally integrating diverse information obtained from multiple sources and evaluating its importance and urgency is not easy. This leads to issues such as missing information or misjudging priorities, ultimately resulting in decreased work efficiency. 【0055】 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. 【0056】 In this invention, the server includes means for the information processing device to acquire information from multiple information sources, means for the information processing device to integrate the acquired information into a single information collection, and means for the information processing device to analyze the information using artificial intelligence techniques and evaluate the importance and urgency of the tasks. As a result, information is properly managed, users can concentrate on the most important tasks, and work efficiency can be greatly improved. 【0057】 An "information processing device" is a device used to acquire, integrate, analyze, and display information. 【0058】 An "information source" is a medium from which an information processing device acquires information, such as email, messaging services, or calendar services. 【0059】 An "information collection" is a collective entity that manages information integrated by an information processing device. 【0060】 "Artificial intelligence methods" are technologies that utilize techniques such as machine learning and natural language processing to analyze information. 【0061】 "Importance" is a measure that indicates the relative importance of a task or work. 【0062】 "Urgency" is a measure that indicates the degree to which a task or work needs to be done urgently. 【0063】 A "user interface" is a screen generated by an information processing device that allows the user to check and manage information and details of their work. 【0064】 "Natural language technology" refers to technologies that enable computers to understand and analyze human language. 【0065】 A "generative artificial intelligence model" is an artificial intelligence model designed to analyze and evaluate information using natural language processing techniques. 【0066】 A description of embodiments for carrying out the present invention will be provided. 【0067】 This system is designed to allow users to efficiently manage important tasks by collecting information from various sources and managing it centrally. 【0068】 First, the server retrieves information from multiple sources, including email services, messaging platforms, and calendar services. These sources include common email platforms, messaging applications, and online calendar tools. The server uses APIs to access these sources and extract the necessary data. 【0069】 Next, the server integrates the acquired information into a single data set. Since the information is acquired in different formats, the server formats it into a unified format. This formatting process often utilizes libraries such as the pandas library in the Python programming language. 【0070】 Next, the server applies a generative AI model to the integrated information. Specifically, it analyzes the information using natural language processing techniques to evaluate the importance and urgency of the tasks. This analysis utilizes natural language processing techniques such as GPT-4®, and each task is assigned a relative score. 【0071】 Furthermore, the server prioritizes tasks based on the generated information and creates a user interface for displaying it to the user. The user interface has an intuitive and visually easy-to-understand design, and is designed to allow users to manage their tasks efficiently. The JavaScript® library React is used to build the interface. 【0072】 As a concrete example, when a user opens their terminal at the start of their workday, the server retrieves recent emails, messages, and calendar events, integrates them, and analyzes them. Then, a list of tasks, prioritized based on importance and urgency, is displayed on the terminal. An example of a prompt message is, "Show priority tasks that need to be completed within the next 24 hours." 【0073】 This system will enable users to prevent overlooking information and significantly improve work efficiency. 【0074】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0075】 Step 1: 【0076】 The server retrieves information from multiple sources. These sources include email services, messaging platforms, and calendar services. Specifically, the server collects data from each source via APIs. For example, it might use an email API to retrieve the body, sender, and date / time of unread emails. It receives access information from each source as input and obtains a raw dataset containing the content of emails and messages as output. 【0077】 Step 2: 【0078】 The server formats the acquired information into a consistent format and compiles it into an integrated data set. Here, the server uses the Python pandas library to convert multiple data formats into a single unified format. For example, it creates a dataframe containing email body, message text, and calendar event details. It takes a raw dataset as input and obtains an integrated dataset as output. 【0079】 Step 3: 【0080】 The server uses a generated AI model to analyze the importance and urgency of the integrated information. The AI ​​model applies natural language processing techniques to understand the context of each task and assign a score. For example, important meetings and tasks with approaching deadlines receive high scores. It takes an integrated dataset as input and outputs a list with each task assigned a score. 【0081】 Step 4: 【0082】 The server prioritizes tasks based on the analyzed data and creates a list of tasks in order of their impact on the user. A Python script is used to sort tasks by their score. It takes a list of scored tasks as input and outputs a prioritized task list. 【0083】 Step 5: 【0084】 The server generates a user interface for displaying a prioritized task list and sends it to the terminal. Here, React is used to build an intuitive UI for the user. Each table row contains a link to a detail page. It takes a prioritized task list as input and generates a user-viewable HTML page as output. 【0085】 Step 6: 【0086】 The user accesses the user interface from their device and begins working based on the displayed task list. The user then reviews the details of tasks in order of priority, opening emails, replying to them, or adjusting calendar appointments. The system receives a generated HTML page as input and the user performs specific tasks as output. 【0087】 (Application Example 1) 【0088】 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." 【0089】 In operating smart cities, efficiently managing information collected from diverse sources within the city and accurately and quickly prioritizing critical operational tasks is challenging. Conventional technologies have insufficient integration of information from multiple sources, requiring significant time and effort from operators to properly evaluate tasks. 【0090】 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. 【0091】 In this invention, the server includes means for acquiring information from multiple information sources, means for integrating the acquired information into a single information set, and means for analyzing the information using an AI algorithm and evaluating the importance and urgency of tasks. This makes it possible to automatically manage and prioritize tasks that are important for the operation and management of a smart city. 【0092】 A "server" is a computer system used to collect, integrate, and analyze information. It is a device that acquires and manages data from multiple information sources. 【0093】 "Information sources" refer to various means of providing specific data or information, such as email services, messaging platforms, calendar services, traffic information, and citizen reports. 【0094】 An "information collection" refers to a dataset created by a server that integrates data from multiple sources and presents it in a consistent format. 【0095】 An "AI algorithm" is a computational method that learns from past data and processing history, and uses natural language processing technology to analyze the importance and urgency of information. 【0096】 "Prioritizing tasks" is the process of rearranging tasks based on the results of information analysis, taking into account the urgency and importance of each task. 【0097】 A "user interface" refers to an interface that visually displays information so that users can check and manage their work. 【0098】 A "smart city" is a concept that utilizes information and communication technology to improve the overall efficiency of a city and optimize urban management. 【0099】 This invention is a system aimed at improving operational efficiency in smart cities. The server automatically acquires data from various information sources within the city. Specifically, it periodically collects data from traffic information systems and citizen reporting applications. This allows the server to obtain real-time information about the city. 【0100】 The server integrates the acquired information and combines it into a single data set. This is done using a database management system (e.g., PostgreSQL) to convert data in different formats into a consistent format. Next, the server uses AI algorithms and natural language processing techniques to automatically assess the importance and urgency of each task. Specifically, it uses Python natural language processing libraries (e.g., NLTK and spaCy) to extract key keywords from the task content and identify tasks requiring immediate processing. 【0101】 The server then displays the prioritized tasks in a user interface. This interface is built using frameworks such as React Native, allowing administrators to visually understand the status of tasks on their smartphones or tablets. For example, if a city is affected by flooding, it is possible to prioritize and display a list of high-priority tasks based on reports from citizens and weather forecast data. This allows administrators to make quick decisions. 【0102】 A concrete example of a prompt is: "Help design a smart city management application that collects task information from multiple data sources, uses AI to determine importance, and adjusts priorities to improve the operational efficiency of the smart city." This prompt allows the generated AI model to provide solutions tailored to the operational management needs. 【0103】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0104】 Step 1: 【0105】 The server automatically retrieves data from various sources within the city. Inputs include data provided via APIs from traffic information systems and citizen reporting applications. This data is stored in temporary storage on the server. Outputs are raw data collected from each source. 【0106】 Step 2: 【0107】 The server integrates the collected raw data and transforms it into a single, consistent set of information. The input is the raw data from each source obtained in Step 1. A database management system (e.g., PostgreSQL) is used to unify the format and structure of the data. The output is the integrated set of information, which makes it easier to compare different types of data. 【0108】 Step 3: 【0109】 The server applies an AI algorithm to the integrated information set to analyze the importance and urgency of tasks. The input is the integrated information set obtained in step 2. Using a natural language processing library (e.g., NLTK or spaCy), important keywords are extracted from the task content. This process assigns a score to each task, and the output is scoring information based on importance and urgency. 【0110】 Step 4: 【0111】 The server prioritizes and lists tasks based on the scoring information. The input is the scoring information determined in step 3. The tasks are sorted in descending order of priority, and the output is a prepared list of tasks for display in the user interface. 【0112】 Step 5: 【0113】 The terminal visually displays a prioritized list of tasks retrieved from the server. The input is the output from step 4. Using the React Native framework, an interface is created that allows users to easily check the details of the tasks. The output is a visual task management screen that helps administrators make quick decisions. 【0114】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0115】 This invention provides a novel system that combines user emotion recognition with conventional data integration and task management systems. This system not only manages information from data sources but also provides information tailored to the user's emotional state. 【0116】 First, the server continuously retrieves and integrates data from email services, messaging platforms, and calendar services. This centralizes all business information related to the user. The server then uses standard AI algorithms to evaluate the importance and urgency of tasks based on this integrated data. 【0117】 Furthermore, the server is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's facial expressions and tone of voice via sensor devices such as cameras and microphones to infer their current emotional state. 【0118】 Based on this, the server combines the user's emotions with AI-driven task evaluation to optimize task prioritization. For example, if a user is feeling stressed, the server can adjust to reduce the user's burden by prioritizing important but low-urgency tasks. 【0119】 The server generates a user interface that reflects the emotional information recognized by the emotion engine and displays it on the user's device. This interface aims to provide the user with the most efficient way to work on tasks by changing the color scheme and how tasks are displayed in response to specific emotions. 【0120】 For example, if the emotion engine detects tension or pressure during a user's daily work, the server adjusts the task order and displays appropriate support messages to provide a comfortable work environment. This process enables users to perform tasks optimally according to their emotional state. 【0121】 The following describes the processing flow. 【0122】 Step 1: 【0123】 The server periodically accesses the APIs of each data source according to the scheduler to retrieve the latest data such as emails, messages, and calendar information. This data is temporarily stored on the server in preparation for subsequent processing. 【0124】 Step 2: 【0125】 The server converts the acquired data into a standard format and organizes it into a single, unified dataset. This includes filtering the data, removing duplicates, and tagging it as needed. 【0126】 Step 3: 【0127】 The server uses an AI algorithm to analyze each task in the integrated dataset and assess its importance and urgency. This assigns an evaluation score to each task. Tasks that are both important and urgent receive a high score. 【0128】 Step 4: 【0129】 The server activates an emotion engine to recognize the user's emotions via sensor devices. For example, it captures facial expressions with a camera and analyzes their emotional state in real time. Simultaneously, it also picks up emotions from voice to improve accuracy. 【0130】 Step 5: 【0131】 The server utilizes the collected emotional data to adjust task priorities based on the user's current emotional state. For example, if a user is feeling stressed, the server will prioritize tasks that are easier. 【0132】 Step 6: 【0133】 The server generates the user interface based on the adjusted task list. It optimizes the screen's color scheme and layout according to the user's emotions, displaying it in a user-friendly format. 【0134】 Step 7: 【0135】 The device displays a user interface and provides the user with a list of current tasks. Based on this information, the user can efficiently process tasks according to the displayed priority. 【0136】 (Example 2) 【0137】 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." 【0138】 In recent years, improving user work efficiency has required centralized information management and streamlined task management. However, conventional systems process tasks mechanically without considering the user's emotional state, potentially increasing user stress and workload. Therefore, a system is needed that can dynamically adjust task priorities according to the user's emotions, enabling efficient work execution. 【0139】 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. 【0140】 In this invention, the server includes means for an information device to acquire information from multiple information sources, means for integrating the acquired information into a single information set, means for the information device to analyze the information using a machine learning algorithm and evaluate the importance and urgency of work items, and means for the information device to analyze the user's emotional state using a sensory recognition device. This enables optimal task prioritization and efficient work execution in accordance with the user's emotions. 【0141】 "Information equipment" refers to mechanical devices that process and manage information, including servers and computers. 【0142】 "Information source" refers to external services or platforms that provide data, such as email services, messaging platforms, and calendar services. 【0143】 An "information set" refers to a dataset created by combining and integrating acquired data into a single entity. 【0144】 A "machine learning algorithm" refers to a type of artificial intelligence technique used to analyze data and discover specific rules or patterns. 【0145】 A "task item" refers to a task or work that a user must process or complete. 【0146】 "Importance" refers to a criterion for evaluating the value and priority that a work item holds in relation to the user's goals and overall business operations. 【0147】 "Urgency" refers to an indicator that shows how quickly a task item should be addressed. 【0148】 A "sensory recognition device" refers to devices such as sensors, cameras, and microphones used to detect a user's emotions and physical state. 【0149】 "Emotional state" refers to the user's current emotions and psychological condition, including stress, relief, and tension. 【0150】 "User interface" refers to the display and operation screen that enables interaction between the user and the information device. 【0151】 In this invention, a server, which is an information device, plays a central role in providing a multi-functional task management system to improve the user's work efficiency. First, the server retrieves data from multiple sources, such as email services, messaging platforms, and calendar services, using APIs. The retrieved data is stored in a database system within the server (e.g., general database management software) and processed as an integrated information set. 【0152】 The server then uses machine learning algorithms to analyze the integrated data and assess the importance and urgency of each work item. In doing so, the server leverages existing generative AI models to efficiently analyze the data. This allows users to flexibly respond to unexpected schedule changes or shifts in work priorities. 【0153】 Furthermore, the server is connected to a sensory recognition device that analyzes the user's emotional state in real time via a camera and microphone. The emotional state is inferred from the user's facial expressions and tone of voice. Based on this, the server re-evaluates the priority of work items and presents tasks that are appropriate to the user's psychological state. 【0154】 The completed user interface is delivered to the user's device. This interface is customized according to the user's emotional state to support efficient task management. For example, if emotional data indicating that the user is stressed is detected by a sensory recognition device, the server will adjust the interface to present the user with high-priority but low-urgency tasks first, thereby reducing their psychological burden. 【0155】 As a concrete example, the server manages the user's schedule while displaying relaxing messages at appropriate times. This allows users to maintain high work performance while preserving their mental well-being. 【0156】 Furthermore, the overall system flow is constructed by providing instructions such as, "Design a system that retrieves data from the user's emails, messages, and calendar, uses an AI algorithm to evaluate the importance and urgency of tasks, and uses a camera and microphone to recognize emotions. Explain, with specific examples, how to adjust task priorities when the user is feeling stressed." 【0157】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0158】 Step 1: 【0159】 The server retrieves data from multiple sources. Specifically, it periodically collects information from email services, messaging platforms, and calendar services using APIs. This input data is obtained in JSON format, which the server parses and extracts the necessary information. The output is a separate set of information before it is integrated into the server's database. 【0160】 Step 2: 【0161】 The server integrates the acquired data into a single information set. The server stores each information set in a database and performs schema mapping and data integrity checks to combine data from different sources. This results in a unified information set. Specifically, it uses a database management system to perform table joins and filtering. 【0162】 Step 3: 【0163】 The server uses a machine learning algorithm to analyze the data set and evaluate the importance and urgency of the work items. The integrated data set is fed into the machine learning model as input. The server uses a generative AI model to calculate a score based on importance and urgency. As a result of this analysis, an evaluated task list is output. Specifically, each task is scored by weighting it according to a particular evaluation metric. 【0164】 Step 4: 【0165】 The server uses sensory recognition devices to analyze the user's emotional state. The server receives real-time data of the user's facial expressions and voice through cameras and microphones. This input data is processed by sensing algorithms to infer the user's emotional state. Emotional labels such as stress and relaxation are generated as output. Image processing and voice analysis technologies are used for the analysis. 【0166】 Step 5: 【0167】 The server optimizes work item priorities by combining emotional data and work item evaluations. Using emotional state labels and an evaluated task list as input, the server calculates new priorities. Specifically, it modifies the importance and urgency scores of tasks based on the emotional state and rearranges the list. The output provides a list of optimized tasks. 【0168】 Step 6: 【0169】 The server generates a user interface displaying optimized work items and delivers it to the terminal. The server uses the optimized task list to generate the interface using a front-end framework and sends it to the terminal in HTML format or similar. Inputs include prioritized task data and emotion-based UI settings, while the output provides an interface displayed with color settings and layout appropriate to the user's emotional state. 【0170】 Step 7: 【0171】 Users manage their work items using a user interface generated by the system. Through the interface displayed on the terminal, users can check the progress of tasks and take action as needed. This enables effective work processing based on information that is dynamically updated in real time. 【0172】 (Application Example 2) 【0173】 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". 【0174】 Traditional task management systems only focused on integrating information and simple prioritization, failing to consider the user's emotional state. This meant that important tasks were sometimes prioritized even when the user was stressed, making effective performance difficult. Furthermore, the lack of user-friendly interface design also contributed to decreased work efficiency. 【0175】 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. 【0176】 In this invention, the server includes means for an information processing device to recognize the user's emotional state using an emotion analysis device and determine an optimized work priority by combining the emotional state and work evaluation; means for the information processing device to generate an operation screen that displays the prioritized work in a display method that is careful not to affect the emotional state; and means for evaluating work using natural language processing and making work suggestions based on the emotional state. This enables optimal work processing according to the user's emotional state. 【0177】 An "information processing device" is a computer system used to integrate and analyze information obtained from multiple sources. 【0178】 A "machine learning algorithm" is a method that uses mathematical models to learn and recognize patterns in data analysis. 【0179】 An "emotion analysis device" is a device that analyzes data such as facial expressions and voice in order to recognize the user's emotional state. 【0180】 An "operation screen" is a visual interface for users to manage their work, and it is a screen that displays information with consideration for the user's feelings. 【0181】 "Natural language processing" is a technology that enables computers to understand and analyze human speech. 【0182】 A "user" is an individual or organization that uses this system to manage their work. 【0183】 The system for carrying out the present invention includes an information processing device, an emotion analysis device, a display device, and network communication means. The information processing device is designed to acquire and integrate information from multiple sources, including various schedule management services and communication platforms. The acquired information is analyzed by machine learning algorithms to evaluate the importance and urgency of the work. 【0184】 The emotion analysis device uses a camera and microphone to analyze the user's facial expressions and voice tone in real time, and estimates the user's emotional state. This analysis uses OpenCV, and for voice analysis, it uses Google® Cloud Speech-to-Text API. 【0185】 The server determines task priorities optimized for the user based on acquired emotional data and task evaluation results, and generates an operation screen with a display method that takes the user's emotional state into consideration. By utilizing natural language processing technology, it is designed so that users can receive task suggestions that are tailored to their emotions. 【0186】 The server aims to provide users with a more comfortable work environment and supports optimal work management regardless of the user's state. For example, if a user is seeking relaxation, it can move lower-priority tasks to the front and play relaxing background sounds. 【0187】 An example of a prompt to input into a generative AI model is, "Suggest an optimal task schedule for a user who is relaxed." 【0188】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0189】 Step 1: 【0190】 The server collects data from the information source. 【0191】 The server retrieves data from information sources such as schedule management services and messaging platforms via APIs and integrates all relevant information into a single data set. At this time, the retrieved data is input and processed to standardize and format each piece of data. 【0192】 Step 2: 【0193】 The server analyzes the integrated data. 【0194】 The server uses machine learning algorithms to analyze integrated data and assess the importance and urgency of each task. The input is an integrated set of information, and a task evaluation score is output. This provides the basis for prioritizing the data. 【0195】 Step 3: 【0196】 The device recognizes the emotional state. 【0197】 Using a camera and microphone connected to the device, the system collects the user's facial expressions and audio data, which are then analyzed in real time by an emotion analysis device. The input consists of audio and video data, and the output is the user's emotional state. OpenCV and the Google Cloud Speech-to-Text API are used for this analysis. 【0198】 Step 4: 【0199】 The server determines the task priority. 【0200】 The server combines the work evaluation score and the user's emotional state to determine the optimal work priority. The inputs are the work evaluation score and the emotional state, and a priority list is formed based on this combination. Tasks are then arranged in the order that best suits the user's current emotional state. 【0201】 Step 5: 【0202】 The terminal generates the user interface. 【0203】 The terminal generates and displays an operation screen to the user based on a priority list provided by the server, using colors and layouts that take the user's emotional state into consideration. The input is the priority list, and the output is a visually optimized operation screen. The color scheme and music are adjusted to create a relaxing environment for the user. 【0204】 Step 6: 【0205】 Users manage their tasks through the user interface. 【0206】 The user selects and executes the presented tasks using the operation screen displayed on the terminal. The system records the user's actions as a log, and this data is used for evaluating future tasks. The input is the user's actions, and the output is the result of those actions. 【0207】 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. 【0208】 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. 【0209】 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. 【0210】 [Second Embodiment] 【0211】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0212】 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. 【0213】 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). 【0214】 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. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 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". 【0223】 The system of this invention is designed to centrally manage information from data sources, enabling users to efficiently manage important tasks. First, the server retrieves data from multiple data sources, including email services, messaging platforms, and calendar services. This aggregates all information relevant to the user. 【0224】 Next, the server integrates the acquired information into a single dataset. This dataset includes information such as emails, messages, and calendar events, and is formatted into a consistent format within the server. 【0225】 The server applies an AI algorithm to this integrated dataset. The AI ​​algorithm learns from past processing history and uses natural language processing techniques to analyze the importance and urgency of tasks. This calculates a score to be assigned to each task. 【0226】 Based on the calculated score, the server automatically prioritizes and lists tasks. This allows users to instantly see the most important tasks, improving work efficiency. 【0227】 Furthermore, the server generates a user interface and provides the functionality to display prioritized tasks on the user's terminal. The user interface is designed to be visually intuitive, and each task has a link to detailed information. Users can access this interface on their terminal and easily manage and review the contents of their tasks. 【0228】 As a concrete example, when a user opens their terminal at the start of the workday, the server retrieves recent emails, messages, and calendar events and evaluates the tasks. Tasks are then displayed in order of priority, allowing the user to view the list and ensure each task is completed. This process prevents users from overlooking information and provides an environment where they can focus on important tasks. 【0229】 The following describes the processing flow. 【0230】 Step 1: 【0231】 The server uses a scheduler to periodically connect to the APIs of each data source (email service, messaging platform, calendar service) to collect new data. The data is stored on the server in a structured format, including subject, body, sender, and time information. 【0232】 Step 2: 【0233】 The server integrates all acquired data into a single dataset. The data is standardized and stored in a consistent format, enabling subsequent analysis. 【0234】 Step 3: 【0235】 The server analyzes text data from the integrated dataset using natural language processing techniques. It extracts important keywords from messages and email content to understand the meaning and context of tasks. Based on the analysis results, the server calculates a score for each task. This score is used to assess importance and urgency. 【0236】 Step 4: 【0237】 The server prioritizes tasks based on the calculated score. Tasks are sorted by importance and urgency and saved in a list format. This makes it clear which tasks should be handled first. 【0238】 Step 5: 【0239】 The server generates a user interface to present the user with an organized task list. This interface is visually structured to make each task easy to understand intuitively. 【0240】 Step 6: 【0241】 The device displays a generated interface to the user, allowing them to check the status of tasks in real time. The user can select any task from the displayed task list to view detailed information and take action as needed. 【0242】 (Example 1) 【0243】 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." 【0244】 In both professional and personal work, it is essential to properly manage information from various sources and efficiently prioritize tasks. However, centrally integrating diverse information obtained from multiple sources and evaluating its importance and urgency is not easy. This leads to issues such as missing information or misjudging priorities, ultimately resulting in decreased work efficiency. 【0245】 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. 【0246】 In this invention, the server includes means for the information processing device to acquire information from multiple information sources, means for the information processing device to integrate the acquired information into a single information collection, and means for the information processing device to analyze the information using artificial intelligence techniques and evaluate the importance and urgency of the tasks. As a result, information is properly managed, users can concentrate on the most important tasks, and work efficiency can be greatly improved. 【0247】 An "information processing device" is a device used to acquire, integrate, analyze, and display information. 【0248】 An "information source" is a medium from which an information processing device acquires information, such as email, messaging services, or calendar services. 【0249】 An "information collection" is a collective entity that manages information integrated by an information processing device. 【0250】 "Artificial intelligence methods" are technologies that utilize techniques such as machine learning and natural language processing to analyze information. 【0251】 "Importance" is a measure that indicates the relative importance of a task or work. 【0252】 "Urgency" is a measure that indicates the degree to which a task or work needs to be done urgently. 【0253】 A "user interface" is a screen generated by an information processing device that allows the user to check and manage information and details of their work. 【0254】 "Natural language technology" refers to technologies that enable computers to understand and analyze human language. 【0255】 A "generative artificial intelligence model" is an artificial intelligence model designed to analyze and evaluate information using natural language processing techniques. 【0256】 A description of embodiments for carrying out the present invention will be provided. 【0257】 This system is designed to allow users to efficiently manage important tasks by collecting information from various sources and managing it centrally. 【0258】 First, the server retrieves information from multiple sources, including email services, messaging platforms, and calendar services. These sources include common email platforms, messaging applications, and online calendar tools. The server uses APIs to access these sources and extract the necessary data. 【0259】 Next, the server integrates the acquired information into a single data set. Since the information is acquired in different formats, the server formats it into a unified format. This formatting process often utilizes libraries such as the pandas library in the Python programming language. 【0260】 Next, the server applies a generative AI model to the integrated information. Specifically, it uses natural language processing techniques to analyze the information and evaluate the importance and urgency of the tasks. This analysis utilizes natural language processing techniques such as GPT-4, and each task is assigned a relative score. 【0261】 Furthermore, the server prioritizes tasks based on the generated information and creates a user interface for displaying it to the user. The user interface is designed to be intuitive and visually easy to understand, enabling users to manage their tasks efficiently. The JavaScript library React is used to build the interface. 【0262】 As a concrete example, when a user opens their terminal at the start of their workday, the server retrieves recent emails, messages, and calendar events, integrates them, and analyzes them. Then, a list of tasks, prioritized based on importance and urgency, is displayed on the terminal. An example of a prompt message is, "Show priority tasks that need to be completed within the next 24 hours." 【0263】 This system will enable users to prevent overlooking information and significantly improve work efficiency. 【0264】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0265】 Step 1: 【0266】 The server retrieves information from multiple sources. These sources include email services, messaging platforms, and calendar services. Specifically, the server collects data from each source via APIs. For example, it might use an email API to retrieve the body, sender, and date / time of unread emails. It receives access information from each source as input and obtains a raw dataset containing the content of emails and messages as output. 【0267】 Step 2: 【0268】 The server formats the acquired information into a consistent format and compiles it into an integrated data set. Here, the server uses the Python pandas library to convert multiple data formats into a single unified format. For example, it creates a dataframe containing email body, message text, and calendar event details. It takes a raw dataset as input and obtains an integrated dataset as output. 【0269】 Step 3: 【0270】 The server uses a generated AI model to analyze the importance and urgency of the integrated information. The AI ​​model applies natural language processing techniques to understand the context of each task and assign a score. For example, important meetings and tasks with approaching deadlines receive high scores. It takes an integrated dataset as input and outputs a list with each task assigned a score. 【0271】 Step 4: 【0272】 The server prioritizes tasks based on the analyzed data and creates a list of tasks in order of their impact on the user. A Python script is used to sort tasks by their score. It takes a list of scored tasks as input and outputs a prioritized task list. 【0273】 Step 5: 【0274】 The server generates a user interface for displaying a prioritized task list and sends it to the terminal. Here, React is used to build an intuitive UI for the user. Each table row contains a link to a detail page. It takes a prioritized task list as input and generates a user-viewable HTML page as output. 【0275】 Step 6: 【0276】 The user accesses the user interface from their device and begins working based on the displayed task list. The user then reviews the details of tasks in order of priority, opening emails, replying to them, or adjusting calendar appointments. The system receives a generated HTML page as input and the user performs specific tasks as output. 【0277】 (Application Example 1) 【0278】 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." 【0279】 In operating smart cities, efficiently managing information collected from diverse sources within the city and accurately and quickly prioritizing critical operational tasks is challenging. Conventional technologies have insufficient integration of information from multiple sources, requiring significant time and effort from operators to properly evaluate tasks. 【0280】 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. 【0281】 In this invention, the server includes means for acquiring information from multiple information sources, means for integrating the acquired information into a single information set, and means for analyzing the information using an AI algorithm and evaluating the importance and urgency of tasks. This makes it possible to automatically manage and prioritize tasks that are important for the operation and management of a smart city. 【0282】 A "server" is a computer system used to collect, integrate, and analyze information. It is a device that acquires and manages data from multiple information sources. 【0283】 "Information sources" refer to various means of providing specific data or information, such as email services, messaging platforms, calendar services, traffic information, and citizen reports. 【0284】 "Information aggregation" means a dataset that the server integrates data obtained from multiple information sources and organizes it in a consistent format. 【0285】 "AI algorithm" is a computational method for learning past data and processing histories and analyzing the importance and urgency of information using natural language processing technology. 【0286】 "Business prioritization" is a process of rearranging business operations based on the results of information analysis, considering the urgency and importance of responses. 【0287】 "User interface" refers to an interface for visually displaying information so that users can view and manage business content. 【0288】 "Smart city" means a concept that utilizes information and communication technologies to improve the efficiency of the entire city and optimize urban management. 【0289】 This invention is a system aimed at improving the operational efficiency in a smart city. The server automatically acquires data from various information sources within the city. Specifically, it regularly collects data from traffic information systems and citizen reporting applications. As a result, the server can obtain real-time information within the city. 【0290】 The server integrates the acquired information and organizes it into a single information aggregation. For this, a database management system (e.g., PostgreSQL) is used to convert data in different formats into a consistent format. Next, the server uses an AI algorithm and utilizes natural language processing technology to automatically evaluate the importance and urgency of each business operation. Specifically, it uses natural language processing libraries in Python (e.g., NLTK and spaCy) to extract important keywords from the business content and identify operations that require prompt processing. 【0291】 The server then displays the prioritized tasks in a user interface. This interface is built using frameworks such as React Native, allowing administrators to visually understand the status of tasks on their smartphones or tablets. For example, if a city is affected by flooding, it is possible to prioritize and display a list of high-priority tasks based on reports from citizens and weather forecast data. This allows administrators to make quick decisions. 【0292】 A concrete example of a prompt is: "Help design a smart city management application that collects task information from multiple data sources, uses AI to determine importance, and adjusts priorities to improve the operational efficiency of the smart city." This prompt allows the generated AI model to provide solutions tailored to the operational management needs. 【0293】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0294】 Step 1: 【0295】 The server automatically retrieves data from various sources within the city. Inputs include data provided via APIs from traffic information systems and citizen reporting applications. This data is stored in temporary storage on the server. Outputs are raw data collected from each source. 【0296】 Step 2: 【0297】 The server integrates the collected raw data and transforms it into a single, consistent set of information. The input is the raw data from each source obtained in Step 1. A database management system (e.g., PostgreSQL) is used to unify the format and structure of the data. The output is the integrated set of information, which makes it easier to compare different types of data. 【0298】 Step 3: 【0299】 The server applies an AI algorithm to the integrated information set to analyze the importance and urgency of tasks. The input is the integrated information set obtained in step 2. Using a natural language processing library (e.g., NLTK or spaCy), important keywords are extracted from the task content. This process assigns a score to each task, and the output is scoring information based on importance and urgency. 【0300】 Step 4: 【0301】 The server prioritizes and lists tasks based on the scoring information. The input is the scoring information determined in step 3. The tasks are sorted in descending order of priority, and the output is a prepared list of tasks for display in the user interface. 【0302】 Step 5: 【0303】 The terminal visually displays a prioritized list of tasks retrieved from the server. The input is the output from step 4. Using the React Native framework, an interface is created that allows users to easily check the details of the tasks. The output is a visual task management screen that helps administrators make quick decisions. 【0304】 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. 【0305】 This invention provides a novel system that combines user emotion recognition with conventional data integration and task management systems. This system not only manages information from data sources but also provides information tailored to the user's emotional state. 【0306】 First, the server continuously obtains and integrates data from the mail service, message platform, and calendar service. As a result, all business information related to the user is unified. The server evaluates the importance and urgency of tasks for the integrated data using ordinary AI algorithms. 【0307】 Furthermore, the server is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine estimates the current emotional state by analyzing the user's facial expressions and voice tones via sensor devices such as cameras and microphones. 【0308】 Based on this, the server combines the user's emotions and the task evaluation by AI to optimize the task prioritization. For example, when the user is feeling stressed, the server can adjust to reduce the user's burden by prioritizing tasks that are important but have low urgency. 【0309】 The server generates a user interface that reflects the emotion information recognized by the emotion engine and displays it on the user's terminal. This interface aims to provide a state where the user can work on tasks most efficiently by changing the color scheme and task display method for specific emotions. 【0310】 As a specific example, when the emotion engine senses tension or pressure during the user's daily work, the server adjusts the task order and displays a support message suitable for the user to provide a comfortable working environment. Through this process, the user can perform optimal business processing according to the emotional state. 【0311】 The following explains the processing flow. 【0312】 Step 1: 【0313】 The server periodically accesses the APIs of each data source according to the scheduler to retrieve the latest data such as emails, messages, and calendar information. This data is temporarily stored on the server in preparation for subsequent processing. 【0314】 Step 2: 【0315】 The server converts the acquired data into a standard format and organizes it into a single, unified dataset. This includes filtering the data, removing duplicates, and tagging it as needed. 【0316】 Step 3: 【0317】 The server uses an AI algorithm to analyze each task in the integrated dataset and assess its importance and urgency. This assigns an evaluation score to each task. Tasks that are both important and urgent receive a high score. 【0318】 Step 4: 【0319】 The server activates an emotion engine to recognize the user's emotions via sensor devices. For example, it captures facial expressions with a camera and analyzes their emotional state in real time. Simultaneously, it also picks up emotions from voice to improve accuracy. 【0320】 Step 5: 【0321】 The server utilizes the collected emotional data to adjust task priorities based on the user's current emotional state. For example, if a user is feeling stressed, the server will prioritize tasks that are easier. 【0322】 Step 6: 【0323】 The server generates the user interface based on the adjusted task list. It optimizes the screen's color scheme and layout according to the user's emotions, displaying it in a user-friendly format. 【0324】 Step 7: 【0325】 The device displays a user interface and provides the user with a list of current tasks. Based on this information, the user can efficiently process tasks according to the displayed priority. 【0326】 (Example 2) 【0327】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0328】 In recent years, improving user work efficiency has required centralized information management and streamlined task management. However, conventional systems process tasks mechanically without considering the user's emotional state, potentially increasing user stress and workload. Therefore, a system is needed that can dynamically adjust task priorities according to the user's emotions, enabling efficient work execution. 【0329】 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. 【0330】 In this invention, the server includes means for an information device to acquire information from multiple information sources, means for integrating the acquired information into a single information set, means for the information device to analyze the information using a machine learning algorithm and evaluate the importance and urgency of work items, and means for the information device to analyze the user's emotional state using a sensory recognition device. This enables optimal task prioritization and efficient work execution in accordance with the user's emotions. 【0331】 "Information equipment" refers to mechanical devices that process and manage information, including servers and computers. 【0332】 "Information source" refers to external services or platforms that provide data, such as email services, messaging platforms, and calendar services. 【0333】 An "information set" refers to a dataset created by combining and integrating acquired data into a single entity. 【0334】 A "machine learning algorithm" refers to a type of artificial intelligence technique used to analyze data and discover specific rules or patterns. 【0335】 A "task item" refers to a task or work that a user must process or complete. 【0336】 "Importance" refers to a criterion for evaluating the value and priority that a work item holds in relation to the user's goals and overall business operations. 【0337】 "Urgency" refers to an indicator that shows how quickly a task item should be addressed. 【0338】 A "sensory recognition device" refers to devices such as sensors, cameras, and microphones used to detect a user's emotions and physical state. 【0339】 "Emotional state" refers to the user's current emotions and psychological condition, including stress, relief, and tension. 【0340】 "User interface" refers to the display and operation screen that enables interaction between the user and the information device. 【0341】 In this invention, a server, which is an information device, plays a central role in providing a multi-functional task management system to improve the user's work efficiency. First, the server retrieves data from multiple sources, such as email services, messaging platforms, and calendar services, using APIs. The retrieved data is stored in a database system within the server (e.g., general database management software) and processed as an integrated information set. 【0342】 The server then uses machine learning algorithms to analyze the integrated data and assess the importance and urgency of each work item. In doing so, the server leverages existing generative AI models to efficiently analyze the data. This allows users to flexibly respond to unexpected schedule changes or shifts in work priorities. 【0343】 Furthermore, the server is connected to a sensory recognition device that analyzes the user's emotional state in real time via a camera and microphone. The emotional state is inferred from the user's facial expressions and tone of voice. Based on this, the server re-evaluates the priority of work items and presents tasks that are appropriate to the user's psychological state. 【0344】 The completed user interface is delivered to the user's device. This interface is customized according to the user's emotional state to support efficient task management. For example, if emotional data indicating that the user is stressed is detected by a sensory recognition device, the server will adjust the interface to present the user with high-priority but low-urgency tasks first, thereby reducing their psychological burden. 【0345】 As a concrete example, the server manages the user's schedule while displaying relaxing messages at appropriate times. This allows users to maintain high work performance while preserving their mental well-being. 【0346】 Furthermore, the overall system flow is constructed by providing instructions such as, "Design a system that retrieves data from the user's emails, messages, and calendar, uses an AI algorithm to evaluate the importance and urgency of tasks, and uses a camera and microphone to recognize emotions. Explain, with specific examples, how to adjust task priorities when the user is feeling stressed." 【0347】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0348】 Step 1: 【0349】 The server retrieves data from multiple sources. Specifically, it periodically collects information from email services, messaging platforms, and calendar services using APIs. This input data is obtained in JSON format, which the server parses and extracts the necessary information. The output is a separate set of information before it is integrated into the server's database. 【0350】 Step 2: 【0351】 The server integrates the acquired data into a single information set. The server stores each information set in a database and performs schema mapping and data integrity checks to combine data from different sources. This results in a unified information set. Specifically, it uses a database management system to perform table joins and filtering. 【0352】 Step 3: 【0353】 The server uses a machine learning algorithm to analyze the data set and evaluate the importance and urgency of the work items. The integrated data set is fed into the machine learning model as input. The server uses a generative AI model to calculate a score based on importance and urgency. As a result of this analysis, an evaluated task list is output. Specifically, each task is scored by weighting it according to a particular evaluation metric. 【0354】 Step 4: 【0355】 The server uses sensory recognition devices to analyze the user's emotional state. The server receives real-time data of the user's facial expressions and voice through cameras and microphones. This input data is processed by sensing algorithms to infer the user's emotional state. Emotional labels such as stress and relaxation are generated as output. Image processing and voice analysis technologies are used for the analysis. 【0356】 Step 5: 【0357】 The server optimizes work item priorities by combining emotional data and work item evaluations. Using emotional state labels and an evaluated task list as input, the server calculates new priorities. Specifically, it modifies the importance and urgency scores of tasks based on the emotional state and rearranges the list. The output provides a list of optimized tasks. 【0358】 Step 6: 【0359】 The server generates a user interface displaying optimized work items and delivers it to the terminal. The server uses the optimized task list to generate the interface using a front-end framework and sends it to the terminal in HTML format or similar. Inputs include prioritized task data and emotion-based UI settings, while the output provides an interface displayed with color settings and layout appropriate to the user's emotional state. 【0360】 Step 7: 【0361】 Users manage their work items using a user interface generated by the system. Through the interface displayed on the terminal, users can check the progress of tasks and take action as needed. This enables effective work processing based on information that is dynamically updated in real time. 【0362】 (Application Example 2) 【0363】 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." 【0364】 Traditional task management systems only focused on integrating information and simple prioritization, failing to consider the user's emotional state. This meant that important tasks were sometimes prioritized even when the user was stressed, making effective performance difficult. Furthermore, the lack of user-friendly interface design also contributed to decreased work efficiency. 【0365】 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. 【0366】 In this invention, the server includes means for an information processing device to recognize the user's emotional state using an emotion analysis device and determine an optimized work priority by combining the emotional state and work evaluation; means for the information processing device to generate an operation screen that displays the prioritized work in a display method that is careful not to affect the emotional state; and means for evaluating work using natural language processing and making work suggestions based on the emotional state. This enables optimal work processing according to the user's emotional state. 【0367】 An "information processing device" is a computer system used to integrate and analyze information obtained from multiple sources. 【0368】 A "machine learning algorithm" is a method that uses mathematical models to learn and recognize patterns in data analysis. 【0369】 An "emotion analysis device" is a device that analyzes data such as facial expressions and voice in order to recognize the user's emotional state. 【0370】 An "operation screen" is a visual interface for users to manage their work, and it is a screen that displays information with consideration for the user's feelings. 【0371】 "Natural language processing" is a technology that enables computers to understand and analyze human speech. 【0372】 A "user" is an individual or organization that uses this system to manage their work. 【0373】 The system for carrying out the present invention includes an information processing device, an emotion analysis device, a display device, and network communication means. The information processing device is designed to acquire and integrate information from multiple sources, including various schedule management services and communication platforms. The acquired information is analyzed by machine learning algorithms to evaluate the importance and urgency of the work. 【0374】 The emotion analysis device combines a camera and microphone to analyze the user's facial expressions and voice tone in real time, and estimates the user's emotional state. This analysis uses OpenCV, and the Google Cloud Speech-to-Text API is used for voice analysis. 【0375】 The server determines task priorities optimized for the user based on acquired emotional data and task evaluation results, and generates an operation screen with a display method that takes the user's emotional state into consideration. By utilizing natural language processing technology, it is designed so that users can receive task suggestions that are tailored to their emotions. 【0376】 The server aims to provide users with a more comfortable work environment and supports optimal work management regardless of the user's state. For example, if a user is seeking relaxation, it can move lower-priority tasks to the front and play relaxing background sounds. 【0377】 An example of a prompt to input into a generative AI model is, "Suggest an optimal task schedule for a user who is relaxed." 【0378】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0379】 Step 1: 【0380】 The server collects data from the information source. 【0381】 The server retrieves data from information sources such as schedule management services and messaging platforms via APIs and integrates all relevant information into a single data set. At this time, the retrieved data is input and processed to standardize and format each piece of data. 【0382】 Step 2: 【0383】 The server analyzes the integrated data. 【0384】 The server uses machine learning algorithms to analyze integrated data and assess the importance and urgency of each task. The input is an integrated set of information, and a task evaluation score is output. This provides the basis for prioritizing the data. 【0385】 Step 3: 【0386】 The device recognizes the emotional state. 【0387】 Using a camera and microphone connected to the device, the system collects the user's facial expressions and audio data, which are then analyzed in real time by an emotion analysis device. The input consists of audio and video data, and the output is the user's emotional state. OpenCV and the Google Cloud Speech-to-Text API are used for this analysis. 【0388】 Step 4: 【0389】 The server determines the task priority. 【0390】 The server combines the work evaluation score and the user's emotional state to determine the optimal work priority. The inputs are the work evaluation score and the emotional state, and a priority list is formed based on this combination. Tasks are then arranged in the order that best suits the user's current emotional state. 【0391】 Step 5: 【0392】 The terminal generates the user interface. 【0393】 The terminal generates and displays an operation screen to the user based on a priority list provided by the server, using colors and layouts that take the user's emotional state into consideration. The input is the priority list, and the output is a visually optimized operation screen. The color scheme and music are adjusted to create a relaxing environment for the user. 【0394】 Step 6: 【0395】 Users manage their tasks through the user interface. 【0396】 The user selects and executes the presented tasks using the operation screen displayed on the terminal. The system records the user's actions as a log, and this data is used for evaluating future tasks. The input is the user's actions, and the output is the result of those actions. 【0397】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0398】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0399】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0400】 [Third Embodiment] 【0401】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0402】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0403】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0404】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0405】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0406】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0407】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0408】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0409】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0410】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0411】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0412】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0413】 The system of this invention is designed to centrally manage information from data sources, enabling users to efficiently manage important tasks. First, the server retrieves data from multiple data sources, including email services, messaging platforms, and calendar services. This aggregates all information relevant to the user. 【0414】 Next, the server integrates the acquired information into a single dataset. This dataset includes information such as emails, messages, and calendar events, and is formatted into a consistent format within the server. 【0415】 The server applies an AI algorithm to this integrated dataset. The AI ​​algorithm learns from past processing history and uses natural language processing techniques to analyze the importance and urgency of tasks. This calculates a score to be assigned to each task. 【0416】 Based on the calculated score, the server automatically prioritizes and lists tasks. This allows users to instantly see the most important tasks, improving work efficiency. 【0417】 Furthermore, the server generates a user interface and provides the functionality to display prioritized tasks on the user's terminal. The user interface is designed to be visually intuitive, and each task has a link to detailed information. Users can access this interface on their terminal and easily manage and review the contents of their tasks. 【0418】 As a concrete example, when a user opens their terminal at the start of the workday, the server retrieves recent emails, messages, and calendar events and evaluates the tasks. Tasks are then displayed in order of priority, allowing the user to view the list and ensure each task is completed. This process prevents users from overlooking information and provides an environment where they can focus on important tasks. 【0419】 The following describes the processing flow. 【0420】 Step 1: 【0421】 The server uses a scheduler to periodically connect to the APIs of each data source (email service, messaging platform, calendar service) to collect new data. The data is stored on the server in a structured format, including subject, body, sender, and time information. 【0422】 Step 2: 【0423】 The server integrates all acquired data into a single dataset. The data is standardized and stored in a consistent format, enabling subsequent analysis. 【0424】 Step 3: 【0425】 The server analyzes text data from the integrated dataset using natural language processing techniques. It extracts important keywords from messages and email content to understand the meaning and context of tasks. Based on the analysis results, the server calculates a score for each task. This score is used to assess importance and urgency. 【0426】 Step 4: 【0427】 The server prioritizes tasks based on the calculated score. Tasks are sorted by importance and urgency and saved in a list format. This makes it clear which tasks should be handled first. 【0428】 Step 5: 【0429】 The server generates a user interface to present the user with an organized task list. This interface is visually structured to make each task easy to understand intuitively. 【0430】 Step 6: 【0431】 The device displays a generated interface to the user, allowing them to check the status of tasks in real time. The user can select any task from the displayed task list to view detailed information and take action as needed. 【0432】 (Example 1) 【0433】 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." 【0434】 In both professional and personal work, it is essential to properly manage information from various sources and efficiently prioritize tasks. However, centrally integrating diverse information obtained from multiple sources and evaluating its importance and urgency is not easy. This leads to issues such as missing information or misjudging priorities, ultimately resulting in decreased work efficiency. 【0435】 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. 【0436】 In this invention, the server includes means for the information processing device to acquire information from multiple information sources, means for the information processing device to integrate the acquired information into a single information collection, and means for the information processing device to analyze the information using artificial intelligence techniques and evaluate the importance and urgency of the tasks. As a result, information is properly managed, users can concentrate on the most important tasks, and work efficiency can be greatly improved. 【0437】 An "information processing device" is a device used to acquire, integrate, analyze, and display information. 【0438】 An "information source" is a medium from which an information processing device acquires information, such as email, messaging services, or calendar services. 【0439】 An "information collection" is a collective entity that manages information integrated by an information processing device. 【0440】 "Artificial intelligence methods" are technologies that utilize techniques such as machine learning and natural language processing to analyze information. 【0441】 "Importance" is a measure that indicates the relative importance of a task or work. 【0442】 "Urgency" is a measure that indicates the degree to which a task or work needs to be done urgently. 【0443】 A "user interface" is a screen generated by an information processing device that allows the user to check and manage information and details of their work. 【0444】 "Natural language technology" refers to technologies that enable computers to understand and analyze human language. 【0445】 A "generative artificial intelligence model" is an artificial intelligence model designed to analyze and evaluate information using natural language processing techniques. 【0446】 A description of embodiments for carrying out the present invention will be provided. 【0447】 This system is designed to allow users to efficiently manage important tasks by collecting information from various sources and managing it centrally. 【0448】 First, the server retrieves information from multiple sources, including email services, messaging platforms, and calendar services. These sources include common email platforms, messaging applications, and online calendar tools. The server uses APIs to access these sources and extract the necessary data. 【0449】 Next, the server integrates the acquired information into a single data set. Since the information is acquired in different formats, the server formats it into a unified format. This formatting process often utilizes libraries such as the pandas library in the Python programming language. 【0450】 Next, the server applies a generative AI model to the integrated information. Specifically, it uses natural language processing techniques to analyze the information and evaluate the importance and urgency of the tasks. This analysis utilizes natural language processing techniques such as GPT-4, and each task is assigned a relative score. 【0451】 Furthermore, the server prioritizes tasks based on the generated information and creates a user interface for displaying it to the user. The user interface is designed to be intuitive and visually easy to understand, enabling users to manage their tasks efficiently. The JavaScript library React is used to build the interface. 【0452】 As a concrete example, when a user opens their terminal at the start of their workday, the server retrieves recent emails, messages, and calendar events, integrates them, and analyzes them. Then, a list of tasks, prioritized based on importance and urgency, is displayed on the terminal. An example of a prompt message is, "Show priority tasks that need to be completed within the next 24 hours." 【0453】 This system will enable users to prevent overlooking information and significantly improve work efficiency. 【0454】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0455】 Step 1: 【0456】 The server retrieves information from multiple sources. These sources include email services, messaging platforms, and calendar services. Specifically, the server collects data from each source via APIs. For example, it might use an email API to retrieve the body, sender, and date / time of unread emails. It receives access information from each source as input and obtains a raw dataset containing the content of emails and messages as output. 【0457】 Step 2: 【0458】 The server formats the acquired information into a consistent format and compiles it into an integrated data set. Here, the server uses the Python pandas library to convert multiple data formats into a single unified format. For example, it creates a dataframe containing email body, message text, and calendar event details. It takes a raw dataset as input and obtains an integrated dataset as output. 【0459】 Step 3: 【0460】 The server uses a generated AI model to analyze the importance and urgency of the integrated information. The AI ​​model applies natural language processing techniques to understand the context of each task and assign a score. For example, important meetings and tasks with approaching deadlines receive high scores. It takes an integrated dataset as input and outputs a list with each task assigned a score. 【0461】 Step 4: 【0462】 The server prioritizes tasks based on the analyzed data and creates a list of tasks in order of their impact on the user. A Python script is used to sort tasks by their score. It takes a list of scored tasks as input and outputs a prioritized task list. 【0463】 Step 5: 【0464】 The server generates a user interface for displaying a prioritized task list and sends it to the terminal. Here, React is used to build an intuitive UI for the user. Each table row contains a link to a detail page. It takes a prioritized task list as input and generates a user-viewable HTML page as output. 【0465】 Step 6: 【0466】 The user accesses the user interface from their device and begins working based on the displayed task list. The user then reviews the details of tasks in order of priority, opening emails, replying to them, or adjusting calendar appointments. The system receives a generated HTML page as input and the user performs specific tasks as output. 【0467】 (Application Example 1) 【0468】 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." 【0469】 In operating smart cities, efficiently managing information collected from diverse sources within the city and accurately and quickly prioritizing critical operational tasks is challenging. Conventional technologies have insufficient integration of information from multiple sources, requiring significant time and effort from operators to properly evaluate tasks. 【0470】 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. 【0471】 In this invention, the server includes means for acquiring information from multiple information sources, means for integrating the acquired information into a single information set, and means for analyzing the information using an AI algorithm and evaluating the importance and urgency of tasks. This makes it possible to automatically manage and prioritize tasks that are important for the operation and management of a smart city. 【0472】 A "server" is a computer system used to collect, integrate, and analyze information. It is a device that acquires and manages data from multiple information sources. 【0473】 "Information sources" refer to various means of providing specific data or information, such as email services, messaging platforms, calendar services, traffic information, and citizen reports. 【0474】 An "information collection" refers to a dataset created by a server that integrates data from multiple sources and presents it in a consistent format. 【0475】 An "AI algorithm" is a computational method that learns from past data and processing history, and uses natural language processing technology to analyze the importance and urgency of information. 【0476】 "Prioritizing tasks" is the process of rearranging tasks based on the results of information analysis, taking into account the urgency and importance of each task. 【0477】 A "user interface" refers to an interface that visually displays information so that users can check and manage their work. 【0478】 A "smart city" is a concept that utilizes information and communication technology to improve the overall efficiency of a city and optimize urban management. 【0479】 This invention is a system aimed at improving operational efficiency in smart cities. The server automatically acquires data from various information sources within the city. Specifically, it periodically collects data from traffic information systems and citizen reporting applications. This allows the server to obtain real-time information about the city. 【0480】 The server integrates the acquired information and combines it into a single data set. This is done using a database management system (e.g., PostgreSQL) to convert data in different formats into a consistent format. Next, the server uses AI algorithms and natural language processing techniques to automatically assess the importance and urgency of each task. Specifically, it uses Python natural language processing libraries (e.g., NLTK and spaCy) to extract key keywords from the task content and identify tasks requiring immediate processing. 【0481】 The server then displays the prioritized tasks in a user interface. This interface is built using frameworks such as React Native, allowing administrators to visually understand the status of tasks on their smartphones or tablets. For example, if a city is affected by flooding, it is possible to prioritize and display a list of high-priority tasks based on reports from citizens and weather forecast data. This allows administrators to make quick decisions. 【0482】 A concrete example of a prompt is: "Help design a smart city management application that collects task information from multiple data sources, uses AI to determine importance, and adjusts priorities to improve the operational efficiency of the smart city." This prompt allows the generated AI model to provide solutions tailored to the operational management needs. 【0483】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0484】 Step 1: 【0485】 The server automatically retrieves data from various sources within the city. Inputs include data provided via APIs from traffic information systems and citizen reporting applications. This data is stored in temporary storage on the server. Outputs are raw data collected from each source. 【0486】 Step 2: 【0487】 The server integrates the collected raw data and transforms it into a single, consistent set of information. The input is the raw data from each source obtained in Step 1. A database management system (e.g., PostgreSQL) is used to unify the format and structure of the data. The output is the integrated set of information, which makes it easier to compare different types of data. 【0488】 Step 3: 【0489】 The server applies an AI algorithm to the integrated information set to analyze the importance and urgency of tasks. The input is the integrated information set obtained in step 2. Using a natural language processing library (e.g., NLTK or spaCy), important keywords are extracted from the task content. This process assigns a score to each task, and the output is scoring information based on importance and urgency. 【0490】 Step 4: 【0491】 The server prioritizes and lists tasks based on the scoring information. The input is the scoring information determined in step 3. The tasks are sorted in descending order of priority, and the output is a prepared list of tasks for display in the user interface. 【0492】 Step 5: 【0493】 The terminal visually displays a prioritized list of tasks retrieved from the server. The input is the output from step 4. Using the React Native framework, an interface is created that allows users to easily check the details of the tasks. The output is a visual task management screen that helps administrators make quick decisions. 【0494】 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. 【0495】 This invention provides a novel system that combines user emotion recognition with conventional data integration and task management systems. This system not only manages information from data sources but also provides information tailored to the user's emotional state. 【0496】 First, the server continuously retrieves and integrates data from email services, messaging platforms, and calendar services. This centralizes all business information related to the user. The server then uses standard AI algorithms to evaluate the importance and urgency of tasks based on this integrated data. 【0497】 Furthermore, the server is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's facial expressions and tone of voice via sensor devices such as cameras and microphones to infer their current emotional state. 【0498】 Based on this, the server combines the user's emotions with AI-driven task evaluation to optimize task prioritization. For example, if a user is feeling stressed, the server can adjust to reduce the user's burden by prioritizing important but low-urgency tasks. 【0499】 The server generates a user interface that reflects the emotional information recognized by the emotion engine and displays it on the user's device. This interface aims to provide the user with the most efficient way to work on tasks by changing the color scheme and how tasks are displayed in response to specific emotions. 【0500】 For example, if the emotion engine detects tension or pressure during a user's daily work, the server adjusts the task order and displays appropriate support messages to provide a comfortable work environment. This process enables users to perform tasks optimally according to their emotional state. 【0501】 The following describes the processing flow. 【0502】 Step 1: 【0503】 The server periodically accesses the APIs of each data source according to the scheduler to retrieve the latest data such as emails, messages, and calendar information. This data is temporarily stored on the server in preparation for subsequent processing. 【0504】 Step 2: 【0505】 The server converts the acquired data into a standard format and organizes it into a single, unified dataset. This includes filtering the data, removing duplicates, and tagging it as needed. 【0506】 Step 3: 【0507】 The server uses an AI algorithm to analyze each task in the integrated dataset and assess its importance and urgency. This assigns an evaluation score to each task. Tasks that are both important and urgent receive a high score. 【0508】 Step 4: 【0509】 The server activates an emotion engine to recognize the user's emotions via sensor devices. For example, it captures facial expressions with a camera and analyzes their emotional state in real time. Simultaneously, it also picks up emotions from voice to improve accuracy. 【0510】 Step 5: 【0511】 The server utilizes the collected emotional data to adjust task priorities based on the user's current emotional state. For example, if a user is feeling stressed, the server will prioritize tasks that are easier. 【0512】 Step 6: 【0513】 The server generates the user interface based on the adjusted task list. It optimizes the screen's color scheme and layout according to the user's emotions, displaying it in a user-friendly format. 【0514】 Step 7: 【0515】 The device displays a user interface and provides the user with a list of current tasks. Based on this information, the user can efficiently process tasks according to the displayed priority. 【0516】 (Example 2) 【0517】 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." 【0518】 In recent years, improving user work efficiency has required centralized information management and streamlined task management. However, conventional systems process tasks mechanically without considering the user's emotional state, potentially increasing user stress and workload. Therefore, a system is needed that can dynamically adjust task priorities according to the user's emotions, enabling efficient work execution. 【0519】 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. 【0520】 In this invention, the server includes means for an information device to acquire information from multiple information sources, means for integrating the acquired information into a single information set, means for the information device to analyze the information using a machine learning algorithm and evaluate the importance and urgency of work items, and means for the information device to analyze the user's emotional state using a sensory recognition device. This enables optimal task prioritization and efficient work execution in accordance with the user's emotions. 【0521】 "Information equipment" refers to mechanical devices that process and manage information, including servers and computers. 【0522】 "Information source" refers to external services or platforms that provide data, such as email services, messaging platforms, and calendar services. 【0523】 An "information set" refers to a dataset created by combining and integrating acquired data into a single entity. 【0524】 A "machine learning algorithm" refers to a type of artificial intelligence technique used to analyze data and discover specific rules or patterns. 【0525】 A "task item" refers to a task or work that a user must process or complete. 【0526】 "Importance" refers to a criterion for evaluating the value and priority that a work item holds in relation to the user's goals and overall business operations. 【0527】 "Urgency" refers to an indicator that shows how quickly a task item should be addressed. 【0528】 A "sensory recognition device" refers to devices such as sensors, cameras, and microphones used to detect a user's emotions and physical state. 【0529】 "Emotional state" refers to the user's current emotions and psychological condition, including stress, relief, and tension. 【0530】 "User interface" refers to the display and operation screen that enables interaction between the user and the information device. 【0531】 In this invention, a server, which is an information device, plays a central role in providing a multi-functional task management system to improve the user's work efficiency. First, the server retrieves data from multiple sources, such as email services, messaging platforms, and calendar services, using APIs. The retrieved data is stored in a database system within the server (e.g., general database management software) and processed as an integrated information set. 【0532】 The server then uses machine learning algorithms to analyze the integrated data and assess the importance and urgency of each work item. In doing so, the server leverages existing generative AI models to efficiently analyze the data. This allows users to flexibly respond to unexpected schedule changes or shifts in work priorities. 【0533】 Furthermore, the server is connected to a sensory recognition device that analyzes the user's emotional state in real time via a camera and microphone. The emotional state is inferred from the user's facial expressions and tone of voice. Based on this, the server re-evaluates the priority of work items and presents tasks that are appropriate to the user's psychological state. 【0534】 The completed user interface is delivered to the user's device. This interface is customized according to the user's emotional state to support efficient task management. For example, if emotional data indicating that the user is stressed is detected by a sensory recognition device, the server will adjust the interface to present the user with high-priority but low-urgency tasks first, thereby reducing their psychological burden. 【0535】 As a concrete example, the server manages the user's schedule while displaying relaxing messages at appropriate times. This allows users to maintain high work performance while preserving their mental well-being. 【0536】 Furthermore, the overall system flow is constructed by providing instructions such as, "Design a system that retrieves data from the user's emails, messages, and calendar, uses an AI algorithm to evaluate the importance and urgency of tasks, and uses a camera and microphone to recognize emotions. Explain, with specific examples, how to adjust task priorities when the user is feeling stressed." 【0537】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0538】 Step 1: 【0539】 The server retrieves data from multiple sources. Specifically, it periodically collects information from email services, messaging platforms, and calendar services using APIs. This input data is obtained in JSON format, which the server parses and extracts the necessary information. The output is a separate set of information before it is integrated into the server's database. 【0540】 Step 2: 【0541】 The server integrates the acquired data into a single information set. The server stores each information set in a database and performs schema mapping and data integrity checks to combine data from different sources. This results in a unified information set. Specifically, it uses a database management system to perform table joins and filtering. 【0542】 Step 3: 【0543】 The server uses a machine learning algorithm to analyze the data set and evaluate the importance and urgency of the work items. The integrated data set is fed into the machine learning model as input. The server uses a generative AI model to calculate a score based on importance and urgency. As a result of this analysis, an evaluated task list is output. Specifically, each task is scored by weighting it according to a particular evaluation metric. 【0544】 Step 4: 【0545】 The server uses sensory recognition devices to analyze the user's emotional state. The server receives real-time data of the user's facial expressions and voice through cameras and microphones. This input data is processed by sensing algorithms to infer the user's emotional state. Emotional labels such as stress and relaxation are generated as output. Image processing and voice analysis technologies are used for the analysis. 【0546】 Step 5: 【0547】 The server optimizes work item priorities by combining emotional data and work item evaluations. Using emotional state labels and an evaluated task list as input, the server calculates new priorities. Specifically, it modifies the importance and urgency scores of tasks based on the emotional state and rearranges the list. The output provides a list of optimized tasks. 【0548】 Step 6: 【0549】 The server generates a user interface displaying optimized work items and delivers it to the terminal. The server uses the optimized task list to generate the interface using a front-end framework and sends it to the terminal in HTML format or similar. Inputs include prioritized task data and emotion-based UI settings, while the output provides an interface displayed with color settings and layout appropriate to the user's emotional state. 【0550】 Step 7: 【0551】 Users manage their work items using a user interface generated by the system. Through the interface displayed on the terminal, users can check the progress of tasks and take action as needed. This enables effective work processing based on information that is dynamically updated in real time. 【0552】 (Application Example 2) 【0553】 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." 【0554】 Traditional task management systems only focused on integrating information and simple prioritization, failing to consider the user's emotional state. This meant that important tasks were sometimes prioritized even when the user was stressed, making effective performance difficult. Furthermore, the lack of user-friendly interface design also contributed to decreased work efficiency. 【0555】 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. 【0556】 In this invention, the server includes means for an information processing device to recognize the user's emotional state using an emotion analysis device and determine an optimized work priority by combining the emotional state and work evaluation; means for the information processing device to generate an operation screen that displays the prioritized work in a display method that is careful not to affect the emotional state; and means for evaluating work using natural language processing and making work suggestions based on the emotional state. This enables optimal work processing according to the user's emotional state. 【0557】 An "information processing device" is a computer system used to integrate and analyze information obtained from multiple sources. 【0558】 A "machine learning algorithm" is a method that uses mathematical models to learn and recognize patterns in data analysis. 【0559】 An "emotion analysis device" is a device that analyzes data such as facial expressions and voice in order to recognize the user's emotional state. 【0560】 An "operation screen" is a visual interface for users to manage their work, and it is a screen that displays information with consideration for the user's feelings. 【0561】 "Natural language processing" is a technology that enables computers to understand and analyze human speech. 【0562】 A "user" is an individual or organization that uses this system to manage their work. 【0563】 The system for carrying out the present invention includes an information processing device, an emotion analysis device, a display device, and network communication means. The information processing device is designed to acquire and integrate information from multiple sources, including various schedule management services and communication platforms. The acquired information is analyzed by machine learning algorithms to evaluate the importance and urgency of the work. 【0564】 The emotion analysis device combines a camera and microphone to analyze the user's facial expressions and voice tone in real time, and estimates the user's emotional state. This analysis uses OpenCV, and the Google Cloud Speech-to-Text API is used for voice analysis. 【0565】 The server determines task priorities optimized for the user based on acquired emotional data and task evaluation results, and generates an operation screen with a display method that takes the user's emotional state into consideration. By utilizing natural language processing technology, it is designed so that users can receive task suggestions that are tailored to their emotions. 【0566】 The server aims to provide users with a more comfortable work environment and supports optimal work management regardless of the user's state. For example, if a user is seeking relaxation, it can move lower-priority tasks to the front and play relaxing background sounds. 【0567】 An example of a prompt to input into a generative AI model is, "Suggest an optimal task schedule for a user who is relaxed." 【0568】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0569】 Step 1: 【0570】 The server collects data from the information source. 【0571】 The server retrieves data from information sources such as schedule management services and messaging platforms via APIs and integrates all relevant information into a single data set. At this time, the retrieved data is input and processed to standardize and format each piece of data. 【0572】 Step 2: 【0573】 The server analyzes the integrated data. 【0574】 The server uses machine learning algorithms to analyze integrated data and assess the importance and urgency of each task. The input is an integrated set of information, and a task evaluation score is output. This provides the basis for prioritizing the data. 【0575】 Step 3: 【0576】 The device recognizes the emotional state. 【0577】 Using a camera and microphone connected to the device, the system collects the user's facial expressions and audio data, which are then analyzed in real time by an emotion analysis device. The input consists of audio and video data, and the output is the user's emotional state. OpenCV and the Google Cloud Speech-to-Text API are used for this analysis. 【0578】 Step 4: 【0579】 The server determines the task priority. 【0580】 The server combines the work evaluation score and the user's emotional state to determine the optimal work priority. The inputs are the work evaluation score and the emotional state, and a priority list is formed based on this combination. Tasks are then arranged in the order that best suits the user's current emotional state. 【0581】 Step 5: 【0582】 The terminal generates the user interface. 【0583】 The terminal generates and displays an operation screen to the user based on a priority list provided by the server, using colors and layouts that take the user's emotional state into consideration. The input is the priority list, and the output is a visually optimized operation screen. The color scheme and music are adjusted to create a relaxing environment for the user. 【0584】 Step 6: 【0585】 Users manage their tasks through the user interface. 【0586】 The user selects and executes the presented tasks using the operation screen displayed on the terminal. The system records the user's actions as a log, and this data is used for evaluating future tasks. The input is the user's actions, and the output is the result of those actions. 【0587】 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. 【0588】 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. 【0589】 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. 【0590】 [Fourth Embodiment] 【0591】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0592】 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. 【0593】 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). 【0594】 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. 【0595】 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. 【0596】 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). 【0597】 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. 【0598】 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. 【0599】 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. 【0600】 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. 【0601】 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. 【0602】 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. 【0603】 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". 【0604】 The system of this invention is designed to centrally manage information from data sources, enabling users to efficiently manage important tasks. First, the server retrieves data from multiple data sources, including email services, messaging platforms, and calendar services. This aggregates all information relevant to the user. 【0605】 Next, the server integrates the acquired information into a single dataset. This dataset includes information such as emails, messages, and calendar events, and is formatted into a consistent format within the server. 【0606】 The server applies an AI algorithm to this integrated dataset. The AI ​​algorithm learns from past processing history and uses natural language processing techniques to analyze the importance and urgency of tasks. This calculates a score to be assigned to each task. 【0607】 Based on the calculated score, the server automatically prioritizes and lists tasks. This allows users to instantly see the most important tasks, improving work efficiency. 【0608】 Furthermore, the server generates a user interface and provides the functionality to display prioritized tasks on the user's terminal. The user interface is designed to be visually intuitive, and each task has a link to detailed information. Users can access this interface on their terminal and easily manage and review the contents of their tasks. 【0609】 As a concrete example, when a user opens their terminal at the start of the workday, the server retrieves recent emails, messages, and calendar events and evaluates the tasks. Tasks are then displayed in order of priority, allowing the user to view the list and ensure each task is completed. This process prevents users from overlooking information and provides an environment where they can focus on important tasks. 【0610】 The following describes the processing flow. 【0611】 Step 1: 【0612】 The server uses a scheduler to periodically connect to the APIs of each data source (email service, messaging platform, calendar service) to collect new data. The data is stored on the server in a structured format, including subject, body, sender, and time information. 【0613】 Step 2: 【0614】 The server integrates all acquired data into a single dataset. The data is standardized and stored in a consistent format, enabling subsequent analysis. 【0615】 Step 3: 【0616】 The server analyzes text data from the integrated dataset using natural language processing techniques. It extracts important keywords from messages and email content to understand the meaning and context of tasks. Based on the analysis results, the server calculates a score for each task. This score is used to assess importance and urgency. 【0617】 Step 4: 【0618】 The server prioritizes tasks based on the calculated score. Tasks are sorted by importance and urgency and saved in a list format. This makes it clear which tasks should be handled first. 【0619】 Step 5: 【0620】 The server generates a user interface to present the user with an organized task list. This interface is visually structured to make each task easy to understand intuitively. 【0621】 Step 6: 【0622】 The device displays a generated interface to the user, allowing them to check the status of tasks in real time. The user can select any task from the displayed task list to view detailed information and take action as needed. 【0623】 (Example 1) 【0624】 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". 【0625】 In both professional and personal work, it is essential to properly manage information from various sources and efficiently prioritize tasks. However, centrally integrating diverse information obtained from multiple sources and evaluating its importance and urgency is not easy. This leads to issues such as missing information or misjudging priorities, ultimately resulting in decreased work efficiency. 【0626】 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. 【0627】 In this invention, the server includes means for the information processing device to acquire information from multiple information sources, means for the information processing device to integrate the acquired information into a single information collection, and means for the information processing device to analyze the information using artificial intelligence techniques and evaluate the importance and urgency of the tasks. As a result, information is properly managed, users can concentrate on the most important tasks, and work efficiency can be greatly improved. 【0628】 An "information processing device" is a device used to acquire, integrate, analyze, and display information. 【0629】 An "information source" is a medium from which an information processing device acquires information, such as email, messaging services, or calendar services. 【0630】 An "information collection" is a collective entity that manages information integrated by an information processing device. 【0631】 "Artificial intelligence methods" are technologies that utilize techniques such as machine learning and natural language processing to analyze information. 【0632】 "Importance" is a measure that indicates the relative importance of a task or work. 【0633】 "Urgency" is a measure that indicates the degree to which a task or work needs to be done urgently. 【0634】 A "user interface" is a screen generated by an information processing device that allows the user to check and manage information and details of their work. 【0635】 "Natural language technology" refers to technologies that enable computers to understand and analyze human language. 【0636】 A "generative artificial intelligence model" is an artificial intelligence model designed to analyze and evaluate information using natural language processing techniques. 【0637】 A description of embodiments for carrying out the present invention will be provided. 【0638】 This system is designed to allow users to efficiently manage important tasks by collecting information from various sources and managing it centrally. 【0639】 First, the server retrieves information from multiple sources, including email services, messaging platforms, and calendar services. These sources include common email platforms, messaging applications, and online calendar tools. The server uses APIs to access these sources and extract the necessary data. 【0640】 Next, the server integrates the acquired information into a single data set. Since the information is acquired in different formats, the server formats it into a unified format. This formatting process often utilizes libraries such as the pandas library in the Python programming language. 【0641】 Next, the server applies a generative AI model to the integrated information. Specifically, it uses natural language processing techniques to analyze the information and evaluate the importance and urgency of the tasks. This analysis utilizes natural language processing techniques such as GPT-4, and each task is assigned a relative score. 【0642】 Furthermore, the server prioritizes tasks based on the generated information and creates a user interface for displaying it to the user. The user interface is designed to be intuitive and visually easy to understand, enabling users to manage their tasks efficiently. The JavaScript library React is used to build the interface. 【0643】 As a concrete example, when a user opens their terminal at the start of their workday, the server retrieves recent emails, messages, and calendar events, integrates them, and analyzes them. Then, a list of tasks, prioritized based on importance and urgency, is displayed on the terminal. An example of a prompt message is, "Show priority tasks that need to be completed within the next 24 hours." 【0644】 This system will enable users to prevent overlooking information and significantly improve work efficiency. 【0645】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0646】 Step 1: 【0647】 The server retrieves information from multiple sources. These sources include email services, messaging platforms, and calendar services. Specifically, the server collects data from each source via APIs. For example, it might use an email API to retrieve the body, sender, and date / time of unread emails. It receives access information from each source as input and obtains a raw dataset containing the content of emails and messages as output. 【0648】 Step 2: 【0649】 The server formats the acquired information into a consistent format and compiles it into an integrated data set. Here, the server uses the Python pandas library to convert multiple data formats into a single unified format. For example, it creates a dataframe containing email body, message text, and calendar event details. It takes a raw dataset as input and obtains an integrated dataset as output. 【0650】 Step 3: 【0651】 The server uses a generated AI model to analyze the importance and urgency of the integrated information. The AI ​​model applies natural language processing techniques to understand the context of each task and assign a score. For example, important meetings and tasks with approaching deadlines receive high scores. It takes an integrated dataset as input and outputs a list with each task assigned a score. 【0652】 Step 4: 【0653】 The server prioritizes tasks based on the analyzed data and creates a list of tasks in order of their impact on the user. A Python script is used to sort tasks by their score. It takes a list of scored tasks as input and outputs a prioritized task list. 【0654】 Step 5: 【0655】 The server generates a user interface for displaying a prioritized task list and sends it to the terminal. Here, React is used to build an intuitive UI for the user. Each table row contains a link to a detail page. It takes a prioritized task list as input and generates a user-viewable HTML page as output. 【0656】 Step 6: 【0657】 The user accesses the user interface from their device and begins working based on the displayed task list. The user then reviews the details of tasks in order of priority, opening emails, replying to them, or adjusting calendar appointments. The system receives a generated HTML page as input and the user performs specific tasks as output. 【0658】 (Application Example 1) 【0659】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0660】 In operating smart cities, efficiently managing information collected from diverse sources within the city and accurately and quickly prioritizing critical operational tasks is challenging. Conventional technologies have insufficient integration of information from multiple sources, requiring significant time and effort from operators to properly evaluate tasks. 【0661】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0662】 In this invention, the server includes means for acquiring information from multiple information sources, means for integrating the acquired information into a single information set, and means for analyzing the information using an AI algorithm and evaluating the importance and urgency of tasks. This makes it possible to automatically manage and prioritize tasks that are important for the operation and management of a smart city. 【0663】 A "server" is a computer system used to collect, integrate, and analyze information. It is a device that acquires and manages data from multiple information sources. 【0664】 "Information sources" refer to various means of providing specific data or information, such as email services, messaging platforms, calendar services, traffic information, and citizen reports. 【0665】 An "information collection" refers to a dataset created by a server that integrates data from multiple sources and presents it in a consistent format. 【0666】 An "AI algorithm" is a computational method that learns from past data and processing history, and uses natural language processing technology to analyze the importance and urgency of information. 【0667】 "Prioritizing tasks" is the process of rearranging tasks based on the results of information analysis, taking into account the urgency and importance of each task. 【0668】 A "user interface" refers to an interface that visually displays information so that users can check and manage their work. 【0669】 A "smart city" is a concept that utilizes information and communication technology to improve the overall efficiency of a city and optimize urban management. 【0670】 This invention is a system aimed at improving operational efficiency in smart cities. The server automatically acquires data from various information sources within the city. Specifically, it periodically collects data from traffic information systems and citizen reporting applications. This allows the server to obtain real-time information about the city. 【0671】 The server integrates the acquired information and combines it into a single data set. This is done using a database management system (e.g., PostgreSQL) to convert data in different formats into a consistent format. Next, the server uses AI algorithms and natural language processing techniques to automatically assess the importance and urgency of each task. Specifically, it uses Python natural language processing libraries (e.g., NLTK and spaCy) to extract key keywords from the task content and identify tasks requiring immediate processing. 【0672】 The server then displays the prioritized tasks in a user interface. This interface is built using frameworks such as React Native, allowing administrators to visually understand the status of tasks on their smartphones or tablets. For example, if a city is affected by flooding, it is possible to prioritize and display a list of high-priority tasks based on reports from citizens and weather forecast data. This allows administrators to make quick decisions. 【0673】 A concrete example of a prompt is: "Help design a smart city management application that collects task information from multiple data sources, uses AI to determine importance, and adjusts priorities to improve the operational efficiency of the smart city." This prompt allows the generated AI model to provide solutions tailored to the operational management needs. 【0674】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0675】 Step 1: 【0676】 The server automatically retrieves data from various sources within the city. Inputs include data provided via APIs from traffic information systems and citizen reporting applications. This data is stored in temporary storage on the server. Outputs are raw data collected from each source. 【0677】 Step 2: 【0678】 The server integrates the collected raw data and transforms it into a single, consistent set of information. The input is the raw data from each source obtained in Step 1. A database management system (e.g., PostgreSQL) is used to unify the format and structure of the data. The output is the integrated set of information, which makes it easier to compare different types of data. 【0679】 Step 3: 【0680】 The server applies an AI algorithm to the integrated information set to analyze the importance and urgency of tasks. The input is the integrated information set obtained in step 2. Using a natural language processing library (e.g., NLTK or spaCy), important keywords are extracted from the task content. This process assigns a score to each task, and the output is scoring information based on importance and urgency. 【0681】 Step 4: 【0682】 The server prioritizes and lists tasks based on the scoring information. The input is the scoring information determined in step 3. The tasks are sorted in descending order of priority, and the output is a prepared list of tasks for display in the user interface. 【0683】 Step 5: 【0684】 The terminal visually displays a prioritized list of tasks retrieved from the server. The input is the output from step 4. Using the React Native framework, an interface is created that allows users to easily check the details of the tasks. The output is a visual task management screen that helps administrators make quick decisions. 【0685】 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. 【0686】 This invention provides a novel system that combines user emotion recognition with conventional data integration and task management systems. This system not only manages information from data sources but also provides information tailored to the user's emotional state. 【0687】 First, the server continuously retrieves and integrates data from email services, messaging platforms, and calendar services. This centralizes all business information related to the user. The server then uses standard AI algorithms to evaluate the importance and urgency of tasks based on this integrated data. 【0688】 Furthermore, the server is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's facial expressions and tone of voice via sensor devices such as cameras and microphones to infer their current emotional state. 【0689】 Based on this, the server combines the user's emotions with AI-driven task evaluation to optimize task prioritization. For example, if a user is feeling stressed, the server can adjust to reduce the user's burden by prioritizing important but low-urgency tasks. 【0690】 The server generates a user interface that reflects the emotional information recognized by the emotion engine and displays it on the user's device. This interface aims to provide the user with the most efficient way to work on tasks by changing the color scheme and how tasks are displayed in response to specific emotions. 【0691】 For example, if the emotion engine detects tension or pressure during a user's daily work, the server adjusts the task order and displays appropriate support messages to provide a comfortable work environment. This process enables users to perform tasks optimally according to their emotional state. 【0692】 The following describes the processing flow. 【0693】 Step 1: 【0694】 The server periodically accesses the APIs of each data source according to the scheduler to retrieve the latest data such as emails, messages, and calendar information. This data is temporarily stored on the server in preparation for subsequent processing. 【0695】 Step 2: 【0696】 The server converts the acquired data into a standard format and organizes it into a single, unified dataset. This includes filtering the data, removing duplicates, and tagging it as needed. 【0697】 Step 3: 【0698】 The server uses an AI algorithm to analyze each task in the integrated dataset and assess its importance and urgency. This assigns an evaluation score to each task. Tasks that are both important and urgent receive a high score. 【0699】 Step 4: 【0700】 The server activates an emotion engine to recognize the user's emotions via sensor devices. For example, it captures facial expressions with a camera and analyzes their emotional state in real time. Simultaneously, it also picks up emotions from voice to improve accuracy. 【0701】 Step 5: 【0702】 The server utilizes the collected emotional data to adjust task priorities based on the user's current emotional state. For example, if a user is feeling stressed, the server will prioritize tasks that are easier. 【0703】 Step 6: 【0704】 The server generates the user interface based on the adjusted task list. It optimizes the screen's color scheme and layout according to the user's emotions, displaying it in a user-friendly format. 【0705】 Step 7: 【0706】 The device displays a user interface and provides the user with a list of current tasks. Based on this information, the user can efficiently process tasks according to the displayed priority. 【0707】 (Example 2) 【0708】 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". 【0709】 In recent years, improving user work efficiency has required centralized information management and streamlined task management. However, conventional systems process tasks mechanically without considering the user's emotional state, potentially increasing user stress and workload. Therefore, a system is needed that can dynamically adjust task priorities according to the user's emotions, enabling efficient work execution. 【0710】 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. 【0711】 In this invention, the server includes means for an information device to acquire information from multiple information sources, means for integrating the acquired information into a single information set, means for the information device to analyze the information using a machine learning algorithm and evaluate the importance and urgency of work items, and means for the information device to analyze the user's emotional state using a sensory recognition device. This enables optimal task prioritization and efficient work execution in accordance with the user's emotions. 【0712】 "Information equipment" refers to mechanical devices that process and manage information, including servers and computers. 【0713】 "Information source" refers to external services or platforms that provide data, such as email services, messaging platforms, and calendar services. 【0714】 An "information set" refers to a dataset created by combining and integrating acquired data into a single entity. 【0715】 A "machine learning algorithm" refers to a type of artificial intelligence technique used to analyze data and discover specific rules or patterns. 【0716】 A "task item" refers to a task or work that a user must process or complete. 【0717】 "Importance" refers to a criterion for evaluating the value and priority that a work item holds in relation to the user's goals and overall business operations. 【0718】 "Urgency" refers to an indicator that shows how quickly a task item should be addressed. 【0719】 A "sensory recognition device" refers to devices such as sensors, cameras, and microphones used to detect a user's emotions and physical state. 【0720】 "Emotional state" refers to the user's current emotions and psychological condition, including stress, relief, and tension. 【0721】 "User interface" refers to the display and operation screen that enables interaction between the user and the information device. 【0722】 In this invention, a server, which is an information device, plays a central role in providing a multi-functional task management system to improve the user's work efficiency. First, the server retrieves data from multiple sources, such as email services, messaging platforms, and calendar services, using APIs. The retrieved data is stored in a database system within the server (e.g., general database management software) and processed as an integrated information set. 【0723】 The server then uses machine learning algorithms to analyze the integrated data and assess the importance and urgency of each work item. In doing so, the server leverages existing generative AI models to efficiently analyze the data. This allows users to flexibly respond to unexpected schedule changes or shifts in work priorities. 【0724】 Furthermore, the server is connected to a sensory recognition device that analyzes the user's emotional state in real time via a camera and microphone. The emotional state is inferred from the user's facial expressions and tone of voice. Based on this, the server re-evaluates the priority of work items and presents tasks that are appropriate to the user's psychological state. 【0725】 The completed user interface is delivered to the user's device. This interface is customized according to the user's emotional state to support efficient task management. For example, if emotional data indicating that the user is stressed is detected by a sensory recognition device, the server will adjust the interface to present the user with high-priority but low-urgency tasks first, thereby reducing their psychological burden. 【0726】 As a concrete example, the server manages the user's schedule while displaying relaxing messages at appropriate times. This allows users to maintain high work performance while preserving their mental well-being. 【0727】 Furthermore, the overall system flow is constructed by providing instructions such as, "Design a system that retrieves data from the user's emails, messages, and calendar, uses an AI algorithm to evaluate the importance and urgency of tasks, and uses a camera and microphone to recognize emotions. Explain, with specific examples, how to adjust task priorities when the user is feeling stressed." 【0728】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0729】 Step 1: 【0730】 The server retrieves data from multiple sources. Specifically, it periodically collects information from email services, messaging platforms, and calendar services using APIs. This input data is obtained in JSON format, which the server parses and extracts the necessary information. The output is a separate set of information before it is integrated into the server's database. 【0731】 Step 2: 【0732】 The server integrates the acquired data into a single information set. The server stores each information set in a database and performs schema mapping and data integrity checks to combine data from different sources. This results in a unified information set. Specifically, it uses a database management system to perform table joins and filtering. 【0733】 Step 3: 【0734】 The server uses a machine learning algorithm to analyze the data set and evaluate the importance and urgency of the work items. The integrated data set is fed into the machine learning model as input. The server uses a generative AI model to calculate a score based on importance and urgency. As a result of this analysis, an evaluated task list is output. Specifically, each task is scored by weighting it according to a particular evaluation metric. 【0735】 Step 4: 【0736】 The server uses sensory recognition devices to analyze the user's emotional state. The server receives real-time data of the user's facial expressions and voice through cameras and microphones. This input data is processed by sensing algorithms to infer the user's emotional state. Emotional labels such as stress and relaxation are generated as output. Image processing and voice analysis technologies are used for the analysis. 【0737】 Step 5: 【0738】 The server optimizes work item priorities by combining emotional data and work item evaluations. Using emotional state labels and an evaluated task list as input, the server calculates new priorities. Specifically, it modifies the importance and urgency scores of tasks based on the emotional state and rearranges the list. The output provides a list of optimized tasks. 【0739】 Step 6: 【0740】 The server generates a user interface displaying optimized work items and delivers it to the terminal. The server uses the optimized task list to generate the interface using a front-end framework and sends it to the terminal in HTML format or similar. Inputs include prioritized task data and emotion-based UI settings, while the output provides an interface displayed with color settings and layout appropriate to the user's emotional state. 【0741】 Step 7: 【0742】 Users manage their work items using a user interface generated by the system. Through the interface displayed on the terminal, users can check the progress of tasks and take action as needed. This enables effective work processing based on information that is dynamically updated in real time. 【0743】 (Application Example 2) 【0744】 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". 【0745】 Traditional task management systems only focused on integrating information and simple prioritization, failing to consider the user's emotional state. This meant that important tasks were sometimes prioritized even when the user was stressed, making effective performance difficult. Furthermore, the lack of user-friendly interface design also contributed to decreased work efficiency. 【0746】 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. 【0747】 In this invention, the server includes means for an information processing device to recognize the user's emotional state using an emotion analysis device and determine an optimized work priority by combining the emotional state and work evaluation; means for the information processing device to generate an operation screen that displays the prioritized work in a display method that is careful not to affect the emotional state; and means for evaluating work using natural language processing and making work suggestions based on the emotional state. This enables optimal work processing according to the user's emotional state. 【0748】 An "information processing device" is a computer system used to integrate and analyze information obtained from multiple sources. 【0749】 A "machine learning algorithm" is a method that uses mathematical models to learn and recognize patterns in data analysis. 【0750】 An "emotion analysis device" is a device that analyzes data such as facial expressions and voice in order to recognize the user's emotional state. 【0751】 An "operation screen" is a visual interface for users to manage their work, and it is a screen that displays information with consideration for the user's feelings. 【0752】 "Natural language processing" is a technology that enables computers to understand and analyze human speech. 【0753】 A "user" is an individual or organization that uses this system to manage their work. 【0754】 The system for carrying out the present invention includes an information processing device, an emotion analysis device, a display device, and network communication means. The information processing device is designed to acquire and integrate information from multiple sources, including various schedule management services and communication platforms. The acquired information is analyzed by machine learning algorithms to evaluate the importance and urgency of the work. 【0755】 The emotion analysis device combines a camera and microphone to analyze the user's facial expressions and voice tone in real time, and estimates the user's emotional state. This analysis uses OpenCV, and the Google Cloud Speech-to-Text API is used for voice analysis. 【0756】 The server determines task priorities optimized for the user based on acquired emotional data and task evaluation results, and generates an operation screen with a display method that takes the user's emotional state into consideration. By utilizing natural language processing technology, it is designed so that users can receive task suggestions that are tailored to their emotions. 【0757】 The server aims to provide users with a more comfortable work environment and supports optimal work management regardless of the user's state. For example, if a user is seeking relaxation, it can move lower-priority tasks to the front and play relaxing background sounds. 【0758】 An example of a prompt to input into a generative AI model is, "Suggest an optimal task schedule for a user who is relaxed." 【0759】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0760】 Step 1: 【0761】 The server collects data from the information source. 【0762】 The server retrieves data from information sources such as schedule management services and messaging platforms via APIs and integrates all relevant information into a single data set. At this time, the retrieved data is input and processed to standardize and format each piece of data. 【0763】 Step 2: 【0764】 The server analyzes the integrated data. 【0765】 The server uses machine learning algorithms to analyze integrated data and assess the importance and urgency of each task. The input is an integrated set of information, and a task evaluation score is output. This provides the basis for prioritizing the data. 【0766】 Step 3: 【0767】 The device recognizes the emotional state. 【0768】 Using a camera and microphone connected to the device, the system collects the user's facial expressions and audio data, which are then analyzed in real time by an emotion analysis device. The input consists of audio and video data, and the output is the user's emotional state. OpenCV and the Google Cloud Speech-to-Text API are used for this analysis. 【0769】 Step 4: 【0770】 The server determines the task priority. 【0771】 The server combines the work evaluation score and the user's emotional state to determine the optimal work priority. The inputs are the work evaluation score and the emotional state, and a priority list is formed based on this combination. Tasks are then arranged in the order that best suits the user's current emotional state. 【0772】 Step 5: 【0773】 The terminal generates the user interface. 【0774】 The terminal generates and displays an operation screen to the user based on a priority list provided by the server, using colors and layouts that take the user's emotional state into consideration. The input is the priority list, and the output is a visually optimized operation screen. The color scheme and music are adjusted to create a relaxing environment for the user. 【0775】 Step 6: 【0776】 Users manage their tasks through the user interface. 【0777】 The user selects and executes the presented tasks using the operation screen displayed on the terminal. The system records the user's actions as a log, and this data is used for evaluating future tasks. The input is the user's actions, and the output is the result of those actions. 【0778】 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. 【0779】 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. 【0780】 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. 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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. 【0785】 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. 【0786】 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." 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 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. 【0798】 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. 【0799】 The following is further disclosed regarding the embodiments described above. 【0800】 (Claim 1) 【0801】 A means by which a server retrieves data from multiple data sources, 【0802】 A method for integrating data acquired by the server into a single dataset, 【0803】 A server uses an AI algorithm to analyze data and evaluate the importance and urgency of tasks. 【0804】 A method for prioritizing and listing tasks based on the data evaluated by the server, 【0805】 A means for generating a user interface that displays the server's prioritized tasks, 【0806】 A system that includes this. 【0807】 (Claim 2) 【0808】 The system according to claim 1, wherein the user manages tasks using a generated user interface. 【0809】 (Claim 3) 【0810】 The system according to claim 1, which uses natural language processing to evaluate a task. 【0811】 "Example 1" 【0812】 (Claim 1) 【0813】 A means by which an information processing device acquires information from multiple information sources, 【0814】 A means of integrating information acquired by an information processing device into a single information collection, 【0815】 An information processing device uses artificial intelligence techniques to analyze information and evaluate the importance and urgency of a task. 【0816】 A means of prioritizing and listing tasks based on the information evaluated by the information processing device, 【0817】 A means for generating a user interface that displays prioritized tasks in an information processing device, 【0818】 A means for an information processing device to analyze the details of a task based on natural language technology, 【0819】 An information processing system that includes this. 【0820】 (Claim 2) 【0821】 The information processing system according to claim 1, which allows users to manage tasks using a generated user interface and to check the details of tasks using links to detailed information. 【0822】 (Claim 3) 【0823】 The information processing system according to claim 1, which uses natural language processing technology and calculates importance scores using a generative artificial intelligence model. 【0824】 "Application Example 1" 【0825】 (Claim 1) 【0826】 A means by which a server obtains information from multiple sources, 【0827】 A means of integrating the information acquired by the server into a single information set, 【0828】 A server uses AI algorithms to analyze information and evaluate the importance and urgency of tasks. 【0829】 A method for prioritizing and listing tasks based on the server's evaluation information, 【0830】 A means for generating a user interface that displays the server's prioritized tasks, 【0831】 A server collects various types of information related to the city and provides a means to automatically manage and prioritize tasks that are important for operational management. 【0832】 A system that includes this. 【0833】 (Claim 2) 【0834】 The system according to claim 1, wherein the user manages tasks using a generated user interface. 【0835】 (Claim 3) 【0836】 The system according to claim 1 that evaluates business processes using natural language processing. 【0837】 "Example 2 of combining an emotion engine" 【0838】 (Claim 1) 【0839】 Means by which information devices acquire information from multiple sources, 【0840】 A means of integrating information acquired by information devices into a single information set, 【0841】 A means by which information devices analyze information using machine learning algorithms to evaluate the importance and urgency of work items, 【0842】 A means by which an information device uses a sensory recognition device to analyze the user's emotional state, 【0843】 Information devices combine emotional data and work item evaluations to optimize the priority of work items, 【0844】 A means for generating a user interface that displays optimized work items for information devices, 【0845】 A system that includes this. 【0846】 (Claim 2) 【0847】 The system according to claim 1, wherein the user manages work items using a generated user interface. 【0848】 (Claim 3) 【0849】 The system according to claim 1, which uses natural language processing to evaluate work items. 【0850】 "Application example 2 when combining with an emotional engine" 【0851】 (Claim 1) 【0852】 A means by which an information processing device acquires information from multiple information sources, 【0853】 A means of integrating information acquired by an information processing device into a single information set, 【0854】 An information processing device uses machine learning algorithms to analyze information and evaluate the importance and urgency of a task. 【0855】 A means of prioritizing and listing tasks based on the information evaluated by the information processing device, 【0856】 An information processing device recognizes the user's emotional state using an emotion analysis device, and a means for determining optimized work priorities by combining the emotional state with work evaluation, 【0857】 A means for generating an operation screen that displays prioritized tasks in a display method that takes into consideration the fact that the information processing device does not affect the emotional state, 【0858】 A system that includes this. 【0859】 (Claim 2) 【0860】 The system according to claim 1, wherein a person manages tasks using a generated operation screen. 【0861】 (Claim 3) 【0862】 The system according to claim 1, which uses natural language processing to evaluate work and makes work suggestions based on emotional state. [Explanation of symbols] 【0863】 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 by which a server retrieves data from multiple data sources, A method for integrating data acquired by the server into a single dataset, A server uses an AI algorithm to analyze data and evaluate the importance and urgency of tasks. A method for prioritizing and listing tasks based on the data evaluated by the server, A means for generating a user interface that displays the server's prioritized tasks, A system that includes this. [Claim 2] The system according to claim 1, wherein the user manages tasks using a generated user interface. [Claim 3] The system according to claim 1, which uses natural language processing to evaluate a task.

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