system

Abstract

The system according to this embodiment aims to maximize the use of the limited space in a data center and to enable efficient equipment placement. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects existing equipment layout information and the requirements for new equipment. The analysis unit analyzes the information collected by the collection unit. The generation unit generates an optimal layout based on the information analyzed by the analysis unit. The provision unit provides the layout generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to make the most of the limited space in the data center and perform efficient equipment layout.

[0005] The system according to the embodiment aims to make the most of the limited space in the data center and perform efficient equipment layout.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects existing equipment layout information and the requirements for new equipment. The analysis unit analyzes the information collected by the collection unit. The generation unit generates an optimal layout based on the information analyzed by the analysis unit. The provision unit provides the layout generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can maximize the use of the limited space in a data center and enable efficient equipment layout. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied 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 data center layout optimization system according to an embodiment of the present invention is a system for optimizing the use of space in a data center. This system also takes into account cooling efficiency and power supply. The user inputs existing equipment placement information and requirements for new equipment, and the system analyzes this information to generate an optimal layout. Furthermore, by combining this with AI technology from the architectural field, the entire data center design is optimized. This achieves optimization not only of equipment placement but also of cooling efficiency and power supply. For example, the user inputs the placement of existing server racks and the space requirements for newly installed cooling equipment. This information is input into the data center layout optimization system. Next, the system analyzes the input information and generates an optimal layout. The system considers not only the efficient use of space but also the optimization of cooling efficiency and power supply. For example, by efficiently arranging cooling equipment, the overall cooling efficiency can be improved. The generated layout is provided to the user. The user can review the proposed layout and make modifications as needed. For example, they can change the placement of specific equipment or reflect additional requirements. This mechanism optimizes the use of space in the data center and improves cooling efficiency and power supply. For example, improved cooling efficiency may reduce the operating costs of the data center. Furthermore, optimizing power supply ensures stable operation of equipment. In addition, the data center layout optimization system optimizes the entire data center design by combining AI technology from the architectural field. This optimizes not only equipment placement but also the building structure and layout, improving overall efficiency. For example, optimizing the building structure is expected to further improve cooling efficiency and power supply efficiency. Thus, the data center layout optimization system can optimize data center space utilization and generate an optimal layout that also considers cooling efficiency and power supply.

[0029] The data center layout optimization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects existing equipment placement information and requirements for new equipment. The collection unit can collect information such as physical placement and network placement. The collection unit can also collect requirements such as power requirements, cooling requirements, and space requirements for new equipment. For example, the collection unit can collect placement information of existing server racks and space requirements for newly installed cooling equipment. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information considering, for example, the optimization of cooling efficiency and power supply. For example, the analysis unit can analyze the placement patterns of cooling equipment and methods for evaluating cooling effects. The analysis unit can also analyze power load balancing and methods for reducing power consumption. The generation unit generates an optimal layout based on the information analyzed by the analysis unit. For example, the generation unit can generate a layout that efficiently places cooling equipment. For example, the generation unit can generate an optimal layout based on the placement patterns of cooling equipment. The generation unit can also optimize the overall data center design by combining AI technology from the architectural field. For example, the generation unit can improve cooling efficiency and power supply efficiency by optimizing the building structure and layout. The provision unit provides the layout generated by the generation unit. The provision unit provides the generated layout to the user, for example, allowing the user to make modifications. For example, the provision unit can change the placement of specific equipment or reflect additional requirements. As a result, the data center layout optimization system according to the embodiment can optimize the use of space in the data center and generate an optimal layout that also takes into account cooling efficiency and power supply.

[0030] The data collection unit collects existing equipment layout information and requirements for new equipment. Specifically, it can collect information such as physical and network layouts. For example, it can collect information on the layout of existing server racks and the space requirements for newly installed cooling systems. The data collection unit collects detailed information on the location, connectivity, power consumption, and cooling system layout of each piece of equipment within the data center. This includes the height, width, depth, weight limits, and cable routing of each rack. The data collection unit can also collect requirements for new equipment, such as power requirements, cooling requirements, and space requirements. For example, it can collect detailed information on the power consumption of newly introduced high-performance servers, the cooling capacity of cooling systems, and the dimensions of the installation space. This allows the data collection unit to comprehensively understand the requirements of existing and new equipment and provide the basic data necessary for optimizing the overall layout of the data center. Furthermore, the data collection unit can centrally manage this information and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it can analyze the information considering the optimization of cooling efficiency and power supply. For example, it can analyze the placement patterns of cooling devices and methods for evaluating cooling effectiveness. The analysis unit uses AI technology to analyze the collected data in real time and identify the optimal placement pattern of cooling devices. Based on past data and statistical information, the AI ​​learns placement patterns with high cooling efficiency and proposes the optimal placement. The analysis unit can also analyze methods for power load balancing and reducing power consumption. For example, it can analyze the power consumption of each server and adjust the placement so that the power load is evenly distributed. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can quickly and accurately analyze the collected data and achieve optimization of the data center's cooling efficiency and power supply. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past cooling efficiency data, it can predict fluctuations in cooling efficiency during specific seasons or time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates the optimal layout based on the information analyzed by the analysis unit. Specifically, it can generate a layout that efficiently arranges cooling equipment. For example, it can generate an optimal layout based on the placement pattern of cooling equipment. The generation unit uses AI technology to automatically generate the optimal layout based on the data provided by the analysis unit. The AI ​​can combine design knowledge from the architectural field to optimize the overall design of the data center. For example, optimizing the structure and layout of the building can improve cooling efficiency and power supply efficiency. Furthermore, the generation unit can use simulation technology to evaluate the effects of the generated layout in advance. For example, it can simulate the placement pattern of cooling equipment and predict fluctuations in cooling efficiency and power consumption. This allows the generation unit not only to generate the optimal layout but also to evaluate its effects in advance and make modifications as needed. In addition, the generation unit can visually display the generated layout so that users can easily understand it. For example, it can generate 3D models and drawings so that users can check the details of the layout. This allows the generation unit to efficiently and effectively generate the optimal layout and optimize the overall design of the data center.

[0033] The service provider provides the layout generated by the generation unit. Specifically, it provides the generated layout to the user, allowing the user to make modifications. For example, the user can change the placement of specific equipment or incorporate additional requirements. The service provider visually displays the generated layout to make it easy for the user to understand. For example, it can generate 3D models or drawings so that the user can review the layout details. Furthermore, the service provider can collect feedback from the user and incorporate that feedback into the generation unit. For example, if the user wants to change the placement of specific equipment, the service provider can collect that request, and the generation unit can regenerate the optimal layout. The service provider can also reliably transmit information using multiple communication methods. For example, it can provide the generated layout to the user via email or cloud services. This allows the service provider to provide information to the user quickly and reliably, optimizing the overall data center design. In addition, the service provider can provide interactive tools to allow users to easily modify the generated layout. For example, it can provide an interface with drag-and-drop functionality so that users can intuitively modify the layout. This allows the service provider to flexibly respond to user requests and provide the optimal layout.

[0034] The analysis unit can analyze information while considering the optimization of cooling efficiency and power supply. For example, the analysis unit can analyze specific evaluation criteria and measurement methods for cooling efficiency. For example, the analysis unit can analyze temperature distribution and energy consumption. The analysis unit can also analyze specific criteria and methods for optimizing power supply. For example, the analysis unit can analyze power load balancing and methods for reducing power consumption. This makes it possible to perform analysis that takes into account the optimization of cooling efficiency and power supply. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use an AI model to analyze information in order to perform analysis that takes into account the optimization of cooling efficiency and power supply.

[0035] The generation unit can generate layouts for efficiently arranging cooling devices. For example, the generation unit can generate an optimal layout based on the arrangement pattern of the cooling devices. For example, the generation unit can analyze the arrangement pattern of the cooling devices and generate a layout that maximizes the cooling effect. The generation unit can also generate a layout that improves cooling efficiency based on the arrangement of the cooling devices. For example, the generation unit can improve the overall cooling efficiency by optimizing the arrangement of the cooling devices. This enables efficient arrangement of the cooling devices. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate an efficient arrangement of cooling devices using an AI model that takes the arrangement pattern of the cooling devices as input and outputs an optimal layout.

[0036] The service provider provides the user with a generated layout that the user can modify. For example, the service provider provides the user with a generated layout that the user can change the placement of specific equipment or reflect additional requirements. For example, the service provider allows the user to make modifications through an interface. For example, the service provider provides an interface that allows the user to select and modify specific parts of the layout. The service provider also allows the user to input additional requirements and modify the layout accordingly. This allows the user to modify the generated layout. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated layout into an AI model and provide an interface for reflecting the user's modifications.

[0037] The generation unit can optimize the overall design of a data center by combining AI technologies from the architectural field. For example, the generation unit can use AI technologies from the architectural field to optimize the structure and layout of a building. For example, the generation unit can use architectural design optimization algorithms to optimize the structure of a building. The generation unit can also use simulation technology to optimize the layout of a building. For example, the generation unit can simulate the layout of a building and determine the optimal layout. This allows for the optimization of the overall design of the data center. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use AI models to optimize the overall design of a data center using AI technologies from the architectural field.

[0038] The data collection unit can analyze past equipment layout information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on past equipment layout information. The data collection unit can also prioritize the collection of information that takes a long time to collect from past data. Furthermore, the data collection unit can analyze past data collection history and optimize the frequency and timing of collection. This enables efficient information collection by selecting the optimal data collection method based on past information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past equipment layout information into an AI model and have the AI ​​perform analysis to select the optimal data collection method.

[0039] The data collection unit can filter data center operational status and environmental conditions when collecting equipment layout information. For example, the data collection unit can monitor the data center's operational status in real time and collect information at the optimal time. The data collection unit can also filter the information to be collected by considering environmental conditions (temperature, humidity, etc.). Furthermore, the data collection unit can determine the priority of the information to be collected according to the data center's operational status. This allows for the collection of more relevant information by filtering information based on operational status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the data center's operational status and environmental conditions into an AI model and have the AI ​​perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the data center when collecting equipment layout information. For example, the data collection unit can prioritize the collection of relevant information based on the geographical location of the data center. The data collection unit can also limit the scope of information to be collected based on the geographical location. Furthermore, the data collection unit can determine the priority of information to be collected by considering the geographical location. This allows for the priority collection of highly relevant information by considering the geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the data center into an AI model and have the AI ​​perform analysis to prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the data center's operational history and collect relevant information when collecting equipment layout information. For example, the data collection unit can analyze the data center's operational history and collect information to prevent past problems. The data collection unit can also determine the priority of the information to be collected based on the operational history. Furthermore, the data collection unit can limit the scope of information to be collected, taking the operational history into consideration. This allows for the efficient collection of relevant information by analyzing the operational history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data center's operational history into an AI model and have the AI ​​perform the analysis to collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the equipment during the analysis. For example, the analysis unit can perform a detailed analysis for equipment of high importance. It can also perform a simplified analysis for equipment of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the equipment. This allows for efficient analysis by adjusting the level of detail according to the importance of the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the equipment category during analysis. For example, it can apply different analysis algorithms to server racks and cooling systems. It can also apply different analysis algorithms to power supply equipment and network equipment. Furthermore, the analysis unit can select the optimal analysis algorithm according to the equipment category. By applying the optimal analysis algorithm according to the equipment category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment category data into an AI model and have the AI ​​select the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the installation date of the equipment. For example, the analysis unit can prioritize the analysis of newly installed equipment. The analysis unit can also perform periodic analyses on older equipment. Furthermore, the analysis unit can adjust the frequency of analysis based on the installation date. This enables efficient analysis by determining the priority of analysis based on the installation date of the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment installation date data into an AI model and have the AI ​​perform the determination of analysis priorities.

[0045] The analysis unit can adjust the order of analysis based on the relationships between the equipment during the analysis. For example, the analysis unit can prioritize the analysis of equipment with high relevance. It can also postpone the analysis of equipment with low relevance. Furthermore, the analysis unit can determine the order of analysis based on the relationships between the equipment. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment relationship data into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0046] The generation unit can adjust the level of detail of the layout based on the importance of the equipment during layout generation. For example, the generation unit can generate a detailed layout for equipment with high importance. It can also generate a simplified layout for equipment with low importance. Furthermore, the generation unit can determine the layout priority according to the importance of the equipment. This allows for efficient layout generation by adjusting the level of detail of the layout according to the importance of the equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the layout.

[0047] The generation unit can apply different generation algorithms depending on the equipment category when generating layouts. For example, it can apply different generation algorithms to server racks and cooling equipment. It can also apply different generation algorithms to power supply equipment and network equipment. Furthermore, the generation unit can select the optimal generation algorithm depending on the equipment category. By applying the optimal generation algorithm according to the equipment category, the accuracy of the layout is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment category data into an AI model and have the AI ​​select the optimal generation algorithm.

[0048] The generation unit can determine layout priorities based on the installation dates of equipment during layout generation. For example, the generation unit can prioritize the inclusion of newly installed equipment in the layout. The generation unit can also perform periodic layout updates for older equipment. Furthermore, the generation unit can adjust the frequency of layouts based on the installation dates. This enables efficient layout generation by determining layout priorities based on the installation dates of equipment. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment installation date data into an AI model and have the AI ​​perform the layout priority determination.

[0049] The generation unit can adjust the layout order based on the relationships between the equipment when generating the layout. For example, the generation unit can prioritize reflecting highly relevant equipment in the layout. It can also postpone the generation of less relevant equipment. Furthermore, the generation unit can determine the layout order based on the relationships between the equipment. This allows for efficient layout generation by adjusting the layout order based on the relationships between the equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment relationship data into an AI model and have the AI ​​perform the layout order adjustment.

[0050] The service provider can select the optimal display method by referring to the user's past operation history when providing a layout. For example, the service provider can select the optimal display method based on the user's past operation history. The service provider can also prioritize providing the display method preferred by the user based on past operation history. Furthermore, the service provider can analyze the operation history and suggest improvements to the display method. By selecting the optimal display method based on past operation history, a user-friendly display becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history data into an AI model and have the AI ​​select the optimal display method.

[0051] The service provider can select the optimal display method when providing a layout, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into an AI model and have the AI ​​select the optimal display method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can analyze past equipment layout information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on past equipment layout information. The data collection unit can also prioritize the collection of information that takes a long time to collect from past data. Furthermore, the data collection unit can analyze past data collection history and optimize the frequency and timing of collection. This enables efficient information collection by selecting the optimal data collection method based on past information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past equipment layout information into an AI model and have the AI ​​perform analysis to select the optimal data collection method.

[0054] The data collection unit can filter data center operational status and environmental conditions when collecting equipment layout information. For example, the data collection unit can monitor the data center's operational status in real time and collect information at the optimal time. The data collection unit can also filter the information to be collected by considering environmental conditions (temperature, humidity, etc.). Furthermore, the data collection unit can determine the priority of the information to be collected according to the data center's operational status. This allows for the collection of more relevant information by filtering information based on operational status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the data center's operational status and environmental conditions into an AI model and have the AI ​​perform the filtering.

[0055] The analysis unit can adjust the level of detail of the analysis based on the importance of the equipment during the analysis. For example, the analysis unit can perform a detailed analysis for equipment of high importance. It can also perform a simplified analysis for equipment of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the equipment. This allows for efficient analysis by adjusting the level of detail according to the importance of the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0056] The analysis unit can apply different analysis algorithms depending on the equipment category during analysis. For example, it can apply different analysis algorithms to server racks and cooling systems. It can also apply different analysis algorithms to power supply equipment and network equipment. Furthermore, the analysis unit can select the optimal analysis algorithm according to the equipment category. By applying the optimal analysis algorithm according to the equipment category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment category data into an AI model and have the AI ​​select the optimal analysis algorithm.

[0057] The generation unit can adjust the level of detail of the layout based on the importance of the equipment during layout generation. For example, the generation unit can generate a detailed layout for equipment with high importance. It can also generate a simplified layout for equipment with low importance. Furthermore, the generation unit can determine the layout priority according to the importance of the equipment. This allows for efficient layout generation by adjusting the level of detail of the layout according to the importance of the equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the layout.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects existing equipment layout information and requirements for new equipment. The data collection unit can collect information such as physical layout and network layout. It can also collect requirements such as power requirements, cooling requirements, and space requirements for the new equipment. For example, the data collection unit can collect information on the layout of existing server racks and the space requirements for the newly installed cooling system. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit can analyze the information considering, for example, the optimization of cooling efficiency and power supply. For example, the analysis unit can analyze the arrangement patterns of cooling devices and methods for evaluating cooling effects. The analysis unit can also analyze methods for power load balancing and reducing power consumption. Step 3: The generation unit generates the optimal layout based on the information analyzed by the analysis unit. For example, the generation unit can generate a layout that efficiently arranges cooling equipment. For example, the generation unit can generate the optimal layout based on the arrangement patterns of cooling equipment. The generation unit can also optimize the overall design of a data center by combining AI technology from the architectural field. For example, the generation unit can improve cooling efficiency and power supply efficiency by optimizing the structure and arrangement of the building. Step 4: The provider unit provides the layout generated by the generator unit. The provider unit provides the generated layout to the user, for example, allowing the user to make modifications. For example, the provider unit can change the placement of specific equipment or reflect additional requirements.

[0060] (Example of form 2) The data center layout optimization system according to an embodiment of the present invention is a system for optimizing the use of space in a data center. This system also takes into account cooling efficiency and power supply. The user inputs existing equipment placement information and requirements for new equipment, and the system analyzes this information to generate an optimal layout. Furthermore, by combining this with AI technology from the architectural field, the entire data center design is optimized. This achieves optimization not only of equipment placement but also of cooling efficiency and power supply. For example, the user inputs the placement of existing server racks and the space requirements for newly installed cooling equipment. This information is input into the data center layout optimization system. Next, the system analyzes the input information and generates an optimal layout. The system considers not only the efficient use of space but also the optimization of cooling efficiency and power supply. For example, by efficiently arranging cooling equipment, the overall cooling efficiency can be improved. The generated layout is provided to the user. The user can review the proposed layout and make modifications as needed. For example, they can change the placement of specific equipment or reflect additional requirements. This mechanism optimizes the use of space in the data center and improves cooling efficiency and power supply. For example, improved cooling efficiency may reduce the operating costs of the data center. Furthermore, optimizing power supply ensures stable operation of equipment. In addition, the data center layout optimization system optimizes the entire data center design by combining AI technology from the architectural field. This optimizes not only equipment placement but also the building structure and layout, improving overall efficiency. For example, optimizing the building structure is expected to further improve cooling efficiency and power supply efficiency. Thus, the data center layout optimization system can optimize data center space utilization and generate an optimal layout that also considers cooling efficiency and power supply.

[0061] The data center layout optimization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects existing equipment placement information and requirements for new equipment. The collection unit can collect information such as physical placement and network placement. The collection unit can also collect requirements such as power requirements, cooling requirements, and space requirements for new equipment. For example, the collection unit can collect placement information of existing server racks and space requirements for newly installed cooling equipment. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the information considering, for example, the optimization of cooling efficiency and power supply. For example, the analysis unit can analyze the placement patterns of cooling equipment and methods for evaluating cooling effects. The analysis unit can also analyze power load balancing and methods for reducing power consumption. The generation unit generates an optimal layout based on the information analyzed by the analysis unit. For example, the generation unit can generate a layout that efficiently places cooling equipment. For example, the generation unit can generate an optimal layout based on the placement patterns of cooling equipment. The generation unit can also optimize the overall data center design by combining AI technology from the architectural field. For example, the generation unit can improve cooling efficiency and power supply efficiency by optimizing the building structure and layout. The provision unit provides the layout generated by the generation unit. The provision unit provides the generated layout to the user, for example, allowing the user to make modifications. For example, the provision unit can change the placement of specific equipment or reflect additional requirements. As a result, the data center layout optimization system according to the embodiment can optimize the use of space in the data center and generate an optimal layout that also takes into account cooling efficiency and power supply.

[0062] The data collection unit collects existing equipment layout information and requirements for new equipment. Specifically, it can collect information such as physical and network layouts. For example, it can collect information on the layout of existing server racks and the space requirements for newly installed cooling systems. The data collection unit collects detailed information on the location, connectivity, power consumption, and cooling system layout of each piece of equipment within the data center. This includes the height, width, depth, weight limits, and cable routing of each rack. The data collection unit can also collect requirements for new equipment, such as power requirements, cooling requirements, and space requirements. For example, it can collect detailed information on the power consumption of newly introduced high-performance servers, the cooling capacity of cooling systems, and the dimensions of the installation space. This allows the data collection unit to comprehensively understand the requirements of existing and new equipment and provide the basic data necessary for optimizing the overall layout of the data center. Furthermore, the data collection unit can centrally manage this information and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0063] The analysis unit analyzes the information collected by the data collection unit. Specifically, it can analyze the information considering the optimization of cooling efficiency and power supply. For example, it can analyze the placement patterns of cooling devices and methods for evaluating cooling effectiveness. The analysis unit uses AI technology to analyze the collected data in real time and identify the optimal placement pattern of cooling devices. Based on past data and statistical information, the AI ​​learns placement patterns with high cooling efficiency and proposes the optimal placement. The analysis unit can also analyze methods for power load balancing and reducing power consumption. For example, it can analyze the power consumption of each server and adjust the placement so that the power load is evenly distributed. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. As a result, the analysis unit can quickly and accurately analyze the collected data and achieve optimization of the data center's cooling efficiency and power supply. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past cooling efficiency data, it can predict fluctuations in cooling efficiency during specific seasons or time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0064] The generation unit generates the optimal layout based on the information analyzed by the analysis unit. Specifically, it can generate a layout that efficiently arranges cooling equipment. For example, it can generate an optimal layout based on the placement pattern of cooling equipment. The generation unit uses AI technology to automatically generate the optimal layout based on the data provided by the analysis unit. The AI ​​can combine design knowledge from the architectural field to optimize the overall design of the data center. For example, optimizing the structure and layout of the building can improve cooling efficiency and power supply efficiency. Furthermore, the generation unit can use simulation technology to evaluate the effects of the generated layout in advance. For example, it can simulate the placement pattern of cooling equipment and predict fluctuations in cooling efficiency and power consumption. This allows the generation unit not only to generate the optimal layout but also to evaluate its effects in advance and make modifications as needed. In addition, the generation unit can visually display the generated layout so that users can easily understand it. For example, it can generate 3D models and drawings so that users can check the details of the layout. This allows the generation unit to efficiently and effectively generate the optimal layout and optimize the overall design of the data center.

[0065] The service provider provides the layout generated by the generation unit. Specifically, it provides the generated layout to the user, allowing the user to make modifications. For example, the user can change the placement of specific equipment or incorporate additional requirements. The service provider visually displays the generated layout to make it easy for the user to understand. For example, it can generate 3D models or drawings so that the user can review the layout details. Furthermore, the service provider can collect feedback from the user and incorporate that feedback into the generation unit. For example, if the user wants to change the placement of specific equipment, the service provider can collect that request, and the generation unit can regenerate the optimal layout. The service provider can also reliably transmit information using multiple communication methods. For example, it can provide the generated layout to the user via email or cloud services. This allows the service provider to provide information to the user quickly and reliably, optimizing the overall data center design. In addition, the service provider can provide interactive tools to allow users to easily modify the generated layout. For example, it can provide an interface with drag-and-drop functionality so that users can intuitively modify the layout. This allows the service provider to flexibly respond to user requests and provide the optimal layout.

[0066] The analysis unit can analyze information while considering the optimization of cooling efficiency and power supply. For example, the analysis unit can analyze specific evaluation criteria and measurement methods for cooling efficiency. For example, the analysis unit can analyze temperature distribution and energy consumption. The analysis unit can also analyze specific criteria and methods for optimizing power supply. For example, the analysis unit can analyze power load balancing and methods for reducing power consumption. This makes it possible to perform analysis that takes into account the optimization of cooling efficiency and power supply. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can use an AI model to analyze information in order to perform analysis that takes into account the optimization of cooling efficiency and power supply.

[0067] The generation unit can generate layouts for efficiently arranging cooling devices. For example, the generation unit can generate an optimal layout based on the arrangement pattern of the cooling devices. For example, the generation unit can analyze the arrangement pattern of the cooling devices and generate a layout that maximizes the cooling effect. The generation unit can also generate a layout that improves cooling efficiency based on the arrangement of the cooling devices. For example, the generation unit can improve the overall cooling efficiency by optimizing the arrangement of the cooling devices. This enables efficient arrangement of the cooling devices. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate an efficient arrangement of cooling devices using an AI model that takes the arrangement pattern of the cooling devices as input and outputs an optimal layout.

[0068] The service provider provides the user with a generated layout that the user can modify. For example, the service provider provides the user with a generated layout that the user can change the placement of specific equipment or reflect additional requirements. For example, the service provider allows the user to make modifications through an interface. For example, the service provider provides an interface that allows the user to select and modify specific parts of the layout. The service provider also allows the user to input additional requirements and modify the layout accordingly. This allows the user to modify the generated layout. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated layout into an AI model and provide an interface for reflecting the user's modifications.

[0069] The generation unit can optimize the overall design of a data center by combining AI technologies from the architectural field. For example, the generation unit can use AI technologies from the architectural field to optimize the structure and layout of a building. For example, the generation unit can use architectural design optimization algorithms to optimize the structure of a building. The generation unit can also use simulation technology to optimize the layout of a building. For example, the generation unit can simulate the layout of a building and determine the optimal layout. This allows for the optimization of the overall design of the data center. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use AI models to optimize the overall design of a data center using AI technologies from the architectural field.

[0070] The data collection unit estimates the user's emotions and adjusts the timing of collecting equipment layout information based on the estimated emotions. The data collection unit can estimate the user's emotions and adjust the timing of collecting equipment layout information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. Also, if the user is in a hurry, the data collection unit can advance the collection timing to collect data quickly. Furthermore, if the user is concentrating, the data collection unit can adjust the collection timing to avoid interrupting the user's work. In this way, by adjusting the collection timing according to the user's emotions, information can be collected at a more appropriate time. 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. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the collection timing.

[0071] The data collection unit can analyze past equipment layout information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on past equipment layout information. The data collection unit can also prioritize the collection of information that takes a long time to collect from past data. Furthermore, the data collection unit can analyze past data collection history and optimize the frequency and timing of collection. This enables efficient information collection by selecting the optimal data collection method based on past information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past equipment layout information into an AI model and have the AI ​​perform analysis to select the optimal data collection method.

[0072] The data collection unit can filter data center operational status and environmental conditions when collecting equipment layout information. For example, the data collection unit can monitor the data center's operational status in real time and collect information at the optimal time. The data collection unit can also filter the information to be collected by considering environmental conditions (temperature, humidity, etc.). Furthermore, the data collection unit can determine the priority of the information to be collected according to the data center's operational status. This allows for the collection of more relevant information by filtering information based on operational status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the data center's operational status and environmental conditions into an AI model and have the AI ​​perform the filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information of high importance. If the user is relaxed, the data collection unit can also collect detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize information that can be collected quickly. In this way, by prioritizing information according to the user's emotions, more important information can be collected preferentially. 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. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the data center when collecting equipment layout information. For example, the data collection unit can prioritize the collection of relevant information based on the geographical location of the data center. The data collection unit can also limit the scope of information to be collected based on the geographical location. Furthermore, the data collection unit can determine the priority of information to be collected by considering the geographical location. This allows for the priority collection of highly relevant information by considering the geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the data center into an AI model and have the AI ​​perform analysis to prioritize the collection of highly relevant information.

[0075] The data collection unit can analyze the data center's operational history and collect relevant information when collecting equipment layout information. For example, the data collection unit can analyze the data center's operational history and collect information to prevent past problems. The data collection unit can also determine the priority of the information to be collected based on the operational history. Furthermore, the data collection unit can limit the scope of information to be collected, taking the operational history into consideration. This allows for the efficient collection of relevant information by analyzing the operational history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data center's operational history into an AI model and have the AI ​​perform the analysis to collect relevant information.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the equipment during the analysis. For example, the analysis unit can perform a detailed analysis for equipment of high importance. It can also perform a simplified analysis for equipment of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the equipment. This allows for efficient analysis by adjusting the level of detail according to the importance of the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the equipment category during analysis. For example, it can apply different analysis algorithms to server racks and cooling systems. It can also apply different analysis algorithms to power supply equipment and network equipment. Furthermore, the analysis unit can select the optimal analysis algorithm according to the equipment category. By applying the optimal analysis algorithm according to the equipment category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment category data into an AI model and have the AI ​​select the optimal analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the installation date of the equipment. For example, the analysis unit can prioritize the analysis of newly installed equipment. The analysis unit can also perform periodic analyses on older equipment. Furthermore, the analysis unit can adjust the frequency of analysis based on the installation date. This enables efficient analysis by determining the priority of analysis based on the installation date of the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment installation date data into an AI model and have the AI ​​perform the determination of analysis priorities.

[0081] The analysis unit can adjust the order of analysis based on the relationships between the equipment during the analysis. For example, the analysis unit can prioritize the analysis of equipment with high relevance. It can also postpone the analysis of equipment with low relevance. Furthermore, the analysis unit can determine the order of analysis based on the relationships between the equipment. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment relationship data into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0082] The generation unit can estimate the user's emotions and adjust the way the generated layout is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a layout that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a layout that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a layout with visually stimulating effects. This allows for the provision of a more appropriate layout by adjusting the way the layout is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the layout is presented.

[0083] The generation unit can adjust the level of detail of the layout based on the importance of the equipment during layout generation. For example, the generation unit can generate a detailed layout for equipment with high importance. It can also generate a simplified layout for equipment with low importance. Furthermore, the generation unit can determine the layout priority according to the importance of the equipment. This allows for efficient layout generation by adjusting the level of detail of the layout according to the importance of the equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the layout.

[0084] The generation unit can apply different generation algorithms depending on the equipment category when generating layouts. For example, it can apply different generation algorithms to server racks and cooling equipment. It can also apply different generation algorithms to power supply equipment and network equipment. Furthermore, the generation unit can select the optimal generation algorithm depending on the equipment category. By applying the optimal generation algorithm according to the equipment category, the accuracy of the layout is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment category data into an AI model and have the AI ​​select the optimal generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the length of the generated layout based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise layout. If the user is relaxed, the generation unit can generate a longer layout with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a layout with visually stimulating effects. By adjusting the layout length according to the user's emotions, a more appropriate layout can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the layout length.

[0086] The generation unit can determine layout priorities based on the installation dates of equipment during layout generation. For example, the generation unit can prioritize the inclusion of newly installed equipment in the layout. The generation unit can also perform periodic layout updates for older equipment. Furthermore, the generation unit can adjust the frequency of layouts based on the installation dates. This enables efficient layout generation by determining layout priorities based on the installation dates of equipment. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment installation date data into an AI model and have the AI ​​perform the layout priority determination.

[0087] The generation unit can adjust the layout order based on the relationships between the equipment when generating the layout. For example, the generation unit can prioritize reflecting highly relevant equipment in the layout. It can also postpone the generation of less relevant equipment. Furthermore, the generation unit can determine the layout order based on the relationships between the equipment. This allows for efficient layout generation by adjusting the layout order based on the relationships between the equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment relationship data into an AI model and have the AI ​​perform the layout order adjustment.

[0088] The service provider can estimate the user's emotions and adjust the display method of the layout based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the display method according to the user's emotions, a more appropriate layout can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0089] The service provider can select the optimal display method by referring to the user's past operation history when providing a layout. For example, the service provider can select the optimal display method based on the user's past operation history. The service provider can also prioritize providing the display method preferred by the user based on past operation history. Furthermore, the service provider can analyze the operation history and suggest improvements to the display method. By selecting the optimal display method based on past operation history, a user-friendly display becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history data into an AI model and have the AI ​​select the optimal display method.

[0090] The service provider can estimate the user's emotions and adjust the operation procedures of the layout based on the estimated emotions. For example, if the user is tense, the service provider can provide simple and intuitive operation procedures. If the user is relaxed, the service provider can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. By adjusting the operation procedures according to the user's emotions, more appropriate operation becomes possible. 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of operation procedures.

[0091] The service provider can select the optimal display method when providing a layout, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into an AI model and have the AI ​​select the optimal display method.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The data collection unit can estimate the user's emotions and adjust the timing of data collection for equipment placement information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. Conversely, if the user is in a hurry, the data collection unit can speed up the collection timing to collect data quickly. Furthermore, if the user is concentrating, the data collection unit can adjust the collection timing to avoid interrupting the user's work. This allows for data collection at a more appropriate time by adjusting the collection timing 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0095] The generation unit can estimate the user's emotions and adjust the way the generated layout is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a layout that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a layout that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a layout with visually stimulating effects. This allows for the provision of a more appropriate layout by adjusting the way the layout is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the layout is presented.

[0096] The service provider can estimate the user's emotions and adjust the display method of the layout based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the display method according to the user's emotions, a more appropriate layout can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0097] The service provider can estimate the user's emotions and adjust the operation procedures of the layout based on the estimated emotions. For example, if the user is tense, the service provider can provide simple and intuitive operation procedures. If the user is relaxed, the service provider can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. By adjusting the operation procedures according to the user's emotions, more appropriate operation becomes possible. 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of operation procedures.

[0098] The data collection unit can analyze past equipment layout information and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method based on past equipment layout information. The data collection unit can also prioritize the collection of information that takes a long time to collect from past data. Furthermore, the data collection unit can analyze past data collection history and optimize the frequency and timing of collection. This enables efficient information collection by selecting the optimal data collection method based on past information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past equipment layout information into an AI model and have the AI ​​perform analysis to select the optimal data collection method.

[0099] The data collection unit can filter data center operational status and environmental conditions when collecting equipment layout information. For example, the data collection unit can monitor the data center's operational status in real time and collect information at the optimal time. The data collection unit can also filter the information to be collected by considering environmental conditions (temperature, humidity, etc.). Furthermore, the data collection unit can determine the priority of the information to be collected according to the data center's operational status. This allows for the collection of more relevant information by filtering information based on operational status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the data center's operational status and environmental conditions into an AI model and have the AI ​​perform the filtering.

[0100] The analysis unit can adjust the level of detail of the analysis based on the importance of the equipment during the analysis. For example, the analysis unit can perform a detailed analysis for equipment of high importance. It can also perform a simplified analysis for equipment of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the equipment. This allows for efficient analysis by adjusting the level of detail according to the importance of the equipment. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0101] The analysis unit can apply different analysis algorithms depending on the equipment category during analysis. For example, it can apply different analysis algorithms to server racks and cooling systems. It can also apply different analysis algorithms to power supply equipment and network equipment. Furthermore, the analysis unit can select the optimal analysis algorithm according to the equipment category. By applying the optimal analysis algorithm according to the equipment category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input equipment category data into an AI model and have the AI ​​select the optimal analysis algorithm.

[0102] The generation unit can adjust the level of detail of the layout based on the importance of the equipment during layout generation. For example, the generation unit can generate a detailed layout for equipment with high importance. It can also generate a simplified layout for equipment with low importance. Furthermore, the generation unit can determine the layout priority according to the importance of the equipment. This allows for efficient layout generation by adjusting the level of detail of the layout according to the importance of the equipment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input equipment importance data into an AI model and have the AI ​​perform the adjustment of the level of detail of the layout.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects existing equipment layout information and requirements for new equipment. The data collection unit can collect information such as physical layout and network layout. It can also collect requirements such as power requirements, cooling requirements, and space requirements for the new equipment. For example, the data collection unit can collect information on the layout of existing server racks and the space requirements for the newly installed cooling system. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis unit can analyze the information considering, for example, the optimization of cooling efficiency and power supply. For example, the analysis unit can analyze the arrangement patterns of cooling devices and methods for evaluating cooling effects. The analysis unit can also analyze methods for power load balancing and reducing power consumption. Step 3: The generation unit generates the optimal layout based on the information analyzed by the analysis unit. For example, the generation unit can generate a layout that efficiently arranges cooling equipment. For example, the generation unit can generate the optimal layout based on the arrangement patterns of cooling equipment. The generation unit can also optimize the overall design of a data center by combining AI technology from the architectural field. For example, the generation unit can improve cooling efficiency and power supply efficiency by optimizing the structure and arrangement of the building. Step 4: The provider unit provides the layout generated by the generator unit. The provider unit provides the generated layout to the user, for example, allowing the user to make modifications. For example, the provider unit can change the placement of specific equipment or reflect additional requirements.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects existing equipment layout information and requirements for new equipment. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated layout to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects existing equipment layout information and requirements for new equipment. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated layout to the user. 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.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects existing equipment layout information and requirements for new equipment. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an optimal layout based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated layout to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects existing equipment layout information and requirements for new equipment. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal layout based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated layout to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A data collection unit that collects existing equipment layout information and requirements for new equipment, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates an optimal layout based on the information analyzed by the analysis unit, The system comprises a providing unit that provides the layout generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the information while considering cooling efficiency and power supply optimization. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate a layout for efficiently arranging cooling equipment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated layout is provided to the user, who can then make modifications to it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Combining AI technology in the architectural field to optimize the entire data center design. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates user emotions and adjusts the timing of equipment placement information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past equipment layout data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting equipment layout information, filtering is performed based on the data center's operational status and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting equipment layout information, the geographical location of the data center is taken into consideration to prioritize the collection of highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting equipment layout information, we analyze the data center's operational history and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the level of detail is adjusted based on the importance of the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied depending on the equipment category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the installation date of the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relationships between the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the way the generated layout is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating the layout, adjust the level of detail in the layout based on the importance of the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating layouts, different generation algorithms are applied depending on the equipment category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the generated layout based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating the layout, the layout priority is determined based on the installation date of the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating the layout, adjust the layout order based on the relationships between the equipment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the layout is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing a layout, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the operation procedures of the layout provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a layout, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects existing equipment layout information and requirements for new equipment, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates an optimal layout based on the information analyzed by the analysis unit, The system comprises a providing unit that provides the layout generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the information while considering cooling efficiency and power supply optimization. The system according to feature 1.

3. The generating unit is Generate a layout for efficiently arranging cooling equipment. The system according to feature 1.

4. The aforementioned supply unit is, The generated layout is provided to the user, who can then make modifications to it. The system according to feature 1.

5. The generating unit is Combining AI technology in the architectural field to optimize the entire data center design. The system according to feature 1.

6. The aforementioned collection unit is The system estimates user emotions and adjusts the timing of equipment placement information collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past equipment layout data to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting equipment layout information, filtering is performed based on the data center's operational status and environmental conditions. The system according to feature 1.

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