System

Abstract

An object of a system according to an embodiment is to reduce a business commitment cost in a system operation.SOLUTION: A system includes an automatization tool introduction part, a business process review part, a skill-up training execution part, an outsourcing review part, a remote work promotion part, a data analysis use part, a communication tool introduction part, and a continuous improvement activity part. The automation tool introduction unit introduces an automation tool. The business process review unit reviews the business process. The upskilling training execution part executes upskilling training. The outsourcing review unit reviews outsourcing. The remote work propulsion unit propels the remote work. The data analysis and utilization unit utilizes data analysis. The communication tool introduction unit introduces a communication tool. The continuous improvement activity unit performs a continuous improvement activity.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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 Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not sufficiently reduced outsourcing costs in system operation, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce outsourcing costs in system operation. [Means for solving the problem]

[0006] The system according to the embodiment includes an automation tool introduction unit, a business process review unit, a skill improvement training implementation unit, an outsourcing review unit, a remote work promotion unit, a data analysis utilization unit, a communication tool introduction unit, and a continuous improvement activity unit. The automation tool introduction unit introduces automation tools. The business process review unit reviews business processes. The skill improvement training implementation unit implements skill improvement training. The outsourcing review unit reviews outsourcing. The remote work promotion unit promotes remote work. The data analysis utilization unit utilizes data analysis. The communication tool introduction unit introduces communication tools. The continuous improvement activity unit carries out continuous improvement activities. [Effects of the Invention]

[0007] The system according to the embodiment can reduce outsourcing costs for system operation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to an embodiment of the present invention is a multifaceted improvement method for reducing regular OPEX (outsourcing costs) in the department that operates the system, with the goal of contributing to the company's profits. This system achieves efficient and effective operations by implementing the following eight improvements while maintaining the current work quality. As a result, the system achieves efficient and effective operations and contributes to the company's profits.

[0029] The system according to the embodiment includes an automation tool, business process review, skill development training, outsourcing review, remote work promotion, data analysis, communication tools, and continuous improvement activities. The automation tool automates part of the system operation. For example, by offloading regular backup tasks and log monitoring to the automation tool, human resources are reduced. The business process review involves a detailed analysis of current business processes to eliminate unnecessary procedures and redundant work. For example, data is centrally managed to eliminate the need for multiple departments to enter the same data. The skill development training involves regular training to improve employee skills. For example, training to learn how to use new systems and tools. The outsourcing review involves evaluating the cost-effectiveness of outsourced work and reviewing it as necessary. For example, costs are reduced by in-house outsourcing of high-cost work. The remote work promotion system reduces office operating costs by promoting remote work. For example, commuting expenses and office utility costs can be reduced. The data analysis system utilizes data analysis to improve business efficiency and detect problems early. For example, system operation status is monitored in real time to quickly respond to abnormalities. Regarding communication tools, appropriate tools are introduced to ensure efficient communication. For example, chat tools and video conferencing systems are used to quickly share information. Regarding continuous improvement activities, operations are reviewed periodically to promote continuous improvement activities. For example, regular meetings are held to share and implement improvements. This allows the system to be operated efficiently and effectively, contributing to the company's profits.

[0030] Automation tools use generative AI to propose and execute optimal backup schedules in real time. For example, automation tools use generative AI to analyze the system's operating status and the importance of data, and automatically propose optimal backup schedules. For example, backups can be performed outside of business hours or according to the frequency of data updates. This allows the stable operation of the system to be maintained by proposing and executing the optimal backup schedule.

[0031] Automation tools add the ability to automatically repair systems as soon as they detect anomalies, minimizing downtime. For example, automation tools can detect system anomalies in real time and immediately perform automatic repairs. For example, when a server overload is detected, resources can be automatically reallocated. This allows for system anomaly detection and automatic repair to minimize downtime.

[0032] Automation tools can be applied not only to system operations but also to other business processes, improving efficiency across the entire company. For example, automation tools can be applied to financial management to automate periodic accounting and budget management. For example, data aggregation and report creation for monthly financial statements can be automated. In this way, applying automation tools to other business processes can improve efficiency across the entire company.

[0033] An automation tool can add a voice recognition function and provide an interface that allows system operation by voice commands. An automation tool can add a voice recognition function and provide an interface that allows system operation by voice commands. For example, a backup operation can be instructed by voice. By adding the voice recognition function, an interface that allows system operation by voice commands can be provided.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The system may further include an energy management unit for optimizing energy consumption. For example, the system may adjust power consumption according to the system's operating status. It may also perform backup operations during non-operating hours to reduce power consumption during peak hours. It may also analyze energy consumption data and suggest efficient energy usage methods. By optimizing energy consumption, it is possible to reduce costs and protect the environment.

[0036] The system may further include a security monitoring unit for strengthening security. For example, unauthorized access to the system can be detected in real time and countermeasures can be taken immediately. Regular security scans can also be performed to detect vulnerabilities early. Furthermore, the history of security incidents can be analyzed and future risks can be predicted. This strengthens security and improves the safety of the system.

[0037] The system may further include a health management unit that monitors the user's health condition. For example, the system can measure the user's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. It can also conduct regular health checks and track changes in the user's health condition. It can also provide health advice and support the user in maintaining their health. By monitoring the user's health condition, health risks can be reduced.

[0038] The system also includes an environmental monitoring component that can optimize the office environment. For example, it can monitor temperature, humidity, and CO2 concentration in real time and automatically adjust the air conditioning system to maintain a comfortable environment. It can also adjust lighting brightness to reduce energy consumption. It can also monitor noise levels and take soundproofing measures as needed. This allows for an optimized office environment, improving employee comfort and productivity.

[0039] The system also includes a project management section that allows for real-time monitoring of project progress. For example, it can visualize task progress and issue alerts if delays occur. It can also optimize resource allocation and support efficient project management. It can also evaluate project results and suggest areas for improvement for the next project. This makes project management more efficient and increases the success rate.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: Introduce automation tools to automate some aspects of system operations. For example, by using automation tools to perform regular backups and log monitoring, you can reduce the amount of human resources required. Step 2: Review business processes, analyze current processes in detail, and eliminate unnecessary steps and redundant work. For example, centralize data management to eliminate the need for multiple departments to enter the same data. Step 3: Implement upskilling training and regularly hold training sessions to improve employee skills, such as training on how to use new systems and tools. Step 4: Review outsourcing, evaluate the cost-effectiveness of outsourced work, and revise as necessary. For example, reduce costs by bringing high-cost work in-house. Step 5: Promote remote work and reduce office operating costs by promoting remote work. For example, you can reduce commuting costs and office utility costs. Step 6: Use data analysis to improve business efficiency and detect problems early. For example, monitor the system's operating status in real time and respond quickly if an abnormality occurs. Step 7: Introduce appropriate communication tools to ensure efficient communication. For example, use chat tools or video conferencing systems to quickly share information. Step 8: Implement continuous improvement activities, periodically review operations, and promote continuous improvement activities. For example, hold regular meetings to share and implement improvements.

[0042] (Example 2) The system according to an embodiment of the present invention is a multifaceted improvement method for reducing regular OPEX (outsourcing costs) in the department that operates the system, with the goal of contributing to the company's profits. This system achieves efficient and effective operations by implementing the following eight improvements while maintaining the current work quality. As a result, the system achieves efficient and effective operations and contributes to the company's profits.

[0043] The system according to the embodiment includes an automation tool, business process review, skill development training, outsourcing review, remote work promotion, data analysis, communication tools, and continuous improvement activities. The automation tool automates part of the system operation. For example, by offloading regular backup tasks and log monitoring to the automation tool, human resources are reduced. The business process review involves a detailed analysis of current business processes to eliminate unnecessary procedures and redundant work. For example, data is centrally managed to eliminate the need for multiple departments to enter the same data. The skill development training involves regular training to improve employee skills. For example, training to learn how to use new systems and tools. The outsourcing review involves evaluating the cost-effectiveness of outsourced work and reviewing it as necessary. For example, costs are reduced by in-house outsourcing of high-cost work. The remote work promotion system reduces office operating costs by promoting remote work. For example, commuting expenses and office utility costs can be reduced. The data analysis system utilizes data analysis to improve business efficiency and detect problems early. For example, system operation status is monitored in real time to quickly respond to abnormalities. Regarding communication tools, appropriate tools are introduced to ensure efficient communication. For example, chat tools and video conferencing systems are used to quickly share information. Regarding continuous improvement activities, operations are reviewed periodically to promote continuous improvement activities. For example, regular meetings are held to share and implement improvements. This allows the system to be operated efficiently and effectively, contributing to the company's profits.

[0044] Automation tools use generative AI to propose and execute optimal backup schedules in real time. For example, automation tools use generative AI to analyze the system's operating status and the importance of data, and automatically propose optimal backup schedules. For example, backups can be performed outside of business hours or according to the frequency of data updates. This allows the stable operation of the system to be maintained by proposing and executing the optimal backup schedule.

[0045] Automation tools add the ability to automatically repair systems as soon as they detect anomalies, minimizing downtime. For example, automation tools can detect system anomalies in real time and immediately perform automatic repairs. For example, when a server overload is detected, resources can be automatically reallocated. This allows for system anomaly detection and automatic repair to minimize downtime.

[0046] The automation tool uses an emotion estimation function to monitor the stress level of the operations personnel, and can take over the work when stress increases. The automation tool, for example, uses an emotion estimation function to monitor the stress level of the operations personnel in real time. For example, it analyzes facial expressions and voice tone to calculate a stress score. This allows the automation tool to monitor the stress level of the operations personnel, and when stress increases, the automation tool can take over the work, thereby reducing the burden on the operations personnel.

[0047] Automation tools can be applied not only to system operations but also to other business processes, improving efficiency across the entire company. For example, automation tools can be applied to financial management to automate periodic accounting and budget management. For example, data aggregation and report creation for monthly financial statements can be automated. In this way, applying automation tools to other business processes can improve efficiency across the entire company.

[0048] An automation tool can add a voice recognition function and provide an interface that allows system operation by voice commands. An automation tool can add a voice recognition function and provide an interface that allows system operation by voice commands. For example, a backup operation can be instructed by voice. By adding the voice recognition function, an interface that allows system operation by voice commands can be provided.

[0049] The automation tool uses an emotion estimation function to customize the interface according to the user's emotion, thereby improving operability. The automation tool, for example, uses the emotion estimation function to customize the interface according to the user's emotion. For example, a simple interface is provided when the user is under high stress. In this way, the emotion estimation function can be used to customize the interface according to the user's emotion, thereby improving operability.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The system may further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide advice on how to relax. If the user is satisfied, the feedback unit can make suggestions on how to maintain that state. If the user is confused, the feedback unit can present a specific solution. Thus, by providing appropriate feedback according to the user's emotions, it is possible to improve the user's satisfaction.

[0052] The system may further include a priority adjustment unit that estimates the user's emotions and automatically adjusts task priorities based on the estimated emotions. For example, if the user is feeling stressed, the priority adjustment unit can prioritize simple tasks. Also, if the user is concentrating, the priority adjustment unit can prioritize complex tasks. Furthermore, if the user is tired, the priority adjustment unit can recommend taking a break. In this way, work efficiency can be improved by adjusting task priorities according to the user's emotions.

[0053] The system may further include a learning support unit that estimates the user's emotions and provides appropriate learning content based on the estimated emotions. For example, if the user is interested, the learning support unit can provide content with advanced content. If the user is tired, the learning support unit can provide content with simple content. If the user is confused, the learning support unit can provide content using specific examples. This can improve learning effectiveness by providing appropriate learning content according to the user's emotions.

[0054] The system may further include a communication support unit that estimates the user's emotions and selects an appropriate communication method based on the estimated emotions. For example, if the user is feeling stressed, the communication support unit can select gentle language. If the user is excited, the communication support unit can select calm language. If the user is confused, the communication support unit can select a specific explanation. This allows the quality of communication to be improved by selecting an appropriate communication method according to the user's emotions.

[0055] The system may further include a reminder unit that estimates the user's emotions and provides appropriate reminders based on the estimated emotions. For example, if the user is feeling stressed, the reminder unit can prompt the user to take a break. If the user is concentrating, the reminder unit can remind the user to complete an important task. If the user is tired, the reminder unit can prompt the user to finish work early. This allows for improved work efficiency by providing appropriate reminders according to the user's emotions.

[0056] The system may further include an energy management unit for optimizing energy consumption. For example, the system may adjust power consumption according to the system's operating status. It may also perform backup operations during non-operating hours to reduce power consumption during peak hours. It may also analyze energy consumption data and suggest efficient energy usage methods. By optimizing energy consumption, it is possible to reduce costs and protect the environment.

[0057] The system may further include a security monitoring unit for strengthening security. For example, unauthorized access to the system can be detected in real time and countermeasures can be taken immediately. Regular security scans can also be performed to detect vulnerabilities early. Furthermore, the history of security incidents can be analyzed and future risks can be predicted. This strengthens security and improves the safety of the system.

[0058] The system may further include a health management unit that monitors the user's health condition. For example, the system can measure the user's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. It can also conduct regular health checks and track changes in the user's health condition. It can also provide health advice and support the user in maintaining their health. By monitoring the user's health condition, health risks can be reduced.

[0059] The system also includes an environmental monitoring component that can optimize the office environment. For example, it can monitor temperature, humidity, and CO2 concentration in real time and automatically adjust the air conditioning system to maintain a comfortable environment. It can also adjust lighting brightness to reduce energy consumption. It can also monitor noise levels and take soundproofing measures as needed. This allows for an optimized office environment, improving employee comfort and productivity.

[0060] The system also includes a project management section that allows for real-time monitoring of project progress. For example, it can visualize task progress and issue alerts if delays occur. It can also optimize resource allocation and support efficient project management. It can also evaluate project results and suggest areas for improvement for the next project. This makes project management more efficient and increases the success rate.

[0061] The processing flow of the second embodiment will be briefly explained below.

[0062] Step 1: Introduce automation tools to automate some aspects of system operations. For example, by using automation tools to perform regular backups and log monitoring, you can reduce the amount of human resources required. Step 2: Review business processes, analyze current processes in detail, and eliminate unnecessary steps and redundant work. For example, centralize data management to eliminate the need for multiple departments to enter the same data. Step 3: Implement upskilling training and regularly hold training sessions to improve employee skills, such as training on how to use new systems and tools. Step 4: Review outsourcing, evaluate the cost-effectiveness of outsourced work, and revise as necessary. For example, reduce costs by bringing high-cost work in-house. Step 5: Promote remote work and reduce office operating costs by promoting remote work. For example, you can reduce commuting costs and office utility costs. Step 6: Use data analysis to improve business efficiency and detect problems early. For example, monitor the system's operating status in real time and respond quickly if an abnormality occurs. Step 7: Introduce appropriate communication tools to ensure efficient communication. For example, use chat tools or video conferencing systems to quickly share information. Step 8: Implement continuous improvement activities, periodically review operations, and promote continuous improvement activities. For example, hold regular meetings to share and implement improvements.

[0063] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0064] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0065] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0066] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0067] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0068] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0069] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0070] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0071] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0072] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0073] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0074] 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.

[0075] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0076] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0077] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0078] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0079] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0080] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0081] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0082] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0083] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0084] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0085] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0086] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0087] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0088] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0089] 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.

[0090] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0093] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0096] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0104] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0113] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0114] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0115] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0116] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0117] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0118] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0119] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0120] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0121] 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.

[0122] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0123] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0124] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0125] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0126] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0127] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0128] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0129] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0130] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Introducing automation tools Reviewing business processes, Conducting skill-up training Reconsider outsourcing Promoting remote work, Utilizing data analysis, Introducing communication tools Implement continuous improvement activities A system characterized by:

2. The automation tool includes: Adds a function that automatically repairs the system upon detecting an abnormality, minimizing downtime.

2. The system of claim 1.

3. The automation tool includes: Applying this not only to system operations but also to other business processes to improve efficiency across the company 2. The system of claim 1.

4. The automation tool includes: Adding voice recognition functionality and providing an interface that allows system operation via voice commands 2. The system of claim 1.

5. The automation tool includes: Monitor the stress levels of your operations staff and use automated tools to take over when stress levels rise 2. The system of claim 1.

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