system

Abstract

We provide the system. [Solution] A data collection method that collects past work data from a work history database and uses it to create necessary instruction manuals, A data processing means that automatically generates work procedure manuals using generative artificial intelligence based on collected data, A work support system that presents generated work procedure manuals to on-site workers and provides real-time instructions via a terminal, A feedback processing mechanism that collects feedback from on-site workers after the work is completed and incorporates it into the generation of the next work procedure manual, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Conventionally, for work in a network center or a communication exchange station, it has been necessary to create manual work procedures and work instructions, and this process has been a factor hindering the efficiency of operations. Also, the quality of the procedures and on-site responses depend heavily on the experience of the workers, and it tends to be a personalized task. As a result, there are risks of work errors and efficiency degradation, and there is a problem that it is difficult to maintain a unified quality even in standard operations. 【Means for Solving the Problems】 【0005】 This invention provides a system that utilizes generative artificial intelligence to automatically collect past work data from a work history database and automatically generate optimized work procedures based on that data. This system presents the generated procedures to on-site workers in real time and provides instructions via voice and text, thereby improving work safety and efficiency. Furthermore, by collecting feedback after work completion, the system can be continuously improved by incorporating it into the generation of the next set of procedures. In this way, this invention achieves standardization and efficiency in work and supports high-quality operations by eliminating human-dependent elements. 【0006】 A "work history database" is an information system used to store and manage data and the history of past business operations and tasks. 【0007】 "Generative artificial intelligence" refers to artificial intelligence technology that has the function of generating new content and information based on data. 【0008】 A "work procedure manual" is a document that details the steps and methods necessary to perform a specific task. 【0009】 A "data collection method" refers to a module or process designed to acquire information and incorporate it into a system. 【0010】 "Data processing means" refers to a process or apparatus for analyzing collected information and converting it into a useful format. 【0011】 "Work support means" are interfaces and tools that assist field workers in performing their tasks efficiently and safely. 【0012】 A "feedback processing method" is a process or device for collecting evaluations and opinions after work is completed and using them to improve the system. 【0013】 A "terminal" is a computer device or equipment used for inputting or outputting information. 【0014】 An "algorithm" is a set of procedures or calculation methods defined to solve a specific problem. 【0015】 A "user interface" is an interface used for information exchange between a user and a machine or system. [Brief explanation of the drawing] 【0016】 [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine. 【Mode for Carrying Out the Invention】 【0017】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0018】 First, the terms used in the following description will be explained. 【0019】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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. 【0020】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0021】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0022】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0023】 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." 【0024】 [First Embodiment] 【0025】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0026】 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. 【0027】 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). 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0033】 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. 【0034】 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. 【0035】 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. 【0036】 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". 【0037】 This invention provides a system for automatically generating work procedures and providing work support in network centers and telecommunications exchanges. This system utilizes generative artificial intelligence to collect past work data from a work history database and automatically creates efficient and safe work procedures based on that data, thereby improving the efficiency of operations. 【0038】 Specifically, the server first accesses a work history database to collect past data related to a particular task. This collected data is then analyzed by generative artificial intelligence, and work procedures that prioritize safety and efficiency are automatically generated. For example, the server analyzes data related to network equipment configuration changes and creates a procedure manual that includes everything from necessary preparations to specific operating steps. 【0039】 The generated procedure manual is presented to the on-site worker via a terminal. The terminal provides real-time work instructions to the worker through visual or audio guidance. For example, the terminal might instruct the worker to "check the port settings as the next step" and then clarify the subsequent steps. 【0040】 After completing a task, the user (worker) provides feedback on the work performed at their terminal. This feedback is collected by the server and used to generate future work instructions. For example, the user might report specific improvements, such as "The cable specified in step 3 was actually unnecessary." 【0041】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The server accesses the work history database and collects past work data related to a specific task. During this process, the server filters the relevant history based on the specified work content and equipment, and extracts the necessary data. 【0045】 Step 2: 【0046】 The server analyzes the collected data and automatically generates new work procedures using generative artificial intelligence. The server uses past successful examples as a reference to create optimal procedures that consider safety and efficiency. 【0047】 Step 3: 【0048】 The server sends the generated work instructions to the terminal. The terminal receives these instructions and presents them to the worker before they begin work. For example, the terminal displays the instructions on its screen and also allows the worker to view a list of the tools and materials needed for the work. 【0049】 Step 4: 【0050】 The terminal provides real-time instructions to the worker as the work progresses. This includes communicating the next steps and points to note via voice or text. For example, the terminal might give specific instructions such as, "Check the current connection and ensure there are no problems." 【0051】 Step 5: 【0052】 The user (worker) enters feedback via a terminal after completing the task. This includes suggestions for improvement in the procedure manual and observations made during the work. The worker reports comments such as "Step 5 was unclear" to the system. 【0053】 Step 6: 【0054】 The server receives feedback from workers and incorporates it into the generation of the next work procedure manual. The server analyzes the feedback and stores it as data to improve the content of the procedure manual, thereby enabling continuous improvement of the system. 【0055】 (Example 1) 【0056】 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." 【0057】 Traditional work procedure manuals rely on manual processes, limiting improvements in work efficiency and safety. Furthermore, the lack of standardized instructions for on-site workers makes it difficult to maintain consistent work quality. Additionally, feedback after work completion may not be adequately reflected in subsequent work. There is a need to solve these problems and improve work efficiency, safety, and quality. 【0058】 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. 【0059】 In this invention, the server includes an information gathering means for collecting past work information from a work history information recording device and using it to create necessary work instructions; an information processing means for automatically generating work procedures using a generative information processing device based on the collected information; and a work support means via an operating device that presents the generated work procedures to on-site workers and provides instructions in real time. This enables improved work efficiency, enhanced safety, maintenance of consistent work quality, and effective utilization of feedback. 【0060】 A "work history information recording device" is a device that stores information about past work and functions as a database that can be accessed as needed. 【0061】 "Information gathering means" refers to a method or apparatus that provides the function of acquiring necessary work information from a work history information recording device and making it available for subsequent processing. 【0062】 A "generative information processing device" is a device equipped with artificial intelligence technology to analyze large amounts of collected data and automatically generate optimized work procedures. 【0063】 "Information processing means" refers to a method or apparatus that provides a process or function for generating necessary work instructions based on collected information data. 【0064】 An "operating device" is a device equipped with an interface for displaying the generated work procedure manual to on-site workers and providing them with necessary work instructions. 【0065】 "Work support means" refers to a method or device for providing real-time work instructions to on-site workers via an operating device, thereby supporting the smooth progress of work. 【0066】 A "data feedback means" is a device that provides a method or function for collecting feedback obtained from on-site workers after the completion of work and using it to improve the next work instructions. 【0067】 This invention is a system for reducing the workload on workers and improving work efficiency in network management and communication infrastructure operation. The system consists mainly of a server, terminals, and a generative AI model. 【0068】 The server collects past work information using a work history information recording device. This information covers a wide range of topics, including the type of work, conditions, and results, and each piece of data is systematically stored. The collected information is sent to a generative information processing device. 【0069】 The generative information processing device analyzes received data using, for example, a machine learning model implemented in Python. In particular, it utilizes algorithms that prioritize safety and efficiency, and has the function of automatically generating ideal work procedures. The generative AI model is instructed with a specific prompt message such as, "Based on data from past network equipment configuration changes, please generate safe and efficient work procedures." 【0070】 The terminal is responsible for providing the generated procedure manuals to field workers. The terminal not only displays the procedure manuals visually but also provides voice guidance to assist the workers in their manual operations. For example, instructions such as "Next, check the operation of port 2" may be displayed on the screen or spoken aloud. 【0071】 The user (worker) can efficiently complete tasks by following instructions provided in real time via a terminal. After completing a task, the worker provides feedback using the terminal, and this information is collected by the server and used to improve the next procedure manual generation process. Specifically, the user might input feedback such as "The tool specified in step 4 was not needed," and this will be reflected in the next work procedure manual. 【0072】 In this way, workers receive support throughout the entire system to complete tasks efficiently and safely, resulting in improved overall productivity. 【0073】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0074】 Step 1: 【0075】 The server connects to a work history information recording device to collect past work information. Inputs include the type and duration of the work, and based on this, it collects relevant past work data. This data includes work procedures, tools used, and work time. The output is a structured dataset to be passed to a generative information processing device. Specifically, it executes database queries to extract the required information. 【0076】 Step 2: 【0077】 The server inputs the collected dataset into a generating AI model. The prompt is "Generate safe and efficient procedure manuals based on past work data." The AI ​​model analyzes this data and executes machine learning algorithms to design optimal work procedures. The output is a draft of a detailed procedure manual. Specific operations include data cleaning, feature extraction, and model inference. 【0078】 Step 3: 【0079】 The server transfers the generated procedure manuals to the information management module for review and approval. The input is a procedure manual draft from the AI ​​model, which is then validated for safety and efficiency. The output is the final, revised, and approved work procedure manual. Specific actions include collecting feedback from the review system and editing the procedure manuals as needed. 【0080】 Step 4: 【0081】 The terminal receives the final work instructions and presents them to the field worker visually or audibly. Input is a digital file of the final instructions, and output is a real-time display of instructions to the field worker. Specific operations include step-by-step display on the screen and playback of instructions through the audio speaker. 【0082】 Step 5: 【0083】 The user (worker) performs the task according to the instructions displayed on the terminal. After completing the task, feedback is entered via the terminal. The input consists of opinions and improvement suggestions from the worker, and the output is digital data sent to the server as a feedback record. Specifically, the process involves entering and submitting feedback in a feedback form. 【0084】 Step 6: 【0085】 The server collects feedback submitted by workers and stores it in a database for use in generating future work procedure manuals. The input is feedback data from each worker, and the output is a history record of improved versions that are reflected in past work data. Specifically, the server writes to the database and integrates it into an analysis dataset. 【0086】 (Application Example 1) 【0087】 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." 【0088】 Traditional maintenance procedures have involved manual creation of procedure manuals, leading to challenges in efficiency and safety. Furthermore, insufficient instructions to field workers resulted in inconsistent work quality. Additionally, feedback after maintenance work is not adequately utilized for subsequent work, highlighting areas for improvement. 【0089】 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. 【0090】 In this invention, the server includes an information gathering means for collecting past maintenance work information and using it to create necessary instruction manuals; an information processing means for automatically generating maintenance manuals using generative artificial intelligence based on the collected information; and a work support means via a terminal equipped with a user interface that presents the generated maintenance manuals to on-site workers visually and audibly and provides instructions in real time. This makes it possible to improve the efficiency and safety of maintenance work. 【0091】 "Maintenance work" refers to inspections, repairs, adjustments, and other tasks performed periodically or as needed to maintain the normal operation of equipment and systems. 【0092】 "Information gathering means" refers to methods and devices for efficiently collecting data and information related to past maintenance work. 【0093】 "Generative artificial intelligence" is an artificial intelligence technology that can automatically create new information and procedures based on given data. 【0094】 "Information processing means" refers to technologies or devices for analyzing collected data, extracting necessary information, and processing it. 【0095】 A "user interface" is a means or design for exchanging information bidirectionally between a terminal and a field worker. 【0096】 A "feedback processing method" is a method of collecting and analyzing feedback from workers after maintenance work and using it to help generate future procedure manuals. 【0097】 This invention is a system that supports efficient and safe maintenance work in factories. The system is composed of three key elements: a server, terminals, and users. 【0098】 The server is equipped with information gathering mechanisms to collect past maintenance data. This data is primarily stored in a database, which the server accesses to retrieve the necessary information. The retrieved data is analyzed using information processing mechanisms that utilize generative artificial intelligence, and efficient and safe maintenance procedures are automatically generated. For example, the OpenAI® API is used as the generative artificial intelligence in this process. 【0099】 The terminal plays a role in supporting on-site maintenance work. The terminal presents maintenance procedures, generated through a user interface, to the worker visually and audibly. By wearing AR smart glasses, the worker can visually review the procedures and receive voice instructions. This allows for efficient work without the need for manual intervention. 【0100】 After completing maintenance work, the user (worker) enters feedback into a terminal. This feedback is collected by the server and used to generate the next procedure manual through a feedback processing system. For example, if the worker reports improvements such as "the specified part was not actually necessary," a more optimized procedure manual will be generated next time. 【0101】 As an example of a prompt, by giving the generative artificial intelligence the instruction, "We have maintenance data for a robotic arm. Based on this, please create an efficient and safe maintenance procedure manual in Japanese," an appropriate procedure manual will be generated. 【0102】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0103】 Step 1: 【0104】 The server accesses the maintenance work database to collect past work information. The input is past work records stored in the database, and the output is a set of data related to a specific maintenance task. At this time, relevant information is extracted using efficient data collection methods and converted into a format suitable for analysis. 【0105】 Step 2: 【0106】 The server passes the collected data to a generative artificial intelligence for analysis. The input is the maintenance work data obtained in step 1, and the output is a draft of a maintenance procedure manual that emphasizes efficient and safe procedures. The generative AI model is used to analyze the data and generate the optimal procedure. 【0107】 Step 3: 【0108】 The server sends the generated maintenance procedure manual to the terminal. The input is the draft of the procedure manual created in step 2, and the output is the maintenance procedure manual converted into a format viewable on the terminal. At this point, formatting adjustments are made, enabling visual and audio instructions on the terminal. 【0109】 Step 4: 【0110】 The terminal presents maintenance procedures to field workers visually and audibly. Input is the procedures received from the server, and output is real-time work instructions for the worker. Smart glasses and voice systems are used to support workers in performing their tasks efficiently. 【0111】 Step 5: 【0112】 The user (worker) enters feedback into the terminal after completing the task. The input consists of suggestions for improvement and comments from the worker, and the output is feedback data used to generate the next procedure manual. The terminal collects the feedback and sends it to the server. 【0113】 Step 6: 【0114】 The server analyzes the feedback received from users and uses it to improve the next procedure manual generation. The input is the feedback data collected in step 5, and the output is guidance for the next improved procedure manual generation. This includes a process of incorporating the feedback into the generation AI model. 【0115】 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. 【0116】 This invention provides a system for network centers and communication exchanges that combines generative artificial intelligence and an emotion engine to automatically generate work procedures and provide work support. This system not only automatically generates optimal procedures from work history data, but also recognizes the user's emotions in real time and adjusts the work environment to further support more efficient and comfortable work. 【0117】 Specifically, the server accesses a work history database and collects historical data related to a particular task. An analysis module then uses generative artificial intelligence to automatically generate optimized work procedures based on this data. At this stage, the server specifically identifies past successes and constructs procedures that combine efficiency and safety. 【0118】 The generated procedure manuals are presented to field workers via a terminal. The terminal not only provides real-time instructions via voice and text, but its built-in emotion engine analyzes the user's facial expressions and voice to determine their emotional state. This emotion recognition technology detects the user's stress level and anxiety, enabling appropriate work support. 【0119】 For example, if the device detects tension from the user's facial expressions, it will pause the work process and provide advice to help the user relax or a direct link to troubleshooting. The device will also adjust the pace of the process based on the user's emotional state, supporting them in a way that is easy to understand. 【0120】 After completing a task, the user enters feedback via their terminal, and this information is sent to the server. The server collects this feedback data and uses it to generate future work instructions and adjust the emotion engine, thereby continuously improving the system. 【0121】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. Furthermore, by taking into account the emotional state of the user, it reduces worker stress and creates a comfortable and efficient work environment. 【0122】 The following describes the processing flow. 【0123】 Step 1: 【0124】 The server accesses the work history database and extracts past work data related to a specific task. The server then filters this data to collect the most relevant information based on the nature and conditions of the task. 【0125】 Step 2: 【0126】 The server analyzes the collected data and uses generative artificial intelligence to automatically generate optimal work procedures. By analyzing past success stories and constructing efficient and safe procedures, the server incorporates best practices for the work. 【0127】 Step 3: 【0128】 The server sends the generated work procedure manual to the terminal. The terminal displays this manual in its user interface, allowing the field worker to review the procedure before starting work. 【0129】 Step 4: 【0130】 The terminal provides the worker with voice and text instructions in real time while they are working. The terminal's emotion engine analyzes the user's facial expressions and tone of voice in real time to understand their emotional state. For example, if the terminal detects that the user is stressed, it will slow down the pace of the work and provide calmer instructions. 【0131】 Step 5: 【0132】 The user, acting as the worker, follows instructions received from the terminal as the task progresses and completes the procedures. The terminal periodically checks the user's emotional state and adjusts the support provided as needed. 【0133】 Step 6: 【0134】 After completing a task, users provide feedback on the process and interface via their terminal. This includes suggestions for improving the procedure manual and direct emotional responses. 【0135】 Step 7: 【0136】 The server collects feedback data from terminals and uses it to generate future work instructions and improve the emotion engine. This allows the server to continuously improve the system and provide a better user experience. 【0137】 (Example 2) 【0138】 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." 【0139】 In on-site work, the burden on workers when creating instruction information manually and the stress caused by work guidance that does not take into account their emotional state are problematic. Conventional systems fail to provide sufficient support to improve work safety and efficiency, and lack adjustment functions that take into account the emotional state of workers. As a result, it is difficult to improve work productivity and reduce the burden on workers. 【0140】 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. 【0141】 In this invention, the server includes a numerical data collection means for collecting past work values ​​from a work history database and using them to create necessary instruction information, a numerical data processing means for automatically generating work instruction information using generative artificial intelligence based on the collected numerical values, and a work support means via a display device that presents the generated work instruction information to on-site workers and provides instructions in real time. This reduces the manual workload of on-site workers, improves work efficiency and safety, and enables stress reduction by utilizing emotion recognition. 【0142】 A "work history database" is an information aggregation system that stores numerical data related to all work performed in the past. 【0143】 A "numerical data collection device" is a device that has the function of acquiring numerical data from a database in order to collect necessary work information. 【0144】 "Generative artificial intelligence" is a technology that automatically generates optimal work instruction information based on collected data. 【0145】 A "numerical processing device" is a device that uses collected numerical data and generative artificial intelligence to generate work instruction information. 【0146】 "Instructional information" refers to information that includes specific procedures and instructions that workers should follow when performing their tasks. 【0147】 A "display device" is a device used to present work instruction information to on-site workers visually or audibly. 【0148】 A "human-machine interface" is an interactive user interface that enables the exchange of instruction information between a worker and a machine. 【0149】 "Emotion recognition means" refers to a device that includes technology for analyzing a worker's facial expressions and voice to determine their emotional state. 【0150】 A "feedback processing device" is a device that has the function of collecting numerical feedback from workers and reflecting it in the generation of the next work instruction information. 【0151】 This invention is a system that utilizes a work history database and generative artificial intelligence to provide work support that takes into account the worker's emotions. The server collects past work data by accessing the work history database. Specifically, it uses an SQL server to query the necessary values ​​and retrieve the data. The collected values ​​are passed by the server to a generative artificial intelligence model using Python, which generates optimal work instruction information. This model identifies past success patterns and creates instructions that take safety and efficiency into consideration. 【0152】 The generated work instructions are presented to the field worker via a terminal. The terminal consists of a display device equipped with a speech synthesis system and a display, providing instructions in both voice and text formats. The terminal also has emotion recognition capabilities, using a camera and microphone to determine the user's emotional state from their facial expressions and voice. For example, if the user is feeling frustrated, the terminal will slow down the work speed and provide supplementary explanations. 【0153】 After completing a task, the user provides feedback through the terminal. The terminal sends this feedback data to the server, which uses it to improve the generation of future instructions and the emotion recognition function. 【0154】 As a concrete example, the prompt is as follows: "Create example relaxation advice to provide when the user is feeling anxious." By prompting the AI ​​model with such a prompt, it becomes possible to provide appropriate support according to the user's emotional state. 【0155】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0156】 Step 1: 【0157】 The server accesses the work history database to collect past work metrics. Specifically, the server executes SQL queries to filter data related to a particular work. The input is the specific conditions of the work (e.g., success rate, duration), and the output is a set of work history metrics that match these conditions. These metrics serve as the basis for the next analysis step. 【0158】 Step 2: 【0159】 The server inputs the collected data into an AI model to generate optimal work instruction information. The server uses Python to activate the AI ​​model, identifying past success patterns and using an algorithm to construct the optimal procedure. The input is the work history data obtained in step 1, and the output is automatically generated work instruction information. This instruction information includes details that enhance safety and efficiency. 【0160】 Step 3: 【0161】 The terminal presents the generated work instruction information to the field worker via a display device. Specifically, the terminal uses speech synthesis software to output text instructions as voice. It also visually displays the instruction content on the display. The input is the work instruction information generated in step 2, and the output is real-time instructions to the user via both visual and auditory means. 【0162】 Step 4: 【0163】 The device analyzes the user's emotional state using built-in emotion recognition technology. Specifically, it determines emotions by capturing the user's facial expressions with a camera and analyzing their voice tone with a microphone. The input is the user's facial expressions and voice, captured in real time, and the output is an evaluation of the user's emotional state (e.g., tension, stress). Based on this, the work speed is adjusted and support content is suggested. 【0164】 Step 5: 【0165】 After completing a task, users provide feedback through a terminal. The terminal offers a dedicated form where users can enter comments and ratings. The input is the user's feedback value, and the output is organized feedback information. This information is sent to the server and used to generate future instructions and improve the system. 【0166】 (Application Example 2) 【0167】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0168】 Conventional work procedure creation systems lack sufficient means to reduce the emotional burden on workers while prioritizing their efficiency and safety. This leads to problems such as work errors and slower work speeds due to worker stress and anxiety. Furthermore, the lack of dynamic procedure adjustments to accommodate workers' emotional states makes it difficult to provide optimal work content for individual workers. 【0169】 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. 【0170】 In this invention, the server includes data collection means for collecting past work data from a work history database and using it to create necessary instruction manuals; data processing means for automatically generating work procedure manuals using generative artificial intelligence based on the collected data; and emotion recognition and work environment adaptation means for analyzing the worker's facial expressions and voice to determine their emotional state and adapting the work environment based on the determined emotional state. This enables dynamic procedure adjustments in accordance with the emotional state of on-site workers, making it possible to reduce emotional burden while providing an efficient and safe work environment. 【0171】 A "work history database" is an information repository that stores information about work performed in the past. 【0172】 "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates the optimal work procedure based on collected data. 【0173】 A "work procedure manual" is a document that contains a series of instructions for performing a specific task efficiently and safely. 【0174】 A "terminal" is a device used to present information and transmit instructions to on-site workers. 【0175】 "Emotion recognition and work environment adaptation means" refers to a system that analyzes the facial expressions and voice of workers and adjusts the work environment according to their emotional state. 【0176】 A "feedback processing mechanism" is a system that analyzes opinions and feedback collected from workers after their work is completed and uses this information to improve future procedures and systems. 【0177】 An "algorithm" is a set of procedures or calculation steps for solving a problem. 【0178】 A "user interface" refers to the interactive operating screens and input devices that operators use when operating machinery or equipment. 【0179】 The system for realizing this invention mainly consists of a server, a terminal, and a user. The server collects historical data from a work history database and automatically generates work instructions using generative artificial intelligence. These instructions are based on the user's past successful work examples and are optimized for safety and efficiency. The generated instructions are presented to the field worker via the terminal, and real-time instructions are provided via voice and text. 【0180】 The device incorporates emotion recognition and work environment adaptation mechanisms, analyzing the user's facial expressions and voice to determine their emotional state. Based on this information, the device is designed to dynamically adjust the work environment and reduce stress. For example, if it determines that the worker is stressed, it will display advice to help them relax and, if necessary, pause the work. 【0181】 After completing a task, users enter feedback via their terminal. This feedback is sent to the server and used to generate future work instructions and adjust the sentiment engine. This ensures continuous improvement of the system. 【0182】 As a concrete example, in the assembly process of a certain product, if a user experiences emotional stress, the terminal could temporarily suspend the work procedure and display instructions such as, "Take a deep breath before continuing. Please refer to the support documentation for details." In this way, the system achieves both smooth workflow and reduction of the user's emotional burden. 【0183】 Examples of prompts used in the generating AI model include: "Generate the optimal assembly procedure based on the work history data. Consider the user's stress level, adjust the pace if necessary, and incorporate real-time feedback." This makes it possible to provide the user with the best possible work experience. 【0184】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0185】 Step 1: 【0186】 The server collects past work data from a work history database. The input is the work history stored in the database, and the output is structured work data for input into generative artificial intelligence. This data collection provides diverse information, including past successes and failures. 【0187】 Step 2: 【0188】 The server automatically generates work procedures using generative artificial intelligence based on the collected data. The input is structured work data, and the output is an optimized work procedure document. The generative AI model's algorithm is applied to formulate efficient and safe procedures. 【0189】 Step 3: 【0190】 The terminal displays generated work instructions to field workers and provides real-time instructions via voice and text. The input is the work instructions received from the server, and the output is clear instructions for the workers. The terminal communicates instructions clearly through its user interface. 【0191】 Step 4: 【0192】 The device analyzes the user's facial expressions and voice to determine their emotional state. Input is visual and audio data acquired through the camera and microphone, and output is a determination of the user's emotional state (e.g., stress, relaxation). Emotion recognition technology is used in the analysis to assess the user's current emotional health. 【0193】 Step 5: 【0194】 The device dynamically adapts the work environment based on the user's emotional state. Input is the result of the emotional state assessment, and output includes adjustments to work procedures and advice to promote relaxation. For example, if the device determines that the user is stressed, it will display a message such as "Let's take a short break." 【0195】 Step 6: 【0196】 After completing a task, the user enters feedback into the terminal. The input consists of the worker's subjective impressions and experiences, while the output is feedback data for improvement. This feedback is then sent back to the server and used to generate the next work procedure manual and adjust the system. This ensures continuous system improvement. 【0197】 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. 【0198】 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. 【0199】 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. 【0200】 [Second Embodiment] 【0201】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0202】 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. 【0203】 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). 【0204】 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. 【0205】 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. 【0206】 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). 【0207】 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. 【0208】 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. 【0209】 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. 【0210】 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. 【0211】 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. 【0212】 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". 【0213】 This invention provides a system for automatically generating work procedures and providing work support in network centers and telecommunications exchanges. This system utilizes generative artificial intelligence to collect past work data from a work history database and automatically creates efficient and safe work procedures based on that data, thereby improving the efficiency of operations. 【0214】 Specifically, the server first accesses a work history database to collect past data related to a particular task. This collected data is then analyzed by generative artificial intelligence, and work procedures that prioritize safety and efficiency are automatically generated. For example, the server analyzes data related to network equipment configuration changes and creates a procedure manual that includes everything from necessary preparations to specific operating steps. 【0215】 The generated procedure manual is presented to the on-site worker via a terminal. The terminal provides real-time work instructions to the worker through visual or audio guidance. For example, the terminal might instruct the worker to "check the port settings as the next step" and then clarify the subsequent steps. 【0216】 After completing a task, the user (worker) provides feedback on the work performed at their terminal. This feedback is collected by the server and used to generate future work instructions. For example, the user might report specific improvements, such as "The cable specified in step 3 was actually unnecessary." 【0217】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. 【0218】 The following describes the processing flow. 【0219】 Step 1: 【0220】 The server accesses the work history database and collects past work data related to a specific task. During this process, the server filters the relevant history based on the specified work content and equipment, and extracts the necessary data. 【0221】 Step 2: 【0222】 The server analyzes the collected data and automatically generates new work procedures using generative artificial intelligence. The server uses past successful examples as a reference to create optimal procedures that consider safety and efficiency. 【0223】 Step 3: 【0224】 The server sends the generated work instructions to the terminal. The terminal receives these instructions and presents them to the worker before they begin work. For example, the terminal displays the instructions on its screen and also allows the worker to view a list of the tools and materials needed for the work. 【0225】 Step 4: 【0226】 The terminal provides real-time instructions to the worker as the work progresses. This includes communicating the next steps and points to note via voice or text. For example, the terminal might give specific instructions such as, "Check the current connection and ensure there are no problems." 【0227】 Step 5: 【0228】 The user (worker) enters feedback via a terminal after completing the task. This includes suggestions for improvement in the procedure manual and observations made during the work. The worker reports comments such as "Step 5 was unclear" to the system. 【0229】 Step 6: 【0230】 The server receives feedback from workers and incorporates it into the generation of the next work procedure manual. The server analyzes the feedback and stores it as data to improve the content of the procedure manual, thereby enabling continuous improvement of the system. 【0231】 (Example 1) 【0232】 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." 【0233】 Traditional work procedure manuals rely on manual processes, limiting improvements in work efficiency and safety. Furthermore, the lack of standardized instructions for on-site workers makes it difficult to maintain consistent work quality. Additionally, feedback after work completion may not be adequately reflected in subsequent work. There is a need to solve these problems and improve work efficiency, safety, and quality. 【0234】 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. 【0235】 In this invention, the server includes an information gathering means for collecting past work information from a work history information recording device and using it to create necessary work instructions; an information processing means for automatically generating work procedures using a generative information processing device based on the collected information; and a work support means via an operating device that presents the generated work procedures to on-site workers and provides instructions in real time. This enables improved work efficiency, enhanced safety, maintenance of consistent work quality, and effective utilization of feedback. 【0236】 A "work history information recording device" is a device that stores information about past work and functions as a database that can be accessed as needed. 【0237】 "Information gathering means" refers to a method or apparatus that provides the function of acquiring necessary work information from a work history information recording device and making it available for subsequent processing. 【0238】 A "generative information processing device" is a device equipped with artificial intelligence technology to analyze large amounts of collected data and automatically generate optimized work procedures. 【0239】 "Information processing means" refers to a method or apparatus that provides a process or function for generating necessary work instructions based on collected information data. 【0240】 An "operating device" is a device equipped with an interface for displaying the generated work procedure manual to on-site workers and providing them with necessary work instructions. 【0241】 "Work support means" refers to a method or device for providing real-time work instructions to on-site workers via an operating device, thereby supporting the smooth progress of work. 【0242】 A "data feedback means" is a device that provides a method or function for collecting feedback obtained from on-site workers after the completion of work and using it to improve the next work instructions. 【0243】 This invention is a system for reducing the workload on workers and improving work efficiency in network management and communication infrastructure operation. The system consists mainly of a server, terminals, and a generative AI model. 【0244】 The server collects past work information using a work history information recording device. This information covers a wide range of topics, including the type of work, conditions, and results, and each piece of data is systematically stored. The collected information is sent to a generative information processing device. 【0245】 The generative information processing device analyzes received data using, for example, a machine learning model implemented in Python. In particular, it utilizes algorithms that prioritize safety and efficiency, and has the function of automatically generating ideal work procedures. The generative AI model is instructed with a specific prompt message such as, "Based on data from past network equipment configuration changes, please generate safe and efficient work procedures." 【0246】 The terminal is responsible for providing the generated procedure manuals to field workers. The terminal not only displays the procedure manuals visually but also provides voice guidance to assist the workers in their manual operations. For example, instructions such as "Next, check the operation of port 2" may be displayed on the screen or spoken aloud. 【0247】 The user (worker) can efficiently complete tasks by following instructions provided in real time via a terminal. After completing a task, the worker provides feedback using the terminal, and this information is collected by the server and used to improve the next procedure manual generation process. Specifically, the user might input feedback such as "The tool specified in step 4 was not needed," and this will be reflected in the next work procedure manual. 【0248】 In this way, workers receive support throughout the entire system to complete tasks efficiently and safely, resulting in improved overall productivity. 【0249】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0250】 Step 1: 【0251】 The server connects to a work history information recording device to collect past work information. Inputs include the type and duration of the work, and based on this, it collects relevant past work data. This data includes work procedures, tools used, and work time. The output is a structured dataset to be passed to a generative information processing device. Specifically, it executes database queries to extract the required information. 【0252】 Step 2: 【0253】 The server inputs the collected dataset into a generating AI model. The prompt is "Generate safe and efficient procedure manuals based on past work data." The AI ​​model analyzes this data and executes machine learning algorithms to design optimal work procedures. The output is a draft of a detailed procedure manual. Specific operations include data cleaning, feature extraction, and model inference. 【0254】 Step 3: 【0255】 The server transfers the generated procedure manuals to the information management module for review and approval. The input is a procedure manual draft from the AI ​​model, which is then validated for safety and efficiency. The output is the final, revised, and approved work procedure manual. Specific actions include collecting feedback from the review system and editing the procedure manuals as needed. 【0256】 Step 4: 【0257】 The terminal receives the final work instructions and presents them to the field worker visually or audibly. Input is a digital file of the final instructions, and output is a real-time display of instructions to the field worker. Specific operations include step-by-step display on the screen and playback of instructions through the audio speaker. 【0258】 Step 5: 【0259】 The user (worker) performs the task according to the instructions displayed on the terminal. After completing the task, feedback is entered via the terminal. The input consists of opinions and improvement suggestions from the worker, and the output is digital data sent to the server as a feedback record. Specifically, the process involves entering and submitting feedback in a feedback form. 【0260】 Step 6: 【0261】 The server collects feedback submitted by workers and stores it in a database for use in generating future work procedure manuals. The input is feedback data from each worker, and the output is a history record of improved versions that are reflected in past work data. Specifically, the server writes to the database and integrates it into an analysis dataset. 【0262】 (Application Example 1) 【0263】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0264】 Traditional maintenance procedures have involved manual creation of procedure manuals, leading to challenges in efficiency and safety. Furthermore, insufficient instructions to field workers resulted in inconsistent work quality. Additionally, feedback after maintenance work is not adequately utilized for subsequent work, highlighting areas for improvement. 【0265】 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. 【0266】 In this invention, the server includes an information gathering means for collecting past maintenance work information and using it to create necessary instruction manuals; an information processing means for automatically generating maintenance manuals using generative artificial intelligence based on the collected information; and a work support means via a terminal equipped with a user interface that presents the generated maintenance manuals to on-site workers visually and audibly and provides instructions in real time. This makes it possible to improve the efficiency and safety of maintenance work. 【0267】 "Maintenance work" refers to inspections, repairs, adjustments, and other tasks performed periodically or as needed to maintain the normal operation of equipment and systems. 【0268】 "Information gathering means" refers to methods and devices for efficiently collecting data and information related to past maintenance work. 【0269】 "Generative artificial intelligence" is an artificial intelligence technology that can automatically create new information and procedures based on given data. 【0270】 "Information processing means" refers to technologies or devices for analyzing collected data, extracting necessary information, and processing it. 【0271】 A "user interface" is a means or design for exchanging information bidirectionally between a terminal and a field worker. 【0272】 A "feedback processing method" is a method of collecting and analyzing feedback from workers after maintenance work and using it to help generate future procedure manuals. 【0273】 This invention is a system that supports efficient and safe maintenance work in factories. The system is composed of three key elements: a server, terminals, and users. 【0274】 The server is equipped with information gathering mechanisms to collect past maintenance data. This data is primarily stored in a database, which the server accesses to retrieve the necessary information. The retrieved data is analyzed using information processing mechanisms that utilize generative artificial intelligence, and efficient and safe maintenance procedures are automatically generated. For example, the OpenAI API is used as the generative artificial intelligence in this process. 【0275】 The terminal plays a role in supporting on-site maintenance work. The terminal presents maintenance procedures, generated through a user interface, to the worker visually and audibly. By wearing AR smart glasses, the worker can visually review the procedures and receive voice instructions. This allows for efficient work without the need for manual intervention. 【0276】 After completing maintenance work, the user (worker) enters feedback into a terminal. This feedback is collected by the server and used to generate the next procedure manual through a feedback processing system. For example, if the worker reports improvements such as "the specified part was not actually necessary," a more optimized procedure manual will be generated next time. 【0277】 As an example of a prompt sentence, by giving an instruction such as "There is maintenance data for a robotic arm. Based on this, please create an efficient and safe maintenance procedure manual in Japanese." to a generative artificial intelligence, an appropriate procedure manual is generated. 【0278】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0279】 Step 1: 【0280】 The server accesses the maintenance work database to collect past work information. The input is the past work records stored in the database, and the output is a set of data related to specific maintenance work. At this time, relevant information is extracted using an efficient data collection means and converted into a form suitable for analysis. 【0281】 Step 2: 【0282】 The server passes the collected data to the generative artificial intelligence for analysis. The input is the maintenance work data obtained in Step 1, and the output is a draft of the maintenance procedure manual that emphasizes efficient and safe procedures. The data is analyzed using a generative AI model to generate optimal procedures. 【0283】 Step 3: 【0284】 The server transmits the generated maintenance procedure manual to the terminal. The input is the draft of the procedure manual created in Step 2, and the output is the maintenance procedure manual converted into a form that can be displayed on the terminal. Here, format adjustment is performed so that visual and voice instructions on the terminal are possible. 【0285】 Step 4: 【0286】 The terminal presents the maintenance procedure manual to the on-site worker visually and audibly. The input is the procedure manual received from the server, and the output is real-time work instructions to the worker. Smart glasses and a voice system are used to assist the worker in working efficiently. 【0287】 Step 5: 【0288】 After the work is completed, the user (operator) inputs feedback into the terminal. The input is the points for improvement and impressions of the work provided by the operator, and the output is feedback data that will be utilized for generating the next procedure manual. The terminal collects the feedback and transmits it to the server. 【0289】 Step 6: 【0290】 The server analyzes the feedback obtained from the user and utilizes it for generating the next procedure manual. The input is the feedback data collected in Step 5, and the output is guidelines for generating the next improved procedure manual. The process of reflecting the feedback in the generation AI model is included. 【0291】 Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion. 【0292】 The present invention is a system that combines a generative artificial intelligence and an emotion engine in a network center or a communication exchange station to provide automatic generation of work procedure manuals and work support. This system not only automatically generates an optimal procedure manual from work history data, but also recognizes the user's emotion in real time and supports more efficient and comfortable work by adjusting the work environment. 【0293】 Specifically, the server accesses the work history database and collects past data related to a specific work. Based on this data, the analysis module automatically generates an optimized work procedure manual by utilizing generative artificial intelligence. At this stage, the server particularly identifies past successful cases and assembles procedures that兼备 efficiency and safety. 【0294】 The generated procedure manuals are presented to field workers via a terminal. The terminal not only provides real-time instructions via voice and text, but its built-in emotion engine analyzes the user's facial expressions and voice to determine their emotional state. This emotion recognition technology detects the user's stress level and anxiety, enabling appropriate work support. 【0295】 For example, if the device detects tension from the user's facial expressions, it will pause the work process and provide advice to help the user relax or a direct link to troubleshooting. The device will also adjust the pace of the process based on the user's emotional state, supporting them in a way that is easy to understand. 【0296】 After completing a task, the user enters feedback via their terminal, and this information is sent to the server. The server collects this feedback data and uses it to generate future work instructions and adjust the emotion engine, thereby continuously improving the system. 【0297】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. Furthermore, by taking into account the emotional state of the user, it reduces worker stress and creates a comfortable and efficient work environment. 【0298】 The following describes the processing flow. 【0299】 Step 1: 【0300】 The server accesses the work history database and extracts past work data related to a specific task. The server then filters this data to collect the most relevant information based on the nature and conditions of the task. 【0301】 Step 2: 【0302】 The server analyzes the collected data and automatically generates an optimal work procedure manual using generative artificial intelligence. The server analyzes past successful cases and assembles efficient and safe procedures to reflect best practices in the work. 【0303】 Step 3: 【0304】 The server sends the generated work procedure manual to the terminal. The terminal displays this procedure manual on the user interface so that on-site workers can check the procedures at the start of work. 【0305】 Step 4: 【0306】 During work, the terminal provides voice and text instructions to the worker in real time. The terminal's emotion engine analyzes the user's facial expressions and voice tone in real time to grasp the emotional state. For example, when the terminal recognizes that the user is nervous, it slows down the work pace and provides calming instructions. 【0307】 Step 5: 【0308】 The worker, who is the user, follows the instructions received from the terminal according to the progress of the work and performs the procedures. The terminal periodically checks the user's emotional state and adjusts the support content as needed. 【0309】 Step 6: 【0310】 After the work is completed, the user inputs feedback on the work and the interface through the terminal. This includes opinions on improvement points of the procedure manual and direct emotional reactions. 【0311】 Step 7: 【0312】 The server collects feedback data from the terminal and utilizes it for the generation of the next work procedure manual and the improvement of the emotion engine. By doing so, the server continuously improves the system to provide a better user experience. 【0313】 (Example 2) 【0314】 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". 【0315】 In on-site work, the burden on workers when creating instruction information manually and the stress caused by work guidance that does not take into account their emotional state are problematic. Conventional systems fail to provide sufficient support to improve work safety and efficiency, and lack adjustment functions that take into account the emotional state of workers. As a result, it is difficult to improve work productivity and reduce the burden on workers. 【0316】 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. 【0317】 In this invention, the server includes a numerical data collection means for collecting past work values ​​from a work history database and using them to create necessary instruction information, a numerical data processing means for automatically generating work instruction information using generative artificial intelligence based on the collected numerical values, and a work support means via a display device that presents the generated work instruction information to on-site workers and provides instructions in real time. This reduces the manual workload of on-site workers, improves work efficiency and safety, and enables stress reduction by utilizing emotion recognition. 【0318】 A "work history database" is an information aggregation system that stores numerical data related to all work performed in the past. 【0319】 A "numerical data collection device" is a device that has the function of acquiring numerical data from a database in order to collect necessary work information. 【0320】 "Generative artificial intelligence" is a technology that automatically generates optimal work instruction information based on collected data. 【0321】 A "numerical processing device" is a device that uses collected numerical data and generative artificial intelligence to generate work instruction information. 【0322】 "Instructional information" refers to information that includes specific procedures and instructions that workers should follow when performing their tasks. 【0323】 A "display device" is a device used to present work instruction information to on-site workers visually or audibly. 【0324】 A "human-machine interface" is an interactive user interface that enables the exchange of instruction information between a worker and a machine. 【0325】 "Emotion recognition means" refers to a device that includes technology for analyzing a worker's facial expressions and voice to determine their emotional state. 【0326】 A "feedback processing device" is a device that has the function of collecting numerical feedback from workers and reflecting it in the generation of the next work instruction information. 【0327】 This invention is a system that utilizes a work history database and generative artificial intelligence to provide work support that takes into account the worker's emotions. The server collects past work data by accessing the work history database. Specifically, it uses an SQL server to query the necessary values ​​and retrieve the data. The collected values ​​are passed by the server to a generative artificial intelligence model using Python, which generates optimal work instruction information. This model identifies past success patterns and creates instructions that take safety and efficiency into consideration. 【0328】 The generated work instructions are presented to the field worker via a terminal. The terminal consists of a display device equipped with a speech synthesis system and a display, providing instructions in both voice and text formats. The terminal also has emotion recognition capabilities, using a camera and microphone to determine the user's emotional state from their facial expressions and voice. For example, if the user is feeling frustrated, the terminal will slow down the work speed and provide supplementary explanations. 【0329】 After completing a task, the user provides feedback through the terminal. The terminal sends this feedback data to the server, which uses it to improve the generation of future instructions and the emotion recognition function. 【0330】 As a concrete example, the prompt is as follows: "Create example relaxation advice to provide when the user is feeling anxious." By prompting the AI ​​model with such a prompt, it becomes possible to provide appropriate support according to the user's emotional state. 【0331】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0332】 Step 1: 【0333】 The server accesses the work history database to collect past work metrics. Specifically, the server executes SQL queries to filter data related to a particular work. The input is the specific conditions of the work (e.g., success rate, duration), and the output is a set of work history metrics that match these conditions. These metrics serve as the basis for the next analysis step. 【0334】 Step 2: 【0335】 The server inputs the collected data into an AI model to generate optimal work instruction information. The server uses Python to activate the AI ​​model, identifying past success patterns and using an algorithm to construct the optimal procedure. The input is the work history data obtained in step 1, and the output is automatically generated work instruction information. This instruction information includes details that enhance safety and efficiency. 【0336】 Step 3: 【0337】 The terminal presents the generated work instruction information to the field worker via a display device. Specifically, the terminal uses speech synthesis software to output text instructions as voice. It also visually displays the instruction content on the display. The input is the work instruction information generated in step 2, and the output is real-time instructions to the user via both visual and auditory means. 【0338】 Step 4: 【0339】 The device analyzes the user's emotional state using built-in emotion recognition technology. Specifically, it determines emotions by capturing the user's facial expressions with a camera and analyzing their voice tone with a microphone. The input is the user's facial expressions and voice, captured in real time, and the output is an evaluation of the user's emotional state (e.g., tension, stress). Based on this, the work speed is adjusted and support content is suggested. 【0340】 Step 5: 【0341】 After completing a task, users provide feedback through a terminal. The terminal offers a dedicated form where users can enter comments and ratings. The input is the user's feedback value, and the output is organized feedback information. This information is sent to the server and used to generate future instructions and improve the system. 【0342】 (Application Example 2) 【0343】 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." 【0344】 Conventional work procedure creation systems lack sufficient means to reduce the emotional burden on workers while prioritizing their efficiency and safety. This leads to problems such as work errors and slower work speeds due to worker stress and anxiety. Furthermore, the lack of dynamic procedure adjustments to accommodate workers' emotional states makes it difficult to provide optimal work content for individual workers. 【0345】 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. 【0346】 In this invention, the server includes data collection means for collecting past work data from a work history database and using it to create necessary instruction manuals; data processing means for automatically generating work procedure manuals using generative artificial intelligence based on the collected data; and emotion recognition and work environment adaptation means for analyzing the worker's facial expressions and voice to determine their emotional state and adapting the work environment based on the determined emotional state. This enables dynamic procedure adjustments in accordance with the emotional state of on-site workers, making it possible to reduce emotional burden while providing an efficient and safe work environment. 【0347】 A "work history database" is an information repository that stores information about work performed in the past. 【0348】 "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates the optimal work procedure based on collected data. 【0349】 A "work procedure manual" is a document that contains a series of instructions for performing a specific task efficiently and safely. 【0350】 A "terminal" is a device used to present information and transmit instructions to on-site workers. 【0351】 "Emotion recognition and work environment adaptation means" refers to a system that analyzes the facial expressions and voice of workers and adjusts the work environment according to their emotional state. 【0352】 A "feedback processing mechanism" is a system that analyzes opinions and feedback collected from workers after their work is completed and uses this information to improve future procedures and systems. 【0353】 An "algorithm" is a set of procedures or calculation steps for solving a problem. 【0354】 A "user interface" refers to the interactive operating screens and input devices that operators use when operating machinery or equipment. 【0355】 The system for realizing this invention mainly consists of a server, a terminal, and a user. The server collects historical data from a work history database and automatically generates work instructions using generative artificial intelligence. These instructions are based on the user's past successful work examples and are optimized for safety and efficiency. The generated instructions are presented to the field worker via the terminal, and real-time instructions are provided via voice and text. 【0356】 The device incorporates emotion recognition and work environment adaptation mechanisms, analyzing the user's facial expressions and voice to determine their emotional state. Based on this information, the device is designed to dynamically adjust the work environment and reduce stress. For example, if it determines that the worker is stressed, it will display advice to help them relax and, if necessary, pause the work. 【0357】 After completing a task, users enter feedback via their terminal. This feedback is sent to the server and used to generate future work instructions and adjust the sentiment engine. This ensures continuous improvement of the system. 【0358】 As a concrete example, in the assembly process of a certain product, if a user experiences emotional stress, the terminal could temporarily suspend the work procedure and display instructions such as, "Take a deep breath before continuing. Please refer to the support documentation for details." In this way, the system achieves both smooth workflow and reduction of the user's emotional burden. 【0359】 Examples of prompts used in the generating AI model include: "Generate the optimal assembly procedure based on the work history data. Consider the user's stress level, adjust the pace if necessary, and incorporate real-time feedback." This makes it possible to provide the user with the best possible work experience. 【0360】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0361】 Step 1: 【0362】 The server collects past work data from a work history database. The input is the work history stored in the database, and the output is structured work data for input into generative artificial intelligence. This data collection provides diverse information, including past successes and failures. 【0363】 Step 2: 【0364】 The server automatically generates work procedures using generative artificial intelligence based on the collected data. The input is structured work data, and the output is an optimized work procedure document. The generative AI model's algorithm is applied to formulate efficient and safe procedures. 【0365】 Step 3: 【0366】 The terminal displays generated work instructions to field workers and provides real-time instructions via voice and text. The input is the work instructions received from the server, and the output is clear instructions for the workers. The terminal communicates instructions clearly through its user interface. 【0367】 Step 4: 【0368】 The device analyzes the user's facial expressions and voice to determine their emotional state. Input is visual and audio data acquired through the camera and microphone, and output is a determination of the user's emotional state (e.g., stress, relaxation). Emotion recognition technology is used in the analysis to assess the user's current emotional health. 【0369】 Step 5: 【0370】 The device dynamically adapts the work environment based on the user's emotional state. Input is the result of the emotional state assessment, and output includes adjustments to work procedures and advice to promote relaxation. For example, if the device determines that the user is stressed, it will display a message such as "Let's take a short break." 【0371】 Step 6: 【0372】 After completing a task, the user enters feedback into the terminal. The input consists of the worker's subjective impressions and experiences, while the output is feedback data for improvement. This feedback is then sent back to the server and used to generate the next work procedure manual and adjust the system. This ensures continuous system improvement. 【0373】 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. 【0374】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0375】 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. 【0376】 [Third Embodiment] 【0377】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0378】 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. 【0379】 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). 【0380】 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. 【0381】 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. 【0382】 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). 【0383】 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. 【0384】 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. 【0385】 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. 【0386】 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. 【0387】 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. 【0388】 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". 【0389】 This invention provides a system for automatically generating work procedures and providing work support in network centers and telecommunications exchanges. This system utilizes generative artificial intelligence to collect past work data from a work history database and automatically creates efficient and safe work procedures based on that data, thereby improving the efficiency of operations. 【0390】 Specifically, the server first accesses a work history database to collect past data related to a particular task. This collected data is then analyzed by generative artificial intelligence, and work procedures that prioritize safety and efficiency are automatically generated. For example, the server analyzes data related to network equipment configuration changes and creates a procedure manual that includes everything from necessary preparations to specific operating steps. 【0391】 The generated procedure manual is presented to the on-site worker via a terminal. The terminal provides real-time work instructions to the worker through visual or audio guidance. For example, the terminal might instruct the worker to "check the port settings as the next step" and then clarify the subsequent steps. 【0392】 After completing a task, the user (worker) provides feedback on the work performed at their terminal. This feedback is collected by the server and used to generate future work instructions. For example, the user might report specific improvements, such as "The cable specified in step 3 was actually unnecessary." 【0393】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. 【0394】 The following describes the processing flow. 【0395】 Step 1: 【0396】 The server accesses the work history database and collects past work data related to a specific task. During this process, the server filters the relevant history based on the specified work content and equipment, and extracts the necessary data. 【0397】 Step 2: 【0398】 The server analyzes the collected data and automatically generates new work procedures using generative artificial intelligence. The server uses past successful examples as a reference to create optimal procedures that consider safety and efficiency. 【0399】 Step 3: 【0400】 The server sends the generated work instructions to the terminal. The terminal receives these instructions and presents them to the worker before they begin work. For example, the terminal displays the instructions on its screen and also allows the worker to view a list of the tools and materials needed for the work. 【0401】 Step 4: 【0402】 The terminal provides real-time instructions to the worker as the work progresses. This includes communicating the next steps and points to note via voice or text. For example, the terminal might give specific instructions such as, "Check the current connection and ensure there are no problems." 【0403】 Step 5: 【0404】 The user (worker) enters feedback via a terminal after completing the task. This includes suggestions for improvement in the procedure manual and observations made during the work. The worker reports comments such as "Step 5 was unclear" to the system. 【0405】 Step 6: 【0406】 The server receives feedback from workers and incorporates it into the generation of the next work procedure manual. The server analyzes the feedback and stores it as data to improve the content of the procedure manual, thereby enabling continuous improvement of the system. 【0407】 (Example 1) 【0408】 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." 【0409】 Traditional work procedure manuals rely on manual processes, limiting improvements in work efficiency and safety. Furthermore, the lack of standardized instructions for on-site workers makes it difficult to maintain consistent work quality. Additionally, feedback after work completion may not be adequately reflected in subsequent work. There is a need to solve these problems and improve work efficiency, safety, and quality. 【0410】 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. 【0411】 In this invention, the server includes an information gathering means for collecting past work information from a work history information recording device and using it to create necessary work instructions; an information processing means for automatically generating work procedures using a generative information processing device based on the collected information; and a work support means via an operating device that presents the generated work procedures to on-site workers and provides instructions in real time. This enables improved work efficiency, enhanced safety, maintenance of consistent work quality, and effective utilization of feedback. 【0412】 A "work history information recording device" is a device that stores information about past work and functions as a database that can be accessed as needed. 【0413】 "Information gathering means" refers to a method or apparatus that provides the function of acquiring necessary work information from a work history information recording device and making it available for subsequent processing. 【0414】 A "generative information processing device" is a device equipped with artificial intelligence technology to analyze large amounts of collected data and automatically generate optimized work procedures. 【0415】 "Information processing means" refers to a method or apparatus that provides a process or function for generating necessary work instructions based on collected information data. 【0416】 An "operating device" is a device equipped with an interface for displaying the generated work procedure manual to on-site workers and providing them with necessary work instructions. 【0417】 "Work support means" refers to a method or device for providing real-time work instructions to on-site workers via an operating device, thereby supporting the smooth progress of work. 【0418】 A "data feedback means" is a device that provides a method or function for collecting feedback obtained from on-site workers after the completion of work and using it to improve the next work instructions. 【0419】 This invention is a system for reducing the workload on workers and improving work efficiency in network management and communication infrastructure operation. The system consists mainly of a server, terminals, and a generative AI model. 【0420】 The server collects past work information using a work history information recording device. This information covers a wide range of topics, including the type of work, conditions, and results, and each piece of data is systematically stored. The collected information is sent to a generative information processing device. 【0421】 The generative information processing device analyzes received data using, for example, a machine learning model implemented in Python. In particular, it utilizes algorithms that prioritize safety and efficiency, and has the function of automatically generating ideal work procedures. The generative AI model is instructed with a specific prompt message such as, "Based on data from past network equipment configuration changes, please generate safe and efficient work procedures." 【0422】 The terminal is responsible for providing the generated procedure manuals to field workers. The terminal not only displays the procedure manuals visually but also provides voice guidance to assist the workers in their manual operations. For example, instructions such as "Next, check the operation of port 2" may be displayed on the screen or spoken aloud. 【0423】 The user (worker) can efficiently complete tasks by following instructions provided in real time via a terminal. After completing a task, the worker provides feedback using the terminal, and this information is collected by the server and used to improve the next procedure manual generation process. Specifically, the user might input feedback such as "The tool specified in step 4 was not needed," and this will be reflected in the next work procedure manual. 【0424】 In this way, workers receive support throughout the entire system to complete tasks efficiently and safely, resulting in improved overall productivity. 【0425】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0426】 Step 1: 【0427】 The server connects to a work history information recording device to collect past work information. Inputs include the type and duration of the work, and based on this, it collects relevant past work data. This data includes work procedures, tools used, and work time. The output is a structured dataset to be passed to a generative information processing device. Specifically, it executes database queries to extract the required information. 【0428】 Step 2: 【0429】 The server inputs the collected dataset into a generating AI model. The prompt is "Generate safe and efficient procedure manuals based on past work data." The AI ​​model analyzes this data and executes machine learning algorithms to design optimal work procedures. The output is a draft of a detailed procedure manual. Specific operations include data cleaning, feature extraction, and model inference. 【0430】 Step 3: 【0431】 The server transfers the generated procedure manuals to the information management module for review and approval. The input is a procedure manual draft from the AI ​​model, which is then validated for safety and efficiency. The output is the final, revised, and approved work procedure manual. Specific actions include collecting feedback from the review system and editing the procedure manuals as needed. 【0432】 Step 4: 【0433】 The terminal receives the final work instructions and presents them to the field worker visually or audibly. Input is a digital file of the final instructions, and output is a real-time display of instructions to the field worker. Specific operations include step-by-step display on the screen and playback of instructions through the audio speaker. 【0434】 Step 5: 【0435】 The user (worker) performs the task according to the instructions displayed on the terminal. After completing the task, feedback is entered via the terminal. The input consists of opinions and improvement suggestions from the worker, and the output is digital data sent to the server as a feedback record. Specifically, the process involves entering and submitting feedback in a feedback form. 【0436】 Step 6: 【0437】 The server collects feedback submitted by workers and stores it in a database for use in generating future work procedure manuals. The input is feedback data from each worker, and the output is a history record of improved versions that are reflected in past work data. Specifically, the server writes to the database and integrates it into an analysis dataset. 【0438】 (Application Example 1) 【0439】 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." 【0440】 Traditional maintenance procedures have involved manual creation of procedure manuals, leading to challenges in efficiency and safety. Furthermore, insufficient instructions to field workers resulted in inconsistent work quality. Additionally, feedback after maintenance work is not adequately utilized for subsequent work, highlighting areas for improvement. 【0441】 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. 【0442】 In this invention, the server includes an information gathering means for collecting past maintenance work information and using it to create necessary instruction manuals; an information processing means for automatically generating maintenance manuals using generative artificial intelligence based on the collected information; and a work support means via a terminal equipped with a user interface that presents the generated maintenance manuals to on-site workers visually and audibly and provides instructions in real time. This makes it possible to improve the efficiency and safety of maintenance work. 【0443】 "Maintenance work" refers to inspections, repairs, adjustments, and other tasks performed periodically or as needed to maintain the normal operation of equipment and systems. 【0444】 "Information gathering means" refers to methods and devices for efficiently collecting data and information related to past maintenance work. 【0445】 "Generative artificial intelligence" is an artificial intelligence technology that can automatically create new information and procedures based on given data. 【0446】 "Information processing means" refers to technologies or devices for analyzing collected data, extracting necessary information, and processing it. 【0447】 A "user interface" is a means or design for exchanging information bidirectionally between a terminal and a field worker. 【0448】 A "feedback processing method" is a method of collecting and analyzing feedback from workers after maintenance work and using it to help generate future procedure manuals. 【0449】 This invention is a system that supports efficient and safe maintenance work in factories. The system is composed of three key elements: a server, terminals, and users. 【0450】 The server is equipped with information gathering mechanisms to collect past maintenance data. This data is primarily stored in a database, which the server accesses to retrieve the necessary information. The retrieved data is analyzed using information processing mechanisms that utilize generative artificial intelligence, and efficient and safe maintenance procedures are automatically generated. For example, the OpenAI API is used as the generative artificial intelligence in this process. 【0451】 The terminal plays a role in supporting on-site maintenance work. The terminal presents maintenance procedures, generated through a user interface, to the worker visually and audibly. By wearing AR smart glasses, the worker can visually review the procedures and receive voice instructions. This allows for efficient work without the need for manual intervention. 【0452】 After completing maintenance work, the user (worker) enters feedback into a terminal. This feedback is collected by the server and used to generate the next procedure manual through a feedback processing system. For example, if the worker reports improvements such as "the specified part was not actually necessary," a more optimized procedure manual will be generated next time. 【0453】 As an example of a prompt, by giving the generative artificial intelligence the instruction, "We have maintenance data for a robotic arm. Based on this, please create an efficient and safe maintenance procedure manual in Japanese," an appropriate procedure manual will be generated. 【0454】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0455】 Step 1: 【0456】 The server accesses the maintenance work database to collect past work information. The input is past work records stored in the database, and the output is a set of data related to a specific maintenance task. At this time, relevant information is extracted using efficient data collection methods and converted into a format suitable for analysis. 【0457】 Step 2: 【0458】 The server passes the collected data to a generative artificial intelligence for analysis. The input is the maintenance work data obtained in step 1, and the output is a draft of a maintenance procedure manual that emphasizes efficient and safe procedures. The generative AI model is used to analyze the data and generate the optimal procedure. 【0459】 Step 3: 【0460】 The server sends the generated maintenance procedure manual to the terminal. The input is the draft of the procedure manual created in step 2, and the output is the maintenance procedure manual converted into a format viewable on the terminal. At this point, formatting adjustments are made, enabling visual and audio instructions on the terminal. 【0461】 Step 4: 【0462】 The terminal presents maintenance procedures to field workers visually and audibly. Input is the procedures received from the server, and output is real-time work instructions for the worker. Smart glasses and voice systems are used to support workers in performing their tasks efficiently. 【0463】 Step 5: 【0464】 The user (worker) enters feedback into the terminal after completing the task. The input consists of suggestions for improvement and comments from the worker, and the output is feedback data used to generate the next procedure manual. The terminal collects the feedback and sends it to the server. 【0465】 Step 6: 【0466】 The server analyzes the feedback received from users and uses it to improve the next procedure manual generation. The input is the feedback data collected in step 5, and the output is guidance for the next improved procedure manual generation. This includes a process of incorporating the feedback into the generation AI model. 【0467】 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. 【0468】 This invention provides a system for network centers and communication exchanges that combines generative artificial intelligence and an emotion engine to automatically generate work procedures and provide work support. This system not only automatically generates optimal procedures from work history data, but also recognizes the user's emotions in real time and adjusts the work environment to further support more efficient and comfortable work. 【0469】 Specifically, the server accesses a work history database and collects historical data related to a particular task. An analysis module then uses generative artificial intelligence to automatically generate optimized work procedures based on this data. At this stage, the server specifically identifies past successes and constructs procedures that combine efficiency and safety. 【0470】 The generated procedure manuals are presented to field workers via a terminal. The terminal not only provides real-time instructions via voice and text, but its built-in emotion engine analyzes the user's facial expressions and voice to determine their emotional state. This emotion recognition technology detects the user's stress level and anxiety, enabling appropriate work support. 【0471】 For example, if the device detects tension from the user's facial expressions, it will pause the work process and provide advice to help the user relax or a direct link to troubleshooting. The device will also adjust the pace of the process based on the user's emotional state, supporting them in a way that is easy to understand. 【0472】 After completing a task, the user enters feedback via their terminal, and this information is sent to the server. The server collects this feedback data and uses it to generate future work instructions and adjust the emotion engine, thereby continuously improving the system. 【0473】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. Furthermore, by taking into account the emotional state of the user, it reduces worker stress and creates a comfortable and efficient work environment. 【0474】 The following describes the processing flow. 【0475】 Step 1: 【0476】 The server accesses the work history database and extracts past work data related to a specific task. The server then filters this data to collect the most relevant information based on the nature and conditions of the task. 【0477】 Step 2: 【0478】 The server analyzes the collected data and uses generative artificial intelligence to automatically generate optimal work procedures. By analyzing past success stories and constructing efficient and safe procedures, the server incorporates best practices for the work. 【0479】 Step 3: 【0480】 The server sends the generated work procedure manual to the terminal. The terminal displays this manual in its user interface, allowing the field worker to review the procedure before starting work. 【0481】 Step 4: 【0482】 The terminal provides the worker with voice and text instructions in real time while they are working. The terminal's emotion engine analyzes the user's facial expressions and tone of voice in real time to understand their emotional state. For example, if the terminal detects that the user is stressed, it will slow down the pace of the work and provide calmer instructions. 【0483】 Step 5: 【0484】 The user, acting as the worker, follows instructions received from the terminal as the task progresses and completes the procedures. The terminal periodically checks the user's emotional state and adjusts the support provided as needed. 【0485】 Step 6: 【0486】 After completing a task, users provide feedback on the process and interface via their terminal. This includes suggestions for improving the procedure manual and direct emotional responses. 【0487】 Step 7: 【0488】 The server collects feedback data from terminals and uses it to generate future work instructions and improve the emotion engine. This allows the server to continuously improve the system and provide a better user experience. 【0489】 (Example 2) 【0490】 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." 【0491】 In on-site work, the burden on workers when creating instruction information manually and the stress caused by work guidance that does not take into account their emotional state are problematic. Conventional systems fail to provide sufficient support to improve work safety and efficiency, and lack adjustment functions that take into account the emotional state of workers. As a result, it is difficult to improve work productivity and reduce the burden on workers. 【0492】 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. 【0493】 In this invention, the server includes a numerical data collection means for collecting past work values ​​from a work history database and using them to create necessary instruction information, a numerical data processing means for automatically generating work instruction information using generative artificial intelligence based on the collected numerical values, and a work support means via a display device that presents the generated work instruction information to on-site workers and provides instructions in real time. This reduces the manual workload of on-site workers, improves work efficiency and safety, and enables stress reduction by utilizing emotion recognition. 【0494】 A "work history database" is an information aggregation system that stores numerical data related to all work performed in the past. 【0495】 A "numerical data collection device" is a device that has the function of acquiring numerical data from a database in order to collect necessary work information. 【0496】 "Generative artificial intelligence" is a technology that automatically generates optimal work instruction information based on collected data. 【0497】 A "numerical processing device" is a device that uses collected numerical data and generative artificial intelligence to generate work instruction information. 【0498】 "Instructional information" refers to information that includes specific procedures and instructions that workers should follow when performing their tasks. 【0499】 A "display device" is a device used to present work instruction information to on-site workers visually or audibly. 【0500】 A "human-machine interface" is an interactive user interface that enables the exchange of instruction information between a worker and a machine. 【0501】 "Emotion recognition means" refers to a device that includes technology for analyzing a worker's facial expressions and voice to determine their emotional state. 【0502】 A "feedback processing device" is a device that has the function of collecting numerical feedback from workers and reflecting it in the generation of the next work instruction information. 【0503】 This invention is a system that utilizes a work history database and generative artificial intelligence to provide work support that takes into account the worker's emotions. The server collects past work data by accessing the work history database. Specifically, it uses an SQL server to query the necessary values ​​and retrieve the data. The collected values ​​are passed by the server to a generative artificial intelligence model using Python, which generates optimal work instruction information. This model identifies past success patterns and creates instructions that take safety and efficiency into consideration. 【0504】 The generated work instructions are presented to the field worker via a terminal. The terminal consists of a display device equipped with a speech synthesis system and a display, providing instructions in both voice and text formats. The terminal also has emotion recognition capabilities, using a camera and microphone to determine the user's emotional state from their facial expressions and voice. For example, if the user is feeling frustrated, the terminal will slow down the work speed and provide supplementary explanations. 【0505】 After completing a task, the user provides feedback through the terminal. The terminal sends this feedback data to the server, which uses it to improve the generation of future instructions and the emotion recognition function. 【0506】 As a concrete example, the prompt is as follows: "Create example relaxation advice to provide when the user is feeling anxious." By prompting the AI ​​model with such a prompt, it becomes possible to provide appropriate support according to the user's emotional state. 【0507】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0508】 Step 1: 【0509】 The server accesses the work history database to collect past work metrics. Specifically, the server executes SQL queries to filter data related to a particular work. The input is the specific conditions of the work (e.g., success rate, duration), and the output is a set of work history metrics that match these conditions. These metrics serve as the basis for the next analysis step. 【0510】 Step 2: 【0511】 The server inputs the collected data into an AI model to generate optimal work instruction information. The server uses Python to activate the AI ​​model, identifying past success patterns and using an algorithm to construct the optimal procedure. The input is the work history data obtained in step 1, and the output is automatically generated work instruction information. This instruction information includes details that enhance safety and efficiency. 【0512】 Step 3: 【0513】 The terminal presents the generated work instruction information to the field worker via a display device. Specifically, the terminal uses speech synthesis software to output text instructions as voice. It also visually displays the instruction content on the display. The input is the work instruction information generated in step 2, and the output is real-time instructions to the user via both visual and auditory means. 【0514】 Step 4: 【0515】 The device analyzes the user's emotional state using built-in emotion recognition technology. Specifically, it determines emotions by capturing the user's facial expressions with a camera and analyzing their voice tone with a microphone. The input is the user's facial expressions and voice, captured in real time, and the output is an evaluation of the user's emotional state (e.g., tension, stress). Based on this, the work speed is adjusted and support content is suggested. 【0516】 Step 5: 【0517】 After completing a task, users provide feedback through a terminal. The terminal offers a dedicated form where users can enter comments and ratings. The input is the user's feedback value, and the output is organized feedback information. This information is sent to the server and used to generate future instructions and improve the system. 【0518】 (Application Example 2) 【0519】 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." 【0520】 Conventional work procedure creation systems lack sufficient means to reduce the emotional burden on workers while prioritizing their efficiency and safety. This leads to problems such as work errors and slower work speeds due to worker stress and anxiety. Furthermore, the lack of dynamic procedure adjustments to accommodate workers' emotional states makes it difficult to provide optimal work content for individual workers. 【0521】 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. 【0522】 In this invention, the server includes data collection means for collecting past work data from a work history database and using it to create necessary instruction manuals; data processing means for automatically generating work procedure manuals using generative artificial intelligence based on the collected data; and emotion recognition and work environment adaptation means for analyzing the worker's facial expressions and voice to determine their emotional state and adapting the work environment based on the determined emotional state. This enables dynamic procedure adjustments in accordance with the emotional state of on-site workers, making it possible to reduce emotional burden while providing an efficient and safe work environment. 【0523】 A "work history database" is an information repository that stores information about work performed in the past. 【0524】 "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates the optimal work procedure based on collected data. 【0525】 A "work procedure manual" is a document that contains a series of instructions for performing a specific task efficiently and safely. 【0526】 A "terminal" is a device used to present information and transmit instructions to on-site workers. 【0527】 "Emotion recognition and work environment adaptation means" refers to a system that analyzes the facial expressions and voice of workers and adjusts the work environment according to their emotional state. 【0528】 A "feedback processing mechanism" is a system that analyzes opinions and feedback collected from workers after their work is completed and uses this information to improve future procedures and systems. 【0529】 An "algorithm" is a set of procedures or calculation steps for solving a problem. 【0530】 A "user interface" refers to the interactive operating screens and input devices that operators use when operating machinery or equipment. 【0531】 The system for realizing this invention mainly consists of a server, a terminal, and a user. The server collects historical data from a work history database and automatically generates work instructions using generative artificial intelligence. These instructions are based on the user's past successful work examples and are optimized for safety and efficiency. The generated instructions are presented to the field worker via the terminal, and real-time instructions are provided via voice and text. 【0532】 The device incorporates emotion recognition and work environment adaptation mechanisms, analyzing the user's facial expressions and voice to determine their emotional state. Based on this information, the device is designed to dynamically adjust the work environment and reduce stress. For example, if it determines that the worker is stressed, it will display advice to help them relax and, if necessary, pause the work. 【0533】 After completing a task, users enter feedback via their terminal. This feedback is sent to the server and used to generate future work instructions and adjust the sentiment engine. This ensures continuous improvement of the system. 【0534】 As a concrete example, in the assembly process of a certain product, if a user experiences emotional stress, the terminal could temporarily suspend the work procedure and display instructions such as, "Take a deep breath before continuing. Please refer to the support documentation for details." In this way, the system achieves both smooth workflow and reduction of the user's emotional burden. 【0535】 Examples of prompts used in the generating AI model include: "Generate the optimal assembly procedure based on the work history data. Consider the user's stress level, adjust the pace if necessary, and incorporate real-time feedback." This makes it possible to provide the user with the best possible work experience. 【0536】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0537】 Step 1: 【0538】 The server collects past work data from a work history database. The input is the work history stored in the database, and the output is structured work data for input into generative artificial intelligence. This data collection provides diverse information, including past successes and failures. 【0539】 Step 2: 【0540】 The server automatically generates work procedures using generative artificial intelligence based on the collected data. The input is structured work data, and the output is an optimized work procedure document. The generative AI model's algorithm is applied to formulate efficient and safe procedures. 【0541】 Step 3: 【0542】 The terminal displays generated work instructions to field workers and provides real-time instructions via voice and text. The input is the work instructions received from the server, and the output is clear instructions for the workers. The terminal communicates instructions clearly through its user interface. 【0543】 Step 4: 【0544】 The device analyzes the user's facial expressions and voice to determine their emotional state. Input is visual and audio data acquired through the camera and microphone, and output is a determination of the user's emotional state (e.g., stress, relaxation). Emotion recognition technology is used in the analysis to assess the user's current emotional health. 【0545】 Step 5: 【0546】 The device dynamically adapts the work environment based on the user's emotional state. Input is the result of the emotional state assessment, and output includes adjustments to work procedures and advice to promote relaxation. For example, if the device determines that the user is stressed, it will display a message such as "Let's take a short break." 【0547】 Step 6: 【0548】 After completing a task, the user enters feedback into the terminal. The input consists of the worker's subjective impressions and experiences, while the output is feedback data for improvement. This feedback is then sent back to the server and used to generate the next work procedure manual and adjust the system. This ensures continuous system improvement. 【0549】 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. 【0550】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0551】 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. 【0552】 [Fourth Embodiment] 【0553】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0554】 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. 【0555】 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). 【0556】 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. 【0557】 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. 【0558】 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). 【0559】 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. 【0560】 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. 【0561】 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. 【0562】 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. 【0563】 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. 【0564】 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. 【0565】 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". 【0566】 This invention provides a system for automatically generating work procedures and providing work support in network centers and telecommunications exchanges. This system utilizes generative artificial intelligence to collect past work data from a work history database and automatically creates efficient and safe work procedures based on that data, thereby improving the efficiency of operations. 【0567】 Specifically, the server first accesses a work history database to collect past data related to a particular task. This collected data is then analyzed by generative artificial intelligence, and work procedures that prioritize safety and efficiency are automatically generated. For example, the server analyzes data related to network equipment configuration changes and creates a procedure manual that includes everything from necessary preparations to specific operating steps. 【0568】 The generated procedure manual is presented to the on-site worker via a terminal. The terminal provides real-time work instructions to the worker through visual or audio guidance. For example, the terminal might instruct the worker to "check the port settings as the next step" and then clarify the subsequent steps. 【0569】 After completing a task, the user (worker) provides feedback on the work performed at their terminal. This feedback is collected by the server and used to generate future work instructions. For example, the user might report specific improvements, such as "The cable specified in step 3 was actually unnecessary." 【0570】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. 【0571】 The following describes the processing flow. 【0572】 Step 1: 【0573】 The server accesses the work history database and collects past work data related to a specific task. During this process, the server filters the relevant history based on the specified work content and equipment, and extracts the necessary data. 【0574】 Step 2: 【0575】 The server analyzes the collected data and automatically generates new work procedures using generative artificial intelligence. The server uses past successful examples as a reference to create optimal procedures that consider safety and efficiency. 【0576】 Step 3: 【0577】 The server sends the generated work instructions to the terminal. The terminal receives these instructions and presents them to the worker before they begin work. For example, the terminal displays the instructions on its screen and also allows the worker to view a list of the tools and materials needed for the work. 【0578】 Step 4: 【0579】 The terminal provides real-time instructions to the worker as the work progresses. This includes communicating the next steps and points to note via voice or text. For example, the terminal might give specific instructions such as, "Check the current connection and ensure there are no problems." 【0580】 Step 5: 【0581】 The user (worker) enters feedback via a terminal after completing the task. This includes suggestions for improvement in the procedure manual and observations made during the work. The worker reports comments such as "Step 5 was unclear" to the system. 【0582】 Step 6: 【0583】 The server receives feedback from workers and incorporates it into the generation of the next work procedure manual. The server analyzes the feedback and stores it as data to improve the content of the procedure manual, thereby enabling continuous improvement of the system. 【0584】 (Example 1) 【0585】 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". 【0586】 Traditional work procedure manuals rely on manual processes, limiting improvements in work efficiency and safety. Furthermore, the lack of standardized instructions for on-site workers makes it difficult to maintain consistent work quality. Additionally, feedback after work completion may not be adequately reflected in subsequent work. There is a need to solve these problems and improve work efficiency, safety, and quality. 【0587】 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. 【0588】 In this invention, the server includes an information gathering means for collecting past work information from a work history information recording device and using it to create necessary work instructions; an information processing means for automatically generating work procedures using a generative information processing device based on the collected information; and a work support means via an operating device that presents the generated work procedures to on-site workers and provides instructions in real time. This enables improved work efficiency, enhanced safety, maintenance of consistent work quality, and effective utilization of feedback. 【0589】 A "work history information recording device" is a device that stores information about past work and functions as a database that can be accessed as needed. 【0590】 "Information gathering means" refers to a method or apparatus that provides the function of acquiring necessary work information from a work history information recording device and making it available for subsequent processing. 【0591】 A "generative information processing device" is a device equipped with artificial intelligence technology to analyze large amounts of collected data and automatically generate optimized work procedures. 【0592】 "Information processing means" refers to a method or apparatus that provides a process or function for generating necessary work instructions based on collected information data. 【0593】 An "operating device" is a device equipped with an interface for displaying the generated work procedure manual to on-site workers and providing them with necessary work instructions. 【0594】 "Work support means" refers to a method or device for providing real-time work instructions to on-site workers via an operating device, thereby supporting the smooth progress of work. 【0595】 A "data feedback means" is a device that provides a method or function for collecting feedback obtained from on-site workers after the completion of work and using it to improve the next work instructions. 【0596】 This invention is a system for reducing the workload on workers and improving work efficiency in network management and communication infrastructure operation. The system consists mainly of a server, terminals, and a generative AI model. 【0597】 The server collects past work information using a work history information recording device. This information covers a wide range of topics, including the type of work, conditions, and results, and each piece of data is systematically stored. The collected information is sent to a generative information processing device. 【0598】 The generative information processing device analyzes received data using, for example, a machine learning model implemented in Python. In particular, it utilizes algorithms that prioritize safety and efficiency, and has the function of automatically generating ideal work procedures. The generative AI model is instructed with a specific prompt message such as, "Based on data from past network equipment configuration changes, please generate safe and efficient work procedures." 【0599】 The terminal is responsible for providing the generated procedure manuals to field workers. The terminal not only displays the procedure manuals visually but also provides voice guidance to assist the workers in their manual operations. For example, instructions such as "Next, check the operation of port 2" may be displayed on the screen or spoken aloud. 【0600】 The user (worker) can efficiently complete tasks by following instructions provided in real time via a terminal. After completing a task, the worker provides feedback using the terminal, and this information is collected by the server and used to improve the next procedure manual generation process. Specifically, the user might input feedback such as "The tool specified in step 4 was not needed," and this will be reflected in the next work procedure manual. 【0601】 In this way, workers receive support throughout the entire system to complete tasks efficiently and safely, resulting in improved overall productivity. 【0602】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0603】 Step 1: 【0604】 The server connects to a work history information recording device to collect past work information. Inputs include the type and duration of the work, and based on this, it collects relevant past work data. This data includes work procedures, tools used, and work time. The output is a structured dataset to be passed to a generative information processing device. Specifically, it executes database queries to extract the required information. 【0605】 Step 2: 【0606】 The server inputs the collected dataset into a generating AI model. The prompt is "Generate safe and efficient procedure manuals based on past work data." The AI ​​model analyzes this data and executes machine learning algorithms to design optimal work procedures. The output is a draft of a detailed procedure manual. Specific operations include data cleaning, feature extraction, and model inference. 【0607】 Step 3: 【0608】 The server transfers the generated procedure manuals to the information management module for review and approval. The input is a procedure manual draft from the AI ​​model, which is then validated for safety and efficiency. The output is the final, revised, and approved work procedure manual. Specific actions include collecting feedback from the review system and editing the procedure manuals as needed. 【0609】 Step 4: 【0610】 The terminal receives the final work instructions and presents them to the field worker visually or audibly. Input is a digital file of the final instructions, and output is a real-time display of instructions to the field worker. Specific operations include step-by-step display on the screen and playback of instructions through the audio speaker. 【0611】 Step 5: 【0612】 The user (worker) performs the task according to the instructions displayed on the terminal. After completing the task, feedback is entered via the terminal. The input consists of opinions and improvement suggestions from the worker, and the output is digital data sent to the server as a feedback record. Specifically, the process involves entering and submitting feedback in a feedback form. 【0613】 Step 6: 【0614】 The server collects feedback submitted by workers and stores it in a database for use in generating future work procedure manuals. The input is feedback data from each worker, and the output is a history record of improved versions that are reflected in past work data. Specifically, the server writes to the database and integrates it into an analysis dataset. 【0615】 (Application Example 1) 【0616】 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". 【0617】 Traditional maintenance procedures have involved manual creation of procedure manuals, leading to challenges in efficiency and safety. Furthermore, insufficient instructions to field workers resulted in inconsistent work quality. Additionally, feedback after maintenance work is not adequately utilized for subsequent work, highlighting areas for improvement. 【0618】 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. 【0619】 In this invention, the server includes an information gathering means for collecting past maintenance work information and using it to create necessary instruction manuals; an information processing means for automatically generating maintenance manuals using generative artificial intelligence based on the collected information; and a work support means via a terminal equipped with a user interface that presents the generated maintenance manuals to on-site workers visually and audibly and provides instructions in real time. This makes it possible to improve the efficiency and safety of maintenance work. 【0620】 "Maintenance work" refers to inspections, repairs, adjustments, and other tasks performed periodically or as needed to maintain the normal operation of equipment and systems. 【0621】 "Information gathering means" refers to methods and devices for efficiently collecting data and information related to past maintenance work. 【0622】 "Generative artificial intelligence" is an artificial intelligence technology that can automatically create new information and procedures based on given data. 【0623】 "Information processing means" refers to technologies or devices for analyzing collected data, extracting necessary information, and processing it. 【0624】 A "user interface" is a means or design for exchanging information bidirectionally between a terminal and a field worker. 【0625】 A "feedback processing method" is a method of collecting and analyzing feedback from workers after maintenance work and using it to help generate future procedure manuals. 【0626】 This invention is a system that supports efficient and safe maintenance work in factories. The system is composed of three key elements: a server, terminals, and users. 【0627】 The server is equipped with information gathering mechanisms to collect past maintenance data. This data is primarily stored in a database, which the server accesses to retrieve the necessary information. The retrieved data is analyzed using information processing mechanisms that utilize generative artificial intelligence, and efficient and safe maintenance procedures are automatically generated. For example, the OpenAI API is used as the generative artificial intelligence in this process. 【0628】 The terminal plays a role in supporting on-site maintenance work. The terminal presents maintenance procedures, generated through a user interface, to the worker visually and audibly. By wearing AR smart glasses, the worker can visually review the procedures and receive voice instructions. This allows for efficient work without the need for manual intervention. 【0629】 After completing maintenance work, the user (worker) enters feedback into a terminal. This feedback is collected by the server and used to generate the next procedure manual through a feedback processing system. For example, if the worker reports improvements such as "the specified part was not actually necessary," a more optimized procedure manual will be generated next time. 【0630】 As an example of a prompt, by giving the generative artificial intelligence the instruction, "We have maintenance data for a robotic arm. Based on this, please create an efficient and safe maintenance procedure manual in Japanese," an appropriate procedure manual will be generated. 【0631】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0632】 Step 1: 【0633】 The server accesses the maintenance work database to collect past work information. The input is past work records stored in the database, and the output is a set of data related to a specific maintenance task. At this time, relevant information is extracted using efficient data collection methods and converted into a format suitable for analysis. 【0634】 Step 2: 【0635】 The server passes the collected data to a generative artificial intelligence for analysis. The input is the maintenance work data obtained in step 1, and the output is a draft of a maintenance procedure manual that emphasizes efficient and safe procedures. The generative AI model is used to analyze the data and generate the optimal procedure. 【0636】 Step 3: 【0637】 The server sends the generated maintenance procedure manual to the terminal. The input is the draft of the procedure manual created in step 2, and the output is the maintenance procedure manual converted into a format viewable on the terminal. At this point, formatting adjustments are made, enabling visual and audio instructions on the terminal. 【0638】 Step 4: 【0639】 The terminal presents maintenance procedures to field workers visually and audibly. Input is the procedures received from the server, and output is real-time work instructions for the worker. Smart glasses and voice systems are used to support workers in performing their tasks efficiently. 【0640】 Step 5: 【0641】 The user (worker) enters feedback into the terminal after completing the task. The input consists of suggestions for improvement and comments from the worker, and the output is feedback data used to generate the next procedure manual. The terminal collects the feedback and sends it to the server. 【0642】 Step 6: 【0643】 The server analyzes the feedback received from users and uses it to improve the next procedure manual generation. The input is the feedback data collected in step 5, and the output is guidance for the next improved procedure manual generation. This includes a process of incorporating the feedback into the generation AI model. 【0644】 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. 【0645】 This invention provides a system for network centers and communication exchanges that combines generative artificial intelligence and an emotion engine to automatically generate work procedures and provide work support. This system not only automatically generates optimal procedures from work history data, but also recognizes the user's emotions in real time and adjusts the work environment to further support more efficient and comfortable work. 【0646】 Specifically, the server accesses a work history database and collects historical data related to a particular task. An analysis module then uses generative artificial intelligence to automatically generate optimized work procedures based on this data. At this stage, the server specifically identifies past successes and constructs procedures that combine efficiency and safety. 【0647】 The generated procedure manuals are presented to field workers via a terminal. The terminal not only provides real-time instructions via voice and text, but its built-in emotion engine analyzes the user's facial expressions and voice to determine their emotional state. This emotion recognition technology detects the user's stress level and anxiety, enabling appropriate work support. 【0648】 For example, if the device detects tension from the user's facial expressions, it will pause the work process and provide advice to help the user relax or a direct link to troubleshooting. The device will also adjust the pace of the process based on the user's emotional state, supporting them in a way that is easy to understand. 【0649】 After completing a task, the user enters feedback via their terminal, and this information is sent to the server. The server collects this feedback data and uses it to generate future work instructions and adjust the emotion engine, thereby continuously improving the system. 【0650】 In this way, this system eliminates the cumbersome process of manually creating procedure manuals and provides standardized, high-quality work instructions, thereby reducing the burden on on-site workers and improving overall safety and productivity. Furthermore, by taking into account the emotional state of the user, it reduces worker stress and creates a comfortable and efficient work environment. 【0651】 The following describes the processing flow. 【0652】 Step 1: 【0653】 The server accesses the work history database and extracts past work data related to a specific task. The server then filters this data to collect the most relevant information based on the nature and conditions of the task. 【0654】 Step 2: 【0655】 The server analyzes the collected data and uses generative artificial intelligence to automatically generate optimal work procedures. By analyzing past success stories and constructing efficient and safe procedures, the server incorporates best practices for the work. 【0656】 Step 3: 【0657】 The server sends the generated work procedure manual to the terminal. The terminal displays this manual in its user interface, allowing the field worker to review the procedure before starting work. 【0658】 Step 4: 【0659】 The terminal provides the worker with voice and text instructions in real time while they are working. The terminal's emotion engine analyzes the user's facial expressions and tone of voice in real time to understand their emotional state. For example, if the terminal detects that the user is stressed, it will slow down the pace of the work and provide calmer instructions. 【0660】 Step 5: 【0661】 The user, acting as the worker, follows instructions received from the terminal as the task progresses and completes the procedures. The terminal periodically checks the user's emotional state and adjusts the support provided as needed. 【0662】 Step 6: 【0663】 After completing a task, users provide feedback on the process and interface via their terminal. This includes suggestions for improving the procedure manual and direct emotional responses. 【0664】 Step 7: 【0665】 The server collects feedback data from terminals and uses it to generate future work instructions and improve the emotion engine. This allows the server to continuously improve the system and provide a better user experience. 【0666】 (Example 2) 【0667】 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". 【0668】 In on-site work, the burden on workers when creating instruction information manually and the stress caused by work guidance that does not take into account their emotional state are problematic. Conventional systems fail to provide sufficient support to improve work safety and efficiency, and lack adjustment functions that take into account the emotional state of workers. As a result, it is difficult to improve work productivity and reduce the burden on workers. 【0669】 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. 【0670】 In this invention, the server includes a numerical data collection means for collecting past work values ​​from a work history database and using them to create necessary instruction information, a numerical data processing means for automatically generating work instruction information using generative artificial intelligence based on the collected numerical values, and a work support means via a display device that presents the generated work instruction information to on-site workers and provides instructions in real time. This reduces the manual workload of on-site workers, improves work efficiency and safety, and enables stress reduction by utilizing emotion recognition. 【0671】 A "work history database" is an information aggregation system that stores numerical data related to all work performed in the past. 【0672】 A "numerical data collection device" is a device that has the function of acquiring numerical data from a database in order to collect necessary work information. 【0673】 "Generative artificial intelligence" is a technology that automatically generates optimal work instruction information based on collected data. 【0674】 A "numerical processing device" is a device that uses collected numerical data and generative artificial intelligence to generate work instruction information. 【0675】 "Instructional information" refers to information that includes specific procedures and instructions that workers should follow when performing their tasks. 【0676】 A "display device" is a device used to present work instruction information to on-site workers visually or audibly. 【0677】 A "human-machine interface" is an interactive user interface that enables the exchange of instruction information between a worker and a machine. 【0678】 "Emotion recognition means" refers to a device that includes technology for analyzing a worker's facial expressions and voice to determine their emotional state. 【0679】 A "feedback processing device" is a device that has the function of collecting numerical feedback from workers and reflecting it in the generation of the next work instruction information. 【0680】 This invention is a system that utilizes a work history database and generative artificial intelligence to provide work support that takes into account the worker's emotions. The server collects past work data by accessing the work history database. Specifically, it uses an SQL server to query the necessary values ​​and retrieve the data. The collected values ​​are passed by the server to a generative artificial intelligence model using Python, which generates optimal work instruction information. This model identifies past success patterns and creates instructions that take safety and efficiency into consideration. 【0681】 The generated work instructions are presented to the field worker via a terminal. The terminal consists of a display device equipped with a speech synthesis system and a display, providing instructions in both voice and text formats. The terminal also has emotion recognition capabilities, using a camera and microphone to determine the user's emotional state from their facial expressions and voice. For example, if the user is feeling frustrated, the terminal will slow down the work speed and provide supplementary explanations. 【0682】 After completing a task, the user provides feedback through the terminal. The terminal sends this feedback data to the server, which uses it to improve the generation of future instructions and the emotion recognition function. 【0683】 As a concrete example, the prompt is as follows: "Create example relaxation advice to provide when the user is feeling anxious." By prompting the AI ​​model with such a prompt, it becomes possible to provide appropriate support according to the user's emotional state. 【0684】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0685】 Step 1: 【0686】 The server accesses the work history database to collect past work metrics. Specifically, the server executes SQL queries to filter data related to a particular work. The input is the specific conditions of the work (e.g., success rate, duration), and the output is a set of work history metrics that match these conditions. These metrics serve as the basis for the next analysis step. 【0687】 Step 2: 【0688】 The server inputs the collected data into an AI model to generate optimal work instruction information. The server uses Python to activate the AI ​​model, identifying past success patterns and using an algorithm to construct the optimal procedure. The input is the work history data obtained in step 1, and the output is automatically generated work instruction information. This instruction information includes details that enhance safety and efficiency. 【0689】 Step 3: 【0690】 The terminal presents the generated work instruction information to the field worker via a display device. Specifically, the terminal uses speech synthesis software to output text instructions as voice. It also visually displays the instruction content on the display. The input is the work instruction information generated in step 2, and the output is real-time instructions to the user via both visual and auditory means. 【0691】 Step 4: 【0692】 The device analyzes the user's emotional state using built-in emotion recognition technology. Specifically, it determines emotions by capturing the user's facial expressions with a camera and analyzing their voice tone with a microphone. The input is the user's facial expressions and voice, captured in real time, and the output is an evaluation of the user's emotional state (e.g., tension, stress). Based on this, the work speed is adjusted and support content is suggested. 【0693】 Step 5: 【0694】 After completing a task, users provide feedback through a terminal. The terminal offers a dedicated form where users can enter comments and ratings. The input is the user's feedback value, and the output is organized feedback information. This information is sent to the server and used to generate future instructions and improve the system. 【0695】 (Application Example 2) 【0696】 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". 【0697】 Conventional work procedure creation systems lack sufficient means to reduce the emotional burden on workers while prioritizing their efficiency and safety. This leads to problems such as work errors and slower work speeds due to worker stress and anxiety. Furthermore, the lack of dynamic procedure adjustments to accommodate workers' emotional states makes it difficult to provide optimal work content for individual workers. 【0698】 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. 【0699】 In this invention, the server includes data collection means for collecting past work data from a work history database and using it to create necessary instruction manuals; data processing means for automatically generating work procedure manuals using generative artificial intelligence based on the collected data; and emotion recognition and work environment adaptation means for analyzing the worker's facial expressions and voice to determine their emotional state and adapting the work environment based on the determined emotional state. This enables dynamic procedure adjustments in accordance with the emotional state of on-site workers, making it possible to reduce emotional burden while providing an efficient and safe work environment. 【0700】 A "work history database" is an information repository that stores information about work performed in the past. 【0701】 "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates the optimal work procedure based on collected data. 【0702】 A "work procedure manual" is a document that contains a series of instructions for performing a specific task efficiently and safely. 【0703】 A "terminal" is a device used to present information and transmit instructions to on-site workers. 【0704】 "Emotion recognition and work environment adaptation means" refers to a system that analyzes the facial expressions and voice of workers and adjusts the work environment according to their emotional state. 【0705】 A "feedback processing mechanism" is a system that analyzes opinions and feedback collected from workers after their work is completed and uses this information to improve future procedures and systems. 【0706】 An "algorithm" is a set of procedures or calculation steps for solving a problem. 【0707】 A "user interface" refers to the interactive operating screens and input devices that operators use when operating machinery or equipment. 【0708】 The system for realizing this invention mainly consists of a server, a terminal, and a user. The server collects historical data from a work history database and automatically generates work instructions using generative artificial intelligence. These instructions are based on the user's past successful work examples and are optimized for safety and efficiency. The generated instructions are presented to the field worker via the terminal, and real-time instructions are provided via voice and text. 【0709】 The device incorporates emotion recognition and work environment adaptation mechanisms, analyzing the user's facial expressions and voice to determine their emotional state. Based on this information, the device is designed to dynamically adjust the work environment and reduce stress. For example, if it determines that the worker is stressed, it will display advice to help them relax and, if necessary, pause the work. 【0710】 After completing a task, users enter feedback via their terminal. This feedback is sent to the server and used to generate future work instructions and adjust the sentiment engine. This ensures continuous improvement of the system. 【0711】 As a concrete example, in the assembly process of a certain product, if a user experiences emotional stress, the terminal could temporarily suspend the work procedure and display instructions such as, "Take a deep breath before continuing. Please refer to the support documentation for details." In this way, the system achieves both smooth workflow and reduction of the user's emotional burden. 【0712】 Examples of prompts used in the generating AI model include: "Generate the optimal assembly procedure based on the work history data. Consider the user's stress level, adjust the pace if necessary, and incorporate real-time feedback." This makes it possible to provide the user with the best possible work experience. 【0713】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0714】 Step 1: 【0715】 The server collects past work data from a work history database. The input is the work history stored in the database, and the output is structured work data for input into generative artificial intelligence. This data collection provides diverse information, including past successes and failures. 【0716】 Step 2: 【0717】 The server automatically generates work procedures using generative artificial intelligence based on the collected data. The input is structured work data, and the output is an optimized work procedure document. The generative AI model's algorithm is applied to formulate efficient and safe procedures. 【0718】 Step 3: 【0719】 The terminal displays generated work instructions to field workers and provides real-time instructions via voice and text. The input is the work instructions received from the server, and the output is clear instructions for the workers. The terminal communicates instructions clearly through its user interface. 【0720】 Step 4: 【0721】 The device analyzes the user's facial expressions and voice to determine their emotional state. Input is visual and audio data acquired through the camera and microphone, and output is a determination of the user's emotional state (e.g., stress, relaxation). Emotion recognition technology is used in the analysis to assess the user's current emotional health. 【0722】 Step 5: 【0723】 The device dynamically adapts the work environment based on the user's emotional state. Input is the result of the emotional state assessment, and output includes adjustments to work procedures and advice to promote relaxation. For example, if the device determines that the user is stressed, it will display a message such as "Let's take a short break." 【0724】 Step 6: 【0725】 After completing a task, the user enters feedback into the terminal. The input consists of the worker's subjective impressions and experiences, while the output is feedback data for improvement. This feedback is then sent back to the server and used to generate the next work procedure manual and adjust the system. This ensures continuous system improvement. 【0726】 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. 【0727】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0728】 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. 【0729】 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. 【0730】 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. 【0731】 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. 【0732】 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. 【0733】 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. 【0734】 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." 【0735】 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. 【0736】 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. 【0737】 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. 【0738】 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. 【0739】 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. 【0740】 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. 【0741】 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. 【0742】 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. 【0743】 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. 【0744】 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. 【0745】 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. 【0746】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0747】 The following is further disclosed regarding the embodiments described above. 【0748】 (Claim 1) 【0749】 A data collection method that collects past work data from a work history database and uses it to create necessary instruction manuals, 【0750】 A data processing means that automatically generates work procedure manuals using generative artificial intelligence based on collected data, 【0751】 A work support system that presents generated work procedure manuals to on-site workers and provides real-time instructions via a terminal, 【0752】 A feedback processing mechanism that collects feedback from on-site workers after the work is completed and incorporates it into the generation of the next work procedure manual, 【0753】 A system that includes this. 【0754】 (Claim 2) 【0755】 The system according to claim 1, wherein the generative artificial intelligence has an algorithm that identifies past successful work examples and optimizes the safety and efficiency of the procedure manual based on them. 【0756】 (Claim 3) 【0757】 The system according to claim 1, wherein the terminal provides both voice and text instructions and has a user interface to support manual operation by field workers. 【0758】 "Example 1" 【0759】 (Claim 1) 【0760】 Information gathering means for collecting past work information from a work history information recording device and using it to create necessary work instructions, 【0761】 An information processing means that automatically generates work procedure manuals using a generative information processing device based on collected information, 【0762】 A work support means that presents the generated work procedure manual to the on-site worker and provides instructions in real time via an operating device, 【0763】 A data feedback mechanism to collect opinions from on-site workers after the work is completed and to incorporate them into the generation of the next work procedure manual, 【0764】 A system that includes this. 【0765】 (Claim 2) 【0766】 The system according to claim 1, wherein the generative information processing device has a processing method for identifying past successful work examples and optimizing the safety and efficiency of the procedure manual based on them. 【0767】 (Claim 3) 【0768】 The system according to claim 1, wherein the operating device provides both voice and written instructions and has a user interface for supporting manual operation by a field worker. 【0769】 "Application Example 1" 【0770】 (Claim 1) 【0771】 Information gathering means for collecting past maintenance work information and using it to create necessary instruction manuals, 【0772】 An information processing means that automatically generates maintenance procedure manuals using generative artificial intelligence based on collected information, 【0773】 A work support means via a terminal equipped with a user interface that presents generated maintenance procedures to on-site workers visually and audibly, and provides instructions in real time, 【0774】 A feedback processing mechanism that collects feedback from on-site workers after the completion of maintenance work and incorporates it into the generation of the next maintenance procedure manual, 【0775】 A system that includes this. 【0776】 (Claim 2) 【0777】 The system according to claim 1, wherein the generative artificial intelligence has an algorithm that identifies past successful maintenance examples and optimizes the safety and efficiency of the procedure manual based on them. 【0778】 (Claim 3) 【0779】 The system according to claim 1, wherein the terminal is equipped with a visual display device worn by a field worker and visually presents the procedure for maintenance work, and the terminal is also capable of voice instructions and has a user interface to support manual operation by the field worker. 【0780】 "Example 2 of combining an emotion engine" 【0781】 (Claim 1) 【0782】 A means for collecting numerical data to be used to create necessary instruction information by collecting past work values ​​from a work history database, 【0783】 A numerical processing means that automatically generates work instruction information using generative artificial intelligence based on collected numerical data, 【0784】 A work support means that presents generated work instruction information to on-site workers and provides instructions in real time via a display device, 【0785】 An emotion recognition tool for analyzing the emotional state of on-site workers after the completion of work, 【0786】 A feedback processing means for collecting user feedback and reflecting it in the generation of the next work instruction information, 【0787】 A system that includes this. 【0788】 (Claim 2) 【0789】 The system according to claim 1, wherein the generative artificial intelligence has a computing device that identifies past successful work examples and optimizes the safety and efficiency of instruction information based on them. 【0790】 (Claim 3) 【0791】 The system according to claim 1, wherein the display device provides both voice and text instructions, has a human-machine interface to assist in the operation of a field worker, and automatically adjusts the progress speed based on emotional state. 【0792】 "Application example 2 when combining with an emotional engine" 【0793】 (Claim 1) 【0794】 A data collection method that collects past work data from a work history database and uses it to create necessary instruction manuals, 【0795】 A data processing means that automatically generates work procedure manuals using generative artificial intelligence based on collected data, 【0796】 A work support system that presents generated work procedure manuals to on-site workers and provides real-time instructions via a terminal, 【0797】 An emotion recognition and work environment adaptation means that analyzes the worker's facial expressions and voice to determine their emotional state, and adapts the work environment based on the determined emotional state, 【0798】 A feedback processing mechanism that collects feedback from on-site workers after the work is completed and incorporates it into the generation of the next work procedure manual, 【0799】 A system that includes this. 【0800】 (Claim 2) 【0801】 The system according to claim 1, wherein the generative artificial intelligence has an algorithm that identifies past successful work examples and optimizes the safety and efficiency of the procedure manual based on them, and further has a function to dynamically adjust the work procedure according to the user's emotional state. 【0802】 (Claim 3) 【0803】 The system according to claim 1, wherein the terminal provides both voice and text instructions, has a user interface to support manual operation by field workers, and has an interactive function that adjusts the instruction speed and content based on the user's emotional state. [Explanation of symbols] 【0804】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data collection method that collects past work data from a work history database and uses it to create necessary instruction manuals, A data processing means that automatically generates work procedure manuals using generative artificial intelligence based on collected data, A work support system that presents generated work procedure manuals to on-site workers and provides real-time instructions via a terminal, A feedback processing mechanism that collects feedback from on-site workers after the work is completed and incorporates it into the generation of the next work procedure manual, A system that includes this. [Claim 2] The system according to claim 1, wherein the generative artificial intelligence has an algorithm that identifies past successful work examples and optimizes the safety and efficiency of the procedure manual based on them. [Claim 3] The system according to claim 1, wherein the terminal provides both voice and text instructions and has a user interface to support manual operation by field workers.

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