system

Abstract

The system according to the embodiment aims to improve the efficiency of the control and management of building facilities and enable operation in natural language. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, a reception unit, and a control unit. The collection unit collects connection information for each piece of equipment. The analysis unit analyzes the connection information collected by the collection unit. The identification unit identifies a control method for each piece of equipment based on the information analyzed by the analysis unit. The reception unit accepts instructions in natural language. The control unit controls each piece of equipment based on the instructions accepted by the reception unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that the control and management of building facilities requires a lot of system integration work, making efficient management difficult.

[0005] The system according to the embodiment aims to improve the efficiency of the control and management of building facilities and enable operation in natural language. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, a reception unit, and a control unit. The collection unit collects connection information for each piece of equipment. The analysis unit analyzes the connection information collected by the collection unit. The identification unit identifies a control method for each piece of equipment based on the information analyzed by the analysis unit. The reception unit receives instructions in natural language. The control unit controls each piece of equipment based on the instructions received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the control and management of building facilities and enable operation in natural language. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The building facilities management system according to an embodiment of the present invention understands the connection specifications of all building facilities and enables the control and management of various building facilities from a single platform using natural language. This building facilities management system collects connection information for each facility, analyzes the collected connection information, identifies the control method for each facility, accepts natural language instructions, and controls each facility. This system allows building management companies and building users to significantly reduce the system integration work that was previously performed for each building and facility. For example, while air conditioning and elevator control systems previously required separate configurations, the building facilities management system allows these to be controlled from a single platform using natural language. Furthermore, the building facilities management system contributes to increasing the value of older buildings with limited budgets and personnel. For example, even in older buildings, the introduction of the building facilities management system enables smarter building facilities. This increases the value of the building. The building facilities management system significantly reduces the system integration work that building management companies and building users previously performed for each building and facility, thereby contributing to increasing the value of older buildings with limited budgets and personnel.

[0029] A building facilities management system according to an embodiment includes a collection unit, an analysis unit, an identification unit, a reception unit, and a control unit. The collection unit collects connection information for each facility. For example, the collection unit can collect connection information for air conditioning facilities and elevators. The collection unit, for example, collects connection information for air conditioning facilities and determines how each facility is connected. The collection unit can also collect connection information for elevators and determine how each facility is connected. The analysis unit analyzes the connection information collected by the collection unit. For example, the analysis unit can analyze the collected connection information using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI analyzes a control method for each facility based on the collected connection information. The identification unit identifies a control method for each facility based on the information analyzed by the analysis unit. For example, the identification unit can identify the temperature settings for air conditioning facilities and the elevator operation schedule based on the analyzed information. The reception unit accepts instructions in natural language from a building manager. For example, the reception unit can receive a natural language instruction from a building manager, such as "Set the air conditioning to 25 degrees." The control unit controls each piece of equipment based on the instruction received by the reception unit. For example, the control unit can set the air conditioning equipment to 25 degrees based on the received instruction. As a result, the building equipment management system according to the embodiment can collect and analyze connection information for each piece of equipment, identify a control method, receive instructions in natural language, and control each piece of equipment.

[0030] The collection unit can collect connection information about air conditioning equipment or elevators. The collection unit, for example, collects connection information about air conditioning equipment. For example, the collection unit collects connection information about air conditioning equipment and determines how each piece of equipment is connected. The collection unit can also collect connection information about elevators and determine how each piece of equipment is connected. In this way, by collecting connection information about air conditioning equipment and elevators, it is possible to accurately determine the connection information about each piece of equipment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect connection information about air conditioning equipment, input the information to a generation AI, and have the generation AI perform analysis.

[0031] The analysis unit can analyze the connection information collected using the generation AI. The analysis unit analyzes the connection information collected using, for example, the generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI analyzes the control method of each piece of equipment based on the collected connection information. For example, the generation AI analyzes the connection information of air conditioning equipment and identifies the temperature setting method. The generation AI can also analyze the connection information of elevators and identify their operation schedules. In this way, the use of the generation AI improves the analysis accuracy of the collected connection information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected connection information into the generation AI, and the generation AI can perform analysis.

[0032] The identification unit can identify the temperature setting of the air conditioning equipment or the elevator operation schedule based on the analyzed information. The identification unit, for example, identifies the temperature setting of the air conditioning equipment based on the analyzed information. For example, the identification unit identifies a temperature setting method for the air conditioning equipment and sets an appropriate temperature. The identification unit can also identify the elevator operation schedule based on the analyzed information. For example, the identification unit identifies the elevator operation schedule and sets an appropriate operation time. This makes it possible to accurately identify the temperature setting of the air conditioning equipment or the elevator operation schedule based on the analyzed information. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the analyzed information to a generation AI, and the generation AI can perform the identification.

[0033] The reception unit can receive instructions in natural language from the building manager. For example, the reception unit receives instructions in natural language such as "Set the air conditioning to 25 degrees" from the building manager. For example, when the building manager instructs the temperature setting of the air conditioning equipment, the reception unit receives the instruction. Furthermore, when the building manager instructs the elevator operation schedule, the reception unit can also receive the instruction. In this way, receiving instructions in natural language from the building manager enables intuitive operation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the building manager's instructions into a generation AI, which then analyzes the instructions.

[0034] The control unit can set the air conditioning equipment based on the received instructions. The control unit sets the air conditioning equipment based on, for example, the received instructions. For example, if the building manager instructs the control unit to "set the air conditioning to 25 degrees," the control unit sets the air conditioning equipment to 25 degrees based on the instruction. Furthermore, if the building manager instructs the elevator operation schedule, the control unit can also set the elevator operation schedule based on the instruction. This allows the air conditioning equipment to be accurately controlled based on the received instructions. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the building manager's instructions into a generation AI and have the generation AI perform control.

[0035] The collection unit can monitor the operation status of each piece of equipment in real time and automatically collect connection information when an abnormality is detected. The collection unit, for example, monitors the operation status of each piece of equipment in real time and automatically collects connection information when an abnormality is detected. For example, when the collection unit detects an abnormality in air conditioning equipment, it automatically collects the connection information. Furthermore, when the collection unit detects an abnormality in elevators, it can automatically collect the connection information. Furthermore, when the collection unit detects an abnormality in lighting equipment, it can automatically collect the connection information. This enables a rapid response by automatically collecting connection information when an abnormality is detected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input connection information of equipment in which an abnormality is detected to a generation AI, which then performs analysis.

[0036] The collection unit can integrate and analyze multiple sensor data to improve the accuracy of the collected connection information. For example, the collection unit can integrate data from a temperature sensor and a humidity sensor to collect connection information for air conditioning equipment. For example, the collection unit can integrate data from a temperature sensor and data from a humidity sensor to collect connection information for air conditioning equipment. The collection unit can also integrate data from a vibration sensor and a sound sensor to collect connection information for elevators. For example, the collection unit can integrate data from a vibration sensor and data from a sound sensor to collect connection information for elevators. The collection unit can also integrate data from an illuminance sensor and data from a motion sensor to collect connection information for lighting equipment. For example, the collection unit can integrate data from an illuminance sensor and data from a motion sensor to collect connection information for lighting equipment. In this way, the accuracy of the connection information is improved by integrating and analyzing multiple sensor data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input multiple sensor data to a generation AI, which can then perform analysis.

[0037] The collection unit can customize the connection information to be collected based on the geographical and climatic conditions of the building. The collection unit customizes the connection information to be collected, for example, taking into account the geographical and climatic conditions of the building. For example, in a hot and humid region, the collection unit may prioritize collecting connection information for air conditioning equipment. In addition, in a region where earthquakes occur frequently, the collection unit may prioritize collecting connection information for elevators. In addition, in a region where there is a lot of snow, the collection unit may prioritize collecting connection information for heating equipment. This makes it possible to collect information according to geographical and climatic conditions. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the geographical and climatic conditions of the building into a generation AI, which may then perform analysis.

[0038] The collection unit can analyze the behavioral patterns of building users and collect connection information at appropriate times. The collection unit, for example, analyzes the behavioral patterns of building users and collects connection information at optimal times. For example, the collection unit collects connection information for air conditioning equipment during times when there are many building users. The collection unit can also collect connection information for elevators during times when there are few building users. Furthermore, if building users are concentrated in a specific area, the collection unit can preferentially collect connection information for that area. This makes it possible to collect information based on user behavior patterns. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the behavioral patterns of building users to a generation AI, which can then perform analysis.

[0039] The analysis unit can compare data with past data during analysis to detect abnormal values ​​and issue an alert if an abnormality occurs. For example, the analysis unit can compare data with past data during analysis to detect abnormal values ​​and issue an alert if an abnormality occurs. For example, the analysis unit can compare temperature data of air conditioning equipment with past data to detect abnormal values. The analysis unit can also compare elevator operation data with past data to detect abnormal values. The analysis unit can also compare power consumption data of lighting equipment with past data to detect abnormal values. This makes it possible to detect abnormal values ​​and respond quickly. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data to be compared with past data to a generation AI, and the generation AI can perform analysis.

[0040] The analysis unit can visualize the analysis results to enable the building manager to intuitively understand. The analysis unit, for example, displays temperature data of air conditioning equipment in a graph. For example, the analysis unit displays the temperature data of air conditioning equipment in a graph to enable the building manager to intuitively understand. The analysis unit can also display elevator operation data in a chart. For example, the analysis unit displays elevator operation data in a chart to enable the building manager to intuitively understand. The analysis unit can also display power consumption data of lighting equipment in a heat map. For example, the analysis unit displays power consumption data of lighting equipment in a heat map to enable the building manager to intuitively understand. In this way, visualizing the analysis results enables intuitive understanding. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI, which then performs visualization.

[0041] During analysis, the analysis unit can integrate and analyze the energy consumption data of the building to optimize energy efficiency. The analysis unit, for example, integrates and analyzes the energy consumption data of the building to optimize energy efficiency. For example, the analysis unit analyzes the energy consumption data of air conditioning equipment to propose an efficient operation method. The analysis unit can also analyze the energy consumption data of lighting equipment to propose an efficient operation method. The analysis unit can also analyze the energy consumption data of elevators to propose an efficient operation method. In this way, energy efficiency can be optimized by integrating and analyzing the energy consumption data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the energy consumption data to a generation AI and have the generation AI perform the analysis.

[0042] The analysis unit can link the analysis results with other building management systems to achieve comprehensive building management. The analysis unit, for example, shares the analysis results of air conditioning equipment with other building management systems. For example, the analysis unit shares the analysis results of air conditioning equipment with other building management systems to achieve comprehensive building management. The analysis unit can also share the analysis results of elevators with other building management systems. For example, the analysis unit shares the analysis results of elevators with other building management systems to achieve comprehensive building management. The analysis unit can also share the analysis results of lighting equipment with other building management systems. For example, the analysis unit shares the analysis results of lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by linking with other building management systems. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI, and link with other building management systems via the generation AI.

[0043] During identification, the identification unit can select an appropriate control method by taking into account the maintenance history of each piece of equipment. The identification unit, for example, selects the optimal control method by taking into account the maintenance history of each piece of equipment. For example, the identification unit selects the optimal temperature setting by taking into account the maintenance history of air conditioning equipment. The identification unit can also select the optimal operation schedule by taking into account the maintenance history of elevators. The identification unit can also select the optimal lighting schedule by taking into account the maintenance history of lighting equipment. In this way, the optimal control method can be selected by taking the maintenance history into account. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input maintenance history data into a generation AI, which can then perform analysis.

[0044] The identification unit can automatically record the identified control method and use it as future reference data. The identification unit, for example, automatically records the identified control method and uses it as future reference data. For example, the identification unit can automatically record the temperature settings of air conditioning equipment and use it as future reference data. The identification unit can also automatically record elevator operation schedules and use it as future reference data. The identification unit can also automatically record lighting schedules of lighting equipment and use it as future reference data. In this way, by recording the identified control method, it can be used as future reference data. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the identified control method to a generation AI, and the generation AI can record it.

[0045] During identification, the identification unit can select an optimal control method by taking into account the behavioral patterns of building users. The identification unit selects the optimal control method by taking into account, for example, the behavioral patterns of building users. For example, the identification unit selects an optimal temperature setting for air conditioning equipment during times when there are many building users. The identification unit can also select an optimal elevator operation schedule during times when there are few building users. The identification unit can also select an optimal lighting schedule for lighting equipment in a specific area when building users are concentrated in that area. In this way, the optimal control method can be selected by taking into account the behavioral patterns of users. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the behavioral patterns of building users into a generation AI, which can then perform analysis.

[0046] The identification unit can share the identified control method with other building management systems to achieve comprehensive building management. The identification unit, for example, shares a control method for air conditioning equipment with other building management systems. For example, the identification unit can share a control method for air conditioning equipment with other building management systems to achieve comprehensive building management. The identification unit can also share a control method for elevators with other building management systems. For example, the identification unit can share a control method for elevators with other building management systems to achieve comprehensive building management. The identification unit can also share a control method for lighting equipment with other building management systems. For example, the identification unit can share a control method for lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by sharing with other building management systems. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the identified control method to a generation AI, and the generation AI can cooperate with other building management systems.

[0047] The reception unit can analyze the natural language instruction at the time of reception and display a confirmation message to prevent misunderstanding. The reception unit, for example, analyzes the natural language instruction at the time of reception and displays a confirmation message to prevent misunderstanding. For example, the reception unit displays a confirmation message when a user instructs, "Set the air conditioning to 25 degrees." The reception unit can also display a confirmation message when a user instructs, "Call the elevator to the first floor." The reception unit can also display a confirmation message when a user instructs, "Turn off the lights." By displaying a confirmation message to prevent misunderstanding, accurate reception of instructions is possible. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the natural language instruction to a generation AI, which analyzes the instruction and generates a confirmation message.

[0048] The reception unit can automatically record the received instructions and use them as reference data in the future. The reception unit, for example, automatically records the received instructions and uses them as reference data in the future. For example, the reception unit automatically records instructions given by a user to air conditioning equipment. The reception unit can also automatically record instructions given by a user to elevators. The reception unit can also automatically record instructions given by a user to lighting equipment. In this way, by recording the received instructions, the instructions can be used as reference data in the future. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the received instructions to a generation AI, and the generation AI can record them.

[0049] The reception unit can receive optimal instructions taking into consideration the behavioral patterns of building users when receiving the instructions. The reception unit receives optimal instructions taking into consideration, for example, the behavioral patterns of building users. For example, the reception unit receives instructions for optimal temperature settings for air conditioning equipment during times when there are many building users. The reception unit can also receive instructions for optimal elevator operation schedules during times when there are few building users. The reception unit can also receive instructions for optimal lighting schedules for lighting equipment in a specific area when building users are concentrated in that area. This makes it possible to receive optimal instructions by taking into consideration the behavioral patterns of users. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the behavioral patterns of building users into a generation AI, which then performs analysis.

[0050] The reception unit can link the received instructions with other building management systems to achieve comprehensive building management. The reception unit, for example, shares instructions for air conditioning equipment with other building management systems. For example, the reception unit shares instructions for air conditioning equipment with other building management systems to achieve comprehensive building management. The reception unit can also share instructions for elevators with other building management systems. For example, the reception unit shares instructions for elevators with other building management systems to achieve comprehensive building management. The reception unit can also share instructions for lighting equipment with other building management systems. For example, the reception unit shares instructions for lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by linking with other building management systems. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the received instructions to a generation AI, and the generation AI can link with other building management systems.

[0051] During control, the control unit can monitor the operating status of each piece of equipment in real time and automatically adjust the control if an abnormality is detected. For example, during control, the control unit can monitor the operating status of each piece of equipment in real time and automatically adjust the control if an abnormality is detected. For example, when the control unit detects an abnormality in air conditioning equipment, it automatically adjusts the settings. Furthermore, when the control unit detects an abnormality in elevators, it can automatically adjust the operation schedule of the elevators. Furthermore, when the control unit detects an abnormality in lighting equipment, it can automatically adjust the lighting schedule of the lighting equipment. This enables a rapid response by automatically adjusting the control if an abnormality is detected. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input data on equipment in which an abnormality has been detected into a generation AI, which then analyzes the data and adjusts the control.

[0052] The control unit can visualize the control results, allowing the building manager to intuitively understand them. The control unit, for example, displays the control results of air conditioning equipment in a graph. For example, the control unit displays the control results of air conditioning equipment in a graph, allowing the building manager to intuitively understand them. The control unit can also display the control results of elevators in a chart. For example, the control unit displays the control results of elevators in a chart, allowing the building manager to intuitively understand them. The control unit can also display the control results of lighting equipment in a heat map. For example, the control unit displays the control results of lighting equipment in a heat map, allowing the building manager to intuitively understand them. In this way, visualizing the control results enables intuitive understanding. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the control results to a generation AI, which then visualizes the control results.

[0053] During control, the control unit can integrate and control the energy consumption data of the building to optimize energy efficiency. The control unit, for example, integrates and controls the energy consumption data of the building to optimize energy efficiency. For example, the control unit proposes an efficient operation method based on energy consumption data of air conditioning equipment. The control unit can also propose an efficient operation method based on energy consumption data of lighting equipment. The control unit can also propose an efficient operation method based on energy consumption data of elevators. In this way, energy efficiency can be optimized by integrating and controlling the energy consumption data. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input energy consumption data to a generation AI, have the generation AI analyze it, and propose an efficient operation method.

[0054] The control unit can link the control results with other building management systems to achieve comprehensive building management. The control unit, for example, shares the control results of air conditioning equipment with other building management systems. For example, the control unit shares the control results of air conditioning equipment with other building management systems to achieve comprehensive building management. The control unit can also share the control results of elevators with other building management systems. For example, the control unit shares the control results of elevators with other building management systems to achieve comprehensive building management. The control unit can also share the control results of lighting equipment with other building management systems. For example, the control unit shares the control results of lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by linking with other building management systems. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the control results to a generation AI, and link with other building management systems via the generation AI.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The building facility management system can further include a prediction unit. The prediction unit can predict future operating conditions based on past data for each facility. For example, the prediction unit can analyze past temperature setting data for air conditioning facilities to predict future temperature setting trends. The prediction unit can also analyze past elevator operation data to predict future operating schedule trends. The prediction unit can also analyze past power consumption data for lighting facilities to predict future power consumption trends. As a result, the prediction unit can be used to predict future operating conditions and take appropriate measures in advance.

[0057] When collecting connection information for each piece of equipment, the collection unit can determine the priority of the information based on the operating time and frequency of use of the equipment. For example, if the operating time of air conditioning equipment is long, the collection unit can prioritize collecting its connection information. Also, if the frequency of use of elevators is high, the collection unit can also prioritize collecting its connection information. Furthermore, if the frequency of use of lighting equipment is high, the collection unit can also prioritize collecting its connection information. In this way, by determining the priority of information based on the operating time and frequency of use, important information can be collected preferentially.

[0058] When analyzing the connection information of each piece of equipment, the analysis unit can use an anomaly detection algorithm to detect anomalies. For example, the analysis unit can analyze the connection information of air conditioning equipment and detect abnormal temperature fluctuations. The analysis unit can also analyze the connection information of elevators and detect abnormal operation patterns. Furthermore, the analysis unit can analyze the connection information of lighting equipment and detect abnormal power consumption. In this way, by using an anomaly detection algorithm, anomalies in each piece of equipment can be detected early, enabling a prompt response.

[0059] When specifying the control method for each piece of equipment, the specifying unit can select the optimal control method taking energy efficiency into consideration. For example, the specifying unit optimizes the temperature settings of air conditioning equipment from the perspective of energy efficiency. The specifying unit can also optimize elevator operation schedules from the perspective of energy efficiency. Furthermore, the specifying unit can also optimize lighting schedules from the perspective of energy efficiency. In this way, by selecting the optimal control method taking energy efficiency into consideration, it is possible to reduce energy consumption throughout the building.

[0060] When receiving instructions in natural language from a building manager, the reception unit can analyze the content of the instructions and provide appropriate feedback. For example, if the building manager instructs, "Set the air conditioning to 25 degrees," the reception unit can provide feedback confirming that the instruction was correctly received. Also, if the building manager instructs, "Call the elevator to the first floor," the reception unit can provide feedback confirming that the instruction was correctly received. Furthermore, if the building manager instructs, "Turn off the lights," the reception unit can provide feedback confirming that the instruction was correctly received. In this way, by analyzing the content of instructions and providing appropriate feedback, the building manager's sense of security can be increased.

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

[0062] Step 1: The collection unit collects connection information for each piece of equipment. For example, the collection unit collects connection information for air conditioning equipment and elevators, and determines how each piece of equipment is connected. Step 2: The analysis unit analyzes the connection information collected by the collection unit. For example, the analysis unit analyzes the collected connection information using the generation AI and analyzes the control method for each piece of equipment. Step 3: The identification unit identifies a control method for each piece of equipment based on the information analyzed by the analysis unit. For example, the identification unit identifies the temperature settings for air conditioning equipment and elevator operation schedules. Step 4: The reception unit receives instructions in natural language from the building manager. For example, the reception unit receives an instruction in natural language such as "Set the air conditioning to 25 degrees." Step 5: The control unit controls each piece of equipment based on the instructions received by the reception unit. For example, the control unit sets the temperature of the air conditioning equipment to 25 degrees.

[0063] (Example 2) The building facilities management system according to an embodiment of the present invention understands the connection specifications of all building facilities and enables the control and management of various building facilities from a single platform using natural language. This building facilities management system collects connection information for each facility, analyzes the collected connection information, identifies the control method for each facility, accepts natural language instructions, and controls each facility. This system allows building management companies and building users to significantly reduce the system integration work that was previously performed for each building and facility. For example, while air conditioning and elevator control systems previously required separate configurations, the building facilities management system allows these to be controlled from a single platform using natural language. Furthermore, the building facilities management system contributes to increasing the value of older buildings with limited budgets and personnel. For example, even in older buildings, the introduction of the building facilities management system enables smarter building facilities. This increases the value of the building. The building facilities management system significantly reduces the system integration work that building management companies and building users previously performed for each building and facility, thereby contributing to increasing the value of older buildings with limited budgets and personnel.

[0064] A building facilities management system according to an embodiment includes a collection unit, an analysis unit, an identification unit, a reception unit, and a control unit. The collection unit collects connection information for each facility. For example, the collection unit can collect connection information for air conditioning facilities and elevators. The collection unit, for example, collects connection information for air conditioning facilities and determines how each facility is connected. The collection unit can also collect connection information for elevators and determine how each facility is connected. The analysis unit analyzes the connection information collected by the collection unit. For example, the analysis unit can analyze the collected connection information using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI analyzes a control method for each facility based on the collected connection information. The identification unit identifies a control method for each facility based on the information analyzed by the analysis unit. For example, the identification unit can identify the temperature settings for air conditioning facilities and the elevator operation schedule based on the analyzed information. The reception unit accepts instructions in natural language from a building manager. For example, the reception unit can receive a natural language instruction from a building manager, such as "Set the air conditioning to 25 degrees." The control unit controls each piece of equipment based on the instruction received by the reception unit. For example, the control unit can set the air conditioning equipment to 25 degrees based on the received instruction. As a result, the building equipment management system according to the embodiment can collect and analyze connection information for each piece of equipment, identify a control method, receive instructions in natural language, and control each piece of equipment.

[0065] The collection unit can collect connection information about air conditioning equipment or elevators. The collection unit, for example, collects connection information about air conditioning equipment. For example, the collection unit collects connection information about air conditioning equipment and determines how each piece of equipment is connected. The collection unit can also collect connection information about elevators and determine how each piece of equipment is connected. In this way, by collecting connection information about air conditioning equipment and elevators, it is possible to accurately determine the connection information about each piece of equipment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect connection information about air conditioning equipment, input the information to a generation AI, and have the generation AI perform analysis.

[0066] The analysis unit can analyze the connection information collected using the generation AI. The analysis unit analyzes the connection information collected using, for example, the generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI analyzes the control method of each piece of equipment based on the collected connection information. For example, the generation AI analyzes the connection information of air conditioning equipment and identifies the temperature setting method. The generation AI can also analyze the connection information of elevators and identify their operation schedules. In this way, the use of the generation AI improves the analysis accuracy of the collected connection information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected connection information into the generation AI, and the generation AI can perform analysis.

[0067] The identification unit can identify the temperature setting of the air conditioning equipment or the elevator operation schedule based on the analyzed information. The identification unit, for example, identifies the temperature setting of the air conditioning equipment based on the analyzed information. For example, the identification unit identifies a temperature setting method for the air conditioning equipment and sets an appropriate temperature. The identification unit can also identify the elevator operation schedule based on the analyzed information. For example, the identification unit identifies the elevator operation schedule and sets an appropriate operation time. This makes it possible to accurately identify the temperature setting of the air conditioning equipment or the elevator operation schedule based on the analyzed information. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the analyzed information to a generation AI, and the generation AI can perform the identification.

[0068] The reception unit can receive instructions in natural language from the building manager. For example, the reception unit receives instructions in natural language such as "Set the air conditioning to 25 degrees" from the building manager. For example, when the building manager instructs the temperature setting of the air conditioning equipment, the reception unit receives the instruction. Furthermore, when the building manager instructs the elevator operation schedule, the reception unit can also receive the instruction. In this way, receiving instructions in natural language from the building manager enables intuitive operation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the building manager's instructions into a generation AI, which then analyzes the instructions.

[0069] The control unit can set the air conditioning equipment based on the received instructions. The control unit sets the air conditioning equipment based on, for example, the received instructions. For example, if the building manager instructs the control unit to "set the air conditioning to 25 degrees," the control unit sets the air conditioning equipment to 25 degrees based on the instruction. Furthermore, if the building manager instructs the elevator operation schedule, the control unit can also set the elevator operation schedule based on the instruction. This allows the air conditioning equipment to be accurately controlled based on the received instructions. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the building manager's instructions into a generation AI and have the generation AI perform control.

[0070] The collection unit can estimate the user's emotions and determine the priority of the connection information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the connection information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important connection information. Furthermore, when the user is relaxed, the collection unit can equally collect all connection information. Furthermore, when the user is in a hurry, the collection unit can quickly collect the most important connection information. In this way, by determining the priority of connection information based on the user's emotions, important information can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then estimate the emotion.

[0071] The collection unit can monitor the operation status of each piece of equipment in real time and automatically collect connection information when an abnormality is detected. The collection unit, for example, monitors the operation status of each piece of equipment in real time and automatically collects connection information when an abnormality is detected. For example, when the collection unit detects an abnormality in air conditioning equipment, it automatically collects the connection information. Furthermore, when the collection unit detects an abnormality in elevators, it can automatically collect the connection information. Furthermore, when the collection unit detects an abnormality in lighting equipment, it can automatically collect the connection information. This enables a rapid response by automatically collecting connection information when an abnormality is detected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input connection information of equipment in which an abnormality is detected to a generation AI, which then performs analysis.

[0072] The collection unit can integrate and analyze multiple sensor data to improve the accuracy of the collected connection information. For example, the collection unit can integrate data from a temperature sensor and a humidity sensor to collect connection information for air conditioning equipment. For example, the collection unit can integrate data from a temperature sensor and data from a humidity sensor to collect connection information for air conditioning equipment. The collection unit can also integrate data from a vibration sensor and a sound sensor to collect connection information for elevators. For example, the collection unit can integrate data from a vibration sensor and data from a sound sensor to collect connection information for elevators. The collection unit can also integrate data from an illuminance sensor and data from a motion sensor to collect connection information for lighting equipment. For example, the collection unit can integrate data from an illuminance sensor and data from a motion sensor to collect connection information for lighting equipment. In this way, the accuracy of the connection information is improved by integrating and analyzing multiple sensor data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input multiple sensor data to a generation AI, which can then perform analysis.

[0073] The collection unit can estimate the user's emotions and adjust the frequency of collecting connection information based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the frequency of collecting connection information based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can frequently collect important connection information. Also, if the user is relaxed, the collection unit can equally collect all connection information. Also, if the user is in a hurry, the collection unit can quickly collect the most important connection information. This enables efficient information collection by adjusting the frequency of collecting connection information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then estimate the emotion.

[0074] The collection unit can customize the connection information to be collected based on the geographical and climatic conditions of the building. The collection unit customizes the connection information to be collected, for example, taking into account the geographical and climatic conditions of the building. For example, in a hot and humid region, the collection unit may prioritize collecting connection information for air conditioning equipment. In addition, in a region where earthquakes occur frequently, the collection unit may prioritize collecting connection information for elevators. In addition, in a region where there is a lot of snow, the collection unit may prioritize collecting connection information for heating equipment. This makes it possible to collect information according to geographical and climatic conditions. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the geographical and climatic conditions of the building into a generation AI, which may then perform analysis.

[0075] The collection unit can analyze the behavioral patterns of building users and collect connection information at appropriate times. The collection unit, for example, analyzes the behavioral patterns of building users and collects connection information at optimal times. For example, the collection unit collects connection information for air conditioning equipment during times when there are many building users. The collection unit can also collect connection information for elevators during times when there are few building users. Furthermore, if building users are concentrated in a specific area, the collection unit can preferentially collect connection information for that area. This makes it possible to collect information based on user behavior patterns. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the behavioral patterns of building users to a generation AI, which can then perform analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for intuitive understanding by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can then estimate the emotion.

[0077] The analysis unit can compare data with past data during analysis to detect abnormal values ​​and issue an alert if an abnormality occurs. For example, the analysis unit can compare data with past data during analysis to detect abnormal values ​​and issue an alert if an abnormality occurs. For example, the analysis unit can compare temperature data of air conditioning equipment with past data to detect abnormal values. The analysis unit can also compare elevator operation data with past data to detect abnormal values. The analysis unit can also compare power consumption data of lighting equipment with past data to detect abnormal values. This makes it possible to detect abnormal values ​​and respond quickly. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data to be compared with past data to a generation AI, and the generation AI can perform analysis.

[0078] The analysis unit can visualize the analysis results to enable the building manager to intuitively understand. The analysis unit, for example, displays temperature data of air conditioning equipment in a graph. For example, the analysis unit displays the temperature data of air conditioning equipment in a graph to enable the building manager to intuitively understand. The analysis unit can also display elevator operation data in a chart. For example, the analysis unit displays elevator operation data in a chart to enable the building manager to intuitively understand. The analysis unit can also display power consumption data of lighting equipment in a heat map. For example, the analysis unit displays power consumption data of lighting equipment in a heat map to enable the building manager to intuitively understand. In this way, visualizing the analysis results enables intuitive understanding. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI, which then performs visualization.

[0079] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize important analyses. Furthermore, if the user is relaxed, the analysis unit can also perform all analyses equally. Furthermore, if the user is in a hurry, the analysis unit can quickly perform the most important analyses. Thus, by determining the analysis priorities based on the user's emotions, important analyses can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into a generation AI, which then estimates the emotion.

[0080] During analysis, the analysis unit can integrate and analyze the energy consumption data of the building to optimize energy efficiency. The analysis unit, for example, integrates and analyzes the energy consumption data of the building to optimize energy efficiency. For example, the analysis unit analyzes the energy consumption data of air conditioning equipment to propose an efficient operation method. The analysis unit can also analyze the energy consumption data of lighting equipment to propose an efficient operation method. The analysis unit can also analyze the energy consumption data of elevators to propose an efficient operation method. In this way, energy efficiency can be optimized by integrating and analyzing the energy consumption data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the energy consumption data to a generation AI and have the generation AI perform the analysis.

[0081] The analysis unit can link the analysis results with other building management systems to achieve comprehensive building management. The analysis unit, for example, shares the analysis results of air conditioning equipment with other building management systems. For example, the analysis unit shares the analysis results of air conditioning equipment with other building management systems to achieve comprehensive building management. The analysis unit can also share the analysis results of elevators with other building management systems. For example, the analysis unit shares the analysis results of elevators with other building management systems to achieve comprehensive building management. The analysis unit can also share the analysis results of lighting equipment with other building management systems. For example, the analysis unit shares the analysis results of lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by linking with other building management systems. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results to a generation AI, and link with other building management systems via the generation AI.

[0082] The identification unit can estimate the user's emotion and determine the priority of the control methods to be identified based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and determines the priority of the control methods to be identified based on the estimated user's emotion. For example, when the user is feeling stressed, the identification unit can prioritize identifying important control methods. When the user is relaxed, the identification unit can also equally identify all control methods. When the user is in a hurry, the identification unit can quickly identify the most important control method. In this way, by prioritizing the control methods based on the user's emotion, important control methods can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using an AI, for example, or without an AI. For example, the identification unit can input user emotion data into the generation AI, and the generation AI can estimate the emotion.

[0083] During identification, the identification unit can select an appropriate control method by taking into account the maintenance history of each piece of equipment. The identification unit, for example, selects the optimal control method by taking into account the maintenance history of each piece of equipment. For example, the identification unit selects the optimal temperature setting by taking into account the maintenance history of air conditioning equipment. The identification unit can also select the optimal operation schedule by taking into account the maintenance history of elevators. The identification unit can also select the optimal lighting schedule by taking into account the maintenance history of lighting equipment. In this way, the optimal control method can be selected by taking the maintenance history into account. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input maintenance history data into a generation AI, which can then perform analysis.

[0084] The identification unit can automatically record the identified control method and use it as future reference data. The identification unit, for example, automatically records the identified control method and uses it as future reference data. For example, the identification unit can automatically record the temperature settings of air conditioning equipment and use it as future reference data. The identification unit can also automatically record elevator operation schedules and use it as future reference data. The identification unit can also automatically record lighting schedules of lighting equipment and use it as future reference data. In this way, by recording the identified control method, it can be used as future reference data. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the identified control method to a generation AI, and the generation AI can record it.

[0085] The identification unit can estimate the user's emotion and adjust the level of detail of the control method to be identified based on the estimated user's emotion. For example, the identification unit can estimate the user's emotion and adjust the level of detail of the control method to be identified based on the estimated user's emotion. For example, the identification unit can identify a concise control method when the user is stressed. The identification unit can also identify a detailed control method when the user is relaxed. The identification unit can also identify a control method that can be quickly implemented when the user is in a hurry. This allows an appropriate control method to be provided by adjusting the level of detail of the control method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using an AI, for example, or without an AI. For example, the identification unit can input user emotion data into the generation AI, and the generation AI can estimate the emotion.

[0086] During identification, the identification unit can select an optimal control method by taking into account the behavioral patterns of building users. The identification unit selects the optimal control method by taking into account, for example, the behavioral patterns of building users. For example, the identification unit selects an optimal temperature setting for air conditioning equipment during times when there are many building users. The identification unit can also select an optimal elevator operation schedule during times when there are few building users. The identification unit can also select an optimal lighting schedule for lighting equipment in a specific area when building users are concentrated in that area. In this way, the optimal control method can be selected by taking into account the behavioral patterns of users. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the behavioral patterns of building users into a generation AI, which can then perform analysis.

[0087] The identification unit can share the identified control method with other building management systems to achieve comprehensive building management. The identification unit, for example, shares a control method for air conditioning equipment with other building management systems. For example, the identification unit can share a control method for air conditioning equipment with other building management systems to achieve comprehensive building management. The identification unit can also share a control method for elevators with other building management systems. For example, the identification unit can share a control method for elevators with other building management systems to achieve comprehensive building management. The identification unit can also share a control method for lighting equipment with other building management systems. For example, the identification unit can share a control method for lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by sharing with other building management systems. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the identified control method to a generation AI, and the generation AI can cooperate with other building management systems.

[0088] The reception unit can estimate the user's emotions and determine the priority of instructions to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of instructions to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes receiving important instructions. Furthermore, when the user is relaxed, the reception unit can equally receive all instructions. Furthermore, when the user is in a hurry, the reception unit can quickly receive the most important instructions. By determining the priority of instructions based on the user's emotions, important instructions can be preferentially received. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0089] The reception unit can analyze the natural language instruction at the time of reception and display a confirmation message to prevent misunderstanding. The reception unit, for example, analyzes the natural language instruction at the time of reception and displays a confirmation message to prevent misunderstanding. For example, the reception unit displays a confirmation message when a user instructs, "Set the air conditioning to 25 degrees." The reception unit can also display a confirmation message when a user instructs, "Call the elevator to the first floor." The reception unit can also display a confirmation message when a user instructs, "Turn off the lights." By displaying a confirmation message to prevent misunderstanding, accurate reception of instructions is possible. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the natural language instruction to a generation AI, which analyzes the instruction and generates a confirmation message.

[0090] The reception unit can automatically record the received instructions and use them as reference data in the future. The reception unit, for example, automatically records the received instructions and uses them as reference data in the future. For example, the reception unit automatically records instructions given by a user to air conditioning equipment. The reception unit can also automatically record instructions given by a user to elevators. The reception unit can also automatically record instructions given by a user to lighting equipment. In this way, by recording the received instructions, the instructions can be used as reference data in the future. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the received instructions to a generation AI, and the generation AI can record them.

[0091] The reception unit can estimate the user's emotion and adjust the level of detail of the received instruction based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the level of detail of the received instruction based on the estimated user emotion. For example, the reception unit prioritizes receiving concise instructions when the user is stressed. The reception unit can also receive detailed instructions when the user is relaxed. The reception unit can also receive instructions that can be quickly executed when the user is in a hurry. By adjusting the level of detail of the instructions based on the user's emotion, appropriate instructions can be received. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, or may be performed without an AI. For example, the reception unit can input the user's emotion data into a generation AI, which then estimates the emotion.

[0092] The reception unit can receive optimal instructions taking into consideration the behavioral patterns of building users when receiving the instructions. The reception unit receives optimal instructions taking into consideration, for example, the behavioral patterns of building users. For example, the reception unit receives instructions for optimal temperature settings for air conditioning equipment during times when there are many building users. The reception unit can also receive instructions for optimal elevator operation schedules during times when there are few building users. The reception unit can also receive instructions for optimal lighting schedules for lighting equipment in a specific area when building users are concentrated in that area. This makes it possible to receive optimal instructions by taking into consideration the behavioral patterns of users. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the behavioral patterns of building users into a generation AI, which then performs analysis.

[0093] The reception unit can link the received instructions with other building management systems to achieve comprehensive building management. The reception unit, for example, shares instructions for air conditioning equipment with other building management systems. For example, the reception unit shares instructions for air conditioning equipment with other building management systems to achieve comprehensive building management. The reception unit can also share instructions for elevators with other building management systems. For example, the reception unit shares instructions for elevators with other building management systems to achieve comprehensive building management. The reception unit can also share instructions for lighting equipment with other building management systems. For example, the reception unit shares instructions for lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by linking with other building management systems. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the received instructions to a generation AI, and the generation AI can link with other building management systems.

[0094] The control unit can estimate the user's emotions and determine the priority of the equipment to be controlled based on the estimated user emotions. The control unit, for example, estimates the user's emotions and determines the priority of the equipment to be controlled based on the estimated user emotions. For example, when the user is feeling stressed, the control unit prioritizes controlling important equipment. When the user is relaxed, the control unit can also control all equipment equally. When the user is in a hurry, the control unit can also quickly control the most important equipment. In this way, by determining the priority of the equipment based on the user's emotions, important equipment can be controlled preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the control unit may be performed using AI, or may be performed without AI. For example, the control unit can input user emotion data into the generation AI and have the generation AI estimate the emotion.

[0095] During control, the control unit can monitor the operating status of each piece of equipment in real time and automatically adjust the control if an abnormality is detected. For example, during control, the control unit can monitor the operating status of each piece of equipment in real time and automatically adjust the control if an abnormality is detected. For example, when the control unit detects an abnormality in air conditioning equipment, it automatically adjusts the settings. Furthermore, when the control unit detects an abnormality in elevators, it can automatically adjust the operation schedule of the elevators. Furthermore, when the control unit detects an abnormality in lighting equipment, it can automatically adjust the lighting schedule of the lighting equipment. This enables a rapid response by automatically adjusting the control if an abnormality is detected. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input data on equipment in which an abnormality has been detected into a generation AI, which then analyzes the data and adjusts the control.

[0096] The control unit can visualize the control results, allowing the building manager to intuitively understand them. The control unit, for example, displays the control results of air conditioning equipment in a graph. For example, the control unit displays the control results of air conditioning equipment in a graph, allowing the building manager to intuitively understand them. The control unit can also display the control results of elevators in a chart. For example, the control unit displays the control results of elevators in a chart, allowing the building manager to intuitively understand them. The control unit can also display the control results of lighting equipment in a heat map. For example, the control unit displays the control results of lighting equipment in a heat map, allowing the building manager to intuitively understand them. In this way, visualizing the control results enables intuitive understanding. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the control results to a generation AI, which then visualizes the control results.

[0097] The control unit can estimate the user's emotions and adjust the level of detail of the equipment to be controlled based on the estimated user emotions. For example, the control unit can estimate the user's emotions and adjust the level of detail of the equipment to be controlled based on the estimated user emotions. For example, the control unit can provide a simple control method when the user is stressed. The control unit can also provide a detailed control method when the user is relaxed. The control unit can also provide a quickly executable control method when the user is in a hurry. This enables appropriate control by adjusting the level of detail of the equipment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the control unit may be performed using AI, or may be performed without AI. For example, the control unit can input user emotion data into the generation AI, and the generation AI can estimate the emotion.

[0098] During control, the control unit can integrate and control the energy consumption data of the building to optimize energy efficiency. The control unit, for example, integrates and controls the energy consumption data of the building to optimize energy efficiency. For example, the control unit proposes an efficient operation method based on energy consumption data of air conditioning equipment. The control unit can also propose an efficient operation method based on energy consumption data of lighting equipment. The control unit can also propose an efficient operation method based on energy consumption data of elevators. In this way, energy efficiency can be optimized by integrating and controlling the energy consumption data. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input energy consumption data to a generation AI, have the generation AI analyze it, and propose an efficient operation method.

[0099] The control unit can link the control results with other building management systems to achieve comprehensive building management. The control unit, for example, shares the control results of air conditioning equipment with other building management systems. For example, the control unit shares the control results of air conditioning equipment with other building management systems to achieve comprehensive building management. The control unit can also share the control results of elevators with other building management systems. For example, the control unit shares the control results of elevators with other building management systems to achieve comprehensive building management. The control unit can also share the control results of lighting equipment with other building management systems. For example, the control unit shares the control results of lighting equipment with other building management systems to achieve comprehensive building management. This enables comprehensive building management by linking with other building management systems. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the control results to a generation AI, and link with other building management systems via the generation AI. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, identification unit, reception unit, and control unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects connection information of each facility using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected connection information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies a control method for each facility based on the analyzed information. The reception unit is realized, for example, by the control unit 46A of the smart device 14 and receives instructions in natural language from the building manager. The control unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and controls each facility based on the received instructions. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, identification unit, reception unit, and control unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects connection information of each facility using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected connection information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies a control method for each facility based on the analyzed information. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives instructions in natural language from the building manager. The control unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and controls each facility based on the received instructions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, reception unit, and control unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects connection information of each piece of equipment using the camera 42 and communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected connection information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies a control method for each piece of equipment based on the analyzed information. The reception unit is realized, for example, by the control unit 46A of the headset type terminal 314 and receives instructions in natural language from the building manager. The control unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and controls each piece of equipment based on the received instructions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, identification unit, reception unit, and control unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects connection information of each piece of equipment using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected connection information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies a control method for each piece of equipment based on the analyzed information. The reception unit is realized, for example, by the control unit 46A of the robot 414 and receives instructions in natural language from the building manager. The control unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and controls each piece of equipment based on the received instructions.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The building facility management system can further include a prediction unit. The prediction unit can predict future operating conditions based on past data for each facility. For example, the prediction unit can analyze past temperature setting data for air conditioning facilities to predict future temperature setting trends. The prediction unit can also analyze past elevator operation data to predict future operating schedule trends. The prediction unit can also analyze past power consumption data for lighting facilities to predict future power consumption trends. As a result, the prediction unit can be used to predict future operating conditions and take appropriate measures in advance.

[0102] When collecting connection information for each piece of equipment, the collection unit can determine the priority of the information based on the operating time and frequency of use of the equipment. For example, if the operating time of air conditioning equipment is long, the collection unit can prioritize collecting its connection information. Also, if the frequency of use of elevators is high, the collection unit can also prioritize collecting its connection information. Furthermore, if the frequency of use of lighting equipment is high, the collection unit can also prioritize collecting its connection information. In this way, by determining the priority of information based on the operating time and frequency of use, important information can be collected preferentially.

[0103] When analyzing the connection information of each piece of equipment, the analysis unit can use an anomaly detection algorithm to detect anomalies. For example, the analysis unit can analyze the connection information of air conditioning equipment and detect abnormal temperature fluctuations. The analysis unit can also analyze the connection information of elevators and detect abnormal operation patterns. Furthermore, the analysis unit can analyze the connection information of lighting equipment and detect abnormal power consumption. In this way, by using an anomaly detection algorithm, anomalies in each piece of equipment can be detected early, enabling a prompt response.

[0104] When specifying the control method for each piece of equipment, the specifying unit can select the optimal control method taking energy efficiency into consideration. For example, the specifying unit optimizes the temperature settings of air conditioning equipment from the perspective of energy efficiency. The specifying unit can also optimize elevator operation schedules from the perspective of energy efficiency. Furthermore, the specifying unit can also optimize lighting schedules from the perspective of energy efficiency. In this way, by selecting the optimal control method taking energy efficiency into consideration, it is possible to reduce energy consumption throughout the building.

[0105] When receiving instructions in natural language from a building manager, the reception unit can analyze the content of the instructions and provide appropriate feedback. For example, if the building manager instructs, "Set the air conditioning to 25 degrees," the reception unit can provide feedback confirming that the instruction was correctly received. Also, if the building manager instructs, "Call the elevator to the first floor," the reception unit can provide feedback confirming that the instruction was correctly received. Furthermore, if the building manager instructs, "Turn off the lights," the reception unit can provide feedback confirming that the instruction was correctly received. In this way, by analyzing the content of instructions and providing appropriate feedback, the building manager's sense of security can be increased.

[0106] The control unit can estimate the user's emotions when controlling each piece of equipment and adjust the control method based on the estimated user's emotions. For example, if the user is feeling stressed, the control unit can set the temperature of the air conditioning equipment to a comfortable range. Also, if the user is relaxed, the control unit can flexibly adjust the elevator operation schedule. Furthermore, if the user is in a hurry, the control unit can quickly adjust the lighting schedule of the lighting equipment. In this way, by adjusting the control method based on the user's emotions, the user's comfort can be improved.

[0107] The collection unit can estimate the user's emotions and determine the priority of the connection information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting important connection information. When the user is relaxed, the collection unit can also collect all connection information equally. Furthermore, when the user is in a hurry, the collection unit can quickly collect the most important connection information. Thus, by determining the priority of the connection information based on the user's emotions, important information can be collected preferentially.

[0108] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by providing a display method that corresponds to the user's emotions, intuitive understanding is possible.

[0109] The identification unit can estimate the user's emotions and determine the priority of the control methods to be identified based on the estimated user's emotions. For example, when the user is feeling stressed, the identification unit can prioritize identifying important control methods. When the user is relaxed, the identification unit can also identify all control methods equally. Furthermore, when the user is in a hurry, the identification unit can quickly identify the most important control method. In this way, by prioritizing the control methods based on the user's emotions, important control methods can be prioritized.

[0110] The control unit can estimate the user's emotion and adjust the level of detail of the equipment to be controlled based on the estimated user's emotion. For example, the control unit can provide a simple control method when the user is stressed. The control unit can also provide a detailed control method when the user is relaxed. Furthermore, the control unit can also provide a control method that can be quickly executed when the user is in a hurry. This allows appropriate control by adjusting the level of detail of the equipment based on the user's emotion.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects connection information for each piece of equipment. For example, the collection unit collects connection information for air conditioning equipment and elevators, and determines how each piece of equipment is connected. Step 2: The analysis unit analyzes the connection information collected by the collection unit. For example, the analysis unit analyzes the collected connection information using the generation AI and analyzes the control method for each piece of equipment. Step 3: The identification unit identifies a control method for each piece of equipment based on the information analyzed by the analysis unit. For example, the identification unit identifies the temperature settings for air conditioning equipment and elevator operation schedules. Step 4: The reception unit receives instructions in natural language from the building manager. For example, the reception unit receives an instruction in natural language such as "Set the air conditioning to 25 degrees." Step 5: The control unit controls each piece of equipment based on the instructions received by the reception unit. For example, the control unit sets the temperature of the air conditioning equipment to 25 degrees.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects connection information of each facility; an analysis unit that analyzes the connection information collected by the collection unit; an identification unit that identifies a control method for each piece of equipment based on the information analyzed by the analysis unit; a reception unit that receives instructions in natural language; a control unit that controls each piece of equipment based on the instruction received by the reception unit; Equipped with A system characterized by:

2. The collecting unit Collecting HVAC or elevator connection information The system of claim 1 .

3. The analysis unit Analyze collected connection information using generative AI The system of claim 1 .

4. The identification unit Identifying air conditioning temperature settings or elevator schedules based on analyzed information The system of claim 1 .

5. The reception unit Accepts natural language instructions from building managers The system of claim 1 .

6. The control unit Configure the air conditioning equipment based on the received instructions The system of claim 1 .

7. The collecting unit The user's emotions are estimated, and the priority of the connection information to be collected is determined based on the estimated user's emotions. The system of claim 1 .

8. The collecting unit Monitors the operating status of each facility in real time and automatically collects connection information if an abnormality is detected The system of claim 1 .

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