system

Abstract

We provide the system. [Solution] Data collection methods for collecting corporate process data, A data analysis means for analyzing the collected data and extracting best practices, A guideline generation means for generating standardized guidelines based on the extracted best practices, A system including a guideline distribution means for distributing the generated guidelines to a terminal.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In enterprises, standardizing and executing processes compliant with standards is particularly difficult for small and medium-sized enterprises, and it is difficult to secure personnel with specialized knowledge that is costly depending on the scale. As a result, there may be a lack of process consistency and insufficient compliance with regulations and quality control. Furthermore, in the face of the need to quickly respond to changes in regulations in daily operations, the lack of such a system poses a significant burden on enterprises. 【Means for Solving the Problems】 【0005】 This invention solves these problems by providing a system that collects and analyzes corporate process data, extracts best practices, and then generates and distributes standardized guidelines. Furthermore, the terminal has the function of displaying the received guidelines and collecting feedback from user input. The server can continuously update the AI ​​model using this feedback, thereby always providing the latest guidelines. This enables efficient and consistent process operation without relying on specialized personnel, and improves regulatory compliance and quality control. 【0006】 "Data collection methods" refer to the methods and equipment used to acquire and store information generated from various processes within a company. 【0007】 "Data analysis methods" refer to methods and techniques for analyzing collected information and extracting useful patterns and trends from it. 【0008】 "Guideline generation methods" refer to methods and techniques for creating standardized instructions and procedures regarding a company's processes based on analysis results. 【0009】 "Guideline distribution means" refers to methods and technologies for transmitting generated instructions and procedures to the appropriate terminals or departments and making them available. 【0010】 "Device" refers to a device or interface used by users to review guidelines and enter relevant feedback. 【0011】 "Feedback" refers to information that users input and provide to the system, including their experiences and suggestions for improvement during the process. [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, when an emotion engine is combined. [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, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0016】 In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0018】 In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), 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】 This invention is a system for providing efficiency and consistency in the operation of corporate processes, and is implemented as follows. This system primarily functions through three entities: servers, terminals, and users. 【0034】 The server first collects data from various business processes within the company and stores it securely. For example, the server ingests log data from incident management systems and operational management systems. This allows the company to understand the status of the processes it is currently running. 【0035】 Next, the server analyzes the collected data to identify common patterns and best practices. This analysis highlights areas for improvement and effective methods in business operations. For example, analyzing past incident response times can identify process bottlenecks. 【0036】 Furthermore, the server generates standardized guidelines based on the analysis results. These guidelines include specific procedures and priorities. These guidelines aim to improve the quality of work and are used for regulatory compliance and quality control. 【0037】 Guidelines generated by the server are delivered to the terminal. The terminal notifies the user and presents them in a visual interface so that the user can perform the task according to the instructions. For example, the terminal provides step-by-step instructions for creating an incident report. 【0038】 Users perform tasks based on guidelines displayed on their devices. They can input any observations or suggestions for improvement during the task using the device's feedback function. This feedback is then sent back to the server and used to improve the accuracy of the AI ​​model and refine the guidelines for future use. 【0039】 In this way, the present invention aims to standardize and improve the effectiveness of processes, enhance the operational efficiency of companies, and reduce the burden of regulatory compliance. Furthermore, through continuous learning and updates, it is possible to flexibly respond to the latest changes in operational frameworks. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The server collects data from corporate processes. For example, it gathers log information about incidents and performance from various internal systems and stores it in a secure database. During this process, the data is encrypted, and appropriate data protection measures are taken. 【0043】 Step 2: 【0044】 The server analyzes the collected data. Here, AI algorithms are used to perform pattern recognition and trend analysis on the data, identifying particularly efficient processes and potential problems. This analysis yields reliable insights based on historical data. 【0045】 Step 3: 【0046】 The server generates standardized guidelines based on the analysis results. For example, it creates procedures and priority lists aimed at accelerating incident response. These guidelines include specific actions for each step of the process. 【0047】 Step 4: 【0048】 The server distributes the generated guidelines to the terminals. It selects distribution destinations to ensure the guidelines reach the relevant departments and personnel. 【0049】 Step 5: 【0050】 The device notifies the user of the received guidelines and displays them on the screen. For example, it allows the user to quickly check the guidelines through pop-up notifications or displays on the dashboard. 【0051】 Step 6: 【0052】 Users perform their tasks according to the guidelines displayed on their devices. They refer to the guidelines while executing each step in incident handling and quality control. 【0053】 Step 7: 【0054】 The terminal collects user feedback and sends it to the server. It records feedback on areas for improvement and bugs encountered during the process and uses it to improve future model training. 【0055】 Step 8: 【0056】 The server updates the AI ​​model using feedback. This information is then used to generate even more accurate process guidelines in the future. This allows the system to continuously evolve. 【0057】 (Example 1) 【0058】 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." 【0059】 In modern organizations, streamlining and standardizing business processes is a crucial challenge. However, collecting and analyzing business data, extracting best practices for business improvement, and implementing process improvements while considering user feedback are not easy. In particular, there is a need to process large amounts of data in real time and respond flexibly to changing circumstances. 【0060】 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. 【0061】 In this invention, the server includes information gathering means, information analysis means, guideline generation means, guideline distribution means, pattern identification means, and opinion gathering means. This makes it possible to efficiently collect and analyze organizational business data and generate and distribute standardized guidelines. Furthermore, by updating the generated AI model based on user feedback, continuous process improvement and optimization become possible. 【0062】 An "information processing device" is a device that plays a central role in collecting, analyzing, and managing various business data within an organization, and in generating and distributing standardized business guidelines. 【0063】 "Information gathering means" refers to functions that automatically aggregate various types of business-related data from internal or external data sources within an organization, and securely store and manage them. 【0064】 "Information analysis means" refers to a function that uses a generative AI model to analyze collected data and extract patterns and best practices. 【0065】 "Guideline generation means" refers to a function that generates specific guidelines for business standardization based on optimal practice examples obtained through information analysis means. 【0066】 "Guideline distribution means" refers to a function that distributes generated guidelines to information terminals used by users, and uses them to improve business operations. 【0067】 An "information terminal" refers to a device that allows users to receive and display guidelines. This enables users to take specific actions. 【0068】 "Pattern identification means" refers to a function that identifies specific patterns from the analysis results of collected data that can be used for business improvement and standardization. 【0069】 "Methods for gathering feedback" refers to functions that collect user feedback and suggestions for improvement regarding tasks performed in accordance with guidelines. 【0070】 A "generative AI model" refers to an algorithm that uses large amounts of data to learn and generate optimized business guidelines. 【0071】 This invention is an information processing system for efficiently and standardizing business processes within an organization. An embodiment thereof is shown below. 【0072】 The server plays a central role in this system, collecting data from various data sources within the organization using information gathering tools. Specifically, the server retrieves data from existing databases and management systems via APIs and stores it in secure storage. Encryption technologies and access control lists are used to ensure data integrity and security. 【0073】 Next, the server uses a generative AI model as an information analysis tool to analyze the collected data. The generative AI model used here combines machine learning algorithms to efficiently extract patterns and optimal practices from large datasets. This includes data preprocessing, feature extraction, and model training. 【0074】 Once the analysis is complete, the server generates standardized guidelines using a guideline generation mechanism. These guidelines include specific steps and priorities for optimizing business processes. For example, in incident response, they define in detail the initial response procedures and reporting flow. This content is generated as a prompt message, such as "Analyze past incident response logs and propose efficient response procedures." 【0075】 Once guidelines are generated, the server distributes them to information terminals via a guideline distribution system. The information terminals have an interface that visually presents the received guidelines to the user, who then performs their tasks based on these guidelines. This interface includes pop-up notifications and situation-dependent dashboard displays, designed to allow users to intuitively understand and act upon the guidelines. 【0076】 Users input their observations and improvement suggestions as feedback through a feedback collection system while performing tasks via their terminals. This feedback information is then sent back to the server and used to improve the generated AI model and develop next-generation guidelines. This design allows the entire system to continuously learn, improving operational efficiency and quality over time. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 The server collects data from various data sources within the organization using information gathering tools. Inputs include log data obtained in real time via APIs of incident management and operational management systems. This data is encrypted while ensuring integrity and stored in storage. Specifically, the server periodically sends requests to the APIs to retrieve the latest data. 【0080】 Step 2: 【0081】 The server inputs the collected data into an AI model for data analysis. The input here is the log data obtained in the previous step. Data processing includes pre-processing such as noise reduction and missing value imputation. The AI ​​model then performs clustering to detect common patterns and best practices. The output generates identified patterns and significant insights. Specific operations include data cleaning and execution of the AI ​​algorithm. 【0082】 Step 3: 【0083】 The server uses a guideline generation mechanism to generate standardized guidelines based on the analysis results. The input for this step is pattern information, which corresponds to the output of the AI ​​model. The generated guidelines include specific procedures and priorities for business improvement. The output is, for example, in the form of prompt statements such as "Initial response guidelines for system trouble." In terms of specific operation, there is a process that automatically generates guidelines according to a template based on the calculation results of the model. 【0084】 Step 4: 【0085】 The server distributes the generated guidelines to the information terminal using the guidelines distribution means. In this step, the input is the generated guidelines, and the output is the transmission of the guidelines to the terminal. The specific operation includes sending data packets over the network and triggering a notification on the terminal. 【0086】 Step 5: 【0087】 The user performs tasks based on the guidelines displayed on the terminal and inputs feedback. The input for this step is the guidelines displayed on the terminal's interface. The user inputs insights and improvement suggestions gained during the task execution process as feedback through a feedback collection method. The output is the transmission of user feedback data. Specific actions include touch input, keyboard input, and clicking a send button. 【0088】 Step 6: 【0089】 The server utilizes collected user feedback to improve the generated AI model and create next-generation guidelines. The input for this step is user feedback information. Data processing includes text analysis of the feedback and model tuning using regression analysis. The output is the updated AI model and improved guidelines. Specific actions include saving the feedback information to a database and retraining the model. 【0090】 (Application Example 1) 【0091】 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." 【0092】 Industrial automation equipment requires maximizing work efficiency and rapidly detecting anomalies. However, current systems often require manual data analysis and extraction of optimization procedures, making timely responses difficult. Therefore, a system is needed that provides operators with appropriate instructions in real time based on operational conditions and updates operational standards while incorporating improvement suggestions into a feedback loop. 【0093】 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. 【0094】 In this invention, the server includes an information gathering means for collecting operational information relating to industrial automation equipment, an information analysis means for analyzing the collected information and extracting optimization procedures, and an operational criteria generation means for generating operational criteria based on the extracted optimization procedures. This enhances the efficiency and timeliness of operations and enables workers to make optimal decisions. 【0095】 "Industrial automation equipment" refers to devices and systems used to automate processes such as manufacturing and assembly within a factory. 【0096】 "Information gathering means" refers to functions and technologies for collecting data on the operational status of industrial automation equipment. 【0097】 "Information analysis tools" refer to functions and technologies used to analyze collected data and extract procedures and areas for improvement to optimize operations. 【0098】 "Operational standard generation means" refers to functions and technologies that create standards and guidelines for efficient operation based on analysis results. 【0099】 "Operational standards distribution means" refers to functions and technologies for communicating generated operational standards to workers. 【0100】 A "display device" is a device used to visually show workers the operational standards and instructions they have received. 【0101】 An "improvement suggestion" is an opinion or proposal based on the work performed by the worker, aimed at further streamlining operations. 【0102】 An "artificial intelligence model" is a computer program and algorithm that supports prediction and decision-making to improve operational efficiency, based on results obtained from data analysis. 【0103】 The system implementing this invention is primarily configured to improve the operational efficiency of industrial automation equipment. A server plays a central role in this system. The server collects operational information from the industrial automation equipment. Specifically, it uses sensors and communication modules to acquire data on the operating status of the equipment, error codes, and product quality. 【0104】 The server uses this collected data to run data analysis tools and derive information to optimize operations. Here, a machine learning model is built using libraries such as Python's Scikit-learn. This model learns from historical operational data and extracts optimization steps to improve operational efficiency. 【0105】 Subsequently, the server uses the operational standards generation means to create efficient instructions and guidelines for operation based on the results obtained from the analysis. These guidelines include work procedures and the timing of preventive maintenance. The generated standards are transmitted to the display device via the operational standards distribution means. 【0106】 The terminal plays a role in visually displaying the received operational standards to the worker. By using mobile devices or tablet terminals, information is presented in real time. For example, when an anomaly is detected, the cause and corrective procedures are displayed step by step. 【0107】 Users follow instructions and perform tasks via a display device. Users can also input improvement suggestions into the terminal during operation. Effective feedback is collected from users using generated prompt messages. For example, a prompt message might read, "Please enter any problems encountered during today's work and the proposed solutions." 【0108】 Ultimately, the server continuously updates its artificial intelligence model based on collected improvement suggestions and operational results. This ensures continuous operational optimization and establishes a system that can comply with the latest operational standards. 【0109】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0110】 Step 1: 【0111】 The server collects operational information from industrial automation equipment via sensors and communication modules. Inputs include data on the equipment's operating status, error codes, and product quality. This data is securely stored in a database for use in subsequent processing steps. 【0112】 Step 2: 【0113】 The server launches data analysis tools to analyze the collected data. A machine learning model is built using the Python Scikit-learn library. The input is the operational data collected in step 1. The output is optimization procedures and patterns for optimizing operational efficiency. This analysis process includes data preprocessing, feature selection, and model training. 【0114】 Step 3: 【0115】 The server uses an operational standard generation mechanism to create operational standards based on the analysis results. The input is the optimization procedure obtained in step 2. The output is an operational standard that includes work procedures and the timing of preventive maintenance. This standard includes specific instructions to enable users to perform their work smoothly. 【0116】 Step 4: 【0117】 The server transmits the generated operational standards to the terminal via the operational standards distribution means. Specifically, it transfers the data to mobile devices and tablets over the network. The input is the operational standards generated in step 3, and the output is the operational instructions displayed on the terminal. 【0118】 Step 5: 【0119】 The terminal visually displays the received operational standards to the worker. The user then begins their work based on this information. The terminal reflects the information received in step 4 on the screen and presents it in a format that is easy for the worker to understand. This includes a step-by-step display of the cause and corrective procedures when an anomaly is detected. 【0120】 Step 6: 【0121】 Users input improvements they discover during their work into a terminal. The input consists of the user's improvement suggestions and opinions. The output is feedback data sent to the server. This data is effectively collected using prompts from a generated AI model. 【0122】 Step 7: 【0123】 The server analyzes user feedback and continuously updates the artificial intelligence model. The input is the feedback data received from the user in step 6. The output is the latest operational standards and model, which will be used for future operations. This will lead to further improvements in operational efficiency. 【0124】 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. 【0125】 This invention is a system designed to streamline corporate process operations and provide support tailored to the emotional state of individual users. This system primarily functions through four components: a server, a terminal, an emotion engine, and the user. 【0126】 The server first collects log information from process management systems and other related systems to gather data generated within the company. This allows for a comprehensive understanding of the company's operations and the storage of necessary information in a database. The data is appropriately protected and managed with privacy in mind. 【0127】 Next, the server analyzes the collected data. Using AI algorithms, it analyzes patterns and trends within the data to identify efficient business processes and areas requiring improvement. Based on the insights gained during this process, it generates standardized guidelines. 【0128】 The generated guidelines are delivered to the device. The device visually presents the guidelines to the user. At this time, an emotion engine is activated, reading the user's emotions from their voice and facial expressions and adjusting the display method accordingly. For example, if the user is fatigued, measures are taken to simplify the presentation of the guidelines to reduce their burden. 【0129】 Users perform tasks according to guidelines via their devices. During task execution, users can provide feedback, which, including the user's emotional state, is sent from the device to the server. 【0130】 The server receives feedback and performs analysis, including emotional data from the emotion engine. This data is used to update the AI ​​model, helping to further optimize the next set of guidelines generated. In this way, the system provides adaptive support tailored to each user's situation, continuously improving the efficiency of corporate process operations and regulatory compliance. 【0131】 The following describes the processing flow. 【0132】 Step 1: 【0133】 The server collects process data from various systems within the company. For example, it retrieves logs from manufacturing management systems and customer management systems and stores them in a central database. This data is then pre-processed so that it can be analyzed immediately. 【0134】 Step 2: 【0135】 The server analyzes the collected data using an AI engine to extract best practices for improving the efficiency of business processes. This process uses machine learning techniques to identify frequently occurring patterns and success stories. 【0136】 Step 3: 【0137】 The server generates standardized guidelines based on the analysis results. These guidelines include specific work procedures and key performance indicators (KPIs). 【0138】 Step 4: 【0139】 The server distributes the generated guidelines to the terminals. If necessary, the distribution of the guidelines can be restricted to specific departments or individuals. 【0140】 Step 5: 【0141】 The device visually displays the received guidelines. In doing so, it uses an emotion engine to analyze the user's current emotional state. For example, it captures facial expressions with a camera and measures stress and anxiety through voice analysis. 【0142】 Step 6: 【0143】 The device adjusts the display of guidelines according to the user's emotional state. If the user is tired, it uses features to simplify the presentation of information or to show instructions in stages. 【0144】 Step 7: 【0145】 Users perform tasks according to the adjusted guidelines. As they go through each step, they input any problems they find or suggestions for improvement as feedback into their terminal. 【0146】 Step 8: 【0147】 The device sends emotional data acquired by the emotion engine, along with user feedback, to the server. 【0148】 Step 9: 【0149】 The server analyzes the provided feedback and sentiment data to update the AI ​​model. This data is used to generate guidelines and optimize processes for future use, improving the system's accuracy and usefulness. 【0150】 (Example 2) 【0151】 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." 【0152】 In modern business activities, there is a need to improve the efficiency of business processes while providing flexible support that is tailored to the emotional state of users. However, conventional systems have limited mechanisms to effectively address these needs, resulting in challenges such as insufficient improvements in business efficiency and a lack of methods to alleviate the psychological burden on users. 【0153】 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. 【0154】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and extracting the optimal method, and a device for generating standardized guidelines based on the extracted optimal method. This enables flexible support that responds to the user's emotions while promoting the efficiency of the company's operations. 【0155】 An "information-gathering device" is a means of acquiring various data related to business activities, and is a device that plays a role in effectively accumulating database and log information. 【0156】 A "device for extracting optimal methods" is a means of analyzing collected data to identify patterns and trends that contribute to improving business processes, and it is a device that uses analytical techniques to derive the optimal procedures and methods. 【0157】 A "device for generating standardized guidelines" is a device that has the function of forming guidelines and directives necessary to improve the efficiency of operations based on the extracted optimal methods. 【0158】 An "information processing device" is a device that displays information to users, has the function of receiving opinions and feedback from users, and enables two-way information exchange through an interface. 【0159】 An "artificial intelligence model" is a collection of algorithms and systems that continuously learn and improve based on collected information and user feedback, adapting to new data to provide highly accurate analysis and predictions. 【0160】 This invention is a system that streamlines corporate business processes and enables support that responds to users' emotions. The system mainly consists of a server, terminals, and an emotion analysis engine. 【0161】 The server first collects data from various sources within the company. Specifically, it retrieves logs and usage history from the company's process management systems and related information systems using APIs. This data is securely stored in a database for later analysis. 【0162】 Next, the server uses Python libraries (e.g., Pandas and Scikit-learn) to process and analyze the data. Here, AI algorithms are employed to identify optimal business practices and areas for improvement within the data. Based on this analysis, prompts are input to a generative AI model (e.g., a natural language processing model) to generate standardized guidelines. An example of a prompt might be, "Generate guidelines for optimizing the sales process." 【0163】 The generated guidelines are delivered to the device. When the device presents these to the user, it utilizes an emotion analysis engine. The emotion analysis engine incorporates speech recognition and facial expression analysis technologies, and the program evaluates the user's emotional state through Microsoft® Azure® Cognitive Services. Based on this information, the device dynamically adjusts how the guidelines are displayed. For example, if it determines that the user is tired, it simplifies the display of the guidelines to reduce visual burden. 【0164】 Users perform tasks according to guidelines via their devices while also providing feedback. This feedback includes progress, new discoveries, and even emotional states. The devices continuously transmit this information to a server, which uses this data to retrain the AI ​​model. This process optimizes the guidelines provided next time, thereby improving overall operational efficiency within the company. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 The server collects data from the company's process management systems and related information systems. Specifically, it uses APIs to retrieve log data and usage history. The input to this process is data streams from each system, and the output is structured data stored in a database. This allows for the collection of detailed information about the company's activities. 【0168】 Step 2: 【0169】 The server processes and cleans the collected data using Python libraries (such as Pandas and Scikit-learn). The input is structured data in a database, and the output is a clean dataset suitable for analysis. Specifically, it performs missing value imputation and outlier removal. Using this clean data, an AI algorithm identifies patterns and trends. 【0170】 Step 3: 【0171】 The server extracts the optimal method based on identified patterns and trends, and sends prompt messages to the generating AI model to produce standardized guidelines. The input is a prompt message based on the analysis results, and the output is the generated guidelines. Specifically, the server gives the model the prompt, "Generate guidelines for optimizing the sales process." 【0172】 Step 4: 【0173】 The terminal displays guidelines received from the server to the user. The input is the guidelines delivered from the server, and the output is the presentation of visual guidelines to the user. An emotion analysis engine is used to analyze the user's voice and facial expressions through sensors and adjust the display method accordingly. Specifically, it uses Microsoft Azure Cognitive Services to analyze the emotional state. 【0174】 Step 5: 【0175】 Users perform tasks based on guidelines displayed on the terminal. Input is the provided guidelines, and output is data on feedback for completed tasks and their emotional state at the time. Users provide feedback and input information, including their emotional state, into the terminal as they work. 【0176】 Step 6: 【0177】 The terminal sends feedback received from the user to the server. The input is the user feedback data, and the output is the server that received it. The terminal transmits information in real time, which is used by the server to generate the next set of guidelines. 【0178】 Step 7: 【0179】 The server takes in feedback data and uses it to update the AI ​​model. The input is user feedback and sentiment analysis results, and the output is the updated AI model. Through this process, the system continuously optimizes guidelines and improves the company's process management. 【0180】 (Application Example 2) 【0181】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0182】 In production sites such as factories, there is a need to balance efficient operations with reducing the psychological burden on workers. However, currently, it is difficult to grasp the psychological state of workers in real time and provide work instructions that are appropriate to that situation, which can lead to decreased work efficiency and increased stress among workers. 【0183】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0184】 In this invention, the server includes an information gathering means for collecting operational information of a company, an information analysis means for analyzing the collected information and extracting optimal guidelines for improving work efficiency, and a psychological state analysis means for recognizing the psychological state of the worker and adjusting the display method of the instructions. This makes it possible to provide detailed work instructions that are tailored to the psychological state of the worker. 【0185】 "Information gathering means" refers to methods or devices for effectively acquiring and storing operational information of a company in a database. 【0186】 "Information analysis means" refers to a method or device that analyzes collected data and derives guidelines or patterns that contribute to improving operational efficiency. 【0187】 "Instruction generation means" refers to a method or apparatus for generating standardized work instructions based on analysis results and for appropriately transmitting them. 【0188】 "Instruction distribution means" refers to a method or device for transmitting generated instructions to a terminal in a timely manner. 【0189】 A "psychological state analysis means" is a method or device that determines the psychological state of a worker from their facial expressions and voice, and optimizes the method of presenting instructions based on that information. 【0190】 An "AI model" is a mathematical method built using machine learning algorithms based on a large amount of data, with the aim of continuously improving its performance. 【0191】 A "terminal device" is an electronic device used for displaying instructions and collecting responses from workers. 【0192】 "Response" refers to information provided by workers through their devices, specifically feedback related to the work process and their psychological state. 【0193】 In the system realizing this invention, a server, terminal devices, and workers work together in order to efficiently process information. The server stores the necessary data in a database via an information collection means that aggregates operational information from various sensors and information systems of the company. Then, an information analysis means analyzes this data using an AI model to identify guidelines for improving operational efficiency. Standardized work instructions are created from the analysis results by an instruction generation means and transmitted to terminal devices using an instruction distribution means. 【0194】 The terminal device displays the received instructions to the worker. The worker's psychological state is determined using facial expressions and voice data via a psychological state analysis device, and the instruction display method is optimized accordingly. In this process, the terminal device collects the worker's responses and sends them to a server to help update the AI ​​model. 【0195】 A concrete example of this embodiment is a worker on a factory production line. For instance, if a worker is wearing smart glasses, and AI-based psychological state analysis determines that the worker is feeling fatigued, the system sends instructions to suggest a break and displays relaxation methods on the smart glasses' display. This makes it possible to reduce worker stress while improving the efficiency of the production line. 【0196】 A specific example prompt used would be, "Please propose stress reduction measures for the production line. The current emotional state is 'fatigue'." By providing work support tailored to each work environment and situation in this way, it is possible to improve production efficiency and promote worker welfare. 【0197】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0198】 Step 1: 【0199】 The server collects operational information from various sensors and information systems within the company. It receives sensor data and system logs as input and records them in a database. This data is stored accurately and systematically because it will be used for later analysis. 【0200】 Step 2: 【0201】 The server analyzes the collected data using information analysis tools. Input data includes information on work efficiency, error rates, and worker productivity. An AI model is used to identify guidelines for improving work efficiency and generate templates for work instructions as output. Anomaly detection and pattern extraction are performed during the data analysis process. 【0202】 Step 3: 【0203】 The server organizes the generated work instructions using an instruction generation system and distributes them to terminal devices. It receives instruction templates as input and sends standardized work instructions to the terminals as output. The instructions are then presented to the workers as specific guidance content. 【0204】 Step 4: 【0205】 The terminal device displays received work instructions to the worker. Based on the instruction data received as input, it processes it into a clear and visually understandable format and displays it on the display as output. The displayed content includes specific work procedures and points to note. 【0206】 Step 5: 【0207】 The user's psychological state is evaluated by a psychological state analysis tool on the device. Audio and video data are acquired as input, and emotions are determined using a machine learning algorithm based on this data. The output is the evaluation result of the psychological state, which is used to adjust the way instructions are presented. 【0208】 Step 6: 【0209】 The user's responses are collected by the terminal and sent to the server. User feedback and work results are received as input, and processed into data useful for generating the next work instructions as output. The AI ​​model is continuously improved through post-processing feedback. 【0210】 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. 【0211】 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. 【0212】 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. 【0213】 [Second Embodiment] 【0214】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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. 【0224】 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. 【0225】 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". 【0226】 This invention is a system for providing efficiency and consistency in the operation of corporate processes, and is implemented as follows. This system primarily functions through three entities: servers, terminals, and users. 【0227】 The server first collects data from various business processes within the company and stores it securely. For example, the server ingests log data from incident management systems and operational management systems. This allows the company to understand the status of the processes it is currently running. 【0228】 Next, the server analyzes the collected data to identify common patterns and best practices. This analysis highlights areas for improvement and effective methods in business operations. For example, analyzing past incident response times can identify process bottlenecks. 【0229】 Furthermore, the server generates standardized guidelines based on the analysis results. These guidelines include specific procedures and priorities. These guidelines aim to improve the quality of work and are used for regulatory compliance and quality control. 【0230】 Guidelines generated by the server are delivered to the terminal. The terminal notifies the user and presents them in a visual interface so that the user can perform the task according to the instructions. For example, the terminal provides step-by-step instructions for creating an incident report. 【0231】 Users perform tasks based on guidelines displayed on their devices. They can input any observations or suggestions for improvement during the task using the device's feedback function. This feedback is then sent back to the server and used to improve the accuracy of the AI ​​model and refine the guidelines for future use. 【0232】 In this way, the present invention aims to standardize and improve the effectiveness of processes, enhance the operational efficiency of companies, and reduce the burden of regulatory compliance. Furthermore, through continuous learning and updates, it is possible to flexibly respond to the latest changes in operational frameworks. 【0233】 The following describes the processing flow. 【0234】 Step 1: 【0235】 The server collects data from corporate processes. For example, it gathers log information about incidents and performance from various internal systems and stores it in a secure database. During this process, the data is encrypted, and appropriate data protection measures are taken. 【0236】 Step 2: 【0237】 The server analyzes the collected data. Here, AI algorithms are used to perform pattern recognition and trend analysis on the data, identifying particularly efficient processes and potential problems. This analysis yields reliable insights based on historical data. 【0238】 Step 3: 【0239】 The server generates standardized guidelines based on the analysis results. For example, it creates procedures and priority lists aimed at accelerating incident response. These guidelines include specific actions for each step of the process. 【0240】 Step 4: 【0241】 The server distributes the generated guidelines to the terminals. It selects distribution destinations to ensure the guidelines reach the relevant departments and personnel. 【0242】 Step 5: 【0243】 The device notifies the user of the received guidelines and displays them on the screen. For example, it allows the user to quickly check the guidelines through pop-up notifications or displays on the dashboard. 【0244】 Step 6: 【0245】 Users perform their tasks according to the guidelines displayed on their devices. They refer to the guidelines while executing each step in incident handling and quality control. 【0246】 Step 7: 【0247】 The terminal collects user feedback and sends it to the server. It records feedback on areas for improvement and bugs encountered during the process and uses it to improve future model training. 【0248】 Step 8: 【0249】 The server updates the AI ​​model using feedback. This information is then used to generate even more accurate process guidelines in the future. This allows the system to continuously evolve. 【0250】 (Example 1) 【0251】 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." 【0252】 In modern organizations, streamlining and standardizing business processes is a crucial challenge. However, collecting and analyzing business data, extracting best practices for business improvement, and implementing process improvements while considering user feedback are not easy. In particular, there is a need to process large amounts of data in real time and respond flexibly to changing circumstances. 【0253】 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. 【0254】 In this invention, the server includes information gathering means, information analysis means, guideline generation means, guideline distribution means, pattern identification means, and opinion gathering means. This makes it possible to efficiently collect and analyze organizational business data and generate and distribute standardized guidelines. Furthermore, by updating the generated AI model based on user feedback, continuous process improvement and optimization become possible. 【0255】 An "information processing device" is a device that plays a central role in collecting, analyzing, and managing various business data within an organization, and in generating and distributing standardized business guidelines. 【0256】 "Information gathering means" refers to functions that automatically aggregate various types of business-related data from internal or external data sources within an organization, and securely store and manage them. 【0257】 "Information analysis means" refers to a function that uses a generative AI model to analyze collected data and extract patterns and best practices. 【0258】 "Guideline generation means" refers to a function that generates specific guidelines for business standardization based on optimal practice examples obtained through information analysis means. 【0259】 "Guideline distribution means" refers to a function that distributes generated guidelines to information terminals used by users, and uses them to improve business operations. 【0260】 An "information terminal" refers to a device that allows users to receive and display guidelines. This enables users to take specific actions. 【0261】 "Pattern identification means" refers to a function that identifies specific patterns from the analysis results of collected data that can be used for business improvement and standardization. 【0262】 "Methods for gathering feedback" refers to functions that collect user feedback and suggestions for improvement regarding tasks performed in accordance with guidelines. 【0263】 A "generative AI model" refers to an algorithm that uses large amounts of data to learn and generate optimized business guidelines. 【0264】 This invention is an information processing system for efficiently and standardizing business processes within an organization. An embodiment thereof is shown below. 【0265】 The server plays a central role in this system, collecting data from various data sources within the organization using information gathering tools. Specifically, the server retrieves data from existing databases and management systems via APIs and stores it in secure storage. Encryption technologies and access control lists are used to ensure data integrity and security. 【0266】 Next, the server uses a generative AI model as an information analysis tool to analyze the collected data. The generative AI model used here combines machine learning algorithms to efficiently extract patterns and optimal practices from large datasets. This includes data preprocessing, feature extraction, and model training. 【0267】 Once the analysis is complete, the server generates standardized guidelines using a guideline generation mechanism. These guidelines include specific steps and priorities for optimizing business processes. For example, in incident response, they define in detail the initial response procedures and reporting flow. This content is generated as a prompt message, such as "Analyze past incident response logs and propose efficient response procedures." 【0268】 Once guidelines are generated, the server distributes them to information terminals via a guideline distribution system. The information terminals have an interface that visually presents the received guidelines to the user, who then performs their tasks based on these guidelines. This interface includes pop-up notifications and situation-dependent dashboard displays, designed to allow users to intuitively understand and act upon the guidelines. 【0269】 Users input their observations and improvement suggestions as feedback through a feedback collection system while performing tasks via their terminals. This feedback information is then sent back to the server and used to improve the generated AI model and develop next-generation guidelines. This design allows the entire system to continuously learn, improving operational efficiency and quality over time. 【0270】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0271】 Step 1: 【0272】 The server collects data from various data sources within the organization using information gathering tools. Inputs include log data obtained in real time via APIs of incident management and operational management systems. This data is encrypted while ensuring integrity and stored in storage. Specifically, the server periodically sends requests to the APIs to retrieve the latest data. 【0273】 Step 2: 【0274】 The server inputs the collected data into an AI model for data analysis. The input here is the log data obtained in the previous step. Data processing includes pre-processing such as noise reduction and missing value imputation. The AI ​​model then performs clustering to detect common patterns and best practices. The output generates identified patterns and significant insights. Specific operations include data cleaning and execution of the AI ​​algorithm. 【0275】 Step 3: 【0276】 The server uses a guideline generation mechanism to generate standardized guidelines based on the analysis results. The input for this step is pattern information, which corresponds to the output of the AI ​​model. The generated guidelines include specific procedures and priorities for business improvement. The output is, for example, in the form of prompt statements such as "Initial response guidelines for system trouble." In terms of specific operation, there is a process that automatically generates guidelines according to a template based on the calculation results of the model. 【0277】 Step 4: 【0278】 The server distributes the pointers generated using the pointer distribution means to the information terminals. In this step, the input is the pointers generated, and the output is the transmission of the pointers to the terminals. Specific operations include the transmission of data packets via the network and notification triggers at the terminals. 【0279】 Step 5: 【0280】 The user performs operations based on the pointers displayed on the terminal and inputs feedback. The input for this step is the pointers displayed on the terminal interface. The user inputs, as feedback through the opinion collection means, the insights and improvement suggestions obtained during the operation process. As output, the data transmission of user feedback information is performed. Specific operations include touch input by the user, keyboard input, and clicking of the send button. 【0281】 Step 6: 【0282】 The server utilizes the feedback collected from the users for improving the generation AI model and generating next-generation pointers. The input for this step is the feedback information from the users. Data processing includes text analysis of the feedback and tuning of the model using regression analysis. The output is the updated AI model and improved pointers. Specific operations include database storage of the feedback information and retraining of the model. 【0283】 (Application Example 1) 【0284】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0285】 In industrial automation devices, maximizing work efficiency and accelerating anomaly detection are required. However, in current systems, data analysis and extraction of optimization procedures are often performed manually, making it difficult to respond in a timely manner. Therefore, there is a need for a system that provides appropriate instructions to workers in real time according to the operation status, and updates the operation standards while incorporating improvement proposals into the feedback loop. 【0286】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0287】 In this invention, the server includes an information collection means for collecting operation information related to industrial automation devices, an information analysis means for analyzing the collected information and extracting optimization procedures, and an operation standard generation means for generating operation standards based on the extracted optimization procedures. As a result, the efficiency and timeliness of operation are improved, and it becomes possible for workers to make optimal decisions. 【0288】 An "industrial automation device" is a device or system used to automate processes such as manufacturing and assembly in a factory. 【0289】 "Information collection means" is a function or technology for collecting data related to the operation status of industrial automation devices. 【0290】 "Information analysis means" is a function or technology for analyzing the collected data and extracting procedures and improvement points for optimizing operation. 【0291】 "Operation standard generation means" is a function or technology for creating standards and guidelines for efficient operation based on the analysis results. 【0292】 "Operation standard distribution means" is a function or technology for transmitting the generated operation standards to workers. <与えられた運用基準や指示を作業者に視覚的に示すためのデバイスである。 【0293】 A "display device" is a device for visually showing the received operation standards and instructions to workers. 【0294】 An "improvement suggestion" is an opinion or proposal based on the work performed by the worker, aimed at further streamlining operations. 【0295】 An "artificial intelligence model" is a computer program and algorithm that supports prediction and decision-making to improve operational efficiency, based on results obtained from data analysis. 【0296】 The system implementing this invention is primarily configured to improve the operational efficiency of industrial automation equipment. A server plays a central role in this system. The server collects operational information from the industrial automation equipment. Specifically, it uses sensors and communication modules to acquire data on the operating status of the equipment, error codes, and product quality. 【0297】 The server uses this collected data to run data analysis tools and derive information to optimize operations. Here, a machine learning model is built using libraries such as Python's Scikit-learn. This model learns from historical operational data and extracts optimization steps to improve operational efficiency. 【0298】 Subsequently, the server uses the operational standards generation means to create efficient instructions and guidelines for operation based on the results obtained from the analysis. These guidelines include work procedures and the timing of preventive maintenance. The generated standards are transmitted to the display device via the operational standards distribution means. 【0299】 The terminal plays a role in visually displaying the received operational standards to the worker. By using mobile devices or tablet terminals, information is presented in real time. For example, when an anomaly is detected, the cause and corrective procedures are displayed step by step. 【0300】 The user follows the instructions through the display device to perform operations. Also, the user can input improvement suggestions into the terminal during operation. Using the generated prompt text, effective feedback is collected from the user. For example, a prompt text such as "Please input the problems that occurred in today's work and the improvement measures." 【0301】 Finally, based on the collected improvement suggestions and operation results, the server continuously updates the artificial intelligence model. As a result, the optimization of operation is always achieved, and a system that can meet the latest operation standards is established. 【0302】 The flow of specific processing in Application Example 1 will be described using FIG. 12. 【0303】 Step 1: 【0304】 The server collects operation information from industrial automation devices via sensors and communication modules. The input is data related to the operating state of the device, error codes, and product quality. Since these data are used in subsequent processing steps, they are safely stored in the database. 【0305】 Step 2: 【0306】 The server activates data analysis means to analyze the collected data. A machine learning model is constructed using the Scikit-learn library in Python. The input is the operation data collected in Step 1. The output is the optimization procedures and patterns for optimizing operation efficiency. This analysis process includes data preprocessing, feature selection, and model training. 【0307】 Step 3: 【0308】 The server uses an operational standard generation mechanism to create operational standards based on the analysis results. The input is the optimization procedure obtained in step 2. The output is an operational standard that includes work procedures and the timing of preventive maintenance. This standard includes specific instructions to enable users to perform their work smoothly. 【0309】 Step 4: 【0310】 The server transmits the generated operational standards to the terminal via the operational standards distribution means. Specifically, it transfers the data to mobile devices and tablets over the network. The input is the operational standards generated in step 3, and the output is the operational instructions displayed on the terminal. 【0311】 Step 5: 【0312】 The terminal visually displays the received operational standards to the worker. The user then begins their work based on this information. The terminal reflects the information received in step 4 on the screen and presents it in a format that is easy for the worker to understand. This includes a step-by-step display of the cause and corrective procedures when an anomaly is detected. 【0313】 Step 6: 【0314】 Users input improvements they discover during their work into a terminal. The input consists of the user's improvement suggestions and opinions. The output is feedback data sent to the server. This data is effectively collected using prompts from a generated AI model. 【0315】 Step 7: 【0316】 The server analyzes user feedback and continuously updates the artificial intelligence model. The input is the feedback data received from the user in step 6. The output is the latest operational standards and model, which will be used for future operations. This will lead to further improvements in operational efficiency. 【0317】 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. 【0318】 This invention is a system designed to streamline corporate process operations and provide support tailored to the emotional state of individual users. This system primarily functions through four components: a server, a terminal, an emotion engine, and the user. 【0319】 The server first collects log information from process management systems and other related systems to gather data generated within the company. This allows for a comprehensive understanding of the company's operations and the storage of necessary information in a database. The data is appropriately protected and managed with privacy in mind. 【0320】 Next, the server analyzes the collected data. Using AI algorithms, it analyzes patterns and trends within the data to identify efficient business processes and areas requiring improvement. Based on the insights gained during this process, it generates standardized guidelines. 【0321】 The generated guidelines are delivered to the device. The device visually presents the guidelines to the user. At this time, an emotion engine is activated, reading the user's emotions from their voice and facial expressions and adjusting the display method accordingly. For example, if the user is fatigued, measures are taken to simplify the presentation of the guidelines to reduce their burden. 【0322】 Users perform tasks according to guidelines via their devices. During task execution, users can provide feedback, which, including the user's emotional state, is sent from the device to the server. 【0323】 The server receives feedback and performs analysis, including emotional data from the emotion engine. This data is used to update the AI ​​model, helping to further optimize the next set of guidelines generated. In this way, the system provides adaptive support tailored to each user's situation, continuously improving the efficiency of corporate process operations and regulatory compliance. 【0324】 The following describes the processing flow. 【0325】 Step 1: 【0326】 The server collects process data from various systems within the company. For example, it retrieves logs from manufacturing management systems and customer management systems and stores them in a central database. This data is then pre-processed so that it can be analyzed immediately. 【0327】 Step 2: 【0328】 The server analyzes the collected data using an AI engine to extract best practices for improving the efficiency of business processes. This process uses machine learning techniques to identify frequently occurring patterns and success stories. 【0329】 Step 3: 【0330】 The server generates standardized guidelines based on the analysis results. These guidelines include specific work procedures and key performance indicators (KPIs). 【0331】 Step 4: 【0332】 The server distributes the generated guidelines to the terminals. If necessary, the distribution of the guidelines can be restricted to specific departments or individuals. 【0333】 Step 5: 【0334】 The device visually displays the received guidelines. In doing so, it uses an emotion engine to analyze the user's current emotional state. For example, it captures facial expressions with a camera and measures stress and anxiety through voice analysis. 【0335】 Step 6: 【0336】 The device adjusts the display of guidelines according to the user's emotional state. If the user is tired, it uses features to simplify the presentation of information or to show instructions in stages. 【0337】 Step 7: 【0338】 Users perform tasks according to the adjusted guidelines. As they go through each step, they input any problems they find or suggestions for improvement as feedback into their terminal. 【0339】 Step 8: 【0340】 The device sends emotional data acquired by the emotion engine, along with user feedback, to the server. 【0341】 Step 9: 【0342】 The server analyzes the provided feedback and sentiment data to update the AI ​​model. This data is used to generate guidelines and optimize processes for future use, improving the system's accuracy and usefulness. 【0343】 (Example 2) 【0344】 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". 【0345】 In modern business activities, there is a need to improve the efficiency of business processes while providing flexible support that is tailored to the emotional state of users. However, conventional systems have limited mechanisms to effectively address these needs, resulting in challenges such as insufficient improvements in business efficiency and a lack of methods to alleviate the psychological burden on users. 【0346】 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. 【0347】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and extracting the optimal method, and a device for generating standardized guidelines based on the extracted optimal method. This enables flexible support that responds to the user's emotions while promoting the efficiency of the company's operations. 【0348】 An "information-gathering device" is a means of acquiring various data related to business activities, and is a device that plays a role in effectively accumulating database and log information. 【0349】 A "device for extracting optimal methods" is a means of analyzing collected data to identify patterns and trends that contribute to improving business processes, and it is a device that uses analytical techniques to derive the optimal procedures and methods. 【0350】 A "device for generating standardized guidelines" is a device that has the function of forming guidelines and directives necessary to improve the efficiency of operations based on the extracted optimal methods. 【0351】 An "information processing device" is a device that displays information to users, has the function of receiving opinions and feedback from users, and enables two-way information exchange through an interface. 【0352】 An "artificial intelligence model" is a collection of algorithms and systems that continuously learn and improve based on collected information and user feedback, adapting to new data to provide highly accurate analysis and predictions. 【0353】 This invention is a system that streamlines corporate business processes and enables support that responds to users' emotions. The system mainly consists of a server, terminals, and an emotion analysis engine. 【0354】 The server first collects data from various sources within the company. Specifically, it retrieves logs and usage history from the company's process management systems and related information systems using APIs. This data is securely stored in a database for later analysis. 【0355】 Next, the server uses Python libraries (e.g., Pandas and Scikit-learn) to process and analyze the data. Here, AI algorithms are employed to identify optimal business practices and areas for improvement within the data. Based on this analysis, prompts are input to a generative AI model (e.g., a natural language processing model) to generate standardized guidelines. An example of a prompt might be, "Generate guidelines for optimizing the sales process." 【0356】 The generated guidelines are delivered to the device. When the device presents these to the user, it utilizes an emotion analysis engine. The emotion analysis engine incorporates speech recognition and facial expression analysis technologies, and the program evaluates the user's emotional state through Microsoft Azure Cognitive Services. Based on this information, the device dynamically adjusts how the guidelines are displayed. For example, if it determines that the user is tired, it simplifies the display of the guidelines to reduce visual burden. 【0357】 Users perform tasks according to guidelines via their devices while also providing feedback. This feedback includes progress, new discoveries, and even emotional states. The devices continuously transmit this information to a server, which uses this data to retrain the AI ​​model. This process optimizes the guidelines provided next time, thereby improving overall operational efficiency within the company. 【0358】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0359】 Step 1: 【0360】 The server collects data from the company's process management systems and related information systems. Specifically, it uses APIs to retrieve log data and usage history. The input to this process is data streams from each system, and the output is structured data stored in a database. This allows for the collection of detailed information about the company's activities. 【0361】 Step 2: 【0362】 The server processes and cleans the collected data using Python libraries (such as Pandas and Scikit-learn). The input is structured data in a database, and the output is a clean dataset suitable for analysis. Specifically, it performs missing value imputation and outlier removal. Using this clean data, an AI algorithm identifies patterns and trends. 【0363】 Step 3: 【0364】 The server extracts the optimal method based on identified patterns and trends, and sends prompt messages to the generating AI model to produce standardized guidelines. The input is a prompt message based on the analysis results, and the output is the generated guidelines. Specifically, the server gives the model the prompt, "Generate guidelines for optimizing the sales process." 【0365】 Step 4: 【0366】 The terminal displays guidelines received from the server to the user. The input is the guidelines delivered from the server, and the output is the presentation of visual guidelines to the user. An emotion analysis engine is used to analyze the user's voice and facial expressions through sensors and adjust the display method accordingly. Specifically, it uses Microsoft Azure Cognitive Services to analyze the emotional state. 【0367】 Step 5: 【0368】 Users perform tasks based on guidelines displayed on the terminal. Input is the provided guidelines, and output is data on feedback for completed tasks and their emotional state at the time. Users provide feedback and input information, including their emotional state, into the terminal as they work. 【0369】 Step 6: 【0370】 The terminal sends feedback received from the user to the server. The input is the user feedback data, and the output is the server that received it. The terminal transmits information in real time, which is used by the server to generate the next set of guidelines. 【0371】 Step 7: 【0372】 The server takes in feedback data and uses it to update the AI ​​model. The input is user feedback and sentiment analysis results, and the output is the updated AI model. Through this process, the system continuously optimizes guidelines and improves the company's process management. 【0373】 (Application Example 2) 【0374】 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." 【0375】 In production sites such as factories, there is a need to balance efficient operations with reducing the psychological burden on workers. However, currently, it is difficult to grasp the psychological state of workers in real time and provide work instructions that are appropriate to that situation, which can lead to decreased work efficiency and increased stress among workers. 【0376】 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. 【0377】 In this invention, the server includes an information gathering means for collecting operational information of a company, an information analysis means for analyzing the collected information and extracting optimal guidelines for improving work efficiency, and a psychological state analysis means for recognizing the psychological state of the worker and adjusting the display method of the instructions. This makes it possible to provide detailed work instructions that are tailored to the psychological state of the worker. 【0378】 "Information gathering means" refers to methods or devices for effectively acquiring and storing operational information of a company in a database. 【0379】 "Information analysis means" refers to a method or device that analyzes collected data and derives guidelines or patterns that contribute to improving operational efficiency. 【0380】 "Instruction generation means" refers to a method or apparatus for generating standardized work instructions based on analysis results and for appropriately transmitting them. 【0381】 "Instruction distribution means" refers to a method or device for transmitting generated instructions to a terminal in a timely manner. 【0382】 A "psychological state analysis means" is a method or device that determines the psychological state of a worker from their facial expressions and voice, and optimizes the method of presenting instructions based on that information. 【0383】 An "AI model" is a mathematical method built using machine learning algorithms based on a large amount of data, with the aim of continuously improving its performance. 【0384】 A "terminal device" is an electronic device used for displaying instructions and collecting responses from workers. 【0385】 "Response" refers to information provided by workers through their devices, specifically feedback related to the work process and their psychological state. 【0386】 In the system realizing this invention, a server, terminal devices, and workers work together in order to efficiently process information. The server stores the necessary data in a database via an information collection means that aggregates operational information from various sensors and information systems of the company. Then, an information analysis means analyzes this data using an AI model to identify guidelines for improving operational efficiency. Standardized work instructions are created from the analysis results by an instruction generation means and transmitted to terminal devices using an instruction distribution means. 【0387】 The terminal device displays the received instructions to the worker. The worker's psychological state is determined using facial expressions and voice data via a psychological state analysis device, and the instruction display method is optimized accordingly. In this process, the terminal device collects the worker's responses and sends them to a server to help update the AI ​​model. 【0388】 A concrete example of this embodiment is a worker on a factory production line. For instance, if a worker is wearing smart glasses, and AI-based psychological state analysis determines that the worker is feeling fatigued, the system sends instructions to suggest a break and displays relaxation methods on the smart glasses' display. This makes it possible to reduce worker stress while improving the efficiency of the production line. 【0389】 A specific example prompt used would be, "Please propose stress reduction measures for the production line. The current emotional state is 'fatigue'." By providing work support tailored to each work environment and situation in this way, it is possible to improve production efficiency and promote worker welfare. 【0390】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0391】 Step 1: 【0392】 The server collects operational information from various sensors and information systems within the company. It receives sensor data and system logs as input and records them in a database. This data is stored accurately and systematically because it will be used for later analysis. 【0393】 Step 2: 【0394】 The server analyzes the collected data using information analysis tools. Input data includes information on work efficiency, error rates, and worker productivity. An AI model is used to identify guidelines for improving work efficiency and generate templates for work instructions as output. Anomaly detection and pattern extraction are performed during the data analysis process. 【0395】 Step 3: 【0396】 The server organizes the generated work instructions using an instruction generation system and distributes them to terminal devices. It receives instruction templates as input and sends standardized work instructions to the terminals as output. The instructions are then presented to the workers as specific guidance content. 【0397】 Step 4: 【0398】 The terminal device displays received work instructions to the worker. Based on the instruction data received as input, it processes it into a clear and visually understandable format and displays it on the display as output. The displayed content includes specific work procedures and points to note. 【0399】 Step 5: 【0400】 The user's psychological state is evaluated by a psychological state analysis tool on the device. Audio and video data are acquired as input, and emotions are determined using a machine learning algorithm based on this data. The output is the evaluation result of the psychological state, which is used to adjust the way instructions are presented. 【0401】 Step 6: 【0402】 The user's responses are collected by the terminal and sent to the server. User feedback and work results are received as input, and processed into data useful for generating the next work instructions as output. The AI ​​model is continuously improved through post-processing feedback. 【0403】 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. 【0404】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0405】 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. 【0406】 [Third Embodiment] 【0407】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0408】 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. 【0409】 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). 【0410】 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. 【0411】 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. 【0412】 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). 【0413】 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. 【0414】 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. 【0415】 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. 【0416】 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. 【0417】 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. 【0418】 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". 【0419】 This invention is a system for providing efficiency and consistency in the operation of corporate processes, and is implemented as follows. This system primarily functions through three entities: servers, terminals, and users. 【0420】 The server first collects data from various business processes within the company and stores it securely. For example, the server ingests log data from incident management systems and operational management systems. This allows the company to understand the status of the processes it is currently running. 【0421】 Next, the server analyzes the collected data to identify common patterns and best practices. This analysis highlights areas for improvement and effective methods in business operations. For example, analyzing past incident response times can identify process bottlenecks. 【0422】 Furthermore, the server generates standardized guidelines based on the analysis results. These guidelines include specific procedures and priorities. These guidelines aim to improve the quality of work and are used for regulatory compliance and quality control. 【0423】 Guidelines generated by the server are delivered to the terminal. The terminal notifies the user and presents them in a visual interface so that the user can perform the task according to the instructions. For example, the terminal provides step-by-step instructions for creating an incident report. 【0424】 Users perform tasks based on guidelines displayed on their devices. They can input any observations or suggestions for improvement during the task using the device's feedback function. This feedback is then sent back to the server and used to improve the accuracy of the AI ​​model and refine the guidelines for future use. 【0425】 In this way, the present invention aims to standardize and improve the effectiveness of processes, enhance the operational efficiency of companies, and reduce the burden of regulatory compliance. Furthermore, through continuous learning and updates, it is possible to flexibly respond to the latest changes in operational frameworks. 【0426】 The following describes the processing flow. 【0427】 Step 1: 【0428】 The server collects data from corporate processes. For example, it gathers log information about incidents and performance from various internal systems and stores it in a secure database. During this process, the data is encrypted, and appropriate data protection measures are taken. 【0429】 Step 2: 【0430】 The server analyzes the collected data. Here, AI algorithms are used to perform pattern recognition and trend analysis on the data, identifying particularly efficient processes and potential problems. This analysis yields reliable insights based on historical data. 【0431】 Step 3: 【0432】 The server generates standardized guidelines based on the analysis results. For example, it creates procedures and priority lists aimed at accelerating incident response. These guidelines include specific actions for each step of the process. 【0433】 Step 4: 【0434】 The server distributes the generated guidelines to the terminals. It selects distribution destinations to ensure the guidelines reach the relevant departments and personnel. 【0435】 Step 5: 【0436】 The device notifies the user of the received guidelines and displays them on the screen. For example, it allows the user to quickly check the guidelines through pop-up notifications or displays on the dashboard. 【0437】 Step 6: 【0438】 Users perform their tasks according to the guidelines displayed on their devices. They refer to the guidelines while executing each step in incident handling and quality control. 【0439】 Step 7: 【0440】 The terminal collects user feedback and sends it to the server. It records feedback on areas for improvement and bugs encountered during the process and uses it to improve future model training. 【0441】 Step 8: 【0442】 The server updates the AI ​​model using feedback. This information is then used to generate even more accurate process guidelines in the future. This allows the system to continuously evolve. 【0443】 (Example 1) 【0444】 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." 【0445】 In modern organizations, streamlining and standardizing business processes is a crucial challenge. However, collecting and analyzing business data, extracting best practices for business improvement, and implementing process improvements while considering user feedback are not easy. In particular, there is a need to process large amounts of data in real time and respond flexibly to changing circumstances. 【0446】 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. 【0447】 In this invention, the server includes information gathering means, information analysis means, guideline generation means, guideline distribution means, pattern identification means, and opinion gathering means. This makes it possible to efficiently collect and analyze organizational business data and generate and distribute standardized guidelines. Furthermore, by updating the generated AI model based on user feedback, continuous process improvement and optimization become possible. 【0448】 An "information processing device" is a device that plays a central role in collecting, analyzing, and managing various business data within an organization, and in generating and distributing standardized business guidelines. 【0449】 "Information gathering means" refers to functions that automatically aggregate various types of business-related data from internal or external data sources within an organization, and securely store and manage them. 【0450】 "Information analysis means" refers to a function that uses a generative AI model to analyze collected data and extract patterns and best practices. 【0451】 "Guideline generation means" refers to a function that generates specific guidelines for business standardization based on optimal practice examples obtained through information analysis means. 【0452】 "Guideline distribution means" refers to a function that distributes generated guidelines to information terminals used by users, and uses them to improve business operations. 【0453】 An "information terminal" refers to a device that allows users to receive and display guidelines. This enables users to take specific actions. 【0454】 "Pattern identification means" refers to a function that identifies specific patterns from the analysis results of collected data that can be used for business improvement and standardization. 【0455】 "Methods for gathering feedback" refers to functions that collect user feedback and suggestions for improvement regarding tasks performed in accordance with guidelines. 【0456】 A "generative AI model" refers to an algorithm that uses large amounts of data to learn and generate optimized business guidelines. 【0457】 This invention is an information processing system for efficiently and standardizing business processes within an organization. An embodiment thereof is shown below. 【0458】 The server plays a central role in this system, collecting data from various data sources within the organization using information gathering tools. Specifically, the server retrieves data from existing databases and management systems via APIs and stores it in secure storage. Encryption technologies and access control lists are used to ensure data integrity and security. 【0459】 Next, the server uses a generative AI model as an information analysis tool to analyze the collected data. The generative AI model used here combines machine learning algorithms to efficiently extract patterns and optimal practices from large datasets. This includes data preprocessing, feature extraction, and model training. 【0460】 Once the analysis is complete, the server generates standardized guidelines using a guideline generation mechanism. These guidelines include specific steps and priorities for optimizing business processes. For example, in incident response, they define in detail the initial response procedures and reporting flow. This content is generated as a prompt message, such as "Analyze past incident response logs and propose efficient response procedures." 【0461】 Once guidelines are generated, the server distributes them to information terminals via a guideline distribution system. The information terminals have an interface that visually presents the received guidelines to the user, who then performs their tasks based on these guidelines. This interface includes pop-up notifications and situation-dependent dashboard displays, designed to allow users to intuitively understand and act upon the guidelines. 【0462】 Users input their observations and improvement suggestions as feedback through a feedback collection system while performing tasks via their terminals. This feedback information is then sent back to the server and used to improve the generated AI model and develop next-generation guidelines. This design allows the entire system to continuously learn, improving operational efficiency and quality over time. 【0463】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0464】 Step 1: 【0465】 The server collects data from various data sources within the organization using information gathering tools. Inputs include log data obtained in real time via APIs of incident management and operational management systems. This data is encrypted while ensuring integrity and stored in storage. Specifically, the server periodically sends requests to the APIs to retrieve the latest data. 【0466】 Step 2: 【0467】 The server inputs the collected data into an AI model for data analysis. The input here is the log data obtained in the previous step. Data processing includes pre-processing such as noise reduction and missing value imputation. The AI ​​model then performs clustering to detect common patterns and best practices. The output generates identified patterns and significant insights. Specific operations include data cleaning and execution of the AI ​​algorithm. 【0468】 Step 3: 【0469】 The server uses a guideline generation mechanism to generate standardized guidelines based on the analysis results. The input for this step is pattern information, which corresponds to the output of the AI ​​model. The generated guidelines include specific procedures and priorities for business improvement. The output is, for example, in the form of prompt statements such as "Initial response guidelines for system trouble." In terms of specific operation, there is a process that automatically generates guidelines according to a template based on the calculation results of the model. 【0470】 Step 4: 【0471】 The server distributes the generated guidelines to the information terminal using the guidelines distribution means. In this step, the input is the generated guidelines, and the output is the transmission of the guidelines to the terminal. The specific operation includes sending data packets over the network and triggering a notification on the terminal. 【0472】 Step 5: 【0473】 The user performs tasks based on the guidelines displayed on the terminal and inputs feedback. The input for this step is the guidelines displayed on the terminal's interface. The user inputs insights and improvement suggestions gained during the task execution process as feedback through a feedback collection method. The output is the transmission of user feedback data. Specific actions include touch input, keyboard input, and clicking a send button. 【0474】 Step 6: 【0475】 The server utilizes collected user feedback to improve the generated AI model and create next-generation guidelines. The input for this step is user feedback information. Data processing includes text analysis of the feedback and model tuning using regression analysis. The output is the updated AI model and improved guidelines. Specific actions include saving the feedback information to a database and retraining the model. 【0476】 (Application Example 1) 【0477】 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." 【0478】 Industrial automation equipment requires maximizing work efficiency and rapidly detecting anomalies. However, current systems often require manual data analysis and extraction of optimization procedures, making timely responses difficult. Therefore, a system is needed that provides operators with appropriate instructions in real time based on operational conditions and updates operational standards while incorporating improvement suggestions into a feedback loop. 【0479】 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. 【0480】 In this invention, the server includes an information gathering means for collecting operational information relating to industrial automation equipment, an information analysis means for analyzing the collected information and extracting optimization procedures, and an operational criteria generation means for generating operational criteria based on the extracted optimization procedures. This enhances the efficiency and timeliness of operations and enables workers to make optimal decisions. 【0481】 "Industrial automation equipment" refers to devices and systems used to automate processes such as manufacturing and assembly within a factory. 【0482】 "Information gathering means" refers to functions and technologies for collecting data on the operational status of industrial automation equipment. 【0483】 "Information analysis tools" refer to functions and technologies used to analyze collected data and extract procedures and areas for improvement to optimize operations. 【0484】 "Operational standard generation means" refers to functions and technologies that create standards and guidelines for efficient operation based on analysis results. 【0485】 "Operational standards distribution means" refers to functions and technologies for communicating generated operational standards to workers. 【0486】 A "display device" is a device used to visually show workers the operational standards and instructions they have received. 【0487】 An "improvement suggestion" is an opinion or proposal based on the work performed by the worker, aimed at further streamlining operations. 【0488】 An "artificial intelligence model" is a computer program and algorithm that supports prediction and decision-making to improve operational efficiency, based on results obtained from data analysis. 【0489】 The system implementing this invention is primarily configured to improve the operational efficiency of industrial automation equipment. A server plays a central role in this system. The server collects operational information from the industrial automation equipment. Specifically, it uses sensors and communication modules to acquire data on the operating status of the equipment, error codes, and product quality. 【0490】 The server uses this collected data to run data analysis tools and derive information to optimize operations. Here, a machine learning model is built using libraries such as Python's Scikit-learn. This model learns from historical operational data and extracts optimization steps to improve operational efficiency. 【0491】 Subsequently, the server uses the operational standards generation means to create efficient instructions and guidelines for operation based on the results obtained from the analysis. These guidelines include work procedures and the timing of preventive maintenance. The generated standards are transmitted to the display device via the operational standards distribution means. 【0492】 The terminal plays a role in visually displaying the received operational standards to the worker. By using mobile devices or tablet terminals, information is presented in real time. For example, when an anomaly is detected, the cause and corrective procedures are displayed step by step. 【0493】 Users follow instructions and perform tasks via a display device. Users can also input improvement suggestions into the terminal during operation. Effective feedback is collected from users using generated prompt messages. For example, a prompt message might read, "Please enter any problems encountered during today's work and the proposed solutions." 【0494】 Ultimately, the server continuously updates its artificial intelligence model based on collected improvement suggestions and operational results. This ensures continuous operational optimization and establishes a system that can comply with the latest operational standards. 【0495】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0496】 Step 1: 【0497】 The server collects operational information from industrial automation equipment via sensors and communication modules. Inputs include data on the equipment's operating status, error codes, and product quality. This data is securely stored in a database for use in subsequent processing steps. 【0498】 Step 2: 【0499】 The server launches data analysis tools to analyze the collected data. A machine learning model is built using the Python Scikit-learn library. The input is the operational data collected in step 1. The output is optimization procedures and patterns for optimizing operational efficiency. This analysis process includes data preprocessing, feature selection, and model training. 【0500】 Step 3: 【0501】 The server uses an operational standard generation mechanism to create operational standards based on the analysis results. The input is the optimization procedure obtained in step 2. The output is an operational standard that includes work procedures and the timing of preventive maintenance. This standard includes specific instructions to enable users to perform their work smoothly. 【0502】 Step 4: 【0503】 The server transmits the generated operational standards to the terminal via the operational standards distribution means. Specifically, it transfers the data to mobile devices and tablets over the network. The input is the operational standards generated in step 3, and the output is the operational instructions displayed on the terminal. 【0504】 Step 5: 【0505】 The terminal visually displays the received operational standards to the worker. The user then begins their work based on this information. The terminal reflects the information received in step 4 on the screen and presents it in a format that is easy for the worker to understand. This includes a step-by-step display of the cause and corrective procedures when an anomaly is detected. 【0506】 Step 6: 【0507】 Users input improvements they discover during their work into a terminal. The input consists of the user's improvement suggestions and opinions. The output is feedback data sent to the server. This data is effectively collected using prompts from a generated AI model. 【0508】 Step 7: 【0509】 The server analyzes user feedback and continuously updates the artificial intelligence model. The input is the feedback data received from the user in step 6. The output is the latest operational standards and model, which will be used for future operations. This will lead to further improvements in operational efficiency. 【0510】 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. 【0511】 This invention is a system designed to streamline corporate process operations and provide support tailored to the emotional state of individual users. This system primarily functions through four components: a server, a terminal, an emotion engine, and the user. 【0512】 The server first collects log information from process management systems and other related systems to gather data generated within the company. This allows for a comprehensive understanding of the company's operations and the storage of necessary information in a database. The data is appropriately protected and managed with privacy in mind. 【0513】 Next, the server analyzes the collected data. Using AI algorithms, it analyzes patterns and trends within the data to identify efficient business processes and areas requiring improvement. Based on the insights gained during this process, it generates standardized guidelines. 【0514】 The generated guidelines are delivered to the device. The device visually presents the guidelines to the user. At this time, an emotion engine is activated, reading the user's emotions from their voice and facial expressions and adjusting the display method accordingly. For example, if the user is fatigued, measures are taken to simplify the presentation of the guidelines to reduce their burden. 【0515】 Users perform tasks according to guidelines via their devices. During task execution, users can provide feedback, which, including the user's emotional state, is sent from the device to the server. 【0516】 The server receives feedback and performs analysis, including emotional data from the emotion engine. This data is used to update the AI ​​model, helping to further optimize the next set of guidelines generated. In this way, the system provides adaptive support tailored to each user's situation, continuously improving the efficiency of corporate process operations and regulatory compliance. 【0517】 The following describes the processing flow. 【0518】 Step 1: 【0519】 The server collects process data from various systems within the company. For example, it retrieves logs from manufacturing management systems and customer management systems and stores them in a central database. This data is then pre-processed so that it can be analyzed immediately. 【0520】 Step 2: 【0521】 The server analyzes the collected data using an AI engine to extract best practices for improving the efficiency of business processes. This process uses machine learning techniques to identify frequently occurring patterns and success stories. 【0522】 Step 3: 【0523】 The server generates standardized guidelines based on the analysis results. These guidelines include specific work procedures and key performance indicators (KPIs). 【0524】 Step 4: 【0525】 The server distributes the generated guidelines to the terminals. If necessary, the distribution of the guidelines can be restricted to specific departments or individuals. 【0526】 Step 5: 【0527】 The device visually displays the received guidelines. In doing so, it uses an emotion engine to analyze the user's current emotional state. For example, it captures facial expressions with a camera and measures stress and anxiety through voice analysis. 【0528】 Step 6: 【0529】 The device adjusts the display of guidelines according to the user's emotional state. If the user is tired, it uses features to simplify the presentation of information or to show instructions in stages. 【0530】 Step 7: 【0531】 Users perform tasks according to the adjusted guidelines. As they go through each step, they input any problems they find or suggestions for improvement as feedback into their terminal. 【0532】 Step 8: 【0533】 The device sends emotional data acquired by the emotion engine, along with user feedback, to the server. 【0534】 Step 9: 【0535】 The server analyzes the provided feedback and sentiment data to update the AI ​​model. This data is used to generate guidelines and optimize processes for future use, improving the system's accuracy and usefulness. 【0536】 (Example 2) 【0537】 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." 【0538】 In modern business activities, there is a need to improve the efficiency of business processes while providing flexible support that is tailored to the emotional state of users. However, conventional systems have limited mechanisms to effectively address these needs, resulting in challenges such as insufficient improvements in business efficiency and a lack of methods to alleviate the psychological burden on users. 【0539】 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. 【0540】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and extracting the optimal method, and a device for generating standardized guidelines based on the extracted optimal method. This enables flexible support that responds to the user's emotions while promoting the efficiency of the company's operations. 【0541】 An "information-gathering device" is a means of acquiring various data related to business activities, and is a device that plays a role in effectively accumulating database and log information. 【0542】 A "device for extracting optimal methods" is a means of analyzing collected data to identify patterns and trends that contribute to improving business processes, and it is a device that uses analytical techniques to derive the optimal procedures and methods. 【0543】 A "device for generating standardized guidelines" is a device that has the function of forming guidelines and directives necessary to improve the efficiency of operations based on the extracted optimal methods. 【0544】 An "information processing device" is a device that displays information to users, has the function of receiving opinions and feedback from users, and enables two-way information exchange through an interface. 【0545】 An "artificial intelligence model" is a collection of algorithms and systems that continuously learn and improve based on collected information and user feedback, adapting to new data to provide highly accurate analysis and predictions. 【0546】 This invention is a system that streamlines corporate business processes and enables support that responds to users' emotions. The system mainly consists of a server, terminals, and an emotion analysis engine. 【0547】 The server first collects data from various sources within the company. Specifically, it retrieves logs and usage history from the company's process management systems and related information systems using APIs. This data is securely stored in a database for later analysis. 【0548】 Next, the server uses Python libraries (e.g., Pandas and Scikit-learn) to process and analyze the data. Here, AI algorithms are employed to identify optimal business practices and areas for improvement within the data. Based on this analysis, prompts are input to a generative AI model (e.g., a natural language processing model) to generate standardized guidelines. An example of a prompt might be, "Generate guidelines for optimizing the sales process." 【0549】 The generated guidelines are delivered to the device. When the device presents these to the user, it utilizes an emotion analysis engine. The emotion analysis engine incorporates speech recognition and facial expression analysis technologies, and the program evaluates the user's emotional state through Microsoft Azure Cognitive Services. Based on this information, the device dynamically adjusts how the guidelines are displayed. For example, if it determines that the user is tired, it simplifies the display of the guidelines to reduce visual burden. 【0550】 Users perform tasks according to guidelines via their devices while also providing feedback. This feedback includes progress, new discoveries, and even emotional states. The devices continuously transmit this information to a server, which uses this data to retrain the AI ​​model. This process optimizes the guidelines provided next time, thereby improving overall operational efficiency within the company. 【0551】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0552】 Step 1: 【0553】 The server collects data from the company's process management systems and related information systems. Specifically, it uses APIs to retrieve log data and usage history. The input to this process is data streams from each system, and the output is structured data stored in a database. This allows for the collection of detailed information about the company's activities. 【0554】 Step 2: 【0555】 The server processes and cleans the collected data using Python libraries (such as Pandas and Scikit-learn). The input is structured data in a database, and the output is a clean dataset suitable for analysis. Specifically, it performs missing value imputation and outlier removal. Using this clean data, an AI algorithm identifies patterns and trends. 【0556】 Step 3: 【0557】 The server extracts the optimal method based on identified patterns and trends, and sends prompt messages to the generating AI model to produce standardized guidelines. The input is a prompt message based on the analysis results, and the output is the generated guidelines. Specifically, the server gives the model the prompt, "Generate guidelines for optimizing the sales process." 【0558】 Step 4: 【0559】 The terminal displays guidelines received from the server to the user. The input is the guidelines delivered from the server, and the output is the presentation of visual guidelines to the user. An emotion analysis engine is used to analyze the user's voice and facial expressions through sensors and adjust the display method accordingly. Specifically, it uses Microsoft Azure Cognitive Services to analyze the emotional state. 【0560】 Step 5: 【0561】 Users perform tasks based on guidelines displayed on the terminal. Input is the provided guidelines, and output is data on feedback for completed tasks and their emotional state at the time. Users provide feedback and input information, including their emotional state, into the terminal as they work. 【0562】 Step 6: 【0563】 The terminal sends feedback received from the user to the server. The input is the user feedback data, and the output is the server that received it. The terminal transmits information in real time, which is used by the server to generate the next set of guidelines. 【0564】 Step 7: 【0565】 The server takes in feedback data and uses it to update the AI ​​model. The input is user feedback and sentiment analysis results, and the output is the updated AI model. Through this process, the system continuously optimizes guidelines and improves the company's process management. 【0566】 (Application Example 2) 【0567】 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." 【0568】 In production sites such as factories, there is a need to balance efficient operations with reducing the psychological burden on workers. However, currently, it is difficult to grasp the psychological state of workers in real time and provide work instructions that are appropriate to that situation, which can lead to decreased work efficiency and increased stress among workers. 【0569】 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. 【0570】 In this invention, the server includes an information gathering means for collecting operational information of a company, an information analysis means for analyzing the collected information and extracting optimal guidelines for improving work efficiency, and a psychological state analysis means for recognizing the psychological state of the worker and adjusting the display method of the instructions. This makes it possible to provide detailed work instructions that are tailored to the psychological state of the worker. 【0571】 "Information gathering means" refers to methods or devices for effectively acquiring and storing operational information of a company in a database. 【0572】 "Information analysis means" refers to a method or device that analyzes collected data and derives guidelines or patterns that contribute to improving operational efficiency. 【0573】 "Instruction generation means" refers to a method or apparatus for generating standardized work instructions based on analysis results and for appropriately transmitting them. 【0574】 "Instruction distribution means" refers to a method or device for transmitting generated instructions to a terminal in a timely manner. 【0575】 A "psychological state analysis means" is a method or device that determines the psychological state of a worker from their facial expressions and voice, and optimizes the method of presenting instructions based on that information. 【0576】 An "AI model" is a mathematical method built using machine learning algorithms based on a large amount of data, with the aim of continuously improving its performance. 【0577】 A "terminal device" is an electronic device used for displaying instructions and collecting responses from workers. 【0578】 "Response" refers to information provided by workers through their devices, specifically feedback related to the work process and their psychological state. 【0579】 In the system realizing this invention, a server, terminal devices, and workers work together in order to efficiently process information. The server stores the necessary data in a database via an information collection means that aggregates operational information from various sensors and information systems of the company. Then, an information analysis means analyzes this data using an AI model to identify guidelines for improving operational efficiency. Standardized work instructions are created from the analysis results by an instruction generation means and transmitted to terminal devices using an instruction distribution means. 【0580】 The terminal device displays the received instructions to the worker. The worker's psychological state is determined using facial expressions and voice data via a psychological state analysis device, and the instruction display method is optimized accordingly. In this process, the terminal device collects the worker's responses and sends them to a server to help update the AI ​​model. 【0581】 A concrete example of this embodiment is a worker on a factory production line. For instance, if a worker is wearing smart glasses, and AI-based psychological state analysis determines that the worker is feeling fatigued, the system sends instructions to suggest a break and displays relaxation methods on the smart glasses' display. This makes it possible to reduce worker stress while improving the efficiency of the production line. 【0582】 A specific example prompt used would be, "Please propose stress reduction measures for the production line. The current emotional state is 'fatigue'." By providing work support tailored to each work environment and situation in this way, it is possible to improve production efficiency and promote worker welfare. 【0583】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0584】 Step 1: 【0585】 The server collects operational information from various sensors and information systems within the company. It receives sensor data and system logs as input and records them in a database. This data is stored accurately and systematically because it will be used for later analysis. 【0586】 Step 2: 【0587】 The server analyzes the collected data using information analysis tools. Input data includes information on work efficiency, error rates, and worker productivity. An AI model is used to identify guidelines for improving work efficiency and generate templates for work instructions as output. Anomaly detection and pattern extraction are performed during the data analysis process. 【0588】 Step 3: 【0589】 The server organizes the generated work instructions using an instruction generation system and distributes them to terminal devices. It receives instruction templates as input and sends standardized work instructions to the terminals as output. The instructions are then presented to the workers as specific guidance content. 【0590】 Step 4: 【0591】 The terminal device displays received work instructions to the worker. Based on the instruction data received as input, it processes it into a clear and visually understandable format and displays it on the display as output. The displayed content includes specific work procedures and points to note. 【0592】 Step 5: 【0593】 The user's psychological state is evaluated by a psychological state analysis tool on the device. Audio and video data are acquired as input, and emotions are determined using a machine learning algorithm based on this data. The output is the evaluation result of the psychological state, which is used to adjust the way instructions are presented. 【0594】 Step 6: 【0595】 The user's responses are collected by the terminal and sent to the server. User feedback and work results are received as input, and processed into data useful for generating the next work instructions as output. The AI ​​model is continuously improved through post-processing feedback. 【0596】 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. 【0597】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0598】 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. 【0599】 [Fourth Embodiment] 【0600】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0601】 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. 【0602】 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). 【0603】 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. 【0604】 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. 【0605】 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). 【0606】 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. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 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. 【0611】 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. 【0612】 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". 【0613】 This invention is a system for providing efficiency and consistency in the operation of corporate processes, and is implemented as follows. This system primarily functions through three entities: servers, terminals, and users. 【0614】 The server first collects data from various business processes within the company and stores it securely. For example, the server ingests log data from incident management systems and operational management systems. This allows the company to understand the status of the processes it is currently running. 【0615】 Next, the server analyzes the collected data to identify common patterns and best practices. This analysis highlights areas for improvement and effective methods in business operations. For example, analyzing past incident response times can identify process bottlenecks. 【0616】 Furthermore, the server generates standardized guidelines based on the analysis results. These guidelines include specific procedures and priorities. These guidelines aim to improve the quality of work and are used for regulatory compliance and quality control. 【0617】 Guidelines generated by the server are delivered to the terminal. The terminal notifies the user and presents them in a visual interface so that the user can perform the task according to the instructions. For example, the terminal provides step-by-step instructions for creating an incident report. 【0618】 Users perform tasks based on guidelines displayed on their devices. They can input any observations or suggestions for improvement during the task using the device's feedback function. This feedback is then sent back to the server and used to improve the accuracy of the AI ​​model and refine the guidelines for future use. 【0619】 In this way, the present invention aims to standardize and improve the effectiveness of processes, enhance the operational efficiency of companies, and reduce the burden of regulatory compliance. Furthermore, through continuous learning and updates, it is possible to flexibly respond to the latest changes in operational frameworks. 【0620】 The following describes the processing flow. 【0621】 Step 1: 【0622】 The server collects data from corporate processes. For example, it gathers log information about incidents and performance from various internal systems and stores it in a secure database. During this process, the data is encrypted, and appropriate data protection measures are taken. 【0623】 Step 2: 【0624】 The server analyzes the collected data. Here, AI algorithms are used to perform pattern recognition and trend analysis on the data, identifying particularly efficient processes and potential problems. This analysis yields reliable insights based on historical data. 【0625】 Step 3: 【0626】 The server generates standardized guidelines based on the analysis results. For example, it creates procedures and priority lists aimed at accelerating incident response. These guidelines include specific actions for each step of the process. 【0627】 Step 4: 【0628】 The server distributes the generated guidelines to the terminals. It selects distribution destinations to ensure the guidelines reach the relevant departments and personnel. 【0629】 Step 5: 【0630】 The device notifies the user of the received guidelines and displays them on the screen. For example, it allows the user to quickly check the guidelines through pop-up notifications or displays on the dashboard. 【0631】 Step 6: 【0632】 Users perform their tasks according to the guidelines displayed on their devices. They refer to the guidelines while executing each step in incident handling and quality control. 【0633】 Step 7: 【0634】 The terminal collects user feedback and sends it to the server. It records feedback on areas for improvement and bugs encountered during the process and uses it to improve future model training. 【0635】 Step 8: 【0636】 The server updates the AI ​​model using feedback. This information is then used to generate even more accurate process guidelines in the future. This allows the system to continuously evolve. 【0637】 (Example 1) 【0638】 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". 【0639】 In modern organizations, streamlining and standardizing business processes is a crucial challenge. However, collecting and analyzing business data, extracting best practices for business improvement, and implementing process improvements while considering user feedback are not easy. In particular, there is a need to process large amounts of data in real time and respond flexibly to changing circumstances. 【0640】 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. 【0641】 In this invention, the server includes information gathering means, information analysis means, guideline generation means, guideline distribution means, pattern identification means, and opinion gathering means. This makes it possible to efficiently collect and analyze organizational business data and generate and distribute standardized guidelines. Furthermore, by updating the generated AI model based on user feedback, continuous process improvement and optimization become possible. 【0642】 An "information processing device" is a device that plays a central role in collecting, analyzing, and managing various business data within an organization, and in generating and distributing standardized business guidelines. 【0643】 "Information gathering means" refers to functions that automatically aggregate various types of business-related data from internal or external data sources within an organization, and securely store and manage them. 【0644】 "Information analysis means" refers to a function that uses a generative AI model to analyze collected data and extract patterns and best practices. 【0645】 "Guideline generation means" refers to a function that generates specific guidelines for business standardization based on optimal practice examples obtained through information analysis means. 【0646】 "Guideline distribution means" refers to a function that distributes generated guidelines to information terminals used by users, and uses them to improve business operations. 【0647】 An "information terminal" refers to a device that allows users to receive and display guidelines. This enables users to take specific actions. 【0648】 "Pattern identification means" refers to a function that identifies specific patterns from the analysis results of collected data that can be used for business improvement and standardization. 【0649】 "Methods for gathering feedback" refers to functions that collect user feedback and suggestions for improvement regarding tasks performed in accordance with guidelines. 【0650】 A "generative AI model" refers to an algorithm that uses large amounts of data to learn and generate optimized business guidelines. 【0651】 This invention is an information processing system for efficiently and standardizing business processes within an organization. An embodiment thereof is shown below. 【0652】 The server plays a central role in this system, collecting data from various data sources within the organization using information gathering tools. Specifically, the server retrieves data from existing databases and management systems via APIs and stores it in secure storage. Encryption technologies and access control lists are used to ensure data integrity and security. 【0653】 Next, the server uses a generative AI model as an information analysis tool to analyze the collected data. The generative AI model used here combines machine learning algorithms to efficiently extract patterns and optimal practices from large datasets. This includes data preprocessing, feature extraction, and model training. 【0654】 Once the analysis is complete, the server generates standardized guidelines using a guideline generation mechanism. These guidelines include specific steps and priorities for optimizing business processes. For example, in incident response, they define in detail the initial response procedures and reporting flow. This content is generated as a prompt message, such as "Analyze past incident response logs and propose efficient response procedures." 【0655】 Once guidelines are generated, the server distributes them to information terminals via a guideline distribution system. The information terminals have an interface that visually presents the received guidelines to the user, who then performs their tasks based on these guidelines. This interface includes pop-up notifications and situation-dependent dashboard displays, designed to allow users to intuitively understand and act upon the guidelines. 【0656】 Users input their observations and improvement suggestions as feedback through a feedback collection system while performing tasks via their terminals. This feedback information is then sent back to the server and used to improve the generated AI model and develop next-generation guidelines. This design allows the entire system to continuously learn, improving operational efficiency and quality over time. 【0657】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0658】 Step 1: 【0659】 The server collects data from various data sources within the organization using information gathering tools. Inputs include log data obtained in real time via APIs of incident management and operational management systems. This data is encrypted while ensuring integrity and stored in storage. Specifically, the server periodically sends requests to the APIs to retrieve the latest data. 【0660】 Step 2: 【0661】 The server inputs the collected data into an AI model for data analysis. The input here is the log data obtained in the previous step. Data processing includes pre-processing such as noise reduction and missing value imputation. The AI ​​model then performs clustering to detect common patterns and best practices. The output generates identified patterns and significant insights. Specific operations include data cleaning and execution of the AI ​​algorithm. 【0662】 Step 3: 【0663】 The server uses a guideline generation mechanism to generate standardized guidelines based on the analysis results. The input for this step is pattern information, which corresponds to the output of the AI ​​model. The generated guidelines include specific procedures and priorities for business improvement. The output is, for example, in the form of prompt statements such as "Initial response guidelines for system trouble." In terms of specific operation, there is a process that automatically generates guidelines according to a template based on the calculation results of the model. 【0664】 Step 4: 【0665】 The server distributes the generated guidelines to the information terminal using the guidelines distribution means. In this step, the input is the generated guidelines, and the output is the transmission of the guidelines to the terminal. The specific operation includes sending data packets over the network and triggering a notification on the terminal. 【0666】 Step 5: 【0667】 The user performs tasks based on the guidelines displayed on the terminal and inputs feedback. The input for this step is the guidelines displayed on the terminal's interface. The user inputs insights and improvement suggestions gained during the task execution process as feedback through a feedback collection method. The output is the transmission of user feedback data. Specific actions include touch input, keyboard input, and clicking a send button. 【0668】 Step 6: 【0669】 The server utilizes collected user feedback to improve the generated AI model and create next-generation guidelines. The input for this step is user feedback information. Data processing includes text analysis of the feedback and model tuning using regression analysis. The output is the updated AI model and improved guidelines. Specific actions include saving the feedback information to a database and retraining the model. 【0670】 (Application Example 1) 【0671】 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". 【0672】 Industrial automation equipment requires maximizing work efficiency and rapidly detecting anomalies. However, current systems often require manual data analysis and extraction of optimization procedures, making timely responses difficult. Therefore, a system is needed that provides operators with appropriate instructions in real time based on operational conditions and updates operational standards while incorporating improvement suggestions into a feedback loop. 【0673】 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. 【0674】 In this invention, the server includes an information gathering means for collecting operational information relating to industrial automation equipment, an information analysis means for analyzing the collected information and extracting optimization procedures, and an operational criteria generation means for generating operational criteria based on the extracted optimization procedures. This enhances the efficiency and timeliness of operations and enables workers to make optimal decisions. 【0675】 "Industrial automation equipment" refers to devices and systems used to automate processes such as manufacturing and assembly within a factory. 【0676】 "Information gathering means" refers to functions and technologies for collecting data on the operational status of industrial automation equipment. 【0677】 "Information analysis tools" refer to functions and technologies used to analyze collected data and extract procedures and areas for improvement to optimize operations. 【0678】 "Operational standard generation means" refers to functions and technologies that create standards and guidelines for efficient operation based on analysis results. 【0679】 "Operational standards distribution means" refers to functions and technologies for communicating generated operational standards to workers. 【0680】 A "display device" is a device used to visually show workers the operational standards and instructions they have received. 【0681】 An "improvement suggestion" is an opinion or proposal based on the work performed by the worker, aimed at further streamlining operations. 【0682】 An "artificial intelligence model" is a computer program and algorithm that supports prediction and decision-making to improve operational efficiency, based on results obtained from data analysis. 【0683】 The system implementing this invention is primarily configured to improve the operational efficiency of industrial automation equipment. A server plays a central role in this system. The server collects operational information from the industrial automation equipment. Specifically, it uses sensors and communication modules to acquire data on the operating status of the equipment, error codes, and product quality. 【0684】 The server uses this collected data to run data analysis tools and derive information to optimize operations. Here, a machine learning model is built using libraries such as Python's Scikit-learn. This model learns from historical operational data and extracts optimization steps to improve operational efficiency. 【0685】 Subsequently, the server uses the operational standards generation means to create efficient instructions and guidelines for operation based on the results obtained from the analysis. These guidelines include work procedures and the timing of preventive maintenance. The generated standards are transmitted to the display device via the operational standards distribution means. 【0686】 The terminal plays a role in visually displaying the received operational standards to the worker. By using mobile devices or tablet terminals, information is presented in real time. For example, when an anomaly is detected, the cause and corrective procedures are displayed step by step. 【0687】 Users follow instructions and perform tasks via a display device. Users can also input improvement suggestions into the terminal during operation. Effective feedback is collected from users using generated prompt messages. For example, a prompt message might read, "Please enter any problems encountered during today's work and the proposed solutions." 【0688】 Ultimately, the server continuously updates its artificial intelligence model based on collected improvement suggestions and operational results. This ensures continuous operational optimization and establishes a system that can comply with the latest operational standards. 【0689】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0690】 Step 1: 【0691】 The server collects operational information from industrial automation equipment via sensors and communication modules. Inputs include data on the equipment's operating status, error codes, and product quality. This data is securely stored in a database for use in subsequent processing steps. 【0692】 Step 2: 【0693】 The server launches data analysis tools to analyze the collected data. A machine learning model is built using the Python Scikit-learn library. The input is the operational data collected in step 1. The output is optimization procedures and patterns for optimizing operational efficiency. This analysis process includes data preprocessing, feature selection, and model training. 【0694】 Step 3: 【0695】 The server uses an operational standard generation mechanism to create operational standards based on the analysis results. The input is the optimization procedure obtained in step 2. The output is an operational standard that includes work procedures and the timing of preventive maintenance. This standard includes specific instructions to enable users to perform their work smoothly. 【0696】 Step 4: 【0697】 The server transmits the generated operational standards to the terminal via the operational standards distribution means. Specifically, it transfers the data to mobile devices and tablets over the network. The input is the operational standards generated in step 3, and the output is the operational instructions displayed on the terminal. 【0698】 Step 5: 【0699】 The terminal visually displays the received operational standards to the worker. The user then begins their work based on this information. The terminal reflects the information received in step 4 on the screen and presents it in a format that is easy for the worker to understand. This includes a step-by-step display of the cause and corrective procedures when an anomaly is detected. 【0700】 Step 6: 【0701】 Users input improvements they discover during their work into a terminal. The input consists of the user's improvement suggestions and opinions. The output is feedback data sent to the server. This data is effectively collected using prompts from a generated AI model. 【0702】 Step 7: 【0703】 The server analyzes user feedback and continuously updates the artificial intelligence model. The input is the feedback data received from the user in step 6. The output is the latest operational standards and model, which will be used for future operations. This will lead to further improvements in operational efficiency. 【0704】 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. 【0705】 This invention is a system designed to streamline corporate process operations and provide support tailored to the emotional state of individual users. This system primarily functions through four components: a server, a terminal, an emotion engine, and the user. 【0706】 The server first collects log information from process management systems and other related systems to gather data generated within the company. This allows for a comprehensive understanding of the company's operations and the storage of necessary information in a database. The data is appropriately protected and managed with privacy in mind. 【0707】 Next, the server analyzes the collected data. Using AI algorithms, it analyzes patterns and trends within the data to identify efficient business processes and areas requiring improvement. Based on the insights gained during this process, it generates standardized guidelines. 【0708】 The generated guidelines are delivered to the device. The device visually presents the guidelines to the user. At this time, an emotion engine is activated, reading the user's emotions from their voice and facial expressions and adjusting the display method accordingly. For example, if the user is fatigued, measures are taken to simplify the presentation of the guidelines to reduce their burden. 【0709】 Users perform tasks according to guidelines via their devices. During task execution, users can provide feedback, which, including the user's emotional state, is sent from the device to the server. 【0710】 The server receives feedback and performs analysis, including emotional data from the emotion engine. This data is used to update the AI ​​model, helping to further optimize the next set of guidelines generated. In this way, the system provides adaptive support tailored to each user's situation, continuously improving the efficiency of corporate process operations and regulatory compliance. 【0711】 The following describes the processing flow. 【0712】 Step 1: 【0713】 The server collects process data from various systems within the company. For example, it retrieves logs from manufacturing management systems and customer management systems and stores them in a central database. This data is then pre-processed so that it can be analyzed immediately. 【0714】 Step 2: 【0715】 The server analyzes the collected data using an AI engine to extract best practices for improving the efficiency of business processes. This process uses machine learning techniques to identify frequently occurring patterns and success stories. 【0716】 Step 3: 【0717】 The server generates standardized guidelines based on the analysis results. These guidelines include specific work procedures and key performance indicators (KPIs). 【0718】 Step 4: 【0719】 The server distributes the generated guidelines to the terminals. If necessary, the distribution of the guidelines can be restricted to specific departments or individuals. 【0720】 Step 5: 【0721】 The device visually displays the received guidelines. In doing so, it uses an emotion engine to analyze the user's current emotional state. For example, it captures facial expressions with a camera and measures stress and anxiety through voice analysis. 【0722】 Step 6: 【0723】 The device adjusts the display of guidelines according to the user's emotional state. If the user is tired, it uses features to simplify the presentation of information or to show instructions in stages. 【0724】 Step 7: 【0725】 Users perform tasks according to the adjusted guidelines. As they go through each step, they input any problems they find or suggestions for improvement as feedback into their terminal. 【0726】 Step 8: 【0727】 The device sends emotional data acquired by the emotion engine, along with user feedback, to the server. 【0728】 Step 9: 【0729】 The server analyzes the provided feedback and sentiment data to update the AI ​​model. This data is used to generate guidelines and optimize processes for future use, improving the system's accuracy and usefulness. 【0730】 (Example 2) 【0731】 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". 【0732】 In modern business activities, there is a need to improve the efficiency of business processes while providing flexible support that is tailored to the emotional state of users. However, conventional systems have limited mechanisms to effectively address these needs, resulting in challenges such as insufficient improvements in business efficiency and a lack of methods to alleviate the psychological burden on users. 【0733】 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. 【0734】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and extracting the optimal method, and a device for generating standardized guidelines based on the extracted optimal method. This enables flexible support that responds to the user's emotions while promoting the efficiency of the company's operations. 【0735】 An "information-gathering device" is a means of acquiring various data related to business activities, and is a device that plays a role in effectively accumulating database and log information. 【0736】 A "device for extracting optimal methods" is a means of analyzing collected data to identify patterns and trends that contribute to improving business processes, and it is a device that uses analytical techniques to derive the optimal procedures and methods. 【0737】 A "device for generating standardized guidelines" is a device that has the function of forming guidelines and directives necessary to improve the efficiency of operations based on the extracted optimal methods. 【0738】 An "information processing device" is a device that displays information to users, has the function of receiving opinions and feedback from users, and enables two-way information exchange through an interface. 【0739】 An "artificial intelligence model" is a collection of algorithms and systems that continuously learn and improve based on collected information and user feedback, adapting to new data to provide highly accurate analysis and predictions. 【0740】 This invention is a system that streamlines corporate business processes and enables support that responds to users' emotions. The system mainly consists of a server, terminals, and an emotion analysis engine. 【0741】 The server first collects data from various sources within the company. Specifically, it retrieves logs and usage history from the company's process management systems and related information systems using APIs. This data is securely stored in a database for later analysis. 【0742】 Next, the server uses Python libraries (e.g., Pandas and Scikit-learn) to process and analyze the data. Here, AI algorithms are employed to identify optimal business practices and areas for improvement within the data. Based on this analysis, prompts are input to a generative AI model (e.g., a natural language processing model) to generate standardized guidelines. An example of a prompt might be, "Generate guidelines for optimizing the sales process." 【0743】 The generated guidelines are delivered to the device. When the device presents these to the user, it utilizes an emotion analysis engine. The emotion analysis engine incorporates speech recognition and facial expression analysis technologies, and the program evaluates the user's emotional state through Microsoft Azure Cognitive Services. Based on this information, the device dynamically adjusts how the guidelines are displayed. For example, if it determines that the user is tired, it simplifies the display of the guidelines to reduce visual burden. 【0744】 Users perform tasks according to guidelines via their devices while also providing feedback. This feedback includes progress, new discoveries, and even emotional states. The devices continuously transmit this information to a server, which uses this data to retrain the AI ​​model. This process optimizes the guidelines provided next time, thereby improving overall operational efficiency within the company. 【0745】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0746】 Step 1: 【0747】 The server collects data from the company's process management systems and related information systems. Specifically, it uses APIs to retrieve log data and usage history. The input to this process is data streams from each system, and the output is structured data stored in a database. This allows for the collection of detailed information about the company's activities. 【0748】 Step 2: 【0749】 The server processes and cleans the collected data using Python libraries (such as Pandas and Scikit-learn). The input is structured data in a database, and the output is a clean dataset suitable for analysis. Specifically, it performs missing value imputation and outlier removal. Using this clean data, an AI algorithm identifies patterns and trends. 【0750】 Step 3: 【0751】 The server extracts the optimal method based on identified patterns and trends, and sends prompt messages to the generating AI model to produce standardized guidelines. The input is a prompt message based on the analysis results, and the output is the generated guidelines. Specifically, the server gives the model the prompt, "Generate guidelines for optimizing the sales process." 【0752】 Step 4: 【0753】 The terminal displays guidelines received from the server to the user. The input is the guidelines delivered from the server, and the output is the presentation of visual guidelines to the user. An emotion analysis engine is used to analyze the user's voice and facial expressions through sensors and adjust the display method accordingly. Specifically, it uses Microsoft Azure Cognitive Services to analyze the emotional state. 【0754】 Step 5: 【0755】 Users perform tasks based on guidelines displayed on the terminal. Input is the provided guidelines, and output is data on feedback for completed tasks and their emotional state at the time. Users provide feedback and input information, including their emotional state, into the terminal as they work. 【0756】 Step 6: 【0757】 The terminal sends feedback received from the user to the server. The input is the user feedback data, and the output is the server that received it. The terminal transmits information in real time, which is used by the server to generate the next set of guidelines. 【0758】 Step 7: 【0759】 The server takes in feedback data and uses it to update the AI ​​model. The input is user feedback and sentiment analysis results, and the output is the updated AI model. Through this process, the system continuously optimizes guidelines and improves the company's process management. 【0760】 (Application Example 2) 【0761】 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". 【0762】 In production sites such as factories, there is a need to balance efficient operations with reducing the psychological burden on workers. However, currently, it is difficult to grasp the psychological state of workers in real time and provide work instructions that are appropriate to that situation, which can lead to decreased work efficiency and increased stress among workers. 【0763】 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. 【0764】 In this invention, the server includes an information gathering means for collecting operational information of a company, an information analysis means for analyzing the collected information and extracting optimal guidelines for improving work efficiency, and a psychological state analysis means for recognizing the psychological state of the worker and adjusting the display method of the instructions. This makes it possible to provide detailed work instructions that are tailored to the psychological state of the worker. 【0765】 "Information gathering means" refers to methods or devices for effectively acquiring and storing operational information of a company in a database. 【0766】 "Information analysis means" refers to a method or device that analyzes collected data and derives guidelines or patterns that contribute to improving operational efficiency. 【0767】 "Instruction generation means" refers to a method or apparatus for generating standardized work instructions based on analysis results and for appropriately transmitting them. 【0768】 "Instruction distribution means" refers to a method or device for transmitting generated instructions to a terminal in a timely manner. 【0769】 A "psychological state analysis means" is a method or device that determines the psychological state of a worker from their facial expressions and voice, and optimizes the method of presenting instructions based on that information. 【0770】 An "AI model" is a mathematical method built using machine learning algorithms based on a large amount of data, with the aim of continuously improving its performance. 【0771】 A "terminal device" is an electronic device used for displaying instructions and collecting responses from workers. 【0772】 "Response" refers to information provided by workers through their devices, specifically feedback related to the work process and their psychological state. 【0773】 In the system realizing this invention, a server, terminal devices, and workers work together in order to efficiently process information. The server stores the necessary data in a database via an information collection means that aggregates operational information from various sensors and information systems of the company. Then, an information analysis means analyzes this data using an AI model to identify guidelines for improving operational efficiency. Standardized work instructions are created from the analysis results by an instruction generation means and transmitted to terminal devices using an instruction distribution means. 【0774】 The terminal device displays the received instructions to the worker. The worker's psychological state is determined using facial expressions and voice data via a psychological state analysis device, and the instruction display method is optimized accordingly. In this process, the terminal device collects the worker's responses and sends them to a server to help update the AI ​​model. 【0775】 A concrete example of this embodiment is a worker on a factory production line. For instance, if a worker is wearing smart glasses, and AI-based psychological state analysis determines that the worker is feeling fatigued, the system sends instructions to suggest a break and displays relaxation methods on the smart glasses' display. This makes it possible to reduce worker stress while improving the efficiency of the production line. 【0776】 A specific example prompt used would be, "Please propose stress reduction measures for the production line. The current emotional state is 'fatigue'." By providing work support tailored to each work environment and situation in this way, it is possible to improve production efficiency and promote worker welfare. 【0777】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0778】 Step 1: 【0779】 The server collects operational information from various sensors and information systems within the company. It receives sensor data and system logs as input and records them in a database. This data is stored accurately and systematically because it will be used for later analysis. 【0780】 Step 2: 【0781】 The server analyzes the collected data using information analysis tools. Input data includes information on work efficiency, error rates, and worker productivity. An AI model is used to identify guidelines for improving work efficiency and generate templates for work instructions as output. Anomaly detection and pattern extraction are performed during the data analysis process. 【0782】 Step 3: 【0783】 The server organizes the generated work instructions using an instruction generation system and distributes them to terminal devices. It receives instruction templates as input and sends standardized work instructions to the terminals as output. The instructions are then presented to the workers as specific guidance content. 【0784】 Step 4: 【0785】 The terminal device displays received work instructions to the worker. Based on the instruction data received as input, it processes it into a clear and visually understandable format and displays it on the display as output. The displayed content includes specific work procedures and points to note. 【0786】 Step 5: 【0787】 The user's psychological state is evaluated by a psychological state analysis tool on the device. Audio and video data are acquired as input, and emotions are determined using a machine learning algorithm based on this data. The output is the evaluation result of the psychological state, which is used to adjust the way instructions are presented. 【0788】 Step 6: 【0789】 The user's responses are collected by the terminal and sent to the server. User feedback and work results are received as input, and processed into data useful for generating the next work instructions as output. The AI ​​model is continuously improved through post-processing feedback. 【0790】 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. 【0791】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 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. 【0798】 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." 【0799】 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. 【0800】 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. 【0801】 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. 【0802】 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. 【0803】 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. 【0804】 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. 【0805】 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. 【0806】 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. 【0807】 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. 【0808】 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. 【0809】 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. 【0810】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0811】 The following is further disclosed regarding the embodiments described above. 【0812】 (Claim 1) 【0813】 Data collection methods for collecting corporate process data, 【0814】 A data analysis means for analyzing the collected data and extracting best practices, 【0815】 A guideline generation means for generating standardized guidelines based on the extracted best practices, 【0816】 A system including a guideline distribution means for distributing the generated guidelines to a terminal. 【0817】 (Claim 2) 【0818】 The system according to claim 1, wherein the terminal has the function of displaying received guidelines and collecting user input. 【0819】 (Claim 3) 【0820】 The system according to claim 1, wherein the server has a function to take in feedback sent from the user and continuously update the AI ​​model. 【0821】 "Example 1" 【0822】 (Claim 1) 【0823】 An information processing device includes an information collection means for collecting organizational business data, 【0824】 Information analysis means for analyzing the collected information and extracting optimal practice examples, 【0825】 A guideline generation means for generating standardized guidelines based on the extracted optimal practice examples, 【0826】 A guideline distribution means for distributing the generated guideline to an information terminal, 【0827】 A pattern identification means for identifying common patterns from the aforementioned analysis results and extracting points for business improvement, 【0828】 A means for collecting opinions from users who have performed tasks based on the generated guidelines, 【0829】 A system that includes this. 【0830】 (Claim 2) 【0831】 The system according to claim 1, wherein the information terminal has a function to display received guidelines and record opinions entered by the user. 【0832】 (Claim 3) 【0833】 The system according to claim 1, wherein the information processing device has a function to continuously update the generated AI model based on opinions sent from the user and to improve the information analysis results and guidelines. 【0834】 "Application Example 1" 【0835】 (Claim 1) 【0836】 Information gathering means for collecting operational information related to industrial automation equipment, 【0837】 Information analysis means for analyzing the collected information and extracting an optimization procedure, 【0838】 An operational criteria generation means for generating operational criteria based on the extracted optimization procedure, 【0839】 Operational standards distribution means for distributing the generated operational standards to a display device, 【0840】 A system including a display means that shows the procedure for an operator to perform an operation based on the aforementioned distributed operational standards. 【0841】 (Claim 2) 【0842】 The system according to claim 1, wherein the display device has the function of displaying received operational standards and collecting improvement suggestions entered by the worker. 【0843】 (Claim 3) 【0844】 The system according to claim 1, wherein the analysis device has a function to continuously update the artificial intelligence model by incorporating improvement suggestions transmitted from the operator. 【0845】 "Example 2 of combining an emotion engine" 【0846】 (Claim 1) 【0847】 A device for collecting information, 【0848】 A device for analyzing the collected information and extracting the optimal method, 【0849】 A device for generating standardized guidelines based on the extracted optimal method, 【0850】 A system including a device for distributing the generated guidelines to an information processing device. 【0851】 (Claim 2) 【0852】 The system according to claim 1, wherein the information processing device has the function of displaying received guidelines and collecting opinions entered by the user. 【0853】 (Claim 3) 【0854】 The system according to claim 1, wherein the information processing device has a function to analyze the user's emotions from their voice and facial expressions and adjust the display method. 【0855】 (Claim 4) 【0856】 The system according to claim 1, wherein the device has a function to incorporate opinions transmitted from users and continuously update the artificial intelligence model. 【0857】 "Application example 2 when combining with an emotional engine" 【0858】 (Claim 1) 【0859】 Information gathering methods for collecting operational information of companies, 【0860】 Information analysis means for analyzing the collected information and extracting optimal guidelines for improving operational efficiency, 【0861】 Instruction generation means for generating standardized instructions based on the extracted optimal guidelines, 【0862】 Instruction distribution means for distributing the generated instructions to terminal devices, 【0863】 A psychological state analysis means that recognizes the psychological state of the worker and adjusts the display method of the instructions, 【0864】 A system that includes this. 【0865】 (Claim 2) 【0866】 The system according to claim 1, wherein the terminal device has the function of displaying received instructions and collecting responses provided by the worker. 【0867】 (Claim 3) 【0868】 The system according to claim 1, wherein the information processing device has a function to take in responses transmitted from the worker and dynamically update the AI ​​model. [Explanation of Symbols] 【0869】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Data collection methods for collecting corporate process data, A data analysis means for analyzing the collected data and extracting best practices, A guideline generation means for generating standardized guidelines based on the extracted best practices, A system including a guideline distribution means for distributing the generated guidelines to a terminal. [Claim 2] The system according to claim 1, wherein the terminal has the function of displaying received guidelines and collecting user input. [Claim 3] The system according to claim 1, wherein the server has a function to take in feedback sent from the user and continuously update the AI ​​model.

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