System

Abstract

An object of a system according to an embodiment is to understand connection specifications of building facilities and to integrally manage the facilities.SOLUTION: A system includes a connection specification understanding unit, a control instruction generation unit, and a management unit. A connection specification understanding part understands the connection specification of the building facility. The control instruction generation unit generates a control instruction based on the connection specification understood by the connection specification understanding unit. The manager manages the building equipment based on the control instruction generated by the control instruction generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to understand the connection specifications of building equipment and manage each piece of equipment in an integrated manner, and system integration work required a significant amount of man-hours.

[0005] The system according to the embodiment aims to understand the connection specifications of building facilities and manage each facility in an integrated manner. [Means for solving the problem]

[0006] A system according to an embodiment includes a connection specification understanding unit, a control instruction generation unit, and a management unit. The connection specification understanding unit understands the connection specifications of building equipment. The control instruction generation unit generates control instructions based on the connection specifications understood by the connection specification understanding unit. The management unit manages the building equipment based on the control instructions generated by the control instruction generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can understand the connection specifications of building facilities and manage each facility in an integrated manner. [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 Citylink system according to an embodiment of the present invention is a system that understands the connection specifications of building facilities, analyzes natural language commands to generate control instructions, and enables the control and management of building facilities in natural language from a single platform. As a result, the Citylink system can reduce the amount of system integration work previously performed by building management companies and building users to as close to zero as possible, making it possible to realize smart building facilities even in older buildings with limited budgets and personnel.

[0029] The Citylink system according to the embodiment includes a connection specification understanding unit, a control instruction generation unit, and a management unit. The connection specification understanding unit understands the connection specifications of building equipment. For example, the generation AI analyzes data related to the connection specifications of the building equipment and understands the characteristics and connection methods of each piece of equipment. The input to the generation AI is a prompt containing data and instructions related to the connection specifications of the building equipment, and the generation AI performs analysis based on the prompt. The control instruction generation unit generates control instructions based on the connection specifications understood by the connection specification understanding unit. For example, the generation AI analyzes natural language commands entered by a user and understands their content. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates control instructions based on the prompt. The management unit manages the building equipment based on the control instructions generated by the control instruction generation unit. For example, the generation AI can control and manage building equipment in natural language. This enables the Citylink system to control and manage building equipment in natural language from a single platform.

[0030] The connection specification understanding unit can analyze not only the connection specifications of building equipment, but also the maintenance history and failure history, and make suggestions for preventive maintenance. For example, the connection specification understanding unit uses a generation AI to analyze past maintenance history and failure history along with the connection specifications of building equipment. For example, based on past failure data of air conditioning equipment, it predicts future failure risks and makes specific suggestions for preventive maintenance. Furthermore, when understanding the connection specifications of building equipment, the generation AI refers to past maintenance history and analyzes the frequency and content of maintenance. This allows it to propose an optimal maintenance schedule. Furthermore, the connection specification understanding unit uses a generation AI to analyze the connection specifications and failure history of building equipment and make preventive maintenance suggestions for specific equipment. For example, based on the failure history of an elevator, it predicts when parts need to be replaced and suggests replacement at the appropriate time. This enables preventive maintenance of building equipment.

[0031] When understanding the connection specifications of building equipment, the connection specification understanding unit takes into account the characteristics of each manufacturer and model, and can propose the optimal connection method. For example, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes into account the characteristics of each manufacturer and model of the equipment. For example, it proposes the optimal connection method for air conditioning equipment from a specific manufacturer. Furthermore, when analyzing the connection specifications of building equipment, the generation AI retrieves the characteristics of each model of each piece of equipment from a database and proposes the optimal connection method based on that. For example, it considers the connection characteristics of each elevator model. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes into account the characteristics of each manufacturer and model, and optimizes the connection method. For example, it proposes the optimal connection method when integrating equipment from different manufacturers. This makes it possible to propose the optimal connection method for building equipment.

[0032] The connection specification understanding unit can propose the optimal connection method taking into consideration not only the connection specifications of the building equipment but also the surrounding environment. For example, when the generation AI understands the connection specifications of the building equipment, the connection specification understanding unit considers the surrounding environment (e.g., weather and traffic conditions) and proposes the optimal connection method. For example, it proposes connection work that should be performed on a day with bad weather. Furthermore, when analyzing the connection specifications of the building equipment, the connection specification understanding unit has the generation AI collect surrounding environment data and propose the optimal connection method based on that. For example, it adjusts work time taking traffic conditions into consideration. Furthermore, when the generation AI understands the connection specifications of the building equipment, the connection specification understanding unit considers the surrounding environment and proposes the optimal connection method. For example, it proposes connection work that should be performed on a day with good weather. This makes it possible to propose the optimal connection method taking the surrounding environment into consideration.

[0033] The connection specification understanding unit can refer to connection examples from other buildings and introduce best practices. For example, when the generation AI understands the connection specifications of a building's equipment, the connection specification understanding unit refers to connection examples from other buildings and introduces best practices. For example, it refers to the connection methods of buildings with similar equipment. Furthermore, when the connection specification understanding unit analyzes the connection specifications of a building's equipment, the generation AI retrieves connection examples from other buildings from a database and proposes the optimal connection method based on those examples. For example, it optimizes the connection method based on successful examples. Furthermore, when the generation AI understands the connection specifications of a building's equipment, the connection specification understanding unit refers to connection examples from other buildings and introduces best practices. For example, it proposes a connection method based on past successful examples. This makes it possible to propose the optimal connection method based on successful examples from other buildings.

[0034] The control instruction generation unit can analyze not only natural language commands but also commands given by voice or gestures to control building facilities. The control instruction generation unit, for example, uses a generation AI to analyze not only natural language commands but also commands given by voice or gestures to control building facilities. For example, adjusting the air conditioning with a voice command. In addition, in controlling building facilities, the generation AI analyzes voice and gestures and combines them with natural language commands to generate control instructions. For example, calling an elevator with a gesture. In addition, when the generation AI analyzes natural language commands, the control instruction generation unit simultaneously analyzes voice and gestures to control building facilities. For example, adjusting the lighting by combining voice and gestures. In this way, commands given by voice and gestures can also be analyzed to control building facilities.

[0035] When analyzing natural language commands, the control instruction generation unit learns the user's past command history and can generate more accurate control instructions. For example, when the generation AI analyzes natural language commands, the control instruction generation unit learns the user's past command history and generates more accurate control instructions. For example, adjusting air conditioning based on past settings. In addition, when controlling building facilities, the control instruction generation unit refers to the user's past command history and generates optimal control instructions. For example, learning elevator call patterns. In addition, when the generation AI analyzes natural language commands, the control instruction generation unit learns the user's past command history and generates more accurate control instructions. For example, adjusting lighting settings based on past history. In this way, the generation AI learns the user's past command history and can generate more accurate control instructions.

[0036] The control instruction generation unit can analyze not only natural language commands but also commands given through a visual interface to control building facilities. For example, using a generation AI, the control instruction generation unit can analyze not only natural language commands but also commands given through a visual interface to control building facilities. For example, adjusting the air conditioning using a touch panel. In addition, when controlling building facilities, the generation AI analyzes the visual interface and combines it with natural language commands to generate control instructions. For example, calling an elevator using a graphical operation. In addition, when the generation AI analyzes natural language commands, the control instruction generation unit can simultaneously analyze the visual interface to control building facilities. For example, adjusting the lighting using a touch screen. This allows commands given through a visual interface to be analyzed and building facilities to be controlled.

[0037] The control instruction generation unit can accommodate different languages ​​and dialects when analyzing natural language commands, thereby catering to global users. For example, when the generation AI analyzes natural language commands, the control instruction generation unit can accommodate different languages ​​and dialects, thereby catering to global users. For example, it analyzes commands in English and Chinese. Furthermore, when controlling building facilities, the control instruction generation unit analyzes different languages ​​and dialects and generates optimal control instructions. For example, it analyzes commands in Spanish and French. Furthermore, when the generation AI analyzes natural language commands, the control instruction generation unit can accommodate different languages ​​and dialects, thereby catering to global users. For example, it analyzes dialects for each region. This allows it to accommodate different languages ​​and dialects, thereby catering to global users.

[0038] The management department can not only perform system integration work, but also automatically diagnose building equipment and make repair proposals. For example, using generative AI, the management department can not only perform system integration work, but also automatically diagnose building equipment and make repair proposals. For example, it can detect abnormalities in air conditioning equipment and make repair proposals. Furthermore, when the management department understands the connection specifications of building equipment, the generative AI can perform automatic diagnosis and generate necessary repair proposals. For example, it can detect abnormalities in elevators and make repair proposals. Furthermore, when the generative AI performs system integration work, the management department can automatically diagnose building equipment and generate repair proposals. For example, it can detect abnormalities in lighting equipment and make repair proposals. This makes it possible to automatically diagnose building equipment and make repair proposals.

[0039] The management department can receive real-time feedback when performing system integration work and optimize the work. For example, when the generation AI performs system integration work, the management department receives real-time feedback and optimizes the work. For example, it can immediately solve any problems that arise during the connection work. In addition, when the management department understands the connection specifications of the building equipment, the generation AI receives real-time feedback and suggests the optimal connection method. For example, it can immediately correct any errors that occur during the connection work. In addition, the management department receives real-time feedback when the generation AI performs system integration work and optimizes the work. For example, it can monitor the progress of the connection work and suggest the optimal procedure. This makes it possible to receive real-time feedback and optimize the work.

[0040] The management department can not only perform system integration work, but also optimize the energy efficiency of building facilities. For example, using generative AI, the management department can not only perform system integration work, but also optimize the energy efficiency of building facilities. For example, optimizing the energy consumption of air conditioning facilities. Furthermore, when the management department understands the connection specifications of building facilities, the generative AI can optimize energy efficiency and propose the optimal connection method. For example, optimizing the energy consumption of elevators. Furthermore, when the generative AI performs system integration work, the management department can optimize the energy efficiency of building facilities. For example, optimizing the energy consumption of lighting facilities. This makes it possible to optimize the energy efficiency of building facilities.

[0041] When performing system integration work, the management department can refer to integration examples from other buildings and introduce optimal work procedures. For example, when the generation AI performs system integration work, the management department can refer to integration examples from other buildings and introduce optimal work procedures. For example, it can refer to work procedures from buildings with similar equipment. Furthermore, when the management department understands the connection specifications of building equipment, the generation AI can retrieve integration examples from other buildings from a database and propose optimal work procedures based on those. For example, it can optimize work procedures based on successful examples. Furthermore, when the generation AI performs system integration work, the management department can refer to integration examples from other buildings and introduce optimal work procedures. For example, it can propose work procedures based on past successful examples. This makes it possible to introduce optimal work procedures based on successful examples from other buildings.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the energy consumption patterns of the equipment and propose ways to optimize energy efficiency. For example, it can analyze energy consumption data for air conditioning equipment and propose an optimal operation schedule. When analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve energy consumption patterns from a database and propose the optimal connection method based on that. For example, it can propose a connection method that minimizes the energy consumption of elevators. When the generation AI understands the connection specifications of building equipment, the connection specification understanding unit also takes energy consumption patterns into account and optimizes the connection method. For example, it can propose a connection method that minimizes the energy consumption of lighting equipment. This makes it possible to propose a connection method that optimizes the energy efficiency of building equipment.

[0044] When understanding the connection specifications of building equipment, the connection specification understanding unit takes into account the durability of the equipment and can propose connection methods that will extend its lifespan. For example, it analyzes durability data for air conditioning equipment and proposes optimal operating conditions. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve durability data from a database and propose the optimal connection method based on that data. For example, it proposes a connection method to improve the durability of elevators. Furthermore, when the connection specification understanding unit understands the connection specifications of building equipment, it takes durability into account and optimizes the connection method. For example, it proposes a connection method to improve the durability of lighting equipment. This makes it possible to propose connection methods that improve the durability of building equipment.

[0045] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the security risks of the equipment and propose a connection method that minimizes the risks. For example, it can analyze security risk data for air conditioning equipment and propose optimal security measures. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve security risk data from a database and propose the optimal connection method based on that data. For example, it can propose a connection method that minimizes the security risk of elevators. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes security risks into consideration and optimizes the connection method. For example, it can propose a connection method that minimizes the security risk of lighting equipment. This makes it possible to propose a connection method that minimizes the security risk of building equipment.

[0046] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the maintenance costs of the equipment and propose a connection method that minimizes costs. For example, it can analyze maintenance cost data for air conditioning equipment and propose an optimal maintenance schedule. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve maintenance cost data from a database and propose the optimal connection method based on that data. For example, it can propose a connection method that minimizes elevator maintenance costs. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes maintenance costs into account and optimizes the connection method. For example, it can propose a connection method that minimizes the maintenance costs of lighting equipment. This makes it possible to propose a connection method that minimizes the maintenance costs of building equipment.

[0047] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the environmental load of the equipment and propose a connection method that minimizes the environmental load. For example, it can analyze environmental load data for air conditioning equipment and propose optimal operating conditions. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve environmental load data from a database and propose the optimal connection method based on that data. For example, it can propose a connection method that minimizes the environmental load of elevators. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes environmental load into consideration and optimizes the connection method. For example, it can propose a connection method that minimizes the environmental load of lighting equipment. This makes it possible to propose a connection method that minimizes the environmental load of building equipment.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The connection specification understanding unit understands the connection specifications of the building equipment. For example, the generation AI analyzes data related to the connection specifications of the building equipment and understands the characteristics and connection methods of each piece of equipment. The input to the generation AI is a prompt containing data and instructions related to the connection specifications of the building equipment, and the generation AI performs analysis based on that prompt. Step 2: The control instruction generation unit generates control instructions based on the connection specifications understood by the connection specification understanding unit. For example, the generation AI analyzes natural language commands entered by the user and understands their content. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates control instructions based on that prompt. Step 3: The management unit manages the building facilities based on the control instructions generated by the control instruction generation unit. For example, the generation AI can control and manage the building facilities in natural language. This enables the Citylink system to control and manage building facilities in natural language from a single platform.

[0050] (Example 2) The Citylink system according to an embodiment of the present invention is a system that understands the connection specifications of building facilities, analyzes natural language commands to generate control instructions, and enables the control and management of building facilities in natural language from a single platform. As a result, the Citylink system can reduce the amount of system integration work previously performed by building management companies and building users to as close to zero as possible, making it possible to realize smart building facilities even in older buildings with limited budgets and personnel.

[0051] The Citylink system according to the embodiment includes a connection specification understanding unit, a control instruction generation unit, and a management unit. The connection specification understanding unit understands the connection specifications of building equipment. For example, the generation AI analyzes data related to the connection specifications of the building equipment and understands the characteristics and connection methods of each piece of equipment. The input to the generation AI is a prompt containing data and instructions related to the connection specifications of the building equipment, and the generation AI performs analysis based on the prompt. The control instruction generation unit generates control instructions based on the connection specifications understood by the connection specification understanding unit. For example, the generation AI analyzes natural language commands entered by a user and understands their content. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates control instructions based on the prompt. The management unit manages the building equipment based on the control instructions generated by the control instruction generation unit. For example, the generation AI can control and manage building equipment in natural language. This enables the Citylink system to control and manage building equipment in natural language from a single platform.

[0052] The connection specification understanding unit can analyze not only the connection specifications of building equipment, but also the maintenance history and failure history, and make suggestions for preventive maintenance. For example, the connection specification understanding unit uses a generation AI to analyze past maintenance history and failure history along with the connection specifications of building equipment. For example, based on past failure data of air conditioning equipment, it predicts future failure risks and makes specific suggestions for preventive maintenance. Furthermore, when understanding the connection specifications of building equipment, the generation AI refers to past maintenance history and analyzes the frequency and content of maintenance. This allows it to propose an optimal maintenance schedule. Furthermore, the connection specification understanding unit uses a generation AI to analyze the connection specifications and failure history of building equipment and make preventive maintenance suggestions for specific equipment. For example, based on the failure history of an elevator, it predicts when parts need to be replaced and suggests replacement at the appropriate time. This enables preventive maintenance of building equipment.

[0053] When understanding the connection specifications of building equipment, the connection specification understanding unit takes into account the characteristics of each manufacturer and model, and can propose the optimal connection method. For example, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes into account the characteristics of each manufacturer and model of the equipment. For example, it proposes the optimal connection method for air conditioning equipment from a specific manufacturer. Furthermore, when analyzing the connection specifications of building equipment, the generation AI retrieves the characteristics of each model of each piece of equipment from a database and proposes the optimal connection method based on that. For example, it considers the connection characteristics of each elevator model. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes into account the characteristics of each manufacturer and model, and optimizes the connection method. For example, it proposes the optimal connection method when integrating equipment from different manufacturers. This makes it possible to propose the optimal connection method for building equipment.

[0054] The connection specification understanding unit can use the emotion estimation function to analyze the stress level of the building manager and propose optimal connection work procedures for a low-stress state. For example, the connection specification understanding unit can use the emotion estimation function to analyze the stress level of the building manager in real time and propose optimal connection work procedures for a low-stress state. For example, it can adjust the timing and order of work. The connection specification understanding unit can also analyze the stress level of the building manager and have the generation AI propose connection work procedures for a low-stress state. For example, it can present specific steps to reduce the burden of work. The connection specification understanding unit can also use the emotion estimation function to monitor the stress level of the building manager and propose optimal connection work procedures for a low-stress state. For example, it can adjust the timing of work breaks. This reduces the stress of the building manager and enables efficient work.

[0055] The connection specification understanding unit can propose the optimal connection method taking into consideration not only the connection specifications of the building equipment but also the surrounding environment. For example, when the generation AI understands the connection specifications of the building equipment, the connection specification understanding unit considers the surrounding environment (e.g., weather and traffic conditions) and proposes the optimal connection method. For example, it proposes connection work that should be performed on a day with bad weather. Furthermore, when analyzing the connection specifications of the building equipment, the connection specification understanding unit has the generation AI collect surrounding environment data and propose the optimal connection method based on that. For example, it adjusts work time taking traffic conditions into consideration. Furthermore, when the generation AI understands the connection specifications of the building equipment, the connection specification understanding unit considers the surrounding environment and proposes the optimal connection method. For example, it proposes connection work that should be performed on a day with good weather. This makes it possible to propose the optimal connection method taking the surrounding environment into consideration.

[0056] The connection specification understanding unit can refer to connection examples from other buildings and introduce best practices. For example, when the generation AI understands the connection specifications of a building's equipment, the connection specification understanding unit refers to connection examples from other buildings and introduces best practices. For example, it refers to the connection methods of buildings with similar equipment. Furthermore, when the connection specification understanding unit analyzes the connection specifications of a building's equipment, the generation AI retrieves connection examples from other buildings from a database and proposes the optimal connection method based on those examples. For example, it optimizes the connection method based on successful examples. Furthermore, when the generation AI understands the connection specifications of a building's equipment, the connection specification understanding unit refers to connection examples from other buildings and introduces best practices. For example, it proposes a connection method based on past successful examples. This makes it possible to propose the optimal connection method based on successful examples from other buildings.

[0057] The connection specification understanding unit can use the emotion estimation function to analyze the comfort level of building users and propose connection methods that will increase comfort level. For example, the connection specification understanding unit can use the emotion estimation function to analyze the comfort level of building users in real time and propose connection methods that will increase comfort level. For example, adjusting the settings of air conditioning equipment. The connection specification understanding unit can also analyze the comfort level of building users, and the generation AI can propose connection methods that will increase comfort level. For example, presenting a connection method that shortens elevator waiting time. The connection specification understanding unit can also use the emotion estimation function to monitor the comfort level of building users and propose connection methods that will increase comfort level. For example, adjusting lighting settings. This makes it possible to propose connection methods that will increase the comfort level of building users.

[0058] The control instruction generation unit can analyze not only natural language commands but also commands given by voice or gestures to control building facilities. The control instruction generation unit, for example, uses a generation AI to analyze not only natural language commands but also commands given by voice or gestures to control building facilities. For example, adjusting the air conditioning with a voice command. In addition, in controlling building facilities, the generation AI analyzes voice and gestures and combines them with natural language commands to generate control instructions. For example, calling an elevator with a gesture. In addition, when the generation AI analyzes natural language commands, the control instruction generation unit simultaneously analyzes voice and gestures to control building facilities. For example, adjusting the lighting by combining voice and gestures. In this way, commands given by voice and gestures can also be analyzed to control building facilities.

[0059] When analyzing natural language commands, the control instruction generation unit learns the user's past command history and can generate more accurate control instructions. For example, when the generation AI analyzes natural language commands, the control instruction generation unit learns the user's past command history and generates more accurate control instructions. For example, adjusting air conditioning based on past settings. In addition, when controlling building facilities, the control instruction generation unit refers to the user's past command history and generates optimal control instructions. For example, learning elevator call patterns. In addition, when the generation AI analyzes natural language commands, the control instruction generation unit learns the user's past command history and generates more accurate control instructions. For example, adjusting lighting settings based on past history. In this way, the generation AI learns the user's past command history and can generate more accurate control instructions.

[0060] The control instruction generation unit can analyze the user's emotional state using the emotion estimation function and generate optimal control instructions according to the emotion. The control instruction generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generate optimal control instructions according to the emotion. For example, adjusting the lighting when the user wants to relax. The control instruction generation unit also analyzes the user's emotional state, and the generation AI generates optimal control instructions according to the emotion. For example, adjusting the air conditioning when the user is under high stress. The control instruction generation unit also uses the emotion estimation function to monitor the user's emotional state and generate optimal control instructions according to the emotion. For example, playing music according to the emotion. This makes it possible to generate optimal control instructions according to the user's emotional state.

[0061] The control instruction generation unit can analyze not only natural language commands but also commands given through a visual interface to control building facilities. For example, using a generation AI, the control instruction generation unit can analyze not only natural language commands but also commands given through a visual interface to control building facilities. For example, adjusting the air conditioning using a touch panel. In addition, when controlling building facilities, the generation AI analyzes the visual interface and combines it with natural language commands to generate control instructions. For example, calling an elevator using a graphical operation. In addition, when the generation AI analyzes natural language commands, the control instruction generation unit can simultaneously analyze the visual interface to control building facilities. For example, adjusting the lighting using a touch screen. This allows commands given through a visual interface to be analyzed and building facilities to be controlled.

[0062] The control instruction generation unit can accommodate different languages ​​and dialects when analyzing natural language commands, thereby catering to global users. For example, when the generation AI analyzes natural language commands, the control instruction generation unit can accommodate different languages ​​and dialects, thereby catering to global users. For example, it analyzes commands in English and Chinese. Furthermore, when controlling building facilities, the control instruction generation unit analyzes different languages ​​and dialects and generates optimal control instructions. For example, it analyzes commands in Spanish and French. Furthermore, when the generation AI analyzes natural language commands, the control instruction generation unit can accommodate different languages ​​and dialects, thereby catering to global users. For example, it analyzes dialects for each region. This allows it to accommodate different languages ​​and dialects, thereby catering to global users.

[0063] The control instruction generation unit can use the emotion estimation function to analyze the emotional state of the building user and automatically adjust lighting and music settings according to the emotion. The control instruction generation unit, for example, uses the emotion estimation function to analyze the emotional state of the building user in real time and automatically adjust lighting and music settings according to the emotion. For example, adjusting lighting when the user wants to relax. The control instruction generation unit also analyzes the emotional state of the building user, and the generation AI automatically adjusts lighting and music settings according to the emotion. For example, playing music when stress is high. The control instruction generation unit also uses the emotion estimation function to monitor the emotional state of the building user and automatically adjusts lighting and music settings according to the emotion. For example, changing the color of lighting according to the emotion. This makes it possible to automatically adjust lighting and music settings according to the emotional state of the building user.

[0064] The management department can not only perform system integration work, but also automatically diagnose building equipment and make repair proposals. For example, using generative AI, the management department can not only perform system integration work, but also automatically diagnose building equipment and make repair proposals. For example, it can detect abnormalities in air conditioning equipment and make repair proposals. Furthermore, when the management department understands the connection specifications of building equipment, the generative AI can perform automatic diagnosis and generate necessary repair proposals. For example, it can detect abnormalities in elevators and make repair proposals. Furthermore, when the generative AI performs system integration work, the management department can automatically diagnose building equipment and generate repair proposals. For example, it can detect abnormalities in lighting equipment and make repair proposals. This makes it possible to automatically diagnose building equipment and make repair proposals.

[0065] The management department can receive real-time feedback when performing system integration work and optimize the work. For example, when the generation AI performs system integration work, the management department receives real-time feedback and optimizes the work. For example, it can immediately solve any problems that arise during the connection work. In addition, when the management department understands the connection specifications of the building equipment, the generation AI receives real-time feedback and suggests the optimal connection method. For example, it can immediately correct any errors that occur during the connection work. In addition, the management department receives real-time feedback when the generation AI performs system integration work and optimizes the work. For example, it can monitor the progress of the connection work and suggest the optimal procedure. This makes it possible to receive real-time feedback and optimize the work.

[0066] The management department can use the emotion estimation function to analyze the workload of the building manager and propose work procedures that will reduce the burden. For example, the management department can use the emotion estimation function to analyze the workload of the building manager in real time and propose work procedures that will reduce the burden. For example, it can adjust the timing and order of work. The management department can also analyze the workload of the building manager and have the generation AI propose work procedures that will reduce the burden. For example, it can present specific steps to reduce the workload. The management department can also use the emotion estimation function to monitor the workload of the building manager and propose work procedures that will reduce the burden. For example, it can adjust the timing of work breaks. This reduces the workload of the building manager and enables more efficient work.

[0067] The management department can not only perform system integration work, but also optimize the energy efficiency of building facilities. For example, using generative AI, the management department can not only perform system integration work, but also optimize the energy efficiency of building facilities. For example, optimizing the energy consumption of air conditioning facilities. Furthermore, when the management department understands the connection specifications of building facilities, the generative AI can optimize energy efficiency and propose the optimal connection method. For example, optimizing the energy consumption of elevators. Furthermore, when the generative AI performs system integration work, the management department can optimize the energy efficiency of building facilities. For example, optimizing the energy consumption of lighting facilities. This makes it possible to optimize the energy efficiency of building facilities.

[0068] When performing system integration work, the management department can refer to integration examples from other buildings and introduce optimal work procedures. For example, when the generation AI performs system integration work, the management department can refer to integration examples from other buildings and introduce optimal work procedures. For example, it can refer to work procedures from buildings with similar equipment. Furthermore, when the management department understands the connection specifications of building equipment, the generation AI can retrieve integration examples from other buildings from a database and propose optimal work procedures based on those. For example, it can optimize work procedures based on successful examples. Furthermore, when the generation AI performs system integration work, the management department can refer to integration examples from other buildings and introduce optimal work procedures. For example, it can propose work procedures based on past successful examples. This makes it possible to introduce optimal work procedures based on successful examples from other buildings.

[0069] The management unit can use the emotion estimation function to analyze building user satisfaction and propose system integration work that will increase satisfaction. For example, the management unit can use the emotion estimation function to analyze building user satisfaction in real time and propose system integration work that will increase satisfaction. For example, adjusting the settings of air conditioning equipment. The management unit also analyzes building user satisfaction and the generation AI proposes system integration work that will increase satisfaction. For example, presenting work procedures to shorten elevator waiting times. The management unit also uses the emotion estimation function to monitor building user satisfaction and propose system integration work that will increase satisfaction. For example, adjusting lighting settings. This makes it possible to propose system integration work that will increase building user satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the energy consumption patterns of the equipment and propose ways to optimize energy efficiency. For example, it can analyze energy consumption data for air conditioning equipment and propose an optimal operation schedule. When analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve energy consumption patterns from a database and propose the optimal connection method based on that. For example, it can propose a connection method that minimizes the energy consumption of elevators. When the generation AI understands the connection specifications of building equipment, the connection specification understanding unit also takes energy consumption patterns into account and optimizes the connection method. For example, it can propose a connection method that minimizes the energy consumption of lighting equipment. This makes it possible to propose a connection method that optimizes the energy efficiency of building equipment.

[0072] When understanding the connection specifications of building equipment, the connection specification understanding unit takes into account the durability of the equipment and can propose connection methods that will extend its lifespan. For example, it analyzes durability data for air conditioning equipment and proposes optimal operating conditions. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve durability data from a database and propose the optimal connection method based on that data. For example, it proposes a connection method to improve the durability of elevators. Furthermore, when the connection specification understanding unit understands the connection specifications of building equipment, it takes durability into account and optimizes the connection method. For example, it proposes a connection method to improve the durability of lighting equipment. This makes it possible to propose connection methods that improve the durability of building equipment.

[0073] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the security risks of the equipment and propose a connection method that minimizes the risks. For example, it can analyze security risk data for air conditioning equipment and propose optimal security measures. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve security risk data from a database and propose the optimal connection method based on that data. For example, it can propose a connection method that minimizes the security risk of elevators. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes security risks into consideration and optimizes the connection method. For example, it can propose a connection method that minimizes the security risk of lighting equipment. This makes it possible to propose a connection method that minimizes the security risk of building equipment.

[0074] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the maintenance costs of the equipment and propose a connection method that minimizes costs. For example, it can analyze maintenance cost data for air conditioning equipment and propose an optimal maintenance schedule. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve maintenance cost data from a database and propose the optimal connection method based on that data. For example, it can propose a connection method that minimizes elevator maintenance costs. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes maintenance costs into account and optimizes the connection method. For example, it can propose a connection method that minimizes the maintenance costs of lighting equipment. This makes it possible to propose a connection method that minimizes the maintenance costs of building equipment.

[0075] When understanding the connection specifications of building equipment, the connection specification understanding unit can analyze the environmental load of the equipment and propose a connection method that minimizes the environmental load. For example, it can analyze environmental load data for air conditioning equipment and propose optimal operating conditions. Furthermore, when analyzing the connection specifications of building equipment, the connection specification understanding unit allows the generation AI to retrieve environmental load data from a database and propose the optimal connection method based on that data. For example, it can propose a connection method that minimizes the environmental load of elevators. Furthermore, when the generation AI understands the connection specifications of building equipment, the connection specification understanding unit takes environmental load into consideration and optimizes the connection method. For example, it can propose a connection method that minimizes the environmental load of lighting equipment. This makes it possible to propose a connection method that minimizes the environmental load of building equipment.

[0076] The connection specification understanding unit uses the emotion estimation function to analyze the motivation of building managers and propose connection work procedures that will increase their motivation. For example, it can adjust the timing and order of work. The connection specification understanding unit also analyzes the motivation of building managers, and the generation AI proposes connection work procedures that will increase their motivation. For example, it can present specific steps to reduce the burden of work. The connection specification understanding unit also uses the emotion estimation function to monitor the motivation of building managers and propose connection work procedures that will increase their motivation. For example, it can adjust the timing of work breaks. This increases the motivation of building managers and enables more efficient work.

[0077] The connection specification understanding unit uses the emotion estimation function to analyze the stress levels of building users and propose optimal connection work procedures for low-stress situations. For example, it adjusts the timing and order of work. The connection specification understanding unit also analyzes the stress levels of building users, and the generation AI proposes connection work procedures for low-stress situations. For example, it presents specific steps to reduce the burden of work. The connection specification understanding unit also uses the emotion estimation function to monitor the stress levels of building users and proposes optimal connection work procedures for low-stress situations. For example, it adjusts the timing of work breaks. This reduces stress for building users and enables more efficient work.

[0078] The connection specification understanding unit uses the emotion estimation function to analyze the comfort level of building users and propose connection methods that will increase comfort. For example, adjusting the settings of air conditioning equipment. The connection specification understanding unit also analyzes the comfort level of building users, and the generation AI proposes connection methods that will increase comfort. For example, it presents connection methods that reduce elevator waiting times. The connection specification understanding unit also uses the emotion estimation function to monitor the comfort level of building users and proposes connection methods that will increase comfort. For example, it adjusts lighting settings. This makes it possible to propose connection methods that will increase the comfort level of building users.

[0079] The connection specification understanding unit uses the emotion estimation function to analyze the satisfaction of building users and propose connection methods that will increase satisfaction. For example, adjusting the settings of air conditioning equipment. The connection specification understanding unit also analyzes the satisfaction of building users, and the generation AI proposes connection methods that will increase satisfaction. For example, it presents connection methods that shorten elevator waiting times. The connection specification understanding unit also uses the emotion estimation function to monitor the satisfaction of building users and propose connection methods that will increase satisfaction. For example, it adjusts lighting settings. This makes it possible to propose connection methods that will increase building user satisfaction.

[0080] The connection specification understanding unit uses the emotion estimation function to analyze the stress level of the building manager and propose optimal connection work procedures for a low-stress state. For example, it adjusts the timing and order of work. The connection specification understanding unit also analyzes the stress level of the building manager and the generation AI proposes connection work procedures for a low-stress state. For example, it presents specific steps to reduce the burden of work. The connection specification understanding unit also uses the emotion estimation function to monitor the stress level of the building manager and proposes optimal connection work procedures for a low-stress state. For example, it adjusts the timing of work breaks. This reduces the stress of the building manager and enables more efficient work.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The connection specification understanding unit understands the connection specifications of the building equipment. For example, the generation AI analyzes data related to the connection specifications of the building equipment and understands the characteristics and connection methods of each piece of equipment. The input to the generation AI is a prompt containing data and instructions related to the connection specifications of the building equipment, and the generation AI performs analysis based on that prompt. Step 2: The control instruction generation unit generates control instructions based on the connection specifications understood by the connection specification understanding unit. For example, the generation AI analyzes natural language commands entered by the user and understands their content. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates control instructions based on that prompt. Step 3: The management unit manages the building facilities based on the control instructions generated by the control instruction generation unit. For example, the generation AI can control and manage the building facilities in natural language. This enables the Citylink system to control and manage building facilities in natural language from a single platform.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

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

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

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

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 connection specification understanding department that understands the connection specifications of building equipment; a control instruction generation unit that generates a control instruction based on the connection specification understood by the connection specification understanding unit; a management unit that manages building facilities based on the control instructions generated by the control instruction generation unit. A system characterized by:

2. The connection specification understanding unit Analyze not only the connection specifications of the building equipment but also the maintenance and failure history, and make proposals for preventive maintenance.

2. The system of claim 1.

3. The connection specification understanding unit When understanding the connection specifications of the building equipment, we will consider the characteristics of each manufacturer and model and propose the optimal connection method.

2. The system of claim 1.

4. The connection specification understanding unit Analyze the stress level of building managers and propose optimal connection procedures to keep them in a low-stress state 2. The system of claim 1.

5. The connection specification understanding unit We propose optimal connection methods that take into account not only the connection specifications for the building equipment but also the surrounding environment.

2. The system of claim 1.

6. The connection specification understanding unit See how other buildings are connected and adopt best practices 2. The system of claim 1.

7. The connection specification understanding unit Analyze the comfort level of building users and propose connection methods that will increase that level.

2. The system of claim 1.

8. The control instruction generation unit Analyzes commands by voice or gestures as well as natural language commands to control the building facilities.

2. The system of claim 1.

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