system

Abstract

The system according to this embodiment aims to automate the infrastructure design process from the initial stages to detailed design, thereby optimizing resources, costs, and schedules. [Solution] The system according to the embodiment comprises an execution unit, a proposal unit, a response unit, and an allocation unit. The execution unit automatically executes infrastructure design from the initial stage to detailed design. The proposal unit optimizes and proposes the necessary resources, costs, and schedule based on the design executed by the execution unit. The response unit responds to design changes in real time based on the content proposed by the proposal unit. The allocation unit optimizes resource allocation based on the design changes addressed by the response unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] 3]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] ' In the conventional technology, the process from the initial stage of infrastructure design to the detailed design is performed manually, so there is a problem that the efficiency is low and it is difficult to optimize resources, costs, and schedules.

[0005] The system according to the embodiment aims to automate the process from the initial stage of infrastructure design to the detailed design and optimize resources, costs, and schedules.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an execution unit, a proposal unit, a response unit, and an allocation unit. The execution unit automatically performs infrastructure design from the initial stage to detailed design. The proposal unit optimizes and proposes the necessary resources, costs, and schedule based on the design performed by the execution unit. The response unit responds to design changes in real time based on the content proposed by the proposal unit. The allocation unit optimizes resource allocation based on the design changes addressed by the response unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the entire process from the initial stages of infrastructure design to detailed design, and optimize resources, costs, and schedules. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 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 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the 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 infrastructure design automation system according to an embodiment of the present invention is a system that automatically executes infrastructure design from the initial stages to detailed design, and proposes optimized resources, costs, and schedules. This system can reduce the complexity of design and the time-consuming adjustment work, and optimize resources and costs. It also enables rapid response to design changes. For example, an AI agent is built that automatically executes infrastructure design from the initial stages to detailed design. In this process, the tools and work procedures used by design engineers are understood, and the design processes to be automated (network design, server placement, storage planning, etc.) are clarified. Next, the necessary functions (resource estimation, design optimization, cost calculation, etc.) are specifically defined, and the data sources to be used (existing infrastructure, design drawings, best practice collections, etc.) are identified. Next, the AI ​​agent is trained based on the collected data. The generation AI model is trained using past design data, and hyperparameters are adjusted and the model is optimized. The model's performance is evaluated (accuracy, recall, F1 score, etc.), and the model is improved and retrained as needed. Furthermore, the AI ​​agent automates and optimizes the design process. Specifically, the AI ​​agent performs design generation, resource optimization, cost estimation, etc. For example, an AI agent can automatically perform network design and propose optimal server placement and storage plans. Furthermore, if design changes occur, the AI ​​agent can respond to the changes in real time and provide cost estimates. This system reduces the complexity and time-consuming adjustment work of the design, optimizing resources and costs. It also enables rapid response to design changes. For instance, by having the AI ​​agent reflect design changes in real time and provide cost estimates, design engineers can respond quickly. In addition, the AI ​​agent performs efficient resource allocation and management. For example, the AI ​​agent monitors resource usage and proposes optimal resource allocation. This reduces resource waste and enables efficient resource management. In this way, utilizing an AI agent improves the efficiency and accuracy of infrastructure design, reducing the burden on design engineers.As a result, the infrastructure design automation system can automatically execute infrastructure design from the initial stages to detailed design, optimize and propose resources, costs, and schedules, respond to design changes in real time, and optimize resource allocation.

[0029] The infrastructure design automation system according to the embodiment comprises an execution unit, a proposal unit, a response unit, and an allocation unit. The execution unit automatically executes infrastructure design from the initial stage to detailed design. The execution unit automatically executes design processes such as network design, server placement, and storage planning. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. The proposal unit optimizes and proposes the necessary resources, costs, and schedules based on the design executed by the execution unit. The proposal unit performs tasks such as resource estimation, design optimization, and cost calculation. The proposal unit monitors resource usage and proposes the optimal resource allocation. The response unit responds to design changes in real time based on the content proposed by the proposal unit. For example, if a design change occurs, the response unit responds to the change in real time and performs cost estimation. By reflecting design changes in real time and performing cost estimation, the response unit enables design engineers to respond quickly. The allocation unit optimizes resource allocation based on the design changes handled by the response unit. For example, the allocation unit monitors resource usage and proposes the optimal resource allocation. The allocation unit reduces resource waste and enables efficient resource management. As a result, the infrastructure design automation system according to this embodiment can automatically execute infrastructure design from the initial stages to detailed design, propose optimized resources, costs, and schedules, respond to design changes in real time, and optimize resource allocation.

[0030] The execution unit automates infrastructure design from the initial stages to detailed design. Specifically, it automates design processes such as network design, server placement, and storage planning. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. For example, in network design, it automates topology selection, routing configuration, and security policy application. In server placement, it automates physical location selection, virtual machine placement, and load balancing configuration. In storage planning, it automates data storage location selection, backup policy configuration, and data redundancy. By automating these design processes, the execution unit reduces the workload of design engineers and improves the accuracy and efficiency of the design. Furthermore, the execution unit can integrate with the tools and software used by design engineers and centrally manage design data. This eliminates the need for design engineers to use multiple tools, allowing the entire design process to proceed efficiently. In addition, the execution unit can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0031] The proposal department optimizes and proposes the necessary resources, costs, and schedules based on the design executed by the execution department. Specifically, it performs resource estimation, design optimization, and cost calculations. The proposal department monitors resource usage and proposes the optimal resource allocation. For example, in network design, it proposes optimizing the number and placement of necessary network equipment to reduce costs. In server placement, it proposes optimizing the number and placement of necessary servers to reduce resource waste. In storage planning, it proposes optimizing the necessary storage capacity and placement, taking data redundancy into consideration. By making these proposals, the proposal department enables design engineers to use resources efficiently and reduce costs. Furthermore, the proposal department can integrate with the tools and software used by design engineers to monitor resource usage in real time. This allows the proposal department to understand resource usage and propose the optimal resource allocation. In addition, the proposal department can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0032] The support department responds to design changes in real time based on the proposals made by the proposal department. Specifically, when a design change occurs, it responds to the change in real time and performs cost estimation. By reflecting design changes in real time and performing cost estimation, the support department enables design engineers to respond quickly. For example, in network design, it responds to topology changes and routing configuration changes and performs cost estimation. In server placement, it responds to adding servers and changing placement locations and performs cost estimation. In storage planning, it responds to adding storage capacity and changing placement locations and performs cost estimation. By performing these tasks, the support department enables design engineers to respond to design changes quickly and optimize costs. Furthermore, the support department can integrate with the tools and software used by design engineers and reflect the details of design changes in real time. This allows design engineers to understand the details of the design changes and respond quickly. In addition, the support department can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0033] The allocation unit optimizes resource allocation based on design changes addressed by the response unit. Specifically, it monitors resource usage and proposes optimal resource allocation. For example, in network design, it proposes optimizing the number and location of necessary network devices to reduce resource waste. In server placement, it proposes optimizing the number and location of necessary servers to reduce resource waste. In storage planning, it proposes optimizing the necessary storage capacity and location, taking data redundancy into consideration. By making these proposals, the allocation unit enables design engineers to use resources efficiently and reduce costs. Furthermore, the allocation unit can integrate with the tools and software used by design engineers to monitor resource usage in real time. This allows the allocation unit to understand resource usage and propose optimal resource allocation. In addition, the allocation unit can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0034] The execution unit can automatically perform design processes such as network design, server placement, and storage planning. For example, the execution unit can automatically perform network design and propose optimal server placement and storage plans. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. Based on the tools and work procedures used by design engineers, the execution unit automatically performs design processes such as network design, server placement, and storage planning. This automates the design process, leading to increased efficiency and improved accuracy in the design process.

[0035] The proposal department can perform resource estimation, design optimization, and cost calculations. For example, the proposal department can estimate resources and optimize the necessary resources. The proposal department can optimize the design to improve efficiency. The proposal department can calculate costs to reduce them. The proposal department can monitor resource usage and propose the optimal resource allocation. Through resource estimation, design optimization, and cost calculations, the proposal department can improve design efficiency and reduce costs.

[0036] The response unit can respond to design changes in real time and perform cost estimations. For example, if a design change occurs, the response unit will respond to the change in real time and perform cost estimations. By reflecting design changes in real time and performing cost estimations, the response unit enables design engineers to respond quickly. This allows for rapid response by responding to design changes in real time and performing cost estimations.

[0037] The allocation unit can monitor resource usage and propose the optimal resource allocation. For example, by monitoring resource usage and proposing the optimal resource allocation, the allocation unit can reduce resource waste and enable efficient resource management. This means that by monitoring resource usage and proposing the optimal resource allocation, resource waste can be reduced and efficient resource management can be achieved.

[0038] The execution unit can analyze past design data and select the optimal design method. For example, the execution unit can analyze data from past successful projects and apply similar design methodologies. The execution unit can analyze data from past failed projects and select design methodologies to avoid the same mistakes. The execution unit can extract and apply the most effective design methodologies under specific conditions from past design data. In this way, by analyzing past design data, the optimal design methodology can be selected and the accuracy of the design can be improved.

[0039] The execution unit can adjust the degree of automation during the design process according to the skill level of the design engineer. For example, for novice design engineers, the execution unit minimizes automation with detailed guidance. For intermediate design engineers, it provides partial automation, allowing for manual adjustments. For advanced design engineers, it automates almost the entire design process to maximize efficiency. This allows for efficient design by adjusting the degree of automation according to the skill level of the design engineer.

[0040] The implementation team can take geographical conditions into consideration when executing the design process to create the optimal design. For example, in areas prone to earthquakes, the implementation team will prioritize seismic design. In areas prone to frequent flooding, the implementation team will prioritize waterproof design. In areas with high temperatures and humidity, the implementation team will prioritize ventilation design. In this way, by considering geographical conditions during the design process, it becomes possible to create a design that is suitable for the region.

[0041] The implementation unit can take relevant laws and regulations into consideration when executing the design process. For example, the implementation unit can design based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By taking relevant laws and regulations into consideration when designing, it becomes possible to create designs that comply with those regulations.

[0042] The proposal department can adjust the level of detail in a proposal based on the importance of the resources. For example, the proposal department will provide detailed proposals for important resources and concise proposals for less important resources. The proposal department will gradually adjust the level of detail in a proposal according to the importance of the resources. This allows for more efficient proposals by adjusting the level of detail based on the importance of the resources.

[0043] The proposal team can apply different proposal algorithms depending on the project's progress. For example, in the initial stages of a project, the team makes proposals to grasp the overall picture. In the middle stages of the project, the team makes proposals for specific resource allocation. In the final stages of the project, the team makes proposals for final adjustments. By applying different proposal algorithms according to the project's progress, appropriate proposals can be made.

[0044] The proposal department can determine the priority of proposals based on the resource acquisition timing. For example, the proposal department will prioritize proposals for resources that need to be acquired early. For resources that need to be acquired later, the proposal department will postpone the proposal. The proposal department adjusts the priority of proposals in stages according to the resource acquisition timing. This allows for efficient proposals by determining the priority of proposals based on the resource acquisition timing.

[0045] The proposal department can adjust the order of proposals based on the relevance of resources. For example, it can propose highly related resources consecutively, and less related resources separately. The proposal department adjusts the order of proposals in stages according to the relevance of resources. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of resources.

[0046] The response unit can select the optimal response method by referring to past change history when a design change is made. For example, the response unit can select a similar response method based on past successful change history. The response unit can select a response method to avoid the same mistakes based on past unsuccessful change history. The response unit can extract and apply the most effective response method under specific conditions from past change history. In this way, by referring to past change history, the optimal response method can be selected and the accuracy of design changes can be improved.

[0047] The response unit can customize its response methods based on the project's progress when design changes occur. For example, in the early stages of the project, the response unit provides flexible response methods. In the middle stages of the project, it provides specific response methods. In the final stages of the project, it provides response methods for final adjustments. This allows for appropriate responses by customizing the response methods based on the project's progress.

[0048] The system allows for the selection of the most appropriate response method when design changes are made, taking geographical conditions into consideration. For example, in areas prone to earthquakes, the system prioritizes changes to seismic design. In areas prone to frequent flooding, the system prioritizes changes to waterproof design. In areas with high temperatures and humidity, the system prioritizes changes to a design that improves ventilation. By making design changes that take geographical conditions into consideration, it becomes possible to implement solutions that are appropriate for the region.

[0049] The compliant parts can take into account relevant laws and regulations when making design changes. For example, the compliant parts can make design changes based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By making design changes while considering relevant laws and regulations, it becomes possible to comply with those regulations.

[0050] The allocation unit can select the optimal allocation method by referring to past resource usage history when allocating resources. For example, the allocation unit can select a similar allocation method based on past successful resource allocation history. The allocation unit can also select an allocation method to avoid the same mistakes based on past unsuccessful resource allocation history. The allocation unit can extract and apply the most effective allocation method under specific conditions from past resource usage history. This enables efficient resource management by selecting the optimal resource allocation method by referring to past resource usage history.

[0051] The allocation unit can customize the allocation method based on the project's progress when allocating resources. For example, in the initial stages of a project, the allocation unit provides flexible resource allocation methods. In the middle stages of a project, the allocation unit provides specific resource allocation methods. In the final stages of a project, the allocation unit provides resource allocation methods for final adjustments. This allows for appropriate resource allocation by customizing the allocation method based on the project's progress.

[0052] The allocation unit can select the optimal allocation method when allocating resources, taking geographical conditions into consideration. For example, in areas prone to earthquakes, the allocation unit will prioritize the allocation of earthquake-resistant resources. In areas prone to frequent flooding, the allocation unit will prioritize the allocation of waterproofing resources. In areas with high temperatures and humidity, the allocation unit will prioritize the allocation of resources that provide good ventilation. By allocating resources while considering geographical conditions, it becomes possible to allocate resources in a manner appropriate to the region.

[0053] The allocation unit can allocate resources while considering relevant laws and regulations. For example, the allocation unit can allocate resources based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By considering relevant laws and regulations when allocating resources, it becomes possible to allocate resources in compliance with those regulations.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The execution unit can analyze past design data and select the optimal design method. For example, it can analyze data from past successful projects and apply similar design methods. It can also analyze data from past failed projects and select design methods to avoid the same mistakes. From past design data, it can extract and apply the most effective design method under specific conditions. In this way, by analyzing past design data, the optimal design method can be selected and the accuracy of the design can be improved.

[0056] The execution unit can adjust the degree of automation during the design process according to the skill level of the design engineer. For example, for novice design engineers, automation is minimized with detailed guidance. For intermediate design engineers, partial automation is provided, allowing for manual adjustments. For advanced design engineers, almost the entire design process is automated to maximize efficiency. This allows for efficient design by adjusting the degree of automation according to the skill level of the design engineer.

[0057] The execution unit can take geographical conditions into consideration when executing the design process to create the optimal design. For example, in areas prone to earthquakes, seismic design will be prioritized. In areas prone to frequent flooding, waterproof design will be prioritized. In areas with high temperatures and humidity, ventilation will be prioritized. In this way, by considering geographical conditions during the design process, it becomes possible to create a design that is suitable for the region.

[0058] The execution unit can design while considering relevant laws and regulations during the design process. For example, it can design based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By considering relevant laws and regulations during the design process, it becomes possible to create designs that comply with those regulations.

[0059] The proposal department can adjust the level of detail in a proposal based on the importance of the resources. For example, a detailed proposal can be made for important resources, while a concise proposal can be made for less important resources. The level of detail in the proposal can be adjusted in stages according to the importance of the resources. This allows for more efficient proposals by adjusting the level of detail based on the importance of the resources.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The execution unit automatically performs the infrastructure design process from the initial stages to the detailed design. Specifically, it automatically executes design processes such as network design, server placement, and storage planning. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. Step 2: The proposal team optimizes and proposes the necessary resources, costs, and schedule based on the design executed by the execution team. Specifically, this involves resource estimation, design optimization, and cost calculation. The proposal team monitors resource usage and proposes the optimal resource allocation. Step 3: The response department will respond to design changes in real time based on the proposals made by the proposal department. Specifically, when design changes occur, the response department will respond to the changes in real time and perform cost estimates. By reflecting design changes in real time and performing cost estimates, the response department enables design engineers to respond quickly. Step 4: The allocation unit optimizes resource allocation based on the design changes addressed by the corresponding unit. Specifically, it monitors resource usage and proposes the optimal resource allocation. The allocation unit reduces resource waste and enables efficient resource management.

[0062] (Example of form 2) The infrastructure design automation system according to an embodiment of the present invention is a system that automatically executes infrastructure design from the initial stages to detailed design, and proposes optimized resources, costs, and schedules. This system can reduce the complexity of design and the time-consuming adjustment work, and optimize resources and costs. It also enables rapid response to design changes. For example, an AI agent is built that automatically executes infrastructure design from the initial stages to detailed design. In this process, the tools and work procedures used by design engineers are understood, and the design processes to be automated (network design, server placement, storage planning, etc.) are clarified. Next, the necessary functions (resource estimation, design optimization, cost calculation, etc.) are specifically defined, and the data sources to be used (existing infrastructure, design drawings, best practice collections, etc.) are identified. Next, the AI ​​agent is trained based on the collected data. The generation AI model is trained using past design data, and hyperparameters are adjusted and the model is optimized. The model's performance is evaluated (accuracy, recall, F1 score, etc.), and the model is improved and retrained as needed. Furthermore, the AI ​​agent automates and optimizes the design process. Specifically, the AI ​​agent performs design generation, resource optimization, cost estimation, etc. For example, an AI agent can automatically perform network design and propose optimal server placement and storage plans. Furthermore, if design changes occur, the AI ​​agent can respond to the changes in real time and provide cost estimates. This system reduces the complexity and time-consuming adjustment work of the design, optimizing resources and costs. It also enables rapid response to design changes. For instance, by having the AI ​​agent reflect design changes in real time and provide cost estimates, design engineers can respond quickly. In addition, the AI ​​agent performs efficient resource allocation and management. For example, the AI ​​agent monitors resource usage and proposes optimal resource allocation. This reduces resource waste and enables efficient resource management. In this way, utilizing an AI agent improves the efficiency and accuracy of infrastructure design, reducing the burden on design engineers.As a result, the infrastructure design automation system can automatically execute infrastructure design from the initial stages to detailed design, optimize and propose resources, costs, and schedules, respond to design changes in real time, and optimize resource allocation.

[0063] The infrastructure design automation system according to the embodiment comprises an execution unit, a proposal unit, a response unit, and an allocation unit. The execution unit automatically executes infrastructure design from the initial stage to detailed design. The execution unit automatically executes design processes such as network design, server placement, and storage planning. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. The proposal unit optimizes and proposes the necessary resources, costs, and schedules based on the design executed by the execution unit. The proposal unit performs tasks such as resource estimation, design optimization, and cost calculation. The proposal unit monitors resource usage and proposes the optimal resource allocation. The response unit responds to design changes in real time based on the content proposed by the proposal unit. For example, if a design change occurs, the response unit responds to the change in real time and performs cost estimation. By reflecting design changes in real time and performing cost estimation, the response unit enables design engineers to respond quickly. The allocation unit optimizes resource allocation based on the design changes handled by the response unit. For example, the allocation unit monitors resource usage and proposes the optimal resource allocation. The allocation unit reduces resource waste and enables efficient resource management. As a result, the infrastructure design automation system according to this embodiment can automatically execute infrastructure design from the initial stages to detailed design, propose optimized resources, costs, and schedules, respond to design changes in real time, and optimize resource allocation.

[0064] The execution unit automates infrastructure design from the initial stages to detailed design. Specifically, it automates design processes such as network design, server placement, and storage planning. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. For example, in network design, it automates topology selection, routing configuration, and security policy application. In server placement, it automates physical location selection, virtual machine placement, and load balancing configuration. In storage planning, it automates data storage location selection, backup policy configuration, and data redundancy. By automating these design processes, the execution unit reduces the workload of design engineers and improves the accuracy and efficiency of the design. Furthermore, the execution unit can integrate with the tools and software used by design engineers and centrally manage design data. This eliminates the need for design engineers to use multiple tools, allowing the entire design process to proceed efficiently. In addition, the execution unit can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0065] The proposal department optimizes and proposes the necessary resources, costs, and schedules based on the design executed by the execution department. Specifically, it performs resource estimation, design optimization, and cost calculations. The proposal department monitors resource usage and proposes the optimal resource allocation. For example, in network design, it proposes optimizing the number and placement of necessary network equipment to reduce costs. In server placement, it proposes optimizing the number and placement of necessary servers to reduce resource waste. In storage planning, it proposes optimizing the necessary storage capacity and placement, taking data redundancy into consideration. By making these proposals, the proposal department enables design engineers to use resources efficiently and reduce costs. Furthermore, the proposal department can integrate with the tools and software used by design engineers to monitor resource usage in real time. This allows the proposal department to understand resource usage and propose the optimal resource allocation. In addition, the proposal department can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0066] The support department responds to design changes in real time based on the proposals made by the proposal department. Specifically, when a design change occurs, it responds to the change in real time and performs cost estimation. By reflecting design changes in real time and performing cost estimation, the support department enables design engineers to respond quickly. For example, in network design, it responds to topology changes and routing configuration changes and performs cost estimation. In server placement, it responds to adding servers and changing placement locations and performs cost estimation. In storage planning, it responds to adding storage capacity and changing placement locations and performs cost estimation. By performing these tasks, the support department enables design engineers to respond to design changes quickly and optimize costs. Furthermore, the support department can integrate with the tools and software used by design engineers and reflect the details of design changes in real time. This allows design engineers to understand the details of the design changes and respond quickly. In addition, the support department can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0067] The allocation unit optimizes resource allocation based on design changes addressed by the response unit. Specifically, it monitors resource usage and proposes optimal resource allocation. For example, in network design, it proposes optimizing the number and location of necessary network devices to reduce resource waste. In server placement, it proposes optimizing the number and location of necessary servers to reduce resource waste. In storage planning, it proposes optimizing the necessary storage capacity and location, taking data redundancy into consideration. By making these proposals, the allocation unit enables design engineers to use resources efficiently and reduce costs. Furthermore, the allocation unit can integrate with the tools and software used by design engineers to monitor resource usage in real time. This allows the allocation unit to understand resource usage and propose optimal resource allocation. In addition, the allocation unit can monitor the progress of the design process in real time and provide feedback to design engineers as needed. This allows design engineers to understand the progress of the design process and respond quickly.

[0068] The execution unit can automatically perform design processes such as network design, server placement, and storage planning. For example, the execution unit can automatically perform network design and propose optimal server placement and storage plans. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. Based on the tools and work procedures used by design engineers, the execution unit automatically performs design processes such as network design, server placement, and storage planning. This automates the design process, leading to increased efficiency and improved accuracy in the design process.

[0069] The proposal department can perform resource estimation, design optimization, and cost calculations. For example, the proposal department can estimate resources and optimize the necessary resources. The proposal department can optimize the design to improve efficiency. The proposal department can calculate costs to reduce them. The proposal department can monitor resource usage and propose the optimal resource allocation. Through resource estimation, design optimization, and cost calculations, the proposal department can improve design efficiency and reduce costs.

[0070] The response unit can respond to design changes in real time and perform cost estimations. For example, if a design change occurs, the response unit will respond to the change in real time and perform cost estimations. By reflecting design changes in real time and performing cost estimations, the response unit enables design engineers to respond quickly. This allows for rapid response by responding to design changes in real time and performing cost estimations.

[0071] The allocation unit can monitor resource usage and propose the optimal resource allocation. For example, by monitoring resource usage and proposing the optimal resource allocation, the allocation unit can reduce resource waste and enable efficient resource management. This means that by monitoring resource usage and proposing the optimal resource allocation, resource waste can be reduced and efficient resource management can be achieved.

[0072] The execution unit can estimate the user's emotions and adjust the execution order of the design process based on the estimated emotions. For example, if the user is stressed, the execution unit simplifies the order of the design process and executes the most important tasks first. If the user is relaxed, the execution unit executes the detailed design process in a sequential manner to deepen the overall understanding. If the user is in a hurry, the execution unit prioritizes tasks that can be completed quickly. By adjusting the execution order of the design process according to the user's emotions, the burden on the user is reduced, and efficient design becomes possible. 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.

[0073] The execution unit can analyze past design data and select the optimal design method. For example, the execution unit can analyze data from past successful projects and apply similar design methodologies. The execution unit can analyze data from past failed projects and select design methodologies to avoid the same mistakes. The execution unit can extract and apply the most effective design methodologies under specific conditions from past design data. In this way, by analyzing past design data, the optimal design methodology can be selected and the accuracy of the design can be improved.

[0074] The execution unit can adjust the degree of automation during the design process according to the skill level of the design engineer. For example, for novice design engineers, the execution unit minimizes automation with detailed guidance. For intermediate design engineers, it provides partial automation, allowing for manual adjustments. For advanced design engineers, it automates almost the entire design process to maximize efficiency. This allows for efficient design by adjusting the degree of automation according to the skill level of the design engineer.

[0075] The execution unit can estimate the user's emotions and prioritize the design process based on those emotions. For example, if the user is stressed, the execution unit will prioritize executing the simplest tasks. If the user is relaxed, the execution unit will prioritize executing complex tasks. If the user is in a hurry, the execution unit will prioritize executing the quickest tasks. This allows for efficient design by prioritizing the design process according to 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.

[0076] The implementation team can take geographical conditions into consideration when executing the design process to create the optimal design. For example, in areas prone to earthquakes, the implementation team will prioritize seismic design. In areas prone to frequent flooding, the implementation team will prioritize waterproof design. In areas with high temperatures and humidity, the implementation team will prioritize ventilation design. In this way, by considering geographical conditions during the design process, it becomes possible to create a design that is suitable for the region.

[0077] The implementation unit can take relevant laws and regulations into consideration when executing the design process. For example, the implementation unit can design based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By taking relevant laws and regulations into consideration when designing, it becomes possible to create designs that comply with those regulations.

[0078] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function will provide suggestions that include detailed information. If the user is in a hurry, the suggestion function will provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. 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.

[0079] The proposal department can adjust the level of detail in a proposal based on the importance of the resources. For example, the proposal department will provide detailed proposals for important resources and concise proposals for less important resources. The proposal department will gradually adjust the level of detail in a proposal according to the importance of the resources. This allows for more efficient proposals by adjusting the level of detail based on the importance of the resources.

[0080] The proposal team can apply different proposal algorithms depending on the project's progress. For example, in the initial stages of a project, the team makes proposals to grasp the overall picture. In the middle stages of the project, the team makes proposals for specific resource allocation. In the final stages of the project, the team makes proposals for final adjustments. By applying different proposal algorithms according to the project's progress, appropriate proposals can be made.

[0081] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide longer suggestions with more detailed information. If the user is in a hurry, the suggestion function will provide short suggestions that can be quickly understood. By adjusting the length of suggestions according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. 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.

[0082] The proposal department can determine the priority of proposals based on the resource acquisition timing. For example, the proposal department will prioritize proposals for resources that need to be acquired early. For resources that need to be acquired later, the proposal department will postpone the proposal. The proposal department adjusts the priority of proposals in stages according to the resource acquisition timing. This allows for efficient proposals by determining the priority of proposals based on the resource acquisition timing.

[0083] The proposal department can adjust the order of proposals based on the relevance of resources. For example, it can propose highly related resources consecutively, and less related resources separately. The proposal department adjusts the order of proposals in stages according to the relevance of resources. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of resources.

[0084] The response unit can estimate the user's emotions and adjust the response method for design changes based on the estimated user emotions. For example, if the user is stressed, the response unit provides a concise and quick response. If the user is relaxed, the response unit provides a response method that includes a detailed explanation. If the user is in a hurry, the response unit provides the quickest possible response method. This enables a quick and appropriate response by adjusting the response method for design changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The response unit can select the optimal response method by referring to past change history when a design change is made. For example, the response unit can select a similar response method based on past successful change history. The response unit can select a response method to avoid the same mistakes based on past unsuccessful change history. The response unit can extract and apply the most effective response method under specific conditions from past change history. In this way, by referring to past change history, the optimal response method can be selected and the accuracy of design changes can be improved.

[0086] The response unit can customize its response methods based on the project's progress when design changes occur. For example, in the early stages of the project, the response unit provides flexible response methods. In the middle stages of the project, it provides specific response methods. In the final stages of the project, it provides response methods for final adjustments. This allows for appropriate responses by customizing the response methods based on the project's progress.

[0087] The response unit can estimate the user's emotions and determine the priority of design changes based on the estimated emotions. For example, if the user is stressed, the response unit will prioritize the most important changes. If the user is relaxed, the response unit will determine the priority of changes considering the overall balance. If the user is in a hurry, the response unit will prioritize changes that can be implemented quickly. This enables efficient design changes by determining the priority of design changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The system allows for the selection of the most appropriate response method when design changes are made, taking geographical conditions into consideration. For example, in areas prone to earthquakes, the system prioritizes changes to seismic design. In areas prone to frequent flooding, the system prioritizes changes to waterproof design. In areas with high temperatures and humidity, the system prioritizes changes to a design that improves ventilation. By making design changes that take geographical conditions into consideration, it becomes possible to implement solutions that are appropriate for the region.

[0089] The compliant parts can take into account relevant laws and regulations when making design changes. For example, the compliant parts can make design changes based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By making design changes while considering relevant laws and regulations, it becomes possible to comply with those regulations.

[0090] The resource allocation unit can estimate the user's emotions and adjust the resource allocation method based on the estimated emotions. For example, if the user is stressed, the allocation unit provides a simple and easy-to-understand resource allocation method. If the user is relaxed, the allocation unit provides a resource allocation method that includes detailed information. If the user is in a hurry, the allocation unit provides a resource allocation method that can be quickly understood. This allows for efficient resource allocation by adjusting the resource allocation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The allocation unit can select the optimal allocation method by referring to past resource usage history when allocating resources. For example, the allocation unit can select a similar allocation method based on past successful resource allocation history. The allocation unit can also select an allocation method to avoid the same mistakes based on past unsuccessful resource allocation history. The allocation unit can extract and apply the most effective allocation method under specific conditions from past resource usage history. This enables efficient resource management by selecting the optimal resource allocation method by referring to past resource usage history.

[0092] The allocation unit can customize the allocation method based on the project's progress when allocating resources. For example, in the initial stages of a project, the allocation unit provides flexible resource allocation methods. In the middle stages of a project, the allocation unit provides specific resource allocation methods. In the final stages of a project, the allocation unit provides resource allocation methods for final adjustments. This allows for appropriate resource allocation by customizing the allocation method based on the project's progress.

[0093] The allocation unit can estimate the user's emotions and determine resource allocation priorities based on the estimated emotions. For example, if the user is stressed, the allocation unit will prioritize allocating the most important resources first. If the user is relaxed, the allocation unit will determine resource allocation priorities considering the overall balance. If the user is in a hurry, the allocation unit will prioritize allocating resources that can respond quickly. This enables efficient resource allocation by determining resource allocation priorities according to 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.

[0094] The allocation unit can select the optimal allocation method when allocating resources, taking geographical conditions into consideration. For example, in areas prone to earthquakes, the allocation unit will prioritize the allocation of earthquake-resistant resources. In areas prone to frequent flooding, the allocation unit will prioritize the allocation of waterproofing resources. In areas with high temperatures and humidity, the allocation unit will prioritize the allocation of resources that provide good ventilation. By allocating resources while considering geographical conditions, it becomes possible to allocate resources in a manner appropriate to the region.

[0095] The allocation unit can allocate resources while considering relevant laws and regulations. For example, the allocation unit can allocate resources based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By considering relevant laws and regulations when allocating resources, it becomes possible to allocate resources in compliance with those regulations.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The execution unit can estimate the user's emotions and adjust the execution order of the design process based on the estimated emotions. For example, if the user is stressed, the order of the design process is simplified, and the most important tasks are executed first. If the user is relaxed, the detailed design process is executed sequentially to deepen overall understanding. If the user is in a hurry, tasks that can be completed quickly are prioritized. By adjusting the execution order of the design process according to the user's emotions, the burden on the user is reduced, and efficient design becomes possible. 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.

[0098] The suggestion function can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is stressed, it will provide a simple and easy-to-understand suggestion. If the user is relaxed, it will provide a suggestion that includes detailed information. If the user is in a hurry, it will provide a short, concise suggestion that gets straight to the point. By adjusting the way the suggestion is presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The response unit can estimate the user's emotions and adjust the response method for design changes based on the estimated user emotions. For example, if the user is stressed, it provides a concise and quick response. If the user is relaxed, it provides a response method that includes a detailed explanation. If the user is in a hurry, it provides the quickest possible response method. This allows for a quick and appropriate response by adjusting the response method for design changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The resource allocation unit can estimate the user's emotions and adjust the resource allocation method based on the estimated emotions. For example, if the user is stressed, it provides a simple and easy-to-understand resource allocation method. If the user is relaxed, it provides a resource allocation method that includes detailed information. If the user is in a hurry, it provides a resource allocation method that can be quickly understood. This allows for efficient resource allocation by adjusting the resource allocation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The system can estimate the user's emotions and prioritize design changes based on those emotions. For example, if the user is stressed, the most important changes will be prioritized. If the user is relaxed, the system will prioritize changes considering the overall balance. If the user is in a hurry, changes that can be implemented quickly will be prioritized. This allows for efficient design changes by prioritizing them according to 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.

[0102] The execution unit can analyze past design data and select the optimal design method. For example, it can analyze data from past successful projects and apply similar design methods. It can also analyze data from past failed projects and select design methods to avoid the same mistakes. From past design data, it can extract and apply the most effective design method under specific conditions. In this way, by analyzing past design data, the optimal design method can be selected and the accuracy of the design can be improved.

[0103] The execution unit can adjust the degree of automation during the design process according to the skill level of the design engineer. For example, for novice design engineers, automation is minimized with detailed guidance. For intermediate design engineers, partial automation is provided, allowing for manual adjustments. For advanced design engineers, almost the entire design process is automated to maximize efficiency. This allows for efficient design by adjusting the degree of automation according to the skill level of the design engineer.

[0104] The execution unit can take geographical conditions into consideration when executing the design process to create the optimal design. For example, in areas prone to earthquakes, seismic design will be prioritized. In areas prone to frequent flooding, waterproof design will be prioritized. In areas with high temperatures and humidity, ventilation will be prioritized. In this way, by considering geographical conditions during the design process, it becomes possible to create a design that is suitable for the region.

[0105] The execution unit can design while considering relevant laws and regulations during the design process. For example, it can design based on the Building Standards Act, the Environmental Protection Act, or the Industrial Safety and Health Act. By considering relevant laws and regulations during the design process, it becomes possible to create designs that comply with those regulations.

[0106] The proposal department can adjust the level of detail in a proposal based on the importance of the resources. For example, a detailed proposal can be made for important resources, while a concise proposal can be made for less important resources. The level of detail in the proposal can be adjusted in stages according to the importance of the resources. This allows for more efficient proposals by adjusting the level of detail based on the importance of the resources.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The execution unit automatically performs the infrastructure design process from the initial stages to the detailed design. Specifically, it automatically executes design processes such as network design, server placement, and storage planning. The execution unit understands the tools and work procedures used by design engineers and clarifies the design processes to be automated. Step 2: The proposal team optimizes and proposes the necessary resources, costs, and schedule based on the design executed by the execution team. Specifically, this involves resource estimation, design optimization, and cost calculation. The proposal team monitors resource usage and proposes the optimal resource allocation. Step 3: The response department will respond to design changes in real time based on the proposals made by the proposal department. Specifically, when design changes occur, the response department will respond to the changes in real time and perform cost estimates. By reflecting design changes in real time and performing cost estimates, the response department enables design engineers to respond quickly. Step 4: The allocation unit optimizes resource allocation based on the design changes addressed by the corresponding unit. Specifically, it monitors resource usage and proposes the optimal resource allocation. The allocation unit reduces resource waste and enables efficient resource management.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the execution unit, proposal unit, response unit, and allocation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the execution unit is implemented by the control unit 46A of the smart device 14 and automatically executes design processes such as network design, server placement, and storage planning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs resource estimation, design optimization, and cost calculation. The response unit is implemented by the control unit 46A of the smart device 14 and responds to design changes in real time and performs cost estimation. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors resource usage and proposes the optimal resource allocation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the execution unit, proposal unit, response unit, and allocation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the execution unit is implemented by the control unit 46A of the smart glasses 214 and automatically executes design processes such as network design, server placement, and storage planning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs resource estimation, design optimization, and cost calculation. The response unit is implemented by the control unit 46A of the smart glasses 214 and responds to design changes in real time and performs cost estimation. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors resource usage and proposes the optimal resource allocation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the execution unit, proposal unit, response unit, and allocation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the execution unit is implemented by the control unit 46A of the headset terminal 314 and automatically executes design processes such as network design, server placement, and storage planning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs resource estimation, design optimization, and cost calculation. The response unit is implemented by the control unit 46A of the headset terminal 314 and responds to design changes in real time and performs cost estimation. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors resource usage and proposes the optimal resource allocation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the execution unit, proposal unit, response unit, and allocation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the execution unit is implemented by the control unit 46A of the robot 414 and automatically executes design processes such as network design, server placement, and storage planning. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs resource estimation, design optimization, and cost calculation. The response unit is implemented by the control unit 46A of the robot 414 and responds to design changes in real time and performs cost estimation. The allocation unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors resource usage and proposes the optimal resource allocation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) An execution unit that automatically performs infrastructure design from the initial stages to detailed design, Based on the design executed by the aforementioned execution unit, a proposal unit optimizes and proposes the necessary resources, costs, and schedule. A response unit that responds to design changes in real time based on the content proposed by the aforementioned proposal unit, The system includes an allocation unit that optimizes resource allocation based on the design changes addressed by the aforementioned corresponding unit. A system characterized by the following features. (Note 2) The execution unit is, Automate design processes such as network design, server placement, and storage planning. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, This involves resource estimation, design optimization, cost calculation, and more. The system described in Appendix 1, characterized by the features described herein. (Note 4) The corresponding part is, Respond to design changes in real time and perform cost estimates. The system described in Appendix 1, characterized by the features described herein. (Note 5) The distribution unit is, It monitors resource usage and proposes the optimal resource allocation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, It estimates user emotions and adjusts the execution order of the design process based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The execution unit is, Analyze past design data and select the optimal design method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The execution unit is, During the execution of the design process, the degree of automation is adjusted according to the skill level of the design engineers. The system described in Appendix 1, characterized by the features described herein. (Note 9) The execution unit is, We estimate user emotions and prioritize the design process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The execution unit is, When executing the design process, the optimal design is made considering geographical conditions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The execution unit is, When executing the design process, the design should take into account relevant laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the resources. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the progress of the project. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the resources will be acquired. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the resources. The system described in Appendix 1, characterized by the features described herein. (Note 18) The corresponding part is, We estimate user emotions and adjust how design changes are handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The corresponding part is, When making design changes, refer to past change history to select the most appropriate solution. The system described in Appendix 1, characterized by the features described herein. (Note 20) The corresponding part is, When design changes occur, customize the response methods based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 21) The corresponding part is, Estimate user emotions and prioritize design changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The corresponding part is, When making design changes, the most suitable approach will be selected, taking geographical conditions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The corresponding part is, When making design changes, we will take relevant laws and regulations into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The distribution unit is, It estimates user sentiment and adjusts resource allocation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The distribution unit is, When allocating resources, the optimal allocation method is selected by referring to past resource usage history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The distribution unit is, When allocating resources, customize the allocation method based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The distribution unit is, It estimates user sentiment and determines resource allocation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The distribution unit is, When allocating resources, the optimal allocation method is selected considering geographical conditions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The distribution unit is, When allocating resources, consider relevant laws and regulations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. An execution unit that automatically performs infrastructure design from the initial stages to detailed design, Based on the design executed by the aforementioned execution unit, a proposal unit optimizes and proposes the necessary resources, costs, and schedule. A response unit that responds to design changes in real time based on the content proposed by the aforementioned proposal unit, The system includes an allocation unit that optimizes resource allocation based on the design changes addressed by the aforementioned corresponding unit. A system characterized by the following features.

2. The execution unit is, Automate design processes such as network design, server placement, and storage planning. The system according to feature 1.

3. The aforementioned proposal section is, This involves resource estimation, design optimization, cost calculation, and more. The system according to feature 1.

4. The corresponding part is, Respond to design changes in real time and perform cost estimates. The system according to feature 1.

5. The distribution unit is, It monitors resource usage and proposes the optimal resource allocation. The system according to feature 1.

6. The execution unit is, It estimates user emotions and adjusts the execution order of the design process based on the estimated user emotions. The system according to feature 1.

7. The execution unit is, Analyze past design data and select the optimal design method. The system according to feature 1.

8. The execution unit is, During the execution of the design process, the degree of automation is adjusted according to the skill level of the design engineers. The system according to feature 1.

9. The execution unit is, We estimate user emotions and prioritize the design process based on those estimated emotions. The system according to feature 1.

10. The execution unit is, When executing the design process, the optimal design is made considering geographical conditions. The system according to feature 1.

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