system

Abstract

We improve the system within the virtual space and provide the optimal UI and functionality. [Solution] The system comprises a user information setting unit, a system import unit, a requirements import unit, a system duplication unit, a simulation execution unit, a feedback unit, an improvement analysis unit, a system improvement unit, an integration unit, and a deliverable creation unit. The user information setting unit sets user information. The system import unit imports the target system into a virtual space. The requirements import unit imports functional requirements. The system duplication unit duplicates the system in the virtual space. The simulation execution unit executes a simulation to operate the duplicated system. The feedback unit provides feedback on areas for improvement once the operation is complete. The improvement analysis unit analyzes improvement methods based on the feedbacked areas for improvement. The system improvement unit executes system improvements. The integration unit integrates the improved systems. The deliverable creation unit creates the final deliverable.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the optimization of the UI and functions of the system depends on human judgment, and there is a problem that improvements using generative AI have not been sufficiently made.

[0005] The system according to the embodiment aims to improve the system in a virtual space and provide an optimal UI and functions.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a user information setting unit, a system import unit, a requirements import unit, a system replication unit, a simulation execution unit, a feedback unit, an improvement analysis unit, a system improvement unit, an integration unit, and a deliverable creation unit. The user information setting unit sets user information. The system import unit imports the target system into a virtual space. The requirements import unit imports functional requirements. The system replication unit replicates the system in the virtual space. The simulation execution unit executes a simulation to operate the replicated system. The feedback unit provides feedback on areas for improvement once the operation is complete. The improvement analysis unit analyzes improvement methods based on the feedbacked areas for improvement. The system improvement unit executes system improvements. The integration unit integrates the improved systems. The deliverable creation unit creates the final deliverable. [Effects of the Invention]

[0007] The system according to this embodiment can improve the system within a virtual space and provide an optimal UI and functions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage

[0019] 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus <> 34. The communication I / F 26 is connected to a network 54. Examples of the network

[0020] 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

[0020] 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see Figure

[0021] 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a simulation platform that uses generative AI to improve an existing system in a virtual space. First, the system sets the user information of the system. Next, it imports the target system into the virtual space, including its functional requirements. The imported system is duplicated a specified number of times within the virtual space. The set user runs a simulation operating each of the duplicated systems a specified number of times, and after the operation is complete, feedback is provided on areas for improvement. Based on the feedback on areas for improvement, the generative AI is used to analyze the optimal improvement method and implement system improvements. This procedure is repeated a specified number of times. Finally, a system with the specified number of improvements implemented is constructed, and these are integrated by the generative AI to create the final deliverable. The final deliverable is retrieved from the virtual space and considered the finished product. This mechanism allows existing systems to evolve into more optimal systems by performing a number of functional improvements in the virtual space that would be impossible to achieve in the real world. Thus, the system can provide a simulation platform that uses generative AI to improve existing systems in a virtual space.

[0029] The system according to the embodiment comprises a user information setting unit, a system import unit, a requirements import unit, a system duplication unit, a simulation execution unit, a feedback unit, an improvement analysis unit, a system improvement unit, an integration unit, and a deliverable creation unit. The user information setting unit sets user information. The user information setting unit can set user information such as the department of use and the method of operation. For example, the user information setting unit can set the name and role of the department of use. The user information setting unit can also set the procedure and difficulty level of the method of operation. The system import unit imports the target system into a virtual space. The system import unit can import the system into a virtual space using, for example, 3D modeling technology. The system import unit can also import the system using technology for constructing a simulation environment. The requirements import unit imports functional requirements. For example, the requirements import unit can import the system's performance requirements and operation requirements. The requirements import unit can also import the system's functional requirements in detail. The system duplication unit duplicates the system a specified number of times in the virtual space. The system replication unit can replicate a system using, for example, a high-precision replication technique. The system replication unit can also replicate a system by specifying the replication range. The simulation execution unit runs a simulation where a designated user operates each replicated system a specified number of times. The simulation execution unit can, for example, execute a simulation by setting a simulation scenario. It can also execute a simulation by setting the simulation execution conditions. The feedback unit provides feedback on areas for improvement after the operation is complete. The feedback unit can, for example, provide feedback on areas for improvement by setting the feedback format. It can also provide feedback on areas for improvement by setting the content of the feedback. The improvement analysis unit analyzes the optimal improvement method based on the feedbacked areas for improvement. The improvement analysis unit can, for example, analyze the optimal improvement method by setting the improvement technique.Furthermore, the Improvement Analysis Unit can set analysis criteria and analyze the optimal improvement method. The System Improvement Unit executes system improvements. The System Improvement Unit can, for example, set improvement procedures and execute system improvements. The System Improvement Unit can also set the scope of improvements and execute system improvements. The Integration Unit integrates the improved system. The Integration Unit can, for example, set integration procedures and integrate the improved system. The Integration Unit can also set the scope of integration and integrate the improved system. The Deliverable Creation Unit creates the final deliverable. The Deliverable Creation Unit can, for example, set the format of the deliverable and create the final deliverable. The Deliverable Creation Unit can also set quality standards for the deliverable and create the final deliverable. As a result, the system according to the embodiment can efficiently perform the following processes: user information setting, system import, requirements import, system replication, simulation execution, feedback, improvement analysis, system improvement, integration, and deliverable creation.

[0030] The User Information Settings section configures user information. For example, it can configure user information such as the department of use and operating methods. Specifically, the User Information Settings section can configure the name and role of the department of use in detail. For example, it can input the name, role, and contact information of a specific department or team within a company, clarifying what operations each department performs. The User Information Settings section can also configure the procedures and difficulty level of operating methods. For example, it can provide a simple operation guide for beginners and detailed operating procedures for advanced users, allowing users to select the appropriate operating method according to their skill level. Furthermore, the User Information Settings section can also configure user permissions. For example, it can grant access to all functions to users with administrator privileges, while limiting the use of functions to only a select few. In this way, the User Information Settings section can provide system users with appropriate information and an appropriate operating environment, supporting the efficient operation of the system.

[0031] The system capture unit captures the target system into a virtual space. For example, the system capture unit can capture a system into the virtual space using 3D modeling technology. Specifically, the system capture unit scans the physical structure and functions of the target system in detail and reproduces them as a 3D model in the virtual space. In this process, each component, wiring, and connection status of the system are accurately reproduced, enabling operation and verification similar to that of the actual system. Furthermore, the system capture unit can also capture a system using technologies for building a simulation environment. For example, it can reproduce the system's operating environment and external interfaces in the virtual space and simulate how the system actually operates. In this way, the system capture unit can reproduce real-world systems in a virtual space with high accuracy, providing a foundation for simulation and verification.

[0032] The requirements input unit incorporates functional requirements. For example, it can incorporate system performance and operational requirements. Specifically, it incorporates detailed performance metrics and operating procedures that the system should achieve. For instance, it sets performance requirements such as system processing speed, response time, and durability, and verifies whether these requirements are met. The requirements input unit can also incorporate detailed functional requirements. For example, it sets operating conditions and interface specifications for each function provided by the system, and verifies whether these requirements are accurately implemented. In this way, the requirements input unit incorporates detailed requirements regarding system performance and functionality, providing a foundation for ensuring the system operates as expected.

[0033] The system replication unit replicates a system a specified number of times within a virtual space. For example, the system replication unit can replicate a system using high-precision replication techniques. Specifically, it accurately reproduces each component, wiring, and connection state of the system within the virtual space, enabling simultaneous operation and verification of multiple systems. Furthermore, the system replication unit can replicate a system by specifying the scope of replication. For example, it can replicate only specific functions or components and individually verify their operation and performance. In this way, the system replication unit can efficiently replicate systems within a virtual space, providing a foundation for diverse simulations and verifications.

[0034] The simulation execution unit runs simulations in which a specified user operates each of the duplicated systems a specified number of times. The simulation execution unit can, for example, set up simulation scenarios and execute the simulation. Specifically, the simulation execution unit sets up detailed scenarios for when the user operates the system and executes the simulation. For example, it sets up specific operating procedures and methods for responding to abnormal occurrences as scenarios, and the user operates the system according to these scenarios. The simulation execution unit can also set up simulation execution conditions and execute the simulation. For example, it sets the system's operating environment and external conditions and simulates how the system operates under these conditions. In this way, the simulation execution unit can efficiently execute simulations of when the user operates the system and provide a foundation for verifying the system's operation and performance.

[0035] The feedback unit provides feedback on areas for improvement once the operation is complete. For example, the feedback unit can provide feedback on areas for improvement by setting the format of the feedback. Specifically, the feedback unit analyzes the results of the user's operation of the system in detail and identifies areas that need improvement. For example, it identifies time-consuming parts of the operation procedure or parts prone to errors, and provides feedback to improve these areas. The feedback unit can also provide feedback on areas for improvement by setting the content of the feedback. For example, it can describe specific improvement methods and procedures in detail, allowing users to improve the system based on this feedback. In this way, the feedback unit can identify areas for improvement in the system based on the user's operation results and provide efficient feedback.

[0036] The Improvement Analysis Department analyzes the optimal improvement methods based on the feedback received regarding areas for improvement. For example, the Improvement Analysis Department can define improvement methods and then analyze the optimal improvement methods. Specifically, the Improvement Analysis Department selects the optimal improvement methods for the feedbacked areas for improvement and formulates a detailed improvement plan. For example, it may select methods to simplify specific operating procedures or technologies to improve system performance, and then create an improvement plan based on these methods. The Improvement Analysis Department can also analyze the optimal improvement methods by setting analysis criteria. For example, it may set criteria such as the effectiveness, cost, and feasibility of the improvement, and then select the optimal improvement method based on these criteria. In this way, the Improvement Analysis Department can analyze the optimal improvement methods for the feedbacked areas for improvement and provide an efficient improvement plan.

[0037] The System Improvement Department executes system improvements. For example, the System Improvement Department can execute system improvements by setting improvement procedures. Specifically, the System Improvement Department executes system improvements based on improvement plans formulated by the Improvement Analysis Department. For instance, it might implement changes to simplify specific operating procedures or upgrades to improve system performance. The System Improvement Department can also execute system improvements by defining the scope of those improvements. For example, it might improve only specific functions or components, ensuring that the overall system is not affected. In this way, the System Improvement Department can efficiently and effectively execute system improvements, enhancing system performance and usability.

[0038] The integration unit integrates the improved system. For example, the integration unit can integrate the improved system by setting up integration procedures. Specifically, the integration unit integrates the improved components and functions into the entire system, ensuring that the system operates as a cohesive unit. For example, it reflects improved operating procedures and performance enhancements throughout the system, ensuring consistent operation. The integration unit can also integrate the improved system by defining the scope of integration. For example, it may integrate only specific functions or components, without affecting other parts. In this way, the integration unit can efficiently integrate the improved system, improving the overall system performance and ease of use.

[0039] The deliverable creation department creates the final deliverables. For example, the deliverable creation department can create the final deliverables by setting the format of the deliverables. Specifically, the deliverable creation department creates the final deliverables based on the results of system improvements. For example, it creates deliverables such as system operation manuals, performance reports, and user guides, and provides them to users. Furthermore, the deliverable creation department can create the final deliverables by setting quality standards for the deliverables. For example, it can set detailed content, format, and quality standards for the deliverables and create deliverables that meet these standards. In this way, the deliverable creation department can create high-quality deliverables based on the results of system improvements and provide them to users.

[0040] The user information setting unit allows you to configure user information such as the department using the system and the method of operation. For example, the user information setting unit can set the name and role of the department using the system. It can also set the procedure and difficulty level of the operation method. This allows for detailed configuration of user information.

[0041] The system capture unit can capture a target system into a virtual space. For example, the system capture unit can capture a system into a virtual space using 3D modeling technology. Furthermore, the system capture unit can capture a system using technology for constructing a simulation environment. This allows the system to be captured into a virtual space.

[0042] The requirements input unit can input functional requirements. For example, it can input system performance requirements and operational requirements. Furthermore, the requirements input unit can also input detailed system functional requirements. This allows for the input of functional requirements.

[0043] The system replication unit can replicate a system a specified number of times within a virtual space. For example, the system replication unit can replicate a system using high-precision replication techniques. Furthermore, the system replication unit can replicate a system by specifying the range of replication. This allows for the replication of a system within a virtual space.

[0044] The simulation execution unit can run simulations in which a user operates each of the duplicated systems a specified number of times. For example, the simulation execution unit can set a simulation scenario and run the simulation. It can also set the simulation execution conditions and run the simulation. This allows for the execution of simulations in which the duplicated systems are operated.

[0045] The feedback unit can provide feedback on areas for improvement once the operation is complete. For example, the feedback unit can provide feedback on areas for improvement by setting the format of the feedback. Furthermore, the feedback unit can provide feedback on areas for improvement by setting the content of the feedback. This allows for the provision of feedback on areas for improvement.

[0046] The Improvement Analysis Department can analyze the optimal improvement method based on the feedback received regarding areas for improvement. For example, the Improvement Analysis Department can define improvement methods and then analyze the optimal improvement method. Furthermore, the Improvement Analysis Department can define analysis criteria and then analyze the optimal improvement method. This allows for the analysis of the most optimal improvement method.

[0047] The system improvement unit can perform system improvements. For example, the system improvement unit can set improvement procedures and then perform system improvements. Furthermore, the system improvement unit can also set the scope of improvements and then perform system improvements. This allows the system improvements to be carried out.

[0048] The integration unit can integrate the improved system. For example, the integration unit can integrate the improved system by setting the integration procedure. The integration unit can also integrate the improved system by setting the scope of the integration. This allows for the integration of the improved system.

[0049] The deliverable creation unit can create the final deliverable. For example, the deliverable creation unit can create the final deliverable by setting the format of the deliverable. Furthermore, the deliverable creation unit can create the final deliverable by setting the quality standards for the deliverable. This allows the final deliverable to be created.

[0050] The user information setting unit can analyze the user's past operation history and select the optimal user information setting method. For example, the user information setting unit can automatically display settings that the user has frequently used in the past as candidates. Furthermore, the user information setting unit can prioritize suggesting operation methods (voice, text, etc.) that the user has used in the past. In addition, the user information setting unit can predict and suggest settings that the user will use during specific time periods based on their past operation history. This allows for the selection of the optimal setting method based on the user's past operation history.

[0051] The user information settings unit can filter the user's settings based on their current projects and areas of interest. For example, it can prioritize displaying settings related to the user's current project. It can also suggest relevant setting options based on the user's areas of interest. Furthermore, it can automatically select appropriate settings according to the user's current project progress. This allows for filtering based on the user's current projects and areas of interest.

[0052] The user information settings unit can prioritize setting highly relevant information by considering the user's geographical location when setting user information. For example, if the user is in a specific region, the user information settings unit will prioritize displaying settings related to that region. The user information settings unit can also suggest optimal setting options based on the user's current location. Furthermore, if the user is on the move, the user information settings unit can automatically update settings according to the user's current location. This allows for the prioritization of highly relevant information based on the user's geographical location.

[0053] The user information settings unit can analyze the user's social media activity and configure relevant information when the user configures their social media profile. For example, the user information settings unit can suggest relevant setting options based on the user's social media activity. It can also automatically select the optimal settings based on the information the user has shared on social media. Furthermore, the user information settings unit can prioritize displaying relevant settings according to the user's areas of interest on social media. This allows relevant information to be configured based on the user's social media activity.

[0054] The system import unit can select the optimal import method by referring to past import history during system import. For example, the system import unit can propose the optimal import procedure based on past successful import methods. Furthermore, the system import unit can select an import method with fewer errors from past import history. In addition, the system import unit can analyze past import history and propose the most efficient import procedure. This allows for the selection of the optimal import method based on past import history.

[0055] The system acquisition unit can apply different acquisition algorithms depending on the system category during system acquisition. For example, the system acquisition unit can select the optimal acquisition algorithm based on the system category. Furthermore, the system acquisition unit can adjust the acquisition algorithm based on the system characteristics. In addition, the system acquisition unit can apply different acquisition algorithms depending on the complexity of the system. This allows for the application of the optimal acquisition algorithm according to the system category.

[0056] The system acquisition unit can perform system acquisition while considering the geographical distribution of the systems. For example, the system acquisition unit can propose an optimal acquisition procedure based on the geographical distribution of the systems. Furthermore, the system acquisition unit can provide procedures for efficiently acquiring geographically dispersed systems. In addition, the system acquisition unit can determine acquisition priorities while considering the geographical distribution of the systems. This allows it to propose an optimal acquisition procedure based on the geographical distribution of the systems.

[0057] The system acquisition unit can improve the accuracy of system acquisition by referring to relevant system documentation during the acquisition process. For example, the system acquisition unit can propose an optimal acquisition procedure based on the relevant system documentation. Furthermore, the system acquisition unit can provide procedures to improve acquisition accuracy by referring to relevant documentation. In addition, the system acquisition unit can analyze the relevant system documentation and apply algorithms to improve acquisition accuracy. This allows for improved acquisition accuracy by referring to relevant system documentation.

[0058] The requirements import unit can select the optimal import method by referring to past requirements import history during requirements import. For example, the requirements import unit can propose the optimal import procedure based on past successful requirements import methods. Furthermore, the requirements import unit can select an import method with fewer errors from past requirements import history. In addition, the requirements import unit can analyze past requirements import history and propose the most efficient import procedure. This allows for the selection of the optimal import method based on past requirements import history.

[0059] The requirements import unit can apply different import algorithms depending on the category of the requirements during the import process. For example, the requirements import unit can select the optimal import algorithm based on the category of the requirements. Furthermore, the requirements import unit can adjust the import algorithm based on the characteristics of the requirements. In addition, the requirements import unit can apply different import algorithms depending on the complexity of the requirements. This allows for the application of the optimal import algorithm according to the category of the requirements.

[0060] The requirements acquisition unit can acquire requirements while considering their geographical distribution. For example, the requirements acquisition unit can propose an optimal acquisition procedure based on the geographical distribution of requirements. Furthermore, the requirements acquisition unit can provide procedures for efficiently acquiring geographically dispersed requirements. In addition, the requirements acquisition unit can determine acquisition priorities while considering the geographical distribution of requirements. This allows it to propose an optimal acquisition procedure based on the geographical distribution of requirements.

[0061] The requirements import unit can improve the accuracy of requirements import by referring to relevant literature during the import process. For example, the requirements import unit proposes an optimal import procedure based on relevant literature. Furthermore, the requirements import unit can provide procedures to improve import accuracy by referring to relevant literature. In addition, the requirements import unit can analyze relevant literature and apply algorithms to improve import accuracy. This allows for improved import accuracy by referring to relevant literature.

[0062] The system replication unit can select the optimal replication method by referring to past replication history during system replication. For example, the system replication unit can propose the optimal replication procedure based on past successful replication methods. Furthermore, the system replication unit can select a replication method with fewer errors from past replication history. In addition, the system replication unit can analyze past replication history and propose the most efficient replication procedure. This allows for the selection of the optimal replication method based on past replication history.

[0063] The system replication unit can apply different replication algorithms depending on the system category during system replication. For example, the system replication unit can select the optimal replication algorithm based on the system category. Furthermore, the system replication unit can adjust the replication algorithm based on the system characteristics. In addition, the system replication unit can apply different replication algorithms depending on the complexity of the system. This allows for the application of the optimal replication algorithm for each system category.

[0064] The system replication unit can perform replication while considering the geographical distribution of the system. For example, the system replication unit can propose an optimal replication procedure based on the geographical distribution of the system. Furthermore, the system replication unit can provide procedures for efficiently replicating geographically dispersed systems. In addition, the system replication unit can determine replication priorities while considering the geographical distribution of the system. This allows it to propose an optimal replication procedure based on the geographical distribution of the system.

[0065] The system replication unit can improve the accuracy of replication by referring to system-related documentation during the replication process. For example, the system replication unit proposes an optimal replication procedure based on system-related documentation. Furthermore, the system replication unit can provide procedures to improve replication accuracy by referring to related documentation. In addition, the system replication unit can analyze system-related documentation and apply algorithms to improve replication accuracy. This allows for improved replication accuracy by referring to system-related documentation.

[0066] The simulation execution unit can select the optimal execution method by referring to past simulation history during simulation execution. For example, the simulation execution unit can propose the optimal execution procedure based on past successful simulation methods. Furthermore, the simulation execution unit can select an execution method with fewer errors from past simulation history. In addition, the simulation execution unit can analyze past simulation history and propose the most efficient execution procedure. This allows for the selection of the optimal execution method based on past simulation history.

[0067] The simulation execution unit can apply different execution algorithms depending on the simulation category during simulation execution. For example, the simulation execution unit can select the optimal execution algorithm based on the simulation category. Furthermore, the simulation execution unit can adjust the execution algorithm based on the characteristics of the simulation. In addition, the simulation execution unit can apply different execution algorithms depending on the complexity of the simulation. This allows for the application of the optimal execution algorithm according to the simulation category.

[0068] The simulation execution unit can perform simulations while considering the geographical distribution of the simulations. For example, the simulation execution unit can propose an optimal execution procedure based on the geographical distribution of the simulations. Furthermore, the simulation execution unit can provide procedures for efficiently executing geographically dispersed simulations. In addition, the simulation execution unit can determine execution priorities while considering the geographical distribution of the simulations. This allows it to propose an optimal execution procedure based on the geographical distribution of the simulations.

[0069] The simulation execution unit can improve the accuracy of the simulation by referring to relevant literature during the simulation execution. For example, the simulation execution unit can propose an optimal execution procedure based on the relevant literature. Furthermore, the simulation execution unit can provide procedures to improve the accuracy of the simulation by referring to relevant literature. In addition, the simulation execution unit can analyze the relevant literature and apply algorithms to improve the accuracy of the simulation. This allows for improved execution accuracy by referring to relevant literature.

[0070] The feedback unit can select the optimal feedback method by referring to past feedback history during the feedback process. For example, the feedback unit can propose the optimal feedback procedure based on past successful feedback methods. Furthermore, the feedback unit can select feedback methods with fewer errors from past feedback history. In addition, the feedback unit can analyze past feedback history and propose the most efficient feedback procedure. This allows for the selection of the optimal feedback method based on past feedback history.

[0071] The feedback unit can apply different feedback algorithms depending on the feedback category. For example, the feedback unit can select the optimal feedback algorithm based on the feedback category. Furthermore, the feedback unit can adjust the feedback algorithm based on the characteristics of the feedback. In addition, the feedback unit can apply different feedback algorithms depending on the complexity of the feedback. This allows for the application of the optimal feedback algorithm for each feedback category.

[0072] The feedback unit can provide feedback while considering the geographical distribution of the feedback. For example, the feedback unit can propose an optimal feedback procedure based on the geographical distribution of the feedback. Furthermore, the feedback unit can provide procedures for efficiently handling geographically dispersed feedback. In addition, the feedback unit can determine the priority of feedback while considering its geographical distribution. This allows it to propose an optimal feedback procedure based on the geographical distribution of the feedback.

[0073] The feedback unit can improve the accuracy of feedback by referring to relevant literature during the feedback process. For example, the feedback unit proposes an optimal feedback procedure based on relevant literature. Furthermore, the feedback unit can provide procedures to improve the accuracy of feedback by referring to relevant literature. In addition, the feedback unit can analyze relevant literature and apply algorithms to improve the accuracy of feedback. This allows for improved feedback accuracy by referring to relevant literature.

[0074] The Improvement Analysis Department can select the optimal analysis method by referring to past improvement analysis history during improvement analysis. For example, the Improvement Analysis Department can propose the optimal analysis procedure based on past successful improvement analysis methods. Furthermore, the Improvement Analysis Department can select an analysis method with fewer errors from past improvement analysis history. In addition, the Improvement Analysis Department can analyze past improvement analysis history and propose the most efficient analysis procedure. This allows for the selection of the optimal analysis method based on past improvement analysis history.

[0075] The Improvement Analysis Unit can apply different analysis algorithms depending on the improvement category during improvement analysis. For example, the Improvement Analysis Unit can select the optimal analysis algorithm based on the improvement category. Furthermore, the Improvement Analysis Unit can adjust the analysis algorithm based on the characteristics of the improvement. In addition, the Improvement Analysis Unit can apply different analysis algorithms depending on the complexity of the improvement. This allows for the application of the optimal analysis algorithm for each improvement category.

[0076] The Improvement Analysis Department can perform improvement analysis while considering the geographical distribution of improvements. For example, the Improvement Analysis Department can propose the optimal analysis procedure based on the geographical distribution of improvements. Furthermore, the Improvement Analysis Department can provide procedures for efficiently analyzing geographically dispersed improvements. In addition, the Improvement Analysis Department can determine analysis priorities while considering the geographical distribution of improvements. This allows it to propose the optimal analysis procedure based on the geographical distribution of improvements.

[0077] The Improvement Analysis Department can improve the accuracy of its analysis by referring to relevant literature on improvement during the improvement analysis process. For example, the Improvement Analysis Department can propose the optimal analysis procedure based on relevant literature on improvement. Furthermore, the Improvement Analysis Department can provide procedures to improve the accuracy of the analysis by referring to relevant literature. In addition, the Improvement Analysis Department can analyze relevant literature on improvement and apply algorithms to improve the accuracy of the analysis. This allows for improved accuracy of the analysis by referring to relevant literature on improvement.

[0078] The system improvement department can select the optimal improvement method by referring to past improvement history when improving the system. For example, the system improvement department can propose the optimal improvement procedure based on past successful improvement methods. Furthermore, the system improvement department can select improvement methods with fewer errors from past improvement history. In addition, the system improvement department can analyze past improvement history and propose the most efficient improvement procedure. This allows for the selection of the optimal improvement method based on past improvement history.

[0079] The system improvement unit can apply different improvement algorithms depending on the improvement category during system improvement. For example, the system improvement unit can select the optimal improvement algorithm according to the improvement category. Furthermore, the system improvement unit can adjust the improvement algorithm based on the characteristics of the improvement. In addition, the system improvement unit can apply different improvement algorithms depending on the complexity of the improvement. This allows for the application of the optimal improvement algorithm according to the improvement category.

[0080] The system improvement unit can perform system improvements while considering the geographical distribution of those improvements. For example, the system improvement unit can propose the optimal improvement procedure based on the geographical distribution of improvements. Furthermore, the system improvement unit can provide procedures for efficiently performing geographically dispersed improvements. In addition, the system improvement unit can determine the priority of improvements while considering their geographical distribution. This allows it to propose the optimal improvement procedure based on the geographical distribution of improvements.

[0081] The system improvement unit can improve the accuracy of improvements by referring to relevant literature during system improvements. For example, the system improvement unit can propose optimal improvement procedures based on relevant literature. Furthermore, the system improvement unit can provide procedures to improve the accuracy of improvements by referring to relevant literature. In addition, the system improvement unit can analyze relevant literature and apply algorithms to improve the accuracy of improvements. This allows for improved accuracy of improvements by referring to relevant literature.

[0082] The integration unit can select the optimal integration method by referring to past integration history during the integration process. For example, the integration unit can propose the optimal integration procedure based on past successful integration methods. Furthermore, the integration unit can select an integration method with fewer errors from past integration history. In addition, the integration unit can analyze past integration history and propose the most efficient integration procedure. This allows for the selection of the optimal integration method based on past integration history.

[0083] The integration unit can apply different integration algorithms depending on the integration category during integration. For example, the integration unit can select the optimal integration algorithm based on the integration category. Furthermore, the integration unit can adjust the integration algorithm based on the characteristics of the integration. In addition, the integration unit can apply different integration algorithms depending on the complexity of the integration. This allows for the application of the optimal integration algorithm according to the integration category.

[0084] The integration unit can perform integrations while considering the geographical distribution of the integrations. For example, the integration unit can propose an optimal integration procedure based on the geographical distribution of the integrations. Furthermore, the integration unit can provide procedures for efficiently performing geographically dispersed integrations. In addition, the integration unit can determine integration priorities while considering the geographical distribution of the integrations. This allows it to propose an optimal integration procedure based on the geographical distribution of the integrations.

[0085] The integration unit can improve the accuracy of the integration by referring to relevant literature during the integration process. For example, the integration unit proposes an optimal integration procedure based on relevant literature. Furthermore, the integration unit can provide procedures to improve the accuracy of the integration by referring to relevant literature. In addition, the integration unit can analyze relevant literature and apply algorithms to improve the accuracy of the integration. This allows for improved integration accuracy by referring to relevant literature.

[0086] The deliverable creation department can select the optimal creation method by referring to past deliverable creation history when creating deliverables. For example, the deliverable creation department can propose the optimal creation procedure based on past successful creation methods. Furthermore, the deliverable creation department can select a creation method with fewer errors from past creation history. In addition, the deliverable creation department can analyze past creation history and propose the most efficient creation procedure. This allows for the selection of the optimal creation method based on past deliverable creation history.

[0087] The deliverable creation unit can apply different creation algorithms depending on the category of the deliverable during creation. For example, the deliverable creation unit can select the optimal creation algorithm according to the category of the deliverable. Furthermore, the deliverable creation unit can adjust the creation algorithm based on the characteristics of the deliverable. In addition, the deliverable creation unit can apply different creation algorithms depending on the complexity of the deliverable. This allows for the application of the optimal creation algorithm according to the category of the deliverable.

[0088] The deliverable creation unit can create deliverables while considering their geographical distribution. For example, the unit can propose the optimal creation procedure based on the geographical distribution of the deliverables. Furthermore, the unit can provide procedures for efficiently creating geographically dispersed deliverables. In addition, the unit can determine the creation priority based on the geographical distribution of the deliverables. This allows the unit to propose the optimal creation procedure based on the geographical distribution of the deliverables.

[0089] The deliverable creation unit can improve the accuracy of deliverable creation by referring to relevant literature during the creation process. For example, the deliverable creation unit can propose the optimal creation procedure based on relevant literature. Furthermore, the deliverable creation unit can provide procedures to improve the accuracy of creation by referring to relevant literature. In addition, the deliverable creation unit can analyze relevant literature and apply algorithms to improve the accuracy of creation. This allows for improved accuracy of creation by referring to relevant literature.

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

[0091] The user information settings unit can analyze the user's past operation history and select the optimal user information settings method. For example, it can automatically display settings that the user has frequently used in the past as candidates. It can also prioritize suggesting operation methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest settings that the user will use during specific time periods based on their past operation history. This allows the system to select the optimal settings method based on the user's past operation history.

[0092] The system import unit can select the optimal import method by referring to past import history during system import. For example, it can propose the optimal import procedure based on past successful import methods. It can also select an import method with fewer errors from past import history. Furthermore, it can analyze past import history and propose the most efficient import procedure. This allows for the selection of the optimal import method based on past import history.

[0093] The requirements acquisition unit can select the optimal acquisition method by referring to past requirements acquisition history during requirements acquisition. For example, it can propose the optimal acquisition procedure based on past successful requirements acquisition methods. It can also select an acquisition method with fewer errors from past requirements acquisition history. Furthermore, it can analyze past requirements acquisition history and propose the most efficient acquisition procedure. This allows for the selection of the optimal acquisition method based on past requirements acquisition history.

[0094] The system replication unit can select the optimal replication method by referring to past replication history during system replication. For example, it can propose the optimal replication procedure based on past successful replication methods. It can also select a replication method with fewer errors from past replication history. Furthermore, it can analyze past replication history and propose the most efficient replication procedure. This allows for the selection of the optimal replication method based on past replication history.

[0095] The simulation execution unit can select the optimal execution method by referring to past simulation history during simulation execution. For example, it can propose the optimal execution procedure based on past successful simulation methods. It can also select an execution method with fewer errors from past simulation history. Furthermore, it can analyze past simulation history and propose the most efficient execution procedure. In this way, the optimal execution method can be selected based on past simulation history.

[0096] The following briefly describes the processing flow for example form 1.

[0097] Step 1: The user information setting section is used to configure user information. For example, user information such as the department of use and the method of operation can be set. Specifically, the name and role of the department of use, and the procedure and difficulty level of the method of operation can be set. Step 2: The system integration unit integrates the target system into the virtual space. For example, it may use 3D modeling technology or technology for building a simulation environment to integrate the system. Step 3: The requirements import unit imports functional requirements. For example, it imports detailed system performance requirements, operational requirements, and functional requirements. Step 4: The system replication unit replicates the system a specified number of times within the virtual space. For example, it is possible to replicate the system using high-precision replication technology and specify the scope of replication. Step 5: The simulation execution unit runs a simulation in which the configured user operates each of the duplicated systems a specified number of times. For example, the simulation scenario and execution conditions are set and the simulation is executed. Step 6: The feedback unit provides feedback on areas for improvement once the operation is complete. For example, it sets the format and content of the feedback and then provides feedback on areas for improvement. Step 7: The Improvement Analysis Department analyzes the optimal improvement methods based on the feedback received regarding areas for improvement. For example, they set improvement methods and analysis criteria to analyze the best improvement methods. Step 8: The system improvement unit executes the system improvements. For example, they set the procedures and scope of the improvements and then execute the system improvements. Step 9: The integration unit integrates the improved system. For example, it integrates the improved system by defining the integration procedures and scope. Step 10: The deliverable creation department creates the final deliverable. For example, they set the format and quality standards for the deliverable and then create the final deliverable.

[0098] (Example of form 2) The system according to an embodiment of the present invention is a simulation platform that uses generative AI to improve an existing system in a virtual space. First, the system sets the user information of the system. Next, it imports the target system into the virtual space, including its functional requirements. The imported system is duplicated a specified number of times within the virtual space. The set user runs a simulation operating each of the duplicated systems a specified number of times, and after the operation is complete, feedback is provided on areas for improvement. Based on the feedback on areas for improvement, the generative AI is used to analyze the optimal improvement method and implement system improvements. This procedure is repeated a specified number of times. Finally, a system with the specified number of improvements implemented is constructed, and these are integrated by the generative AI to create the final deliverable. The final deliverable is retrieved from the virtual space and considered the finished product. This mechanism allows existing systems to evolve into more optimal systems by performing a number of functional improvements in the virtual space that would be impossible to achieve in the real world. Thus, the system can provide a simulation platform that uses generative AI to improve existing systems in a virtual space.

[0099] The system according to the embodiment comprises a user information setting unit, a system import unit, a requirements import unit, a system duplication unit, a simulation execution unit, a feedback unit, an improvement analysis unit, a system improvement unit, an integration unit, and a deliverable creation unit. The user information setting unit sets user information. The user information setting unit can set user information such as the department of use and the method of operation. For example, the user information setting unit can set the name and role of the department of use. The user information setting unit can also set the procedure and difficulty level of the method of operation. The system import unit imports the target system into a virtual space. The system import unit can import the system into a virtual space using, for example, 3D modeling technology. The system import unit can also import the system using technology for constructing a simulation environment. The requirements import unit imports functional requirements. For example, the requirements import unit can import the system's performance requirements and operation requirements. The requirements import unit can also import the system's functional requirements in detail. The system duplication unit duplicates the system a specified number of times in the virtual space. The system replication unit can replicate a system using, for example, a high-precision replication technique. The system replication unit can also replicate a system by specifying the replication range. The simulation execution unit runs a simulation where a designated user operates each replicated system a specified number of times. The simulation execution unit can, for example, execute a simulation by setting a simulation scenario. It can also execute a simulation by setting the simulation execution conditions. The feedback unit provides feedback on areas for improvement after the operation is complete. The feedback unit can, for example, provide feedback on areas for improvement by setting the feedback format. It can also provide feedback on areas for improvement by setting the content of the feedback. The improvement analysis unit analyzes the optimal improvement method based on the feedbacked areas for improvement. The improvement analysis unit can, for example, analyze the optimal improvement method by setting the improvement technique.Furthermore, the Improvement Analysis Unit can set analysis criteria and analyze the optimal improvement method. The System Improvement Unit executes system improvements. The System Improvement Unit can, for example, set improvement procedures and execute system improvements. The System Improvement Unit can also set the scope of improvements and execute system improvements. The Integration Unit integrates the improved system. The Integration Unit can, for example, set integration procedures and integrate the improved system. The Integration Unit can also set the scope of integration and integrate the improved system. The Deliverable Creation Unit creates the final deliverable. The Deliverable Creation Unit can, for example, set the format of the deliverable and create the final deliverable. The Deliverable Creation Unit can also set quality standards for the deliverable and create the final deliverable. As a result, the system according to the embodiment can efficiently perform the following processes: user information setting, system import, requirements import, system replication, simulation execution, feedback, improvement analysis, system improvement, integration, and deliverable creation.

[0100] The User Information Settings section configures user information. For example, it can configure user information such as the department of use and operating methods. Specifically, the User Information Settings section can configure the name and role of the department of use in detail. For example, it can input the name, role, and contact information of a specific department or team within a company, clarifying what operations each department performs. The User Information Settings section can also configure the procedures and difficulty level of operating methods. For example, it can provide a simple operation guide for beginners and detailed operating procedures for advanced users, allowing users to select the appropriate operating method according to their skill level. Furthermore, the User Information Settings section can also configure user permissions. For example, it can grant access to all functions to users with administrator privileges, while limiting the use of functions to only a select few. In this way, the User Information Settings section can provide system users with appropriate information and an appropriate operating environment, supporting the efficient operation of the system.

[0101] The system capture unit captures the target system into a virtual space. For example, the system capture unit can capture a system into the virtual space using 3D modeling technology. Specifically, the system capture unit scans the physical structure and functions of the target system in detail and reproduces them as a 3D model in the virtual space. In this process, each component, wiring, and connection status of the system are accurately reproduced, enabling operation and verification similar to that of the actual system. Furthermore, the system capture unit can also capture a system using technologies for building a simulation environment. For example, it can reproduce the system's operating environment and external interfaces in the virtual space and simulate how the system actually operates. In this way, the system capture unit can reproduce real-world systems in a virtual space with high accuracy, providing a foundation for simulation and verification.

[0102] The requirements input unit incorporates functional requirements. For example, it can incorporate system performance and operational requirements. Specifically, it incorporates detailed performance metrics and operating procedures that the system should achieve. For instance, it sets performance requirements such as system processing speed, response time, and durability, and verifies whether these requirements are met. The requirements input unit can also incorporate detailed functional requirements. For example, it sets operating conditions and interface specifications for each function provided by the system, and verifies whether these requirements are accurately implemented. In this way, the requirements input unit incorporates detailed requirements regarding system performance and functionality, providing a foundation for ensuring the system operates as expected.

[0103] The system replication unit replicates a system a specified number of times within a virtual space. For example, the system replication unit can replicate a system using high-precision replication techniques. Specifically, it accurately reproduces each component, wiring, and connection state of the system within the virtual space, enabling simultaneous operation and verification of multiple systems. Furthermore, the system replication unit can replicate a system by specifying the scope of replication. For example, it can replicate only specific functions or components and individually verify their operation and performance. In this way, the system replication unit can efficiently replicate systems within a virtual space, providing a foundation for diverse simulations and verifications.

[0104] The simulation execution unit runs simulations in which a specified user operates each of the duplicated systems a specified number of times. The simulation execution unit can, for example, set up simulation scenarios and execute the simulation. Specifically, the simulation execution unit sets up detailed scenarios for when the user operates the system and executes the simulation. For example, it sets up specific operating procedures and methods for responding to abnormal occurrences as scenarios, and the user operates the system according to these scenarios. The simulation execution unit can also set up simulation execution conditions and execute the simulation. For example, it sets the system's operating environment and external conditions and simulates how the system operates under these conditions. In this way, the simulation execution unit can efficiently execute simulations of when the user operates the system and provide a foundation for verifying the system's operation and performance.

[0105] The feedback unit provides feedback on areas for improvement once the operation is complete. For example, the feedback unit can provide feedback on areas for improvement by setting the format of the feedback. Specifically, the feedback unit analyzes the results of the user's operation of the system in detail and identifies areas that need improvement. For example, it identifies time-consuming parts of the operation procedure or parts prone to errors, and provides feedback to improve these areas. The feedback unit can also provide feedback on areas for improvement by setting the content of the feedback. For example, it can describe specific improvement methods and procedures in detail, allowing users to improve the system based on this feedback. In this way, the feedback unit can identify areas for improvement in the system based on the user's operation results and provide efficient feedback.

[0106] The Improvement Analysis Department analyzes the optimal improvement methods based on the feedback received regarding areas for improvement. For example, the Improvement Analysis Department can define improvement methods and then analyze the optimal improvement methods. Specifically, the Improvement Analysis Department selects the optimal improvement methods for the feedbacked areas for improvement and formulates a detailed improvement plan. For example, it may select methods to simplify specific operating procedures or technologies to improve system performance, and then create an improvement plan based on these methods. The Improvement Analysis Department can also analyze the optimal improvement methods by setting analysis criteria. For example, it may set criteria such as the effectiveness, cost, and feasibility of the improvement, and then select the optimal improvement method based on these criteria. In this way, the Improvement Analysis Department can analyze the optimal improvement methods for the feedbacked areas for improvement and provide an efficient improvement plan.

[0107] The System Improvement Department executes system improvements. For example, the System Improvement Department can execute system improvements by setting improvement procedures. Specifically, the System Improvement Department executes system improvements based on improvement plans formulated by the Improvement Analysis Department. For instance, it might implement changes to simplify specific operating procedures or upgrades to improve system performance. The System Improvement Department can also execute system improvements by defining the scope of those improvements. For example, it might improve only specific functions or components, ensuring that the overall system is not affected. In this way, the System Improvement Department can efficiently and effectively execute system improvements, enhancing system performance and usability.

[0108] The integration unit integrates the improved system. For example, the integration unit can integrate the improved system by setting up integration procedures. Specifically, the integration unit integrates the improved components and functions into the entire system, ensuring that the system operates as a cohesive unit. For example, it reflects improved operating procedures and performance enhancements throughout the system, ensuring consistent operation. The integration unit can also integrate the improved system by defining the scope of integration. For example, it may integrate only specific functions or components, without affecting other parts. In this way, the integration unit can efficiently integrate the improved system, improving the overall system performance and ease of use.

[0109] The deliverable creation department creates the final deliverables. For example, the deliverable creation department can create the final deliverables by setting the format of the deliverables. Specifically, the deliverable creation department creates the final deliverables based on the results of system improvements. For example, it creates deliverables such as system operation manuals, performance reports, and user guides, and provides them to users. Furthermore, the deliverable creation department can create the final deliverables by setting quality standards for the deliverables. For example, it can set detailed content, format, and quality standards for the deliverables and create deliverables that meet these standards. In this way, the deliverable creation department can create high-quality deliverables based on the results of system improvements and provide them to users.

[0110] The user information setting unit allows you to configure user information such as the department using the system and the method of operation. For example, the user information setting unit can set the name and role of the department using the system. It can also set the procedure and difficulty level of the operation method. This allows for detailed configuration of user information.

[0111] The system capture unit can capture a target system into a virtual space. For example, the system capture unit can capture a system into a virtual space using 3D modeling technology. Furthermore, the system capture unit can capture a system using technology for constructing a simulation environment. This allows the system to be captured into a virtual space.

[0112] The requirements input unit can input functional requirements. For example, it can input system performance requirements and operational requirements. Furthermore, the requirements input unit can input detailed system functional requirements. This allows for the input of functional requirements.

[0113] The system replication unit can replicate a system a specified number of times within a virtual space. For example, the system replication unit can replicate a system using high-precision replication techniques. Furthermore, the system replication unit can replicate a system by specifying the range of replication. This allows for the replication of a system within a virtual space.

[0114] The simulation execution unit can run simulations in which a user operates each of the duplicated systems a specified number of times. For example, the simulation execution unit can set a simulation scenario and run the simulation. It can also set the simulation execution conditions and run the simulation. This allows for the execution of simulations in which the duplicated systems are operated.

[0115] The feedback unit can provide feedback on areas for improvement once the operation is complete. For example, the feedback unit can provide feedback on areas for improvement by setting the format of the feedback. Furthermore, the feedback unit can provide feedback on areas for improvement by setting the content of the feedback. This allows for the provision of feedback on areas for improvement.

[0116] The Improvement Analysis Department can analyze the optimal improvement method based on the feedback received regarding areas for improvement. For example, the Improvement Analysis Department can define improvement methods and then analyze the optimal improvement method. Furthermore, the Improvement Analysis Department can define analysis criteria and then analyze the optimal improvement method. This allows for the analysis of the most optimal improvement method.

[0117] The system improvement unit can perform system improvements. For example, the system improvement unit can set improvement procedures and then perform system improvements. Furthermore, the system improvement unit can also set the scope of improvements and then perform system improvements. This allows the system improvements to be carried out.

[0118] The integration unit can integrate the improved system. For example, the integration unit can integrate the improved system by setting the integration procedure. The integration unit can also integrate the improved system by setting the scope of the integration. This allows for the integration of the improved system.

[0119] The deliverable creation unit can create the final deliverable. For example, the deliverable creation unit can create the final deliverable by setting the format of the deliverable. Furthermore, the deliverable creation unit can create the final deliverable by setting the quality standards for the deliverable. This allows the final deliverable to be created.

[0120] The user information setting unit can estimate the user's emotions and adjust the user information setting method based on the estimated emotions. For example, if the user is stressed, the user information setting unit can provide a simple interface and minimize the input steps. If the user is relaxed, the user information setting unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the user information setting unit can prioritize voice input to allow for quick user information setting. This allows the user information setting method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The user information setting unit can analyze the user's past operation history and select the optimal user information setting method. For example, the user information setting unit can automatically display settings that the user has frequently used in the past as candidates. Furthermore, the user information setting unit can prioritize suggesting operation methods (voice, text, etc.) that the user has used in the past. In addition, the user information setting unit can predict and suggest settings that the user will use during specific time periods based on their past operation history. This allows for the selection of the optimal setting method based on the user's past operation history.

[0122] The user information settings unit can filter the user's settings based on their current projects and areas of interest. For example, it can prioritize displaying settings related to the user's current project. It can also suggest relevant setting options based on the user's areas of interest. Furthermore, it can automatically select appropriate settings according to the user's current project progress. This allows for filtering based on the user's current projects and areas of interest.

[0123] The user information settings unit can estimate the user's emotions and determine the priority of user information to be set based on the estimated emotions. For example, if the user is stressed, the user information settings unit will prioritize displaying important settings. If the user is relaxed, the user information settings unit can also provide detailed settings options. Furthermore, if the user is in a hurry, the user information settings unit can display only the most important settings, allowing for quick completion of the settings. This enables the prioritization of user information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The user information settings unit can prioritize setting highly relevant information by considering the user's geographical location when setting user information. For example, if the user is in a specific region, the user information settings unit will prioritize displaying settings related to that region. The user information settings unit can also suggest optimal setting options based on the user's current location. Furthermore, if the user is on the move, the user information settings unit can automatically update settings according to the user's current location. This allows for the prioritization of highly relevant information based on the user's geographical location.

[0125] The user information settings unit can analyze the user's social media activity and configure relevant information when the user configures their social media profile. For example, the user information settings unit can suggest relevant setting options based on the user's social media activity. It can also automatically select the optimal settings based on the information the user has shared on social media. Furthermore, the user information settings unit can prioritize displaying relevant settings according to the user's areas of interest on social media. This allows relevant information to be configured based on the user's social media activity.

[0126] The system capture unit can estimate the user's emotions and adjust the system capture method based on the estimated user emotions. For example, if the user is stressed, the system capture unit can provide a simple capture procedure. It can also provide detailed capture options if the user is relaxed. Furthermore, if the user is in a hurry, the system capture unit can provide a simplified procedure for quick system capture. This allows the system capture method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The system import unit can select the optimal import method by referring to past import history during system import. For example, the system import unit can propose the optimal import procedure based on past successful import methods. Furthermore, the system import unit can select an import method with fewer errors from past import history. In addition, the system import unit can analyze past import history and propose the most efficient import procedure. This allows for the selection of the optimal import method based on past import history.

[0128] The system acquisition unit can apply different acquisition algorithms depending on the system category during system acquisition. For example, the system acquisition unit can select the optimal acquisition algorithm based on the system category. Furthermore, the system acquisition unit can adjust the acquisition algorithm based on the system characteristics. In addition, the system acquisition unit can apply different acquisition algorithms depending on the complexity of the system. This allows for the application of the optimal acquisition algorithm according to the system category.

[0129] The system capture unit can estimate the user's emotions and determine the priority of systems to capture based on the estimated emotions. For example, if the user is stressed, the system capture unit will prioritize capturing important systems. If the user is relaxed, the system capture unit can also provide detailed system capture options. Furthermore, if the user is in a hurry, the system capture unit can quickly capture the most important systems. This allows for system prioritization based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The system acquisition unit can perform system acquisition while considering the geographical distribution of the systems. For example, the system acquisition unit can propose an optimal acquisition procedure based on the geographical distribution of the systems. Furthermore, the system acquisition unit can provide procedures for efficiently acquiring geographically dispersed systems. In addition, the system acquisition unit can determine acquisition priorities while considering the geographical distribution of the systems. This allows it to propose an optimal acquisition procedure based on the geographical distribution of the systems.

[0131] The system acquisition unit can improve the accuracy of system acquisition by referring to relevant system documentation during the acquisition process. For example, the system acquisition unit can propose an optimal acquisition procedure based on the relevant system documentation. Furthermore, the system acquisition unit can provide procedures to improve acquisition accuracy by referring to relevant documentation. In addition, the system acquisition unit can analyze the relevant system documentation and apply algorithms to improve acquisition accuracy. This allows for improved acquisition accuracy by referring to relevant system documentation.

[0132] The requirements capture unit can estimate the user's emotions and adjust the requirements capture method based on the estimated user emotions. For example, if the user is stressed, the requirements capture unit can provide a simple capture procedure. It can also provide detailed capture options if the user is relaxed. Furthermore, if the user is in a hurry, the requirements capture unit can provide a simplified procedure for quickly capturing requirements. This allows the requirements capture method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0133] The requirements import unit can select the optimal import method by referring to past requirements import history during requirements import. For example, the requirements import unit can propose the optimal import procedure based on past successful requirements import methods. Furthermore, the requirements import unit can select an import method with fewer errors from past requirements import history. In addition, the requirements import unit can analyze past requirements import history and propose the most efficient import procedure. This allows for the selection of the optimal import method based on past requirements import history.

[0134] The requirements import unit can apply different import algorithms depending on the category of the requirements during the import process. For example, the requirements import unit can select the optimal import algorithm based on the category of the requirements. Furthermore, the requirements import unit can adjust the import algorithm based on the characteristics of the requirements. In addition, the requirements import unit can apply different import algorithms depending on the complexity of the requirements. This allows for the application of the optimal import algorithm according to the category of the requirements.

[0135] The requirements capture unit can estimate the user's emotions and determine the priority of requirements to capture based on the estimated emotions. For example, if the user is stressed, the requirements capture unit will prioritize capturing important requirements. If the user is relaxed, the requirements capture unit can also provide detailed requirements capture options. Furthermore, if the user is in a hurry, the requirements capture unit can quickly capture the most important requirements. This allows for the prioritization of requirements based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0136] The requirements acquisition unit can acquire requirements while considering their geographical distribution. For example, the requirements acquisition unit can propose an optimal acquisition procedure based on the geographical distribution of requirements. Furthermore, the requirements acquisition unit can provide procedures for efficiently acquiring geographically dispersed requirements. In addition, the requirements acquisition unit can determine acquisition priorities while considering the geographical distribution of requirements. This allows it to propose an optimal acquisition procedure based on the geographical distribution of requirements.

[0137] The requirements import unit can improve the accuracy of requirements import by referring to relevant literature during the import process. For example, the requirements import unit proposes an optimal import procedure based on relevant literature. Furthermore, the requirements import unit can provide procedures to improve import accuracy by referring to relevant literature. In addition, the requirements import unit can analyze relevant literature and apply algorithms to improve import accuracy. This allows for improved import accuracy by referring to relevant literature.

[0138] The system replication unit can estimate the user's emotions and adjust the system replication method based on the estimated user emotions. For example, if the user is stressed, the system replication unit can provide a simple replication procedure. It can also provide more detailed replication options if the user is relaxed. Furthermore, if the user is in a hurry, the system replication unit can provide a simplified procedure for quickly replicating the system. This allows the system replication method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0139] The system replication unit can select the optimal replication method by referring to past replication history during system replication. For example, the system replication unit can propose the optimal replication procedure based on past successful replication methods. Furthermore, the system replication unit can select a replication method with fewer errors from past replication history. In addition, the system replication unit can analyze past replication history and propose the most efficient replication procedure. This allows for the selection of the optimal replication method based on past replication history.

[0140] The system replication unit can apply different replication algorithms depending on the system category during system replication. For example, the system replication unit can select the optimal replication algorithm based on the system category. Furthermore, the system replication unit can adjust the replication algorithm based on the system characteristics. In addition, the system replication unit can apply different replication algorithms depending on the complexity of the system. This allows for the application of the optimal replication algorithm for each system category.

[0141] The system replication unit can estimate the user's emotions and determine the priority of systems to replicate based on the estimated emotions. For example, if the user is stressed, the system replication unit will prioritize replicating important systems. If the user is relaxed, the system replication unit can also provide more detailed replication options. Furthermore, if the user is in a hurry, the system replication unit can quickly replicate the most important systems. This allows for system prioritization based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0142] The system replication unit can perform replication while considering the geographical distribution of the system. For example, the system replication unit can propose an optimal replication procedure based on the geographical distribution of the system. Furthermore, the system replication unit can provide procedures for efficiently replicating geographically dispersed systems. In addition, the system replication unit can determine replication priorities while considering the geographical distribution of the system. This allows it to propose an optimal replication procedure based on the geographical distribution of the system.

[0143] The system replication unit can improve the accuracy of replication by referring to system-related documentation during the replication process. For example, the system replication unit proposes an optimal replication procedure based on system-related documentation. Furthermore, the system replication unit can provide procedures to improve replication accuracy by referring to related documentation. In addition, the system replication unit can analyze system-related documentation and apply algorithms to improve replication accuracy. This allows for improved replication accuracy by referring to system-related documentation.

[0144] The simulation execution unit can estimate the user's emotions and adjust the simulation execution method based on the estimated user emotions. For example, if the user is stressed, the simulation execution unit can provide a simple simulation procedure. It can also provide detailed simulation options if the user is relaxed. Furthermore, if the user is in a hurry, the simulation execution unit can provide a simplified procedure for quickly executing the simulation. This allows the simulation execution method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0145] The simulation execution unit can select the optimal execution method by referring to past simulation history during simulation execution. For example, the simulation execution unit can propose the optimal execution procedure based on past successful simulation methods. Furthermore, the simulation execution unit can select an execution method with fewer errors from past simulation history. In addition, the simulation execution unit can analyze past simulation history and propose the most efficient execution procedure. This allows for the selection of the optimal execution method based on past simulation history.

[0146] The simulation execution unit can apply different execution algorithms depending on the simulation category during simulation execution. For example, the simulation execution unit can select the optimal execution algorithm based on the simulation category. Furthermore, the simulation execution unit can adjust the execution algorithm based on the characteristics of the simulation. In addition, the simulation execution unit can apply different execution algorithms depending on the complexity of the simulation. This allows for the application of the optimal execution algorithm according to the simulation category.

[0147] The simulation execution unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is stressed, the simulation execution unit will prioritize important simulations. If the user is relaxed, the simulation execution unit can also provide more detailed simulation options. Furthermore, if the user is in a hurry, the simulation execution unit can quickly execute the most important simulations. This allows for the prioritization of simulations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0148] The simulation execution unit can perform simulations while considering the geographical distribution of the simulations. For example, the simulation execution unit can propose an optimal execution procedure based on the geographical distribution of the simulations. Furthermore, the simulation execution unit can provide procedures for efficiently executing geographically dispersed simulations. In addition, the simulation execution unit can determine execution priorities while considering the geographical distribution of the simulations. This allows it to propose an optimal execution procedure based on the geographical distribution of the simulations.

[0149] The simulation execution unit can improve the accuracy of the simulation by referring to relevant literature during the simulation execution. For example, the simulation execution unit can propose an optimal execution procedure based on the relevant literature. Furthermore, the simulation execution unit can provide procedures to improve the accuracy of the simulation by referring to relevant literature. In addition, the simulation execution unit can analyze the relevant literature and apply algorithms to improve the accuracy of the simulation. This allows for improved execution accuracy by referring to relevant literature.

[0150] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide a simple feedback procedure. It can also provide more detailed feedback options if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide a simplified procedure for quick feedback. This allows the feedback method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0151] The feedback unit can select the optimal feedback method by referring to past feedback history during the feedback process. For example, the feedback unit can propose the optimal feedback procedure based on past successful feedback methods. Furthermore, the feedback unit can select feedback methods with fewer errors from past feedback history. In addition, the feedback unit can analyze past feedback history and propose the most efficient feedback procedure. This allows for the selection of the optimal feedback method based on past feedback history.

[0152] The feedback unit can apply different feedback algorithms depending on the feedback category. For example, the feedback unit can select the optimal feedback algorithm based on the feedback category. Furthermore, the feedback unit can adjust the feedback algorithm based on the characteristics of the feedback. In addition, the feedback unit can apply different feedback algorithms depending on the complexity of the feedback. This allows for the application of the optimal feedback algorithm for each feedback category.

[0153] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will prioritize important feedback. If the user is relaxed, the feedback unit can also provide more detailed feedback options. Furthermore, if the user is in a hurry, the feedback unit can quickly provide the most important feedback. This allows for prioritizing feedback based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0154] The feedback unit can provide feedback while considering the geographical distribution of the feedback. For example, the feedback unit can propose an optimal feedback procedure based on the geographical distribution of the feedback. Furthermore, the feedback unit can provide procedures for efficiently handling geographically dispersed feedback. In addition, the feedback unit can determine the priority of feedback while considering its geographical distribution. This allows it to propose an optimal feedback procedure based on the geographical distribution of the feedback.

[0155] The feedback unit can improve the accuracy of feedback by referring to relevant literature during the feedback process. For example, the feedback unit proposes an optimal feedback procedure based on relevant literature. Furthermore, the feedback unit can provide procedures to improve the accuracy of feedback by referring to relevant literature. In addition, the feedback unit can analyze relevant literature and apply algorithms to improve the accuracy of feedback. This allows for improved feedback accuracy by referring to relevant literature.

[0156] The Improvement Analysis Unit can estimate the user's emotions and adjust the improvement analysis method based on the estimated user emotions. For example, if the user is stressed, the Improvement Analysis Unit can provide a simple analysis procedure. It can also provide detailed analysis options if the user is relaxed. Furthermore, if the user is in a hurry, the Improvement Analysis Unit can provide a simplified procedure for rapid analysis. This allows the improvement analysis method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0157] The Improvement Analysis Department can select the optimal analysis method by referring to past improvement analysis history during improvement analysis. For example, the Improvement Analysis Department can propose the optimal analysis procedure based on past successful improvement analysis methods. Furthermore, the Improvement Analysis Department can select an analysis method with fewer errors from past improvement analysis history. In addition, the Improvement Analysis Department can analyze past improvement analysis history and propose the most efficient analysis procedure. This allows for the selection of the optimal analysis method based on past improvement analysis history.

[0158] The Improvement Analysis Unit can apply different analysis algorithms depending on the improvement category during improvement analysis. For example, the Improvement Analysis Unit can select the optimal analysis algorithm based on the improvement category. Furthermore, the Improvement Analysis Unit can adjust the analysis algorithm based on the characteristics of the improvement. In addition, the Improvement Analysis Unit can apply different analysis algorithms depending on the complexity of the improvement. This allows for the application of the optimal analysis algorithm for each improvement category.

[0159] The improvement analysis unit can estimate the user's emotions and prioritize improvement analyses based on those emotions. For example, if the user is stressed, the improvement analysis unit will prioritize important improvement analyses. If the user is relaxed, the improvement analysis unit can also provide more detailed improvement analysis options. Furthermore, if the user is in a hurry, the improvement analysis unit can quickly perform the most important improvement analyses. This allows for prioritization of improvement analyses based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0160] The Improvement Analysis Department can perform improvement analysis while considering the geographical distribution of improvements. For example, the Improvement Analysis Department can propose the optimal analysis procedure based on the geographical distribution of improvements. Furthermore, the Improvement Analysis Department can provide procedures for efficiently analyzing geographically dispersed improvements. In addition, the Improvement Analysis Department can determine analysis priorities while considering the geographical distribution of improvements. This allows it to propose the optimal analysis procedure based on the geographical distribution of improvements.

[0161] The Improvement Analysis Department can improve the accuracy of its analysis by referring to relevant literature on improvement during the improvement analysis process. For example, the Improvement Analysis Department can propose the optimal analysis procedure based on relevant literature on improvement. Furthermore, the Improvement Analysis Department can provide procedures to improve the accuracy of the analysis by referring to relevant literature. In addition, the Improvement Analysis Department can analyze relevant literature on improvement and apply algorithms to improve the accuracy of the analysis. This allows for improved accuracy of the analysis by referring to relevant literature on improvement.

[0162] The system improvement unit can estimate the user's emotions and adjust the system improvement method based on the estimated user emotions. For example, if the user is stressed, the system improvement unit can provide a simple improvement procedure. It can also provide detailed improvement options if the user is relaxed. Furthermore, if the user is in a hurry, the system improvement unit can provide a simplified procedure for quick improvement. This allows the system improvement method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0163] The system improvement department can select the optimal improvement method by referring to past improvement history when improving the system. For example, the system improvement department can propose the optimal improvement procedure based on past successful improvement methods. Furthermore, the system improvement department can select improvement methods with fewer errors from past improvement history. In addition, the system improvement department can analyze past improvement history and propose the most efficient improvement procedure. This allows for the selection of the optimal improvement method based on past improvement history.

[0164] The system improvement unit can apply different improvement algorithms depending on the improvement category during system improvement. For example, the system improvement unit can select the optimal improvement algorithm according to the improvement category. Furthermore, the system improvement unit can adjust the improvement algorithm based on the characteristics of the improvement. In addition, the system improvement unit can apply different improvement algorithms depending on the complexity of the improvement. This allows for the application of the optimal improvement algorithm according to the improvement category.

[0165] The system improvement unit can estimate the user's emotions and determine the priority of system improvements based on the estimated emotions. For example, if the user is stressed, the system improvement unit will prioritize important improvements. If the user is relaxed, the system improvement unit can also provide detailed improvement options. Furthermore, if the user is in a hurry, the system improvement unit can quickly perform the most important improvements. This allows the system improvement unit to determine the priority of system improvements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0166] The system improvement unit can perform system improvements while considering the geographical distribution of those improvements. For example, the system improvement unit can propose the optimal improvement procedure based on the geographical distribution of improvements. Furthermore, the system improvement unit can provide procedures for efficiently performing geographically dispersed improvements. In addition, the system improvement unit can determine the priority of improvements while considering their geographical distribution. This allows it to propose the optimal improvement procedure based on the geographical distribution of improvements.

[0167] The system improvement unit can improve the accuracy of improvements by referring to relevant literature during system improvements. For example, the system improvement unit can propose optimal improvement procedures based on relevant literature. Furthermore, the system improvement unit can provide procedures to improve the accuracy of improvements by referring to relevant literature. In addition, the system improvement unit can analyze relevant literature and apply algorithms to improve the accuracy of improvements. This allows for improved accuracy of improvements by referring to relevant literature.

[0168] The integration unit can estimate the user's emotions and adjust the integration method based on the estimated emotions. For example, if the user is stressed, the integration unit can provide a simple integration procedure. It can also provide more detailed integration options if the user is relaxed. Furthermore, if the user is in a hurry, the integration unit can provide a simplified procedure for quick integration. This allows the integration method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0169] The integration unit can select the optimal integration method by referring to past integration history during the integration process. For example, the integration unit can propose the optimal integration procedure based on past successful integration methods. Furthermore, the integration unit can select an integration method with fewer errors from past integration history. In addition, the integration unit can analyze past integration history and propose the most efficient integration procedure. This allows for the selection of the optimal integration method based on past integration history.

[0170] The integration unit can apply different integration algorithms depending on the integration category during integration. For example, the integration unit can select the optimal integration algorithm based on the integration category. Furthermore, the integration unit can adjust the integration algorithm based on the characteristics of the integration. In addition, the integration unit can apply different integration algorithms depending on the complexity of the integration. This allows for the application of the optimal integration algorithm according to the integration category.

[0171] The integration unit can estimate the user's emotions and determine integration priorities based on those estimated emotions. For example, if the user is stressed, the integration unit will prioritize important integrations. If the user is relaxed, the integration unit can also provide more detailed integration options. Furthermore, if the user is in a hurry, the integration unit can quickly perform the most important integrations. This allows for the prioritization of integrations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0172] The integration unit can perform integrations while considering the geographical distribution of the integrations. For example, the integration unit can propose an optimal integration procedure based on the geographical distribution of the integrations. Furthermore, the integration unit can provide procedures for efficiently performing geographically dispersed integrations. In addition, the integration unit can determine integration priorities while considering the geographical distribution of the integrations. This allows it to propose an optimal integration procedure based on the geographical distribution of the integrations.

[0173] The integration unit can improve the accuracy of the integration by referring to relevant literature during the integration process. For example, the integration unit proposes an optimal integration procedure based on relevant literature. Furthermore, the integration unit can provide procedures to improve the accuracy of the integration by referring to relevant literature. In addition, the integration unit can analyze relevant literature and apply algorithms to improve the accuracy of the integration. This allows for improved integration accuracy by referring to relevant literature.

[0174] The deliverable creation unit can estimate the user's emotions and adjust the deliverable creation method based on the estimated emotions. For example, if the user is stressed, the deliverable creation unit can provide a simple creation procedure. If the user is relaxed, the deliverable creation unit can also provide detailed creation options. Furthermore, if the user is in a hurry, the deliverable creation unit can provide a simplified procedure for quick deliverable creation. This allows the deliverable creation method to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0175] The deliverable creation department can select the optimal creation method by referring to past deliverable creation history when creating deliverables. For example, the deliverable creation department can propose the optimal creation procedure based on past successful creation methods. Furthermore, the deliverable creation department can select a creation method with fewer errors from past creation history. In addition, the deliverable creation department can analyze past creation history and propose the most efficient creation procedure. This allows for the selection of the optimal creation method based on past deliverable creation history.

[0176] The deliverable creation unit can apply different creation algorithms depending on the category of the deliverable during creation. For example, the deliverable creation unit can select the optimal creation algorithm according to the category of the deliverable. Furthermore, the deliverable creation unit can adjust the creation algorithm based on the characteristics of the deliverable. In addition, the deliverable creation unit can apply different creation algorithms depending on the complexity of the deliverable. This allows for the application of the optimal creation algorithm according to the category of the deliverable.

[0177] The deliverable creation unit can estimate the user's emotions and determine the priority of deliverable creation based on the estimated emotions. For example, if the user is stressed, the deliverable creation unit will prioritize creating important deliverables. If the user is relaxed, the deliverable creation unit can also provide detailed creation options. Furthermore, if the user is in a hurry, the deliverable creation unit can quickly create the most important deliverables. This allows for the prioritization of deliverable creation based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0178] The deliverable creation unit can create deliverables while considering their geographical distribution. For example, the unit can propose the optimal creation procedure based on the geographical distribution of the deliverables. Furthermore, the unit can provide procedures for efficiently creating geographically dispersed deliverables. In addition, the unit can determine the creation priority based on the geographical distribution of the deliverables. This allows the unit to propose the optimal creation procedure based on the geographical distribution of the deliverables.

[0179] The deliverable creation unit can improve the accuracy of deliverable creation by referring to relevant literature during the creation process. For example, the deliverable creation unit can propose the optimal creation procedure based on relevant literature. Furthermore, the deliverable creation unit can provide procedures to improve the accuracy of creation by referring to relevant literature. In addition, the deliverable creation unit can analyze relevant literature and apply algorithms to improve the accuracy of creation. This allows for improved accuracy of creation by referring to relevant literature.

[0180] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0181] The user information setting unit can estimate the user's emotions and adjust the user information setting method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick user information setting. In this way, the user information setting method can be adjusted based on the user's emotions.

[0182] The system capture unit can estimate the user's emotions and adjust the system capture method based on those emotions. For example, if the user is stressed, it can provide a simple capture procedure. If the user is relaxed, it can provide more detailed capture options. Furthermore, if the user is in a hurry, it can provide a simplified procedure for quick system capture. This allows the system capture method to be adjusted based on the user's emotions.

[0183] The requirements capture unit can estimate the user's emotions and adjust the requirements capture method based on those emotions. For example, if the user is stressed, it can provide a simple capture procedure. If the user is relaxed, it can provide more detailed capture options. Furthermore, if the user is in a hurry, it can provide a simplified procedure for quickly capturing requirements. This allows the requirements capture method to be adjusted based on the user's emotions.

[0184] The system replication unit can estimate the user's emotions and adjust the system replication method based on those emotions. For example, if the user is stressed, it can provide a simple replication procedure. If the user is relaxed, it can provide more detailed replication options. Furthermore, if the user is in a hurry, it can provide a simplified procedure for quickly replicating the system. This allows the system replication method to be adjusted based on the user's emotions.

[0185] The simulation execution unit can estimate the user's emotions and adjust the simulation execution method based on the estimated emotions. For example, if the user is stressed, it can provide a simple simulation procedure. If the user is relaxed, it can also provide detailed simulation options. Furthermore, if the user is in a hurry, it can provide a simplified procedure for quickly executing the simulation. This allows the simulation execution method to be adjusted based on the user's emotions.

[0186] The user information settings unit can analyze the user's past operation history and select the optimal user information settings method. For example, it can automatically display settings that the user has frequently used in the past as candidates. It can also prioritize suggesting operation methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest settings that the user will use during specific time periods based on their past operation history. This allows the system to select the optimal settings method based on the user's past operation history.

[0187] The system import unit can select the optimal import method by referring to past import history during system import. For example, it can propose the optimal import procedure based on past successful import methods. It can also select an import method with fewer errors from past import history. Furthermore, it can analyze past import history and propose the most efficient import procedure. This allows for the selection of the optimal import method based on past import history.

[0188] The requirements acquisition unit can select the optimal acquisition method by referring to past requirements acquisition history during requirements acquisition. For example, it can propose the optimal acquisition procedure based on past successful requirements acquisition methods. It can also select an acquisition method with fewer errors from past requirements acquisition history. Furthermore, it can analyze past requirements acquisition history and propose the most efficient acquisition procedure. This allows for the selection of the optimal acquisition method based on past requirements acquisition history.

[0189] The system replication unit can select the optimal replication method by referring to past replication history during system replication. For example, it can propose the optimal replication procedure based on past successful replication methods. It can also select a replication method with fewer errors from past replication history. Furthermore, it can analyze past replication history and propose the most efficient replication procedure. This allows for the selection of the optimal replication method based on past replication history.

[0190] The simulation execution unit can select the optimal execution method by referring to past simulation history during simulation execution. For example, it can propose the optimal execution procedure based on past successful simulation methods. It can also select an execution method with fewer errors from past simulation history. Furthermore, it can analyze past simulation history and propose the most efficient execution procedure. In this way, the optimal execution method can be selected based on past simulation history.

[0191] The following briefly describes the processing flow for example form 2.

[0192] Step 1: The user information setting section is used to configure user information. For example, user information such as the department of use and the method of operation can be set. Specifically, the name and role of the department of use, and the procedure and difficulty level of the operation method can be set. Step 2: The system integration unit integrates the target system into the virtual space. For example, it may use 3D modeling technology or technology for building a simulation environment to integrate the system. Step 3: The requirements import unit imports functional requirements. For example, it imports detailed system performance requirements, operational requirements, and functional requirements. Step 4: The system replication unit replicates the system a specified number of times within the virtual space. For example, it is possible to replicate the system using high-precision replication technology and specify the scope of replication. Step 5: The simulation execution unit runs a simulation where the user, as specified, operates each of the duplicated systems a specified number of times. For example, the user can set the simulation scenario and execution conditions and then run the simulation. Step 6: The feedback unit provides feedback on areas for improvement once the operation is complete. For example, it sets the format and content of the feedback and then provides feedback on areas for improvement. Step 7: The Improvement Analysis Department analyzes the optimal improvement methods based on the feedback received regarding areas for improvement. For example, they set improvement methods and analysis criteria to analyze the best improvement methods. Step 8: The system improvement unit executes the system improvements. For example, they set the procedures and scope of the improvements and then execute the system improvements. Step 9: The integration unit integrates the improved system. For example, it integrates the improved system by defining the integration procedures and scope. Step 10: The deliverable creation department creates the final deliverable. For example, they set the format and quality standards for the deliverable and then create the final deliverable.

[0193] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0194] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0195] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0196] Each of the multiple elements described above, including the user information setting unit, system import unit, requirements import unit, system replication unit, simulation execution unit, feedback unit, improvement analysis unit, system improvement unit, integration unit, and deliverable creation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the user information setting unit is implemented by the control unit 46A of the smart device 14 and sets user information. The system import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports the target system into the virtual space. The requirements import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports functional requirements. The system replication unit is implemented by, for example, the control unit 46A of the smart device 14 and replicates the system in the virtual space. The simulation execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and executes a simulation to operate the replicated system. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14 and provides feedback on areas for improvement. The improvement analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the optimal improvement method. The system improvement unit is implemented, for example, by the control unit 46A of the smart device 14, and executes system improvements. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and integrates the improved system. The deliverable creation unit is implemented, for example, by the control unit 46A of the smart device 14, and creates the final deliverable. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0197] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0198] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0199] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0200] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0201] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0203] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0204] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0205] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0206] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0207] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0208] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0209] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0210] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0211] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0212] Each of the multiple elements described above, including the user information setting unit, system import unit, requirements import unit, system duplication unit, simulation execution unit, feedback unit, improvement analysis unit, system improvement unit, integration unit, and deliverable creation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the user information setting unit is implemented by the control unit 46A of the smart glasses 214 and sets user information. The system import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports the target system into the virtual space. The requirements import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports functional requirements. The system duplication unit is implemented by, for example, the control unit 46A of the smart glasses 214 and duplicates the system in the virtual space. The simulation execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and executes a simulation to operate the duplicated system. The feedback unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides feedback on areas for improvement. The improvement analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the optimal improvement method. The system improvement unit is implemented, for example, by the control unit 46A of the smart glasses 214, and executes system improvements. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and integrates the improved system. The deliverable creation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and creates the final deliverable. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0213] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0214] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0216] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0222] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0223] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0224] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0225] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0226] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0227] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0228] Each of the multiple elements described above, including the user information setting unit, system import unit, requirements import unit, system replication unit, simulation execution unit, feedback unit, improvement analysis unit, system improvement unit, integration unit, and deliverable creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the user information setting unit is implemented by the control unit 46A of the headset terminal 314 and sets user information. The system import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports the target system into the virtual space. The requirements import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports functional requirements. The system replication unit is implemented by, for example, the control unit 46A of the headset terminal 314 and replicates the system in the virtual space. The simulation execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and executes a simulation to operate the replicated system. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides feedback on areas for improvement. The improvement analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the optimal improvement method. The system improvement unit is implemented, for example, by the control unit 46A of the headset terminal 314, and executes system improvements. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and integrates the improved system. The deliverable creation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and creates the final deliverable. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0229] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0230] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0231] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0232] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0233] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0235] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0236] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0237] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0239] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0240] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0241] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0242] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0243] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0244] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0245] Each of the multiple elements described above, including the user information setting unit, system import unit, requirements import unit, system replication unit, simulation execution unit, feedback unit, improvement analysis unit, system improvement unit, integration unit, and deliverable creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the user information setting unit is implemented by the control unit 46A of the robot 414 and sets user information. The system import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports the target system into the virtual space. The requirements import unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and imports functional requirements. The system replication unit is implemented by, for example, the control unit 46A of the robot 414 and replicates the system in the virtual space. The simulation execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and executes a simulation to operate the replicated system. The feedback unit is implemented by, for example, the control unit 46A of the robot 414 and provides feedback on areas for improvement. The improvement analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the optimal improvement method. The system improvement unit is implemented, for example, by the control unit 46A of the robot 414, and executes system improvements. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and integrates the improved system. The deliverable creation unit is implemented, for example, by the control unit 46A of the robot 414, and creates the final deliverable. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0246] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0247] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0248] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0249] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0250] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0251] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0252] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0253] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0254] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0255] 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.

[0256] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0257] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0258] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0259] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0260] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0261] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0262] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0263] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0264] (Note 1) A user information setting unit for setting user information, A system integration unit that incorporates the target system into the virtual space, A requirements import unit that takes in functional requirements, A system replication unit that replicates the system in a virtual space, A simulation execution unit that runs a simulation to operate the replicated system, Once the operation is complete, there is a feedback unit that provides feedback on areas for improvement, The Improvement Analysis Department analyzes improvement methods based on the feedback received regarding areas for improvement, The System Improvement Department, which carries out system improvements, An integration unit that integrates the improved system, It comprises a deliverable creation unit that creates the final deliverable, A system characterized by the following features. (Note 2) The user information setting unit is, Set user information for the department using the service and the operating method. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned system acquisition unit is Incorporate the target system into a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 4) The requirements input unit is, Incorporate functional requirements The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned system replication unit is Duplicate the system a specified number of times within the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned simulation execution unit, The system runs a simulation where the configured user operates each of the duplicated systems a specified number of times. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is Feedback on the improved areas after the operation is completed The system according to appended claim 1, characterized in that (Appended claim 8) The improvement analysis unit Analyzes improvement methods based on the feedback on the improved areas The system according to appended claim 1, characterized in that (Appended claim 9) The system improvement unit Performs system improvement The system according to appended claim 1, characterized in that (Appended claim 10) The integration unit Integrates the improved system The system according to appended claim 1, characterized in that (Appended claim 11) The deliverable creation unit Creates the final deliverable The system according to appended claim 1, characterized in that (Appended claim 12) The user information setting unit Estimates the user's emotions and adjusts the method of setting user information based on the estimated user emotions The system according to appended claim 1, characterized in that (Appended claim 13) The user information setting unit Analyzes the user's past operation history and selects the optimal method for setting user information The system according to appended claim 1, characterized in that (Appended claim 14) The user information setting unit Performs filtering based on the user's current project or area of interest when setting user information The system according to appended claim 1, characterized in that (Appended claim 15) The user information setting unit Estimates the user's emotions and determines the priority of the user information to be set based on the estimated user emotions The system according to appended claim 1, characterized in that (Note 16) The user information setting unit is, When setting up user information, the system prioritizes setting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The user information setting unit is, When setting up user information, the system analyzes the user's social media activity and sets relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned system acquisition unit is The system estimates the user's emotions and adjusts the system's data entry method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned system acquisition unit is During system import, the system selects the optimal import method by referring to past import history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned system acquisition unit is During system import, different import algorithms are applied depending on the system category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned system acquisition unit is It estimates the user's emotions and determines the priority of systems to incorporate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned system acquisition unit is During system import, the system's geographical distribution is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned system acquisition unit is When importing into the system, refer to the relevant documents of the system to improve the accuracy of import The system according to Supplementary Note 1, characterized in that (Supplementary Note 24) The requirement import section Estimate the user's emotion and adjust the method of requirement import based on the estimated user's emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 25) The requirement import section When importing requirements, select the optimal import method by referring to the past requirement import history The system according to Supplementary Note 1, characterized in that (Supplementary Note 26) The requirement import section When importing requirements, apply different import algorithms according to the category of requirements The system according to Supplementary Note 1, characterized in that (Supplementary Note 27) The requirement import section Estimate the user's emotion and determine the priority of the requirements to be imported based on the estimated user's emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 28) The requirement import section When importing requirements, perform the import considering the geographical distribution of the requirements The system according to Supplementary Note 1, characterized in that (Supplementary Note 29) The requirement import section When importing requirements, refer to the relevant documents of the requirements to improve the accuracy of import The system according to Supplementary Note 1, characterized in that (Supplementary Note 30) The system replication section Estimate the user's emotion and adjust the method of system replication based on the estimated user's emotion The system according to Supplementary Note 1, characterized in that (Supplementary Note 31) The system replication section When replicating the system, the system selects the optimal replication method by referring to past replication history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned system replication unit is During system replication, different replication algorithms are applied depending on the system category. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned system replication unit is It estimates user sentiment and determines the priority of systems to replicate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned system replication unit is When replicating a system, the system's geographical distribution is taken into consideration during the replication process. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned system replication unit is During system replication, refer to relevant system documentation to improve replication accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned simulation execution unit, It estimates the user's emotions and adjusts how the simulation is run based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned simulation execution unit, When running a simulation, the system selects the optimal execution method by referring to past simulation history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned simulation execution unit, When running a simulation, different execution algorithms are applied depending on the simulation category. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned simulation execution unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned simulation execution unit, When running the simulation, the geographical distribution of the simulation will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned simulation execution unit, When running simulations, we refer to relevant literature to improve the accuracy of the simulations. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned feedback unit is When providing feedback, refer to past feedback history to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the feedback category. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned feedback unit is When providing feedback, consider the geographical distribution of that feedback. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned feedback unit is When giving feedback, refer to relevant literature on the feedback to improve its accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned improvement analysis unit, We estimate user emotions and adjust the improvement analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned improvement analysis unit, When conducting improvement analysis, the optimal analysis method is selected by referring to past improvement analysis history. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned improvement analysis unit, When analyzing improvements, different analysis algorithms are applied depending on the category of improvement. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned improvement analysis unit, We estimate user emotions and prioritize improvement analyses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned improvement analysis unit, When conducting improvement analysis, the geographical distribution of improvements should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned improvement analysis unit, When conducting improvement analysis, refer to relevant literature on improvement to enhance the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned system improvement unit is The system estimates user emotions and adjusts the method of system improvement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned system improvement unit is When improving the system, refer to past improvement history to select the optimal improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned system improvement unit is When improving the system, different improvement algorithms are applied depending on the category of improvement. The system described in Appendix 1, characterized by the features described herein. (Note 57) The aforementioned system improvement unit is The system estimates user sentiment and prioritizes system improvements based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned system improvement unit is When improving the system, the geographical distribution of the improvements should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned system improvement unit is When improving the system, refer to relevant literature on the improvement to enhance the accuracy of the improvement. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned integration unit is It estimates the user's emotions and adjusts the integration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned integration unit is During integration, the optimal integration method is selected by referring to past integration history. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned integration unit is During integration, different integration algorithms are applied depending on the integration category. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned integration unit is It estimates user sentiment and determines integration priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned integration unit is When integrating, the geographical distribution of the integrations should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned integration unit is During integration, refer to relevant literature to improve the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 66) The aforementioned deliverable creation unit, We estimate user emotions and adjust the method of creating deliverables based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 67) The aforementioned deliverable creation unit, When creating deliverables, refer to past deliverable creation history to select the most suitable creation method. The system described in Appendix 1, characterized by the features described herein. (Note 68) The aforementioned deliverable creation unit, When creating deliverables, different creation algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 69) The aforementioned deliverable creation unit, It estimates user emotions and determines the priority of deliverable creation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 70) The aforementioned deliverable creation unit, When creating deliverables, consider the geographical distribution of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 71) The aforementioned deliverable creation unit, When creating deliverables, refer to relevant literature to improve the accuracy of the creation process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0265] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A user information setting unit for setting user information, A system integration unit that incorporates the target system into the virtual space, A requirements import unit that takes in functional requirements, A system replication unit that replicates the system in a virtual space, A simulation execution unit that runs a simulation to operate the replicated system, Once the operation is complete, there is a feedback unit that provides feedback on areas for improvement, The Improvement Analysis Department analyzes improvement methods based on the feedback received regarding areas for improvement, The System Improvement Department, which carries out system improvements, An integration unit that integrates the improved system, It comprises a deliverable creation unit that creates the final deliverable, A system characterized by the following features.

2. The user information setting unit is, Set user information for the department using the service and the operating method. The system according to feature 1.

3. The aforementioned system acquisition unit is Incorporate the target system into a virtual space. The system according to feature 1.

4. The requirements input unit is, Incorporate functional requirements The system according to feature 1.

5. The aforementioned system replication unit is Duplicate the system a specified number of times within the virtual space. The system according to feature 1.

6. The aforementioned simulation execution unit, The system runs a simulation where the configured user operates each of the duplicated systems a specified number of times. The system according to feature 1.

7. The aforementioned feedback unit is Once the operation is complete, please provide feedback on areas for improvement. The system according to feature 1.

8. The aforementioned improvement analysis unit, We will analyze improvement methods based on the feedback we received regarding areas for improvement. The system according to feature 1.

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