System

Abstract

An object of the system according to the embodiment is to overcome the barriers of location, working time, and communication, and to efficiently perform work 24 hours a day, 365 days a year.SOLUTION: A system according to an embodiment includes a high-security spot, an avatar, and a AI translating system. The high-security spots are arranged. The avatar works in the high security spot. The AI translating system is used by an operator who operates an avatar.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 technology has faced challenges in overcoming the barriers of location, working hours, and communication in a 24 / 7 working environment.

[0005] The system of the embodiment aims to overcome the barriers of location, working hours, and communication, and to carry out work efficiently 24 hours a day, 365 days a year. [Means for solving the problem]

[0006] A system according to an embodiment includes a high-security spot, an avatar, and an AI translation system. The high-security spot is placed. The avatar performs work at the high-security spot. The AI ​​translation system is used by an operator who operates the avatar. [Effects of the Invention]

[0007] The system according to the embodiment overcomes the barriers of location, working hours, and communication, enabling efficient work to be carried out 24 hours a day, 365 days a year. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 business support system according to the embodiment of the present invention is a system that sets up high-security spots and uses avatars and an AI translation system to rotate business operations 24 hours a day, 365 days a year around the world. This allows the business support system to rotate business operations 24 hours a day, 365 days a year around the world.

[0029] A business support system according to an embodiment includes a high-security spot, an avatar, and an AI translation system. The high-security spot is installed, for example, in Japan or around the world and operates 24 hours a day, 365 days a year. For example, an American operator can work during nighttime hours in Japan, allowing Japanese operators to avoid night shifts. The avatar is, for example, a virtual entity that can be operated by multiple operators in rotation. For example, people with disabilities who cannot leave their homes, people who cannot work night shifts due to health concerns, or people who cannot come to the office due to childcare or elderly care responsibilities can perform their work through the avatar. The AI ​​translation system, for example, performs real-time translation to facilitate communication between operators who speak different languages. For example, when a Japanese-speaking operator and an English-speaking operator perform the same task, the AI ​​performs real-time translation, ensuring smooth communication. This allows the business support system according to an embodiment to rotate operations 24 hours a day, 365 days a year around the world.

[0030] High-security spots can introduce biometric authentication to strengthen identity verification of operators. For example, high-security spots can introduce a fingerprint authentication system, scanning the fingerprint of an operator when they begin work to verify their identity. This prevents unauthorized access. High-security spots can also use facial recognition technology to scan the face of an operator to verify their identity. This improves the security level. High-security spots can also introduce an iris authentication system, scanning the iris of an operator when they begin work to verify their identity. This achieves even higher security. This strengthens identity verification of operators and prevents unauthorized access.

[0031] High-security spots are recreated using virtual reality technology and can be accessed remotely by operators. For example, high-security spots recreate a work environment using VR technology, allowing operators to work remotely from home using a VR headset. High-security spots also use VR technology to create virtual offices that operators can access and work remotely. High-security spots also recreate a work environment using VR, providing operators with the same operational experience as in an actual office when accessing the environment remotely. This allows operators to work remotely.

[0032] High-security spots can be deployed in different industries to support operations. For example, high-security spots can be deployed in the financial industry to support operations 24 / 7. For example, they can be installed in bank data centers to enhance security. High-security spots can also be deployed in the medical industry to support operations 24 / 7. For example, security spots can be installed to monitor hospital IT systems. High-security spots can also be deployed in the manufacturing industry to support operations 24 / 7. For example, security spots can be installed to monitor factory production lines. This allows high-security spots to be deployed in different industries to support operations 24 / 7.

[0033] High-security spots can reduce their environmental impact by introducing energy-efficient equipment. For example, high-security spots can introduce energy-efficient LED lighting to reduce power consumption. High-security spots can also introduce solar power generation systems to use renewable energy to reduce their environmental impact. High-security spots can also introduce energy-efficient air conditioning systems to provide a comfortable working environment while reducing power consumption. This allows for the introduction of energy-efficient equipment and the reduction of environmental impact.

[0034] The avatar can optimize its movements using machine learning to reduce the burden on the operator. The avatar can optimize its movements using machine learning, for example, to reduce the burden on the operator. For example, it can learn the operator's operation patterns and automatically generate efficient movements. The avatar can also optimize its movements using machine learning, allowing the operator to perform complex movements with fewer operations. For example, it can realize complex movements with simple operations using gesture recognition. The avatar can also optimize its movements using machine learning to reduce the burden on the operator. For example, it can learn the operator's operation history and predict and automatically execute the next movement. This can reduce the burden on the operator.

[0035] Avatar has developed a system that allows multiple avatars to be operated simultaneously, thereby improving the work efficiency of operators. Avatar has developed a system that allows multiple avatars to be operated simultaneously, allowing operators to perform multiple tasks at once. For example, avatars responsible for different tasks can be operated simultaneously. Avatar has also developed a system that allows simultaneous operation, allowing operators to perform their tasks efficiently. For example, multiple avatars can work together to perform a single task. Avatar has also developed a system that allows multiple avatars to be operated simultaneously, thereby improving work efficiency. For example, an operator can communicate with multiple clients at once. This allows a system that allows multiple avatars to be operated simultaneously to improve work efficiency.

[0036] Avatars can also be applied to other fields such as education or medicine to provide remote support. Avatars can be applied, for example, to the education field to provide remote lessons or individualized instruction. For example, a teacher avatar may conduct a lesson while interacting with students. Avatars can also be applied to the medical field to provide remote consultations and counseling. For example, a doctor avatar may conduct a consultation while interacting with a patient. Avatars can also be applied to the nursing field to provide remote nursing support. For example, a caregiver avatar may interact with an elderly person to support them in their daily lives. This allows avatars to be applied to other fields such as education and medicine to provide remote support.

[0037] The appearance or behavior of the avatar can be customized, allowing a work environment tailored to the personality or needs of the operator. For example, the appearance of the avatar can be customized, allowing the operator to set the avatar to suit their own preferences. For example, clothing and hairstyle can be freely changed. The behavior of the avatar can also be customized, allowing a work environment tailored to the operator's needs. For example, specific gestures and actions can be added. The appearance and behavior of the avatar can also be customized, allowing a work environment that reflects the operator's personality. For example, an avatar can be created that matches the operator's hobbies and interests. This makes it possible to provide a work environment tailored to the operator's personality and needs.

[0038] AI translation systems can handle technical terminology or industry-specific language and improve translation accuracy. For example, AI translation systems can add technical terminology dictionaries to provide translations specialized for specific industries or fields. For example, they can accurately translate medical and technical terminology. AI translation systems can also be customized to handle industry-specific language and improve translation accuracy. For example, they can accurately translate technical terminology in the financial industry. AI translation systems can also be trained to handle technical terminology or industry-specific language and improve translation accuracy. For example, they can accurately translate legal and academic terminology. This allows them to handle technical terminology or industry-specific language and improve translation accuracy.

[0039] AI translation systems can combine speech recognition technology to achieve real-time speech translation. For example, AI translation systems integrate speech recognition technology to provide real-time speech translation. For example, they translate conversations during meetings or on the phone in real time. AI translation systems also use speech recognition technology to convert speech into text in real time and translate that text. For example, they translate the content of lectures and presentations in real time. AI translation systems can also combine speech recognition technology to achieve real-time speech translation. For example, they translate conversations while traveling in real time. This makes it possible to combine speech recognition technology to achieve real-time speech translation.

[0040] AI translation systems can be improved to take into account cultural nuances between different languages, promoting deeper understanding. For example, AI translation systems can add features that take cultural nuances into account to accurately translate subtle meanings between different languages. For example, they can properly translate expressions related to greetings and politeness. AI translation systems can also be trained to take into account cultural differences between different languages, providing more natural translations. For example, they can accurately translate humor and metaphors. AI translation systems can also be improved to take into account cultural nuances, promoting deeper understanding between different languages. For example, they can properly translate expressions based on cultural background. This allows AI translation systems to be improved to take into account cultural nuances between different languages, promoting deeper understanding.

[0041] AI translation systems also support visual translation, broadening the scope of communication. For example, AI translation systems can add visual translation functions to translate sign language and gestures in real time. For example, they can convert sign language into text or speech. AI translation systems can also use visual translation functions to recognize gestures and translate their meaning. For example, they can convert pointing and gestures into text. AI translation systems can also support visual translation, broadening the scope of communication. For example, they can translate conversations using sign language and gestures in real time. This allows them to support visual translation, broadening the scope of communication.

[0042] The system can be developed to database the skills and experience of diverse human resources and match them with the most suitable jobs. For example, the system can register the skills and experience of diverse human resources in a database and develop a system that matches them with the most suitable jobs based on that information. For example, human resources with specific skills are assigned to specific projects. The system can also database the skills and experience of human resources and build a system that matches them with the most suitable jobs using AI. For example, it can suggest the most suitable jobs based on past work history. The system can also register the skills and experience of diverse human resources in a database and develop a system that matches them with the most suitable jobs based on that information. For example, human resources with experience in a specific industry are assigned to specific jobs. In this way, the skills and experience of diverse human resources can be databased and matched with the most suitable jobs.

[0043] The system may be implemented to monitor the health condition of an operator in real time and detect health risks early. For example, the system may be implemented to monitor the health condition of an operator in real time and detect health risks early. For example, the system may monitor heart rate and blood pressure. The system may also be implemented to monitor the health condition and issue an alert if an abnormality is detected. For example, if an abnormal heart rate is detected, the system may notify a medical institution. The system may also be implemented to monitor the health condition of an operator in real time and detect health risks early. For example, the system may monitor stress levels and suggest taking a break if an abnormality is detected. In this way, the system may monitor the health condition of an operator in real time and detect health risks early.

[0044] The system can expand the utilization of diverse human resources to other industries and promote the utilization of human resources in a wide range of fields. For example, the system can expand the utilization of diverse human resources to the education industry, providing remote classes and individual instruction. For example, teachers with disabilities can teach classes remotely. The system can also expand the utilization of diverse human resources to the medical industry, providing remote examinations and counseling. For example, doctors with health concerns can conduct examinations remotely. The system can also expand the utilization of diverse human resources to the nursing care industry, providing remote nursing care support. For example, caregivers who cannot come to the office due to childcare or nursing care responsibilities can provide remote support. This can be expanded to other industries and promote the utilization of human resources in a wide range of fields.

[0045] The system can build a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely. For example, the system builds a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely. For example, it provides a remote desktop or video conferencing system. The system also builds a platform that provides the necessary equipment and tools for working remotely, allowing diverse human resources to work efficiently. For example, it provides cloud storage or collaboration tools. The system also builds a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely. For example, it provides security software for remote access. In this way, it is possible to build a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely.

[0046] The system can reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system. For example, the system can reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system. For example, AI can automatically process routine tasks. The system can also introduce an AI-based automation system to automate part of the 24 / 7 / 365 work. For example, AI can automatically perform monitoring tasks. The system can also reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system. For example, AI can automatically perform data entry and report creation. In this way, the system can reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system.

[0047] A system can be introduced that uses data analysis to optimize business processes in order to improve business efficiency. For example, a system can be introduced that uses data analysis to optimize business processes to improve business efficiency. For example, the system can identify bottlenecks in business and propose improvement measures. The system can also optimize business processes using data analysis to improve business efficiency 24 hours a day, 365 days a year. For example, the system can monitor the progress of business in real time and propose efficient schedules. The system can also be introduced that uses data analysis to optimize business processes in order to improve business efficiency. For example, the system can analyze business performance data and propose optimal resource allocation. In this way, a system can be introduced that uses data analysis to optimize business processes in order to improve business efficiency.

[0048] The system can be expanded to other industries for 24 / 7 operations, improving operational efficiency in a wide range of fields. For example, the system can be expanded to the medical industry for 24 / 7 operations, improving operational efficiency. For example, it can provide remote support for nighttime operations at hospitals. The system can also be expanded to the logistics industry for 24 / 7 operations, improving operational efficiency. For example, it can remotely monitor nighttime operations at warehouses. The system can also be expanded to the manufacturing industry for 24 / 7 operations, improving operational efficiency. For example, it can remotely monitor nighttime production lines at factories. This allows 24 / 7 operations to be expanded to other industries, improving operational efficiency in a wide range of fields.

[0049] The system can improve the flexibility of operators' working styles by combining 24 / 7 work with remote work. For example, the system can combine 24 / 7 work with remote work, allowing operators to work from home. For example, the system can perform work using remote desktop. The system can also introduce remote work to enable flexible 24 / 7 work. For example, the system can enable operators to adjust shifts from home. The system can also combine 24 / 7 work with remote work, improving the flexibility of operators' working styles. For example, the system can provide security software for remote access. This can improve the flexibility of operators' working styles by combining 24 / 7 work with remote work.

[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 business support system also includes a voice recognition unit that recognizes the operator's voice instructions in real time, making business operations more efficient. For example, the system automatically responds when the operator issues a voice instruction. The voice recognition unit also supports multiple languages, facilitating smooth communication between operators who speak different languages. Furthermore, the voice recognition unit can analyze the operator's voice data and provide feedback to improve business performance.

[0052] The business support system also includes a health monitoring unit that can monitor the health status of operators in real time. For example, it can monitor heart rate and blood pressure and issue an alert if an abnormality is detected. The health monitoring unit can also analyze the operator's health data and detect health risks early. Furthermore, the health monitoring unit can provide advice based on the operator's health status and support health management.

[0053] The business support system also includes a data analysis unit that analyzes business data in real time to improve business efficiency. For example, it can monitor the progress of business operations and propose efficient schedules. The data analysis unit can also identify business bottlenecks and propose improvement measures. Furthermore, the data analysis unit can analyze business performance data and propose optimal resource allocation.

[0054] The business support system also includes an energy management unit that can optimize the energy efficiency of the business environment. For example, energy-efficient lighting and air conditioning systems can be introduced to reduce power consumption. The energy management unit can also use renewable energy to reduce environmental impact. Furthermore, the energy management unit can analyze energy consumption data and achieve efficient energy management.

[0055] The business support system can also be equipped with a security monitoring unit to strengthen the security of the business environment. For example, surveillance cameras and sensors can be installed to detect unauthorized access. The security monitoring unit can also analyze security data in real time and issue an alert if an abnormality is detected. Furthermore, the security monitoring unit can evaluate security risks and take appropriate measures.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: Deploy high-security spots. High-security spots are set up in Japan or around the world and operate 24 hours a day, 365 days a year. For example, American operators can work during nighttime hours in Japan, allowing Japanese operators to avoid working the night shift. Step 2: Operate the avatar. The avatar is a virtual entity that can be operated by multiple operators in rotation. For example, people with disabilities who cannot leave their homes, people who cannot work night shifts due to health concerns, or people who cannot come to the office due to childcare or elderly care responsibilities can perform their work through an avatar. Step 3: Use an AI translation system. The AI ​​translation system translates in real time, facilitating communication between operators who speak different languages. For example, if a Japanese-speaking operator and an English-speaking operator are performing the same task, the AI ​​will translate in real time, ensuring smooth communication.

[0058] (Example 2) The business support system according to the embodiment of the present invention is a system that sets up high-security spots and uses avatars and an AI translation system to rotate business operations 24 hours a day, 365 days a year around the world. This allows the business support system to rotate business operations 24 hours a day, 365 days a year around the world.

[0059] A business support system according to an embodiment includes a high-security spot, an avatar, and an AI translation system. The high-security spot is installed, for example, in Japan or around the world and operates 24 hours a day, 365 days a year. For example, an American operator can work during nighttime hours in Japan, allowing Japanese operators to avoid night shifts. The avatar is, for example, a virtual entity that can be operated by multiple operators in rotation. For example, people with disabilities who cannot leave their homes, people who cannot work night shifts due to health concerns, or people who cannot come to the office due to childcare or elderly care responsibilities can perform their work through the avatar. The AI ​​translation system, for example, performs real-time translation to facilitate communication between operators who speak different languages. For example, when a Japanese-speaking operator and an English-speaking operator perform the same task, the AI ​​performs real-time translation, ensuring smooth communication. This allows the business support system according to an embodiment to rotate operations 24 hours a day, 365 days a year around the world.

[0060] High-security spots can introduce biometric authentication to strengthen identity verification of operators. For example, high-security spots can introduce a fingerprint authentication system, scanning the fingerprint of an operator when they begin work to verify their identity. This prevents unauthorized access. High-security spots can also use facial recognition technology to scan the face of an operator to verify their identity. This improves the security level. High-security spots can also introduce an iris authentication system, scanning the iris of an operator when they begin work to verify their identity. This achieves even higher security. This strengthens identity verification of operators and prevents unauthorized access.

[0061] High-security spots are recreated using virtual reality technology and can be accessed remotely by operators. For example, high-security spots recreate a work environment using VR technology, allowing operators to work remotely from home using a VR headset. High-security spots also use VR technology to create virtual offices that operators can access and work remotely. High-security spots also recreate a work environment using VR, providing operators with the same operational experience as in an actual office when accessing the environment remotely. This allows operators to work remotely.

[0062] High-security spots can be optimized to environments that reduce the stress levels of operators using an emotion estimation function. For example, high-security spots can be set up in low-stress environments by using the emotion estimation function to monitor the stress levels of operators in real time. Furthermore, high-security spots can be set up in environments where operators can relax (for example, places surrounded by nature) based on emotion estimation data. Furthermore, high-security spots can analyze the emotion data of operators to identify time periods and places where stress is low, and determine the installation location based on that information. This allows for optimization of the environment to reduce the stress levels of operators.

[0063] High-security spots can be deployed in different industries to support operations. For example, high-security spots can be deployed in the financial industry to support operations 24 / 7. For example, they can be installed in bank data centers to enhance security. High-security spots can also be deployed in the medical industry to support operations 24 / 7. For example, security spots can be installed to monitor hospital IT systems. High-security spots can also be deployed in the manufacturing industry to support operations 24 / 7. For example, security spots can be installed to monitor factory production lines. This allows high-security spots to be deployed in different industries to support operations 24 / 7.

[0064] High-security spots can reduce their environmental impact by introducing energy-efficient equipment. For example, high-security spots can introduce energy-efficient LED lighting to reduce power consumption. High-security spots can also introduce solar power generation systems to use renewable energy to reduce their environmental impact. High-security spots can also introduce energy-efficient air conditioning systems to provide a comfortable working environment while reducing power consumption. This allows for the introduction of energy-efficient equipment and the reduction of environmental impact.

[0065] High-security spots are equipped with an emotion estimation function, which allows them to monitor the emotional state of operators in real time and provide appropriate support. High-security spots, for example, are equipped with an emotion estimation function and monitor the emotional state of operators in real time. For example, they can provide relaxing music when stress levels are high. High-security spots also use the emotion estimation function to monitor the emotional state of operators and introduce a system that encourages them to take a break as needed. High-security spots are also equipped with an emotion estimation function and provide support according to the operator's emotional state. For example, they can provide counseling services when the operator is emotionally unstable. This allows them to monitor the emotional state of operators in real time and provide appropriate support.

[0066] The avatar is equipped with an emotion estimation function and can reflect the emotional state of the operator, thereby achieving more natural communication. The avatar, for example, can be equipped with an emotion estimation function and can reflect the emotional state of the operator in real time. For example, when the operator is smiling, the avatar also displays a smiling face. The avatar can also use the emotion estimation function to reflect the emotional state of the operator, thereby achieving more natural facial expressions and movements. For example, when the operator is surprised, the avatar also displays a surprised face. The avatar is also equipped with an emotion estimation function and can reflect the emotional state of the operator, thereby achieving more natural communication. For example, when the operator is tired, the avatar also displays a tired face. In this way, by reflecting the emotional state of the operator, more natural communication can be achieved.

[0067] The avatar can optimize its movements using machine learning to reduce the burden on the operator. The avatar can optimize its movements using machine learning, for example, to reduce the burden on the operator. For example, it can learn the operator's operation patterns and automatically generate efficient movements. The avatar can also optimize its movements using machine learning, allowing the operator to perform complex movements with fewer operations. For example, it can realize complex movements with simple operations using gesture recognition. The avatar can also optimize its movements using machine learning to reduce the burden on the operator. For example, it can learn the operator's operation history and predict and automatically execute the next movement. This can reduce the burden on the operator.

[0068] Avatar has developed a system that allows multiple avatars to be operated simultaneously, thereby improving the work efficiency of operators. Avatar has developed a system that allows multiple avatars to be operated simultaneously, allowing operators to perform multiple tasks at once. For example, avatars responsible for different tasks can be operated simultaneously. Avatar has also developed a system that allows simultaneous operation, allowing operators to perform their tasks efficiently. For example, multiple avatars can work together to perform a single task. Avatar has also developed a system that allows multiple avatars to be operated simultaneously, thereby improving work efficiency. For example, an operator can communicate with multiple clients at once. This allows a system that allows multiple avatars to be operated simultaneously to improve work efficiency.

[0069] Avatars can also be applied to other fields such as education or medicine to provide remote support. Avatars can be applied, for example, to the education field to provide remote lessons or individualized instruction. For example, a teacher avatar may conduct a lesson while interacting with students. Avatars can also be applied to the medical field to provide remote consultations and counseling. For example, a doctor avatar may conduct a consultation while interacting with a patient. Avatars can also be applied to the nursing field to provide remote nursing support. For example, a caregiver avatar may interact with an elderly person to support them in their daily lives. This allows avatars to be applied to other fields such as education and medicine to provide remote support.

[0070] The appearance or behavior of the avatar can be customized, allowing a work environment tailored to the personality or needs of the operator. For example, the appearance of the avatar can be customized, allowing the operator to set the avatar to suit their own preferences. For example, clothing and hairstyle can be freely changed. The behavior of the avatar can also be customized, allowing a work environment tailored to the operator's needs. For example, specific gestures and actions can be added. The appearance and behavior of the avatar can also be customized, allowing a work environment that reflects the operator's personality. For example, an avatar can be created that matches the operator's hobbies and interests. This makes it possible to provide a work environment tailored to the operator's personality and needs.

[0071] The avatar uses the emotion estimation function to provide feedback according to the operator's emotions, thereby reducing work stress. For example, the avatar uses the emotion estimation function to grasp the operator's emotional state in real time and provide appropriate feedback. For example, when the operator is feeling stressed, the avatar gives advice to help the operator relax. The avatar also provides feedback according to the operator's emotions, thereby reducing work stress. For example, when the operator is tired, the avatar encourages the operator to take a break. The avatar also uses the emotion estimation function to provide feedback according to the operator's emotions. For example, when the operator is feeling anxious, the avatar offers words of encouragement. In this way, the avatar provides feedback according to the operator's emotions, thereby reducing work stress.

[0072] An AI translation system can achieve more natural communication by integrating an emotion estimation function and providing translations that are appropriate for the emotion. For example, an AI translation system can provide translations that are appropriate for the emotion, such as providing translations that reflect emotions like joy or sadness. An AI translation system can also use an emotion estimation function to provide translations that are appropriate for the emotion, such as providing translations that soften emotions like anger. An AI translation system can also achieve more natural communication by integrating an emotion estimation function and providing translations that are appropriate for the emotion, such as providing translations that reflect the emotion of surprise. In this way, by providing translations that are appropriate for the emotion, more natural communication can be achieved.

[0073] AI translation systems can handle technical terminology or industry-specific language and improve translation accuracy. For example, AI translation systems can add technical terminology dictionaries to provide translations specialized for specific industries or fields. For example, they can accurately translate medical and technical terminology. AI translation systems can also be customized to handle industry-specific language and improve translation accuracy. For example, they can accurately translate technical terminology in the financial industry. AI translation systems can also be trained to handle technical terminology or industry-specific language and improve translation accuracy. For example, they can accurately translate legal and academic terminology. This allows them to handle technical terminology or industry-specific language and improve translation accuracy.

[0074] AI translation systems can combine speech recognition technology to achieve real-time speech translation. For example, AI translation systems integrate speech recognition technology to provide real-time speech translation. For example, they translate conversations during meetings or on the phone in real time. AI translation systems also use speech recognition technology to convert speech into text in real time and translate that text. For example, they translate the content of lectures and presentations in real time. AI translation systems can also combine speech recognition technology to achieve real-time speech translation. For example, they translate conversations while traveling in real time. This makes it possible to combine speech recognition technology to achieve real-time speech translation.

[0075] AI translation systems can be improved to take into account cultural nuances between different languages, promoting deeper understanding. For example, AI translation systems can add features that take cultural nuances into account to accurately translate subtle meanings between different languages. For example, they can properly translate expressions related to greetings and politeness. AI translation systems can also be trained to take into account cultural differences between different languages, providing more natural translations. For example, they can accurately translate humor and metaphors. AI translation systems can also be improved to take into account cultural nuances, promoting deeper understanding between different languages. For example, they can properly translate expressions based on cultural background. This allows AI translation systems to be improved to take into account cultural nuances between different languages, promoting deeper understanding.

[0076] AI translation systems also support visual translation, broadening the scope of communication. For example, AI translation systems can add visual translation functions to translate sign language and gestures in real time. For example, they can convert sign language into text or speech. AI translation systems can also use visual translation functions to recognize gestures and translate their meaning. For example, they can convert pointing and gestures into text. AI translation systems can also support visual translation, broadening the scope of communication. For example, they can translate conversations using sign language and gestures in real time. This allows them to support visual translation, broadening the scope of communication.

[0077] An AI translation system can use an emotion estimation function to analyze users' emotional reactions to translated content and continuously improve the quality of the translation. For example, an AI translation system can use the emotion estimation function to collect users' emotional reactions to translated content in real time and improve the quality of the translation based on that data. For example, it can prioritize translations that receive a lot of positive reactions. The AI ​​translation system can also analyze user emotional reaction data and build a system that continuously improves the quality of the translation. For example, it can reevaluate translations that receive a lot of negative reactions and identify areas for improvement. The AI ​​translation system can also develop a system that continuously improves the quality of the translation based on the emotion estimation data. For example, it can adjust the translation algorithm according to the user's emotional reactions. This allows the system to analyze users' emotional reactions to translated content and continuously improve the quality of the translation.

[0078] The system uses an emotion estimation function to monitor the emotional state of an operator and provide appropriate support, thereby creating a comfortable working environment. For example, the system uses the emotion estimation function to monitor the emotional state of an operator in real time and provide support to help them relax if they are under high stress. For example, by playing relaxing music. The system also monitors the emotional state of an operator and introduces a system to encourage them to take a break as needed. For example, by suggesting a break if the operator is emotionally unstable. The system also uses the emotion estimation function to provide support according to the operator's emotional state. For example, by displaying an encouraging message if the operator is feeling depressed. In this way, a comfortable working environment can be created by monitoring the emotional state of an operator and providing appropriate support.

[0079] The system can be developed to database the skills and experience of diverse human resources and match them with the most suitable jobs. For example, the system can register the skills and experience of diverse human resources in a database and develop a system that matches them with the most suitable jobs based on that information. For example, human resources with specific skills are assigned to specific projects. The system can also database the skills and experience of human resources and build a system that matches them with the most suitable jobs using AI. For example, it can suggest the most suitable jobs based on past work history. The system can also register the skills and experience of diverse human resources in a database and develop a system that matches them with the most suitable jobs based on that information. For example, human resources with experience in a specific industry are assigned to specific jobs. In this way, the skills and experience of diverse human resources can be databased and matched with the most suitable jobs.

[0080] The system may be implemented to monitor the health condition of an operator in real time and detect health risks early. For example, the system may be implemented to monitor the health condition of an operator in real time and detect health risks early. For example, the system may monitor heart rate and blood pressure. The system may also be implemented to monitor the health condition and issue an alert if an abnormality is detected. For example, if an abnormal heart rate is detected, the system may notify a medical institution. The system may also be implemented to monitor the health condition of an operator in real time and detect health risks early. For example, the system may monitor stress levels and suggest taking a break if an abnormality is detected. In this way, the system may monitor the health condition of an operator in real time and detect health risks early.

[0081] The system can expand the utilization of diverse human resources to other industries and promote the utilization of human resources in a wide range of fields. For example, the system can expand the utilization of diverse human resources to the education industry, providing remote classes and individual instruction. For example, teachers with disabilities can teach classes remotely. The system can also expand the utilization of diverse human resources to the medical industry, providing remote examinations and counseling. For example, doctors with health concerns can conduct examinations remotely. The system can also expand the utilization of diverse human resources to the nursing care industry, providing remote nursing care support. For example, caregivers who cannot come to the office due to childcare or nursing care responsibilities can provide remote support. This can be expanded to other industries and promote the utilization of human resources in a wide range of fields.

[0082] The system can build a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely. For example, the system builds a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely. For example, it provides a remote desktop or video conferencing system. The system also builds a platform that provides the necessary equipment and tools for working remotely, allowing diverse human resources to work efficiently. For example, it provides cloud storage or collaboration tools. The system also builds a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely. For example, it provides security software for remote access. In this way, it is possible to build a platform that provides the necessary equipment and tools to enable diverse human resources to work remotely.

[0083] The system can use the emotion estimation function to analyze the emotional state of diverse human resources and provide feedback to provide a comfortable working environment. For example, the system uses the emotion estimation function to analyze the emotional state of diverse human resources in real time and provide feedback to provide a comfortable working environment. For example, advice on how to relax when stress is high is provided. The system also builds a system that analyzes the emotional state of diverse human resources and provides feedback to provide a comfortable working environment. For example, suggesting a break when emotions are unstable. The system also uses the emotion estimation function to analyze the emotional state of diverse human resources and provide feedback to provide a comfortable working environment. For example, an encouraging message is displayed when emotions are depressed. This makes it possible to analyze the emotional state of diverse human resources and provide feedback to provide a comfortable working environment.

[0084] The system can use the emotion estimation function to monitor the emotional state of the operator and take measures to reduce stress. For example, the system can use the emotion estimation function to monitor the emotional state of the operator in real time and take measures to help the operator relax if stress is high. For example, by playing relaxing music. The system can also monitor the emotional state of the operator and introduce a system to encourage breaks as needed. For example, by suggesting a break if the operator is emotionally unstable. The system can also use the emotion estimation function to monitor the emotional state of the operator and take measures to reduce stress. For example, by displaying an encouraging message if the operator is feeling depressed. This makes it possible to monitor the emotional state of the operator and take measures to reduce stress.

[0085] The system can reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system. For example, the system can reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system. For example, AI can automatically process routine tasks. The system can also introduce an AI-based automation system to automate part of the 24 / 7 / 365 work. For example, AI can automatically perform monitoring tasks. The system can also reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system. For example, AI can automatically perform data entry and report creation. In this way, the system can reduce the burden on operators by combining 24 / 7 / 365 work with an AI-based automation system.

[0086] A system can be introduced that uses data analysis to optimize business processes in order to improve business efficiency. For example, a system can be introduced that uses data analysis to optimize business processes to improve business efficiency. For example, the system can identify bottlenecks in business and propose improvement measures. The system can also optimize business processes using data analysis to improve business efficiency 24 hours a day, 365 days a year. For example, the system can monitor the progress of business in real time and propose efficient schedules. The system can also be introduced that uses data analysis to optimize business processes in order to improve business efficiency. For example, the system can analyze business performance data and propose optimal resource allocation. In this way, a system can be introduced that uses data analysis to optimize business processes in order to improve business efficiency.

[0087] The system can be expanded to other industries for 24 / 7 operations, improving operational efficiency in a wide range of fields. For example, the system can be expanded to the medical industry for 24 / 7 operations, improving operational efficiency. For example, it can provide remote support for nighttime operations at hospitals. The system can also be expanded to the logistics industry for 24 / 7 operations, improving operational efficiency. For example, it can remotely monitor nighttime operations at warehouses. The system can also be expanded to the manufacturing industry for 24 / 7 operations, improving operational efficiency. For example, it can remotely monitor nighttime production lines at factories. This allows 24 / 7 operations to be expanded to other industries, improving operational efficiency in a wide range of fields.

[0088] The system can improve the flexibility of operators' working styles by combining 24 / 7 work with remote work. For example, the system can combine 24 / 7 work with remote work, allowing operators to work from home. For example, the system can perform work using remote desktop. The system can also introduce remote work to enable flexible 24 / 7 work. For example, the system can enable operators to adjust shifts from home. The system can also combine 24 / 7 work with remote work, improving the flexibility of operators' working styles. For example, the system can provide security software for remote access. This can improve the flexibility of operators' working styles by combining 24 / 7 work with remote work.

[0089] The system uses the emotion estimation function to analyze the emotional state of operators working 24 hours a day, 365 days a year and provide appropriate support. For example, the system uses the emotion estimation function to analyze the emotional state of operators working 24 hours a day, 365 days a year in real time and provide appropriate support. For example, it provides advice on how to relax when stress is high. The system also analyzes the emotional state of operators and introduces a system to encourage breaks as needed. For example, it suggests taking a break when emotions are unstable. The system also uses the emotion estimation function to analyze the emotional state of operators working 24 hours a day, 365 days a year and provide appropriate support. For example, it displays an encouraging message when an operator is feeling depressed. This makes it possible to analyze the emotional state of operators working 24 hours a day, 365 days a year and provide appropriate support.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The business support system also includes a voice recognition unit that recognizes the operator's voice instructions in real time, making business operations more efficient. For example, the system automatically responds when the operator issues a voice instruction. The voice recognition unit also supports multiple languages, facilitating smooth communication between operators who speak different languages. Furthermore, the voice recognition unit can analyze the operator's voice data and provide feedback to improve business performance.

[0092] The business support system also includes a health monitoring unit that can monitor the health status of operators in real time. For example, it can monitor heart rate and blood pressure and issue an alert if an abnormality is detected. The health monitoring unit can also analyze the operator's health data and detect health risks early. Furthermore, the health monitoring unit can provide advice based on the operator's health status and support health management.

[0093] The business support system also includes a data analysis unit that analyzes business data in real time to improve business efficiency. For example, it can monitor the progress of business operations and propose efficient schedules. The data analysis unit can also identify business bottlenecks and propose improvement measures. Furthermore, the data analysis unit can analyze business performance data and propose optimal resource allocation.

[0094] The business support system also includes an energy management unit that can optimize the energy efficiency of the business environment. For example, energy-efficient lighting and air conditioning systems can be introduced to reduce power consumption. The energy management unit can also use renewable energy to reduce environmental impact. Furthermore, the energy management unit can analyze energy consumption data and achieve efficient energy management.

[0095] The business support system can also be equipped with a security monitoring unit to strengthen the security of the business environment. For example, surveillance cameras and sensors can be installed to detect unauthorized access. The security monitoring unit can also analyze security data in real time and issue an alert if an abnormality is detected. Furthermore, the security monitoring unit can evaluate security risks and take appropriate measures.

[0096] The work support system can further use an emotion estimation function to monitor the emotional state of the operator and take measures to reduce stress, for example, by playing relaxing music. The system can also monitor the emotional state of the operator and introduce a system to encourage breaks as needed, for example, by suggesting a break if the operator is emotionally unstable. The system can also use an emotion estimation function to monitor the emotional state of the operator and take measures to reduce stress, for example, by displaying an encouraging message if the operator is feeling depressed.

[0097] The work support system can further use an emotion estimation function to analyze the emotional state of the operator and provide appropriate support. For example, if stress levels are high, the system can provide advice on how to relax. The system can also analyze the emotional state of the operator and introduce a system to encourage breaks as needed. For example, the system can suggest taking a break if the operator is emotionally unstable. The system can also use the emotion estimation function to analyze the emotional state of the operator and provide appropriate support. For example, the system can display an encouraging message if the operator is feeling depressed.

[0098] The work support system can further use an emotion estimation function to monitor the emotional state of the operator in real time and provide appropriate support. For example, it can provide relaxing music when stress levels are high. The system also uses the emotion estimation function to monitor the emotional state of the operator and introduces a system that encourages breaks as needed. For example, it can suggest a break when the operator is emotionally unstable. The system also uses the emotion estimation function to provide support according to the operator's emotional state. For example, it can provide counseling services when the operator is emotionally unstable.

[0099] The work support system can also use an emotion estimation function to grasp the emotional state of the operator in real time and provide appropriate feedback. For example, when an operator is feeling stressed, the system can give them advice on how to relax. The system can also provide feedback according to the operator's emotions to reduce work stress. For example, when an operator is tired, the system can encourage them to take a break. The system can also use the emotion estimation function to provide feedback according to the operator's emotions. For example, when an operator is feeling anxious, the system can offer words of encouragement.

[0100] The work support system can further use an emotion estimation function to monitor the emotional state of the operator in real time and provide appropriate support. For example, it can provide relaxing music when stress levels are high. The system also uses the emotion estimation function to monitor the emotional state of the operator and introduces a system that encourages breaks as needed. For example, it can suggest a break when the operator is emotionally unstable. The system also uses the emotion estimation function to provide support according to the operator's emotional state. For example, it can provide counseling services when the operator is emotionally unstable.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: Deploy high-security spots. High-security spots are set up in Japan or around the world and operate 24 hours a day, 365 days a year. For example, American operators can work during nighttime hours in Japan, allowing Japanese operators to avoid working the night shift. Step 2: Operate the avatar. The avatar is a virtual entity that can be operated by multiple operators in rotation. For example, people with disabilities who cannot leave their homes, people who cannot work night shifts due to health concerns, or people who cannot come to the office due to childcare or elderly care responsibilities can perform their work through an avatar. Step 3: Use an AI translation system. The AI ​​translation system translates in real time, facilitating communication between operators who speak different languages. For example, if a Japanese-speaking operator and an English-speaking operator are performing the same task, the AI ​​will translate in real time, ensuring smooth communication.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. High security spots are placed, an avatar that performs work at the high-security spot; an AI translation system used by an operator who operates the avatar; A system characterized by:

2. The high security spot is Introducing biometric authentication to strengthen the identity verification of the operator 2. The system of claim 1.

3. The high security spot is Recreated using virtual reality technology and accessible remotely by the operator 2. The system of claim 1.

4. The high security spot is Optimized for an environment that reduces the operator's stress level 2. The system of claim 1.

5. The high security spot is Deployed in different industries to support the above business 2. The system of claim 1.

Images