system

Abstract

The system according to the embodiment aims to realize device collaboration within an office and improve the efficiency of business processing. [Solution] A system according to an embodiment includes a communication unit, a business efficiency improvement unit, and a device collaboration unit. Specific technical details of the communication unit are described below. The business efficiency improvement unit improves the efficiency of business processing based on the communication environment provided by the communication unit. The device collaboration unit collaborates devices within an office based on the business processing made more efficient by the business efficiency improvement unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately integrate devices within the office or streamline business processes, leaving room for improvement.

[0005] The system according to the embodiment aims to realize device collaboration within an office and improve the efficiency of business processing. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication unit, a business efficiency improvement unit, and a device collaboration unit. Specific technical details of the communication unit are described below. The business efficiency improvement unit improves the efficiency of business processing based on the communication environment provided by the communication unit. The device collaboration unit collaborates with devices in the office based on the business processing made more efficient by the business efficiency improvement unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize device collaboration within an office and improve the efficiency of business processing. [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) A smart office system according to an embodiment of the present invention utilizes 5G technology to provide a high-speed, stable communication environment and combines AI and IoT technologies to achieve efficient business processing. This smart office system provides a flexible office environment that accommodates both remote and office work, realizing a sustainable office space that is both ecological and efficient. It also optimizes the office environment to enable the provision of high-quality services to customers. For example, by utilizing 5G technology to provide a high-speed, stable communication environment, remote employees can access in-office devices and share data in real time. Next, AI technology is used to improve the efficiency of business processing. For example, AI can automatically adjust employee schedules and suggest optimal meeting times. AI can also analyze business data and grasp the progress of work in real time. This improves business efficiency. Furthermore, IoT technology is used to connect in-office devices. For example, IoT sensors can automatically adjust the temperature and lighting in the office to provide a comfortable working environment. IoT devices can also monitor energy consumption in the office to create an eco-friendly office space. Finally, the office environment is optimized to enable the provision of high-quality services to customers. For example, AI can analyze customer needs and propose optimal services. IoT devices can also detect customer visits and respond quickly, improving customer satisfaction. In this way, the combination of 5G, AI, and IoT technologies can create an efficient and sustainable smart office space, enabling the provision of high-quality services to customers. As a result, smart office systems can provide efficient business processes and sustainable office spaces, enabling the provision of high-quality services to customers.

[0029] A smart office system according to an embodiment includes a communication unit, a business efficiency improvement unit, and a device linkage unit. The communication unit provides a high-speed and stable communication environment. The communication unit, for example, uses 5G technology to achieve high-speed and stable communication. The communication unit enables employees working remotely to access devices in the office and share data in real time. For example, the communication unit uses a remote access protocol to allow employees to access an office server from home and obtain necessary data. The communication unit can also achieve secure communication using data encryption technology. The business efficiency improvement unit uses AI technology to improve the efficiency of business processing. For example, the business efficiency improvement unit uses AI to automatically adjust employees' schedules and suggest optimal meeting times. For example, the AI ​​analyzes employees' calendar information and suggests optimal meeting times. The business efficiency improvement unit can also analyze business data and grasp the progress of work in real time. For example, the AI ​​links with a project management tool to monitor the progress of work in real time. The device linkage unit uses IoT technology to link devices in the office. The device linking unit, for example, uses IoT sensors to automatically adjust the temperature and lighting in the office, providing a comfortable working environment. For example, the IoT sensors use temperature sensors and illuminance sensors to monitor the office environment in real time and automatically adjust the temperature and lighting to the optimum level. The device linking unit also uses IoT devices to monitor energy consumption in the office, creating an ecological office space. For example, the IoT devices collect energy consumption data in real time and propose optimal methods for improving energy efficiency. As a result, the smart office system according to the embodiment can provide a high-speed and stable communication environment, streamline business processing, and link devices in the office.

[0030] The smart office system includes an environmental adjustment unit that uses IoT sensors to adjust the temperature or lighting in the office based on specific technical details. The environmental adjustment unit automatically adjusts the temperature and lighting in the office using the IoT sensors. For example, the environmental adjustment unit uses temperature sensors to monitor the temperature in the office in real time and adjust it to an optimal temperature. For example, temperature sensors are installed in each area of ​​the office and collect temperature data for each area. The environmental adjustment unit controls air conditioners and heaters based on the collected temperature data to adjust the temperature to an optimal level. The environmental adjustment unit also automatically adjusts the lighting in the office using illuminance sensors. For example, illuminance sensors are installed in each area of ​​the office and collect illuminance data for each area. The environmental adjustment unit adjusts the brightness of the lighting based on the collected illuminance data to provide a comfortable working environment. This allows the temperature and lighting in the office to be automatically adjusted and a comfortable working environment to be provided. Some or all of the above-described processing in the environmental adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental adjustment unit can input data obtained from a temperature sensor or illuminance sensor into the generation AI and have the generation AI adjust the temperature and lighting to the optimum level.

[0031] The smart office system includes a service proposal unit that uses AI to analyze customer needs and propose services based on specific technical details. The service proposal unit uses AI to analyze customer needs and propose optimal services. For example, the service proposal unit analyzes the customer's past usage history to understand the customer's needs. For example, the AI ​​analyzes the customer's past purchase history and inquiry history to identify the customer's needs. The service proposal unit also understands the customer's current needs in real time and proposes optimal services. For example, the AI ​​analyzes the customer's current behavioral data and real-time feedback to understand the customer's needs. Furthermore, the service proposal unit proposes optimal services taking into account the customer's geographic location information. For example, the AI ​​proposes nearby stores and services based on the customer's location information. This allows for analyzing customer needs and proposing optimal services, thereby improving customer satisfaction. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or without AI. For example, the service proposal unit may input customer usage history data into a generation AI and have the generation AI propose optimal services.

[0032] The smart office system includes an energy monitoring unit in which IoT devices monitor energy consumption based on specific technical details within the office. The energy monitoring unit monitors energy consumption within the office using the IoT devices. For example, the energy monitoring unit collects energy consumption data in real time and proposes optimal methods for improving energy efficiency. For example, the IoT devices collect energy consumption data from each device and send it to the energy monitoring unit. The energy monitoring unit analyzes energy consumption patterns based on the collected data and proposes optimal methods for improving energy efficiency. The energy monitoring unit also analyzes past energy consumption data and automatically proposes optimal methods for improving energy efficiency. For example, the energy monitoring unit proposes optimal energy management methods based on the past energy consumption data, depending on the season and time of day. This allows energy consumption within the office to be monitored, resulting in an eco-friendly office space. Some or all of the above-described processing in the energy monitoring unit may be performed using, or without, AI. For example, the energy monitoring unit may input energy consumption data into a generation AI and have the generation AI propose optimal methods for improving energy efficiency.

[0033] The communications unit enables remote working employees to access in-office devices and share data based on specific technical content. The communications unit enables remote working employees to access in-office devices and share data in real time. For example, the communications unit uses a remote access protocol to allow employees to access in-office servers from home and obtain necessary data. The communications unit can also achieve secure communications using data encryption technology. For example, the communications unit uses a virtual private network (VPN) to allow remote working employees to securely access in-office devices. Furthermore, the communications unit uses remote desktop technology to allow employees to operate in-office computers from home. For example, the communications unit uses the remote desktop protocol (RDP) to allow employees to remotely connect to in-office computers from home and perform their work. This allows remote working employees to access in-office devices and share data in real time.

[0034] In the business efficiency improvement unit, AI adjusts employee schedules based on specific technical details and suggests meeting times. The business efficiency improvement unit automatically adjusts employee schedules using AI and suggests optimal meeting times. For example, the business efficiency improvement unit analyzes employee calendar information and suggests optimal meeting times. For example, the AI ​​identifies each employee's free time based on the employee calendar information and suggests optimal meeting times. The business efficiency improvement unit can also analyze employee workloads and adjust work priorities. For example, the AI ​​automatically adjusts work priorities based on employee work progress data to support efficient work execution. Furthermore, the business efficiency improvement unit can also consider the importance of meetings and the roles of participants when adjusting employee schedules. For example, the AI ​​suggests optimal meeting times based on the importance of meetings and the roles of participants. As a result, the AI ​​automatically adjusts employee schedules and suggests optimal meeting times, thereby improving business efficiency. Some or all of the above-described processing in the business efficiency improvement unit may be performed using AI, for example, or without AI. For example, the business efficiency improvement department can input employee calendar information into the generation AI and have the generation AI suggest optimal meeting times.

[0035] The device linking unit links devices in the office based on specific technical content. The device linking unit links devices in the office using IoT technology. For example, the device linking unit monitors devices in the office in real time using IoT sensors and proposes an optimal linking method. For example, the IoT sensors monitor the operating status of each device in real time and send the data to the device linking unit. The device linking unit optimizes the linkage between devices based on the collected data. The device linking unit can also apply an algorithm to optimize the data transfer speed between devices. For example, the device linking unit monitors the data transfer speed between devices in real time and applies an optimal algorithm. Furthermore, the device linking unit can set an optimal linking method taking into account device location information. For example, the device linking unit sets an optimal linking method based on device location information. This enables efficient business processing by linking devices in the office using IoT technology. Some or all of the above-mentioned processing in the device linking unit may be performed using AI, for example, or without AI. For example, the device linking unit can input device operating status data to a generation AI and have the generation AI propose an optimal linking method.

[0036] The communication unit monitors communication stability based on specific technical content, and selects a communication path based on the specific technical content when communication quality deteriorates. The communication unit monitors communication stability in real time, and automatically selects an optimal communication path when communication quality deteriorates. For example, the communication unit monitors communication quality in real time and detects communication delays and packet loss. For example, the communication unit monitors communication quality in real time using a network monitoring tool. Furthermore, the communication unit automatically selects an optimal communication path when communication quality deteriorates. For example, the communication unit automatically selects an optimal Wi-Fi network when communication quality deteriorates. Furthermore, the communication unit can switch to an optimal mobile data communication when communication is interrupted. Furthermore, the communication unit can select an optimal communication protocol when communication delays occur. As a result, a stable communication environment can be maintained by automatically selecting an optimal communication path when communication quality deteriorates. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input communication quality data into the generation AI and have the generation AI select the optimal communication route.

[0037] The communication unit selects a communication protocol based on specific technical details according to the type of communication data. The communication unit automatically selects the optimal communication protocol according to the type of communication data. For example, the communication unit selects a low-latency communication protocol for a video conference. For example, the communication unit automatically selects a low-latency communication protocol during a video conference to achieve smooth communication. The communication unit can also select a high-speed communication protocol for a file transfer. For example, the communication unit automatically selects a high-speed communication protocol during a large-capacity file transfer to achieve rapid file transfer. The communication unit can also select a lightweight communication protocol for messaging. For example, the communication unit automatically selects a lightweight communication protocol when sending and receiving text messages to achieve efficient communication. This enables efficient communication by selecting the optimal communication protocol according to the type of communication data. Some or all of the above-described processing in the communication unit may be performed using, or without, AI. For example, the communication unit may input the type of communication data to a generation AI and cause the generation AI to select the optimal communication protocol.

[0038] The communication unit sets a communication route based on specific technical details, taking into account the location information of the device in the office. The communication unit sets an optimal communication route, taking into account the location information of the device in the office. For example, the communication unit selects an optimal Wi-Fi access point based on the device's location information. For example, the communication unit acquires device location information in real time and automatically selects an optimal Wi-Fi access point. The communication unit can also set an optimal Bluetooth® connection based on the device's location information. For example, the communication unit automatically sets an optimal Bluetooth connection based on the device's location information. Furthermore, the communication unit can also set an optimal wired connection based on the device's location information. For example, the communication unit automatically sets an optimal wired connection based on the device's location information. This enables efficient communication by setting an optimal communication route based on the device's location information. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit may input device location information data to a generation AI and cause the generation AI to set an optimal communication route.

[0039] The communication unit cooperates with an external cloud service to back up data based on specific technical details. The communication unit cooperates with the external cloud service to automatically back up data. For example, the communication unit periodically backs up data to the cloud service. For example, the communication unit backs up data to the cloud service at a fixed time every day. The communication unit can also immediately back up important data to the cloud service when it is updated. For example, the communication unit backs up data to the cloud service in real time when the data is updated. Furthermore, the communication unit can also back up data to the cloud service when communication quality is stable. For example, the communication unit selects a time period when communication quality is stable and backs up data to the cloud service during that time period. This improves data security by automatically backing up data in cooperation with an external cloud service. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input a data backup schedule to the generation AI and have the generation AI suggest the optimal backup timing.

[0040] The business efficiency improvement unit analyzes employees' past work histories and proposes business processes based on specific technical content. The business efficiency improvement unit analyzes employees' past work histories and proposes optimal business processes. For example, the business efficiency improvement unit proposes an optimal task order based on the past work histories. For example, the business efficiency improvement unit analyzes employees' past work histories and proposes an efficient task order. The business efficiency improvement unit can also propose efficient business procedures based on the past work histories. For example, the business efficiency improvement unit analyzes employees' past work histories and proposes optimal business procedures. Furthermore, the business efficiency improvement unit can also propose optimal task allocation based on the past work histories. For example, the business efficiency improvement unit analyzes employees' past work histories and proposes efficient task allocation. In this way, by analyzing employees' past work histories, optimal business processes are proposed and business efficiency is improved. Some or all of the above-mentioned processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the business efficiency improvement unit can input employees' work history data into a generation AI and have the generation AI execute a proposal for an optimal business process.

[0041] The business efficiency improvement unit monitors the progress of work based on specific technical details, and when a delay occurs, proposes a recovery plan based on the specific technical details. The business efficiency improvement unit monitors the progress of work in real time, and automatically proposes a recovery plan when a delay occurs. For example, the business efficiency improvement unit monitors the progress of work in real time, and proposes an optimal recovery plan when a delay occurs. For example, the business efficiency improvement unit works in conjunction with a project management tool to monitor the progress of work in real time. The business efficiency improvement unit can also propose resource reallocation when a delay occurs. For example, the business efficiency improvement unit proposes resource reallocation when a delay occurs, thereby minimizing work delays. Furthermore, the business efficiency improvement unit can also propose schedule readjustment when a delay occurs. For example, the business efficiency improvement unit proposes schedule readjustment when a delay occurs, thereby smoothly progressing work progress. In this way, by monitoring the progress of work in real time and automatically proposing a recovery plan when a delay occurs, it is possible to minimize work delays. Some or all of the above-described processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the business efficiency improvement department can input business progress data into the generation AI and have the generation AI propose an optimal recovery plan.

[0042] The business efficiency improvement unit monitors device usage in the office and allocates devices based on specific technical details. The business efficiency improvement unit monitors device usage in the office and allocates optimal devices. For example, the business efficiency improvement unit monitors device usage in real time and allocates optimal devices. For example, the business efficiency improvement unit monitors the usage of each device in real time and understands the frequency of use and operating status. The business efficiency improvement unit can also analyze device usage and propose efficient device allocation. For example, the business efficiency improvement unit proposes optimal device placement based on device usage. Furthermore, the business efficiency improvement unit can propose optimal device placement based on device usage. For example, the business efficiency improvement unit proposes optimal device placement based on device usage. In this way, by monitoring device usage in the office, optimal device allocation is achieved, thereby improving business efficiency. Some or all of the above-mentioned processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without AI. For example, the business efficiency improvement unit may input device usage data into a generation AI and have the generation AI execute optimal device allocation.

[0043] The Business Efficiency Improvement Department works in conjunction with an external project management tool to manage the progress of work based on specific technical content. The Business Efficiency Improvement Department works in conjunction with an external project management tool to centrally manage the progress of work. For example, the Business Efficiency Improvement Department works in conjunction with an external project management tool to grasp the progress of work in real time. For example, the Business Efficiency Improvement Department works in conjunction with a project management tool to monitor the progress of each project in real time. The Business Efficiency Improvement Department can also work in conjunction with an external project management tool to centrally manage the progress of work. For example, the Business Efficiency Improvement Department works in conjunction with a project management tool to centrally manage the progress of each project. Furthermore, the Business Efficiency Improvement Department can also work in conjunction with an external project management tool to visualize the progress of work. For example, the Business Efficiency Improvement Department works in conjunction with a project management tool to display the progress of each project as graphs and charts, providing it in a visually easy-to-understand format. In this way, by working in conjunction with an external project management tool, business progress can be centrally managed and efficient business management is possible. Some or all of the above-described processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the business efficiency improvement unit may input data from a project management tool into the generation AI and have the generation AI perform business progress management.

[0044] The device collaboration unit monitors the operating status of devices in the office based on specific technical details and proposes an integration method. The device collaboration unit monitors the operating status of devices in the office in real time and proposes an optimal integration method. For example, the device collaboration unit monitors the operating status of devices in real time and proposes an optimal integration method. For example, the device collaboration unit monitors the operating status of each device in real time and understands the frequency of use and operating status. The device collaboration unit can also analyze the operating status of devices and propose an efficient integration method. For example, the device collaboration unit proposes an optimal integration procedure based on the operating status of the devices. Furthermore, the device collaboration unit can propose an optimal integration procedure based on the operating status of the devices. For example, the device collaboration unit proposes an optimal integration procedure based on the operating status of the devices. In this way, by monitoring the operating status of devices in real time, an optimal integration method is proposed and efficient device collaboration is possible. Some or all of the above-mentioned processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit can input device operating status data to a generation AI and have the generation AI propose an optimal integration method.

[0045] The device collaboration unit applies an algorithm for adjusting the data transfer rate between devices based on specific technical content. The device collaboration unit applies an algorithm for optimizing the data transfer rate between devices. For example, the device collaboration unit monitors the data transfer rate between devices in real time and applies an optimal algorithm. For example, the device collaboration unit monitors the data transfer rate between devices in real time and applies an optimal algorithm. The device collaboration unit can also analyze the data transfer rate between devices and apply an efficient algorithm. For example, the device collaboration unit proposes an optimal data transfer procedure based on the data transfer rate between devices. Furthermore, the device collaboration unit can propose an optimal data transfer procedure based on the data transfer rate between devices. For example, the device collaboration unit proposes an optimal data transfer procedure based on the data transfer rate between devices. This enables efficient data transfer by optimizing the data transfer rate between devices. Some or all of the above-described processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit may input data on the data transfer rate between devices to a generation AI and cause the generation AI to propose an optimal data transfer procedure.

[0046] The device collaboration unit sets an collaboration method based on specific technical details, taking into account the location information of devices in the office. The device collaboration unit sets an optimal collaboration method, taking into account the location information of devices in the office. For example, the device collaboration unit sets an optimal collaboration method based on the device location information. For example, the device collaboration unit acquires device location information in real time and automatically sets an optimal collaboration method. The device collaboration unit can also analyze device location information and set an efficient collaboration method. For example, the device collaboration unit sets an optimal collaboration procedure based on the device location information. Furthermore, the device collaboration unit can also set an optimal collaboration procedure based on the device location information. For example, the device collaboration unit sets an optimal collaboration procedure based on the device location information. This enables efficient device collaboration by setting an optimal collaboration method based on the device location information. Some or all of the above-described processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit may input device location information data to a generation AI and cause the generation AI to propose an optimal collaboration procedure.

[0047] The device collaboration unit collaborates with an external IoT platform to manage devices based on specific technical details. The device collaboration unit collaborates with an external IoT platform to centralize device management. For example, the device collaboration unit collaborates with an external IoT platform to centralize device management. For example, the device collaboration unit collaborates with an external IoT platform to grasp the operating status of each device in real time. The device collaboration unit can also collaborate with an external IoT platform to centrally manage device settings. For example, the device collaboration unit collaborates with an external IoT platform to centrally manage the settings of each device. Furthermore, the device collaboration unit can collaborate with an external IoT platform to monitor the operating status of devices in real time and propose an optimal management method. For example, the device collaboration unit collaborates with an external IoT platform to propose an optimal management method based on the operating status of each device. As a result, collaboration with an external IoT platform centralizes device management and enables efficient device management. Some or all of the above-described processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit can input device operating status data into the generation AI and have the generation AI propose the optimal management method.

[0048] The environmental adjustment unit monitors environmental data within the office in real time and proposes environmental settings based on specific technical details. The environmental adjustment unit monitors environmental data within the office in real time and proposes optimal environmental settings. For example, the environmental adjustment unit monitors environmental data in real time and proposes optimal temperature and lighting. For example, the environmental adjustment unit uses a temperature sensor and an illuminance sensor to collect environmental data within the office in real time and proposes optimal temperature and lighting. The environmental adjustment unit can also analyze the environmental data and propose efficient environmental settings. For example, the environmental adjustment unit proposes optimal environmental settings based on the collected environmental data. Furthermore, the environmental adjustment unit can propose optimal environmental settings based on the environmental data. For example, the environmental adjustment unit proposes optimal environmental settings based on the collected environmental data. In this way, by monitoring environmental data within the office in real time, optimal environmental settings can be proposed and a comfortable work environment can be provided. Some or all of the above-mentioned processing in the environmental adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the environmental adjustment unit can input environmental data to a generation AI and have the generation AI propose optimal environmental settings.

[0049] The environment adjustment unit analyzes past environmental data and sets the environment based on specific technical details corresponding to the season and time of day. The environment adjustment unit analyzes past environmental data and automatically sets the optimal environment setting according to the season and time of day. For example, the environment adjustment unit sets the optimal temperature and lighting according to the season based on the past environmental data. For example, the environment adjustment unit analyzes past environmental data and sets the optimal temperature and lighting according to the season. The environment adjustment unit can also set the optimal environment setting according to the time of day based on the past environmental data. For example, the environment adjustment unit analyzes past environmental data and sets the optimal environment setting according to the time of day. Furthermore, the environment adjustment unit can automatically set an efficient environment setting based on the past environmental data. For example, the environment adjustment unit automatically sets an efficient environment setting based on the past environmental data. In this way, by analyzing past environmental data, the optimal environment setting according to the season and time of day can be automatically set, thereby providing a comfortable working environment. Some or all of the above-mentioned processing in the environment adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental adjustment unit can input past environmental data into the generation AI and have the generation AI propose optimal environmental settings.

[0050] The environment adjustment unit sets the environment based on specific technical details, taking into account the device usage status in the office. The environment adjustment unit sets the optimal environment setting, taking into account the device usage status in the office. For example, the environment adjustment unit monitors device usage status in real time and sets the optimal temperature and lighting. For example, the environment adjustment unit monitors the usage status of each device in real time and sets the optimal temperature and lighting. The environment adjustment unit can also analyze device usage status and propose efficient environment settings. For example, the environment adjustment unit proposes optimal environment settings based on device usage status. Furthermore, the environment adjustment unit can also set the optimal environment setting based on device usage status. For example, the environment adjustment unit sets the optimal environment setting based on device usage status. This enables efficient environment management by setting the optimal environment setting, taking into account the device usage status. Some or all of the above-mentioned processing in the environment adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the environment adjustment unit can input device usage status data to a generation AI and have the generation AI propose optimal environment settings.

[0051] The environmental adjustment unit cooperates with external environmental data to adjust the environmental settings within the office based on specific technical details. The environmental adjustment unit cooperates with external environmental data to optimize the environmental settings within the office. For example, the environmental adjustment unit sets optimal temperature and lighting based on external weather data. For example, the environmental adjustment unit acquires external weather data in real time and sets optimal temperature and lighting. The environmental adjustment unit can also propose efficient environmental settings based on external air quality data. For example, the environmental adjustment unit proposes optimal environmental settings based on external air quality data. Furthermore, the environmental adjustment unit can also set optimal environmental settings based on external environmental data. For example, the environmental adjustment unit sets optimal environmental settings based on external environmental data. In this way, by cooperating with external environmental data, the environmental settings within the office can be optimized and a comfortable working environment can be provided. Some or all of the above-mentioned processing in the environmental adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the environmental adjustment unit can input external environmental data into a generation AI and have the generation AI execute a proposal for optimal environmental settings.

[0052] The service proposal unit analyzes the customer's past usage history and proposes a service based on specific technical content. The service proposal unit analyzes the customer's past usage history and proposes an optimal service. For example, the service proposal unit proposes an optimal service based on the customer's past usage history. For example, the service proposal unit analyzes the customer's past purchase history and inquiry history and proposes an optimal service. The service proposal unit can also analyze the customer's past usage history and make efficient service proposals. For example, the service proposal unit proposes efficient services based on the customer's past usage history. Furthermore, the service proposal unit can also propose customized services based on the customer's past usage history. For example, the service proposal unit proposes customized services based on the customer's past usage history. In this way, by analyzing the customer's past usage history, optimal services can be proposed and customer satisfaction can be improved. Some or all of the above-mentioned processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit can input the customer's usage history data into a generation AI and cause the generation AI to propose an optimal service.

[0053] The service proposal unit grasps the customer's current needs in real time and proposes a service based on specific technical content. The service proposal unit grasps the customer's current needs in real time and proposes an optimal service. For example, the service proposal unit grasps the customer's current needs in real time and proposes an optimal service. For example, the service proposal unit analyzes the customer's current behavioral data and real-time feedback to propose an optimal service. The service proposal unit can also analyze the customer's current needs and make an efficient service proposal. For example, the service proposal unit makes an efficient service proposal based on the customer's current needs. Furthermore, the service proposal unit can also propose a customized service based on the customer's current needs. For example, the service proposal unit proposes a customized service based on the customer's current needs. In this way, by grasping the customer's current needs in real time, the optimal service can be proposed and customer satisfaction can be improved. Some or all of the above-mentioned processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit can input the customer's current needs data into a generation AI and cause the generation AI to propose an optimal service.

[0054] The service proposal unit proposes a service based on specific technical content, taking into account the customer's geographical location information. The service proposal unit proposes an optimal service, taking into account the customer's geographical location information. For example, the service proposal unit proposes an optimal service based on the customer's geographical location information. For example, the service proposal unit acquires the customer's geographical location information in real time and proposes an optimal service. The service proposal unit can also analyze the customer's geographical location information and make efficient service proposals. For example, the service proposal unit proposes efficient services based on the customer's geographical location information. Furthermore, the service proposal unit can also propose customized services based on the customer's geographical location information. For example, the service proposal unit proposes customized services based on the customer's geographical location information. This makes it possible to propose optimal services and improve customer satisfaction by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit may input the customer's geographical location information data into a generation AI and cause the generation AI to propose an optimal service.

[0055] The service proposal unit cooperates with an external customer management system to adjust service proposals based on specific technical content. The service proposal unit cooperates with an external customer management system to optimize service proposals. For example, the service proposal unit cooperates with an external customer management system to grasp customer information in real time. For example, the service proposal unit cooperates with an external customer management system to acquire customer information in real time and propose optimal services. The service proposal unit can also cooperate with an external customer management system to make efficient service proposals. For example, the service proposal unit cooperates with an external customer management system to make efficient service proposals based on customer information. Furthermore, the service proposal unit can also cooperate with an external customer management system to propose customized services. For example, the service proposal unit cooperates with an external customer management system to propose customized services based on customer information. By cooperating with an external customer management system, service proposals can be optimized and customer satisfaction can be improved. Some or all of the above-described processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit can input data from a customer management system into the generation AI and have the generation AI propose the most appropriate service.

[0056] The energy monitoring unit monitors energy consumption data within the office in real time and proposes an energy management method based on specific technical details. The energy monitoring unit monitors energy consumption data within the office in real time and proposes an optimal energy management method. For example, the energy monitoring unit monitors energy consumption data in real time and proposes an optimal energy management method. For example, the energy monitoring unit monitors energy consumption data of each device in real time and proposes an optimal energy management method. The energy monitoring unit can also analyze the energy consumption data and propose an efficient energy management method. For example, the energy monitoring unit proposes an optimal energy management procedure based on the energy consumption data. Furthermore, the energy monitoring unit can propose an optimal energy management procedure based on the energy consumption data. For example, the energy monitoring unit proposes an optimal energy management procedure based on the energy consumption data. In this way, by monitoring energy consumption data within the office in real time, an optimal energy management method can be proposed and energy efficiency can be improved. Some or all of the above-mentioned processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input energy consumption data to a generation AI and have the generation AI propose an optimal energy management procedure.

[0057] The energy monitoring unit analyzes past energy consumption data and proposes a method based on specific technical details for improving energy efficiency. The energy monitoring unit analyzes past energy consumption data and automatically proposes an optimal method for improving energy efficiency. For example, the energy monitoring unit proposes an optimal energy management method based on past energy consumption data. For example, the energy monitoring unit analyzes past energy consumption data and proposes an optimal energy management method. The energy monitoring unit can also propose an efficient energy management method based on past energy consumption data. For example, the energy monitoring unit proposes an efficient energy management method based on past energy consumption data. Furthermore, the energy monitoring unit can also propose an optimal energy management procedure based on past energy consumption data. For example, the energy monitoring unit proposes an optimal energy management procedure based on past energy consumption data. In this way, by analyzing past energy consumption data, an optimal method for improving energy efficiency can be automatically proposed and energy consumption can be optimized. Some or all of the above-described processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input past energy consumption data into a generation AI and have the generation AI execute a proposal for an optimal energy management procedure.

[0058] The energy monitoring unit sets an energy management method based on specific technical details, taking into account the device usage status in the office. The energy monitoring unit sets an optimal energy management method, taking into account the device usage status in the office. For example, the energy monitoring unit monitors device usage status in real time and sets an optimal energy management method. For example, the energy monitoring unit monitors the usage status of each device in real time and sets an optimal energy management method. The energy monitoring unit can also analyze device usage status and set an efficient energy management method. For example, the energy monitoring unit sets an optimal energy management procedure based on the device usage status. Furthermore, the energy monitoring unit can also set an optimal energy management procedure based on the device usage status. For example, the energy monitoring unit sets an optimal energy management procedure based on the device usage status. This enables efficient energy management by setting an optimal energy management method, taking into account the device usage status. Some or all of the above-described processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input device usage status data to a generation AI and have the generation AI propose an optimal energy management procedure.

[0059] The energy monitoring unit cooperates with an external energy management system to adjust energy consumption based on specific technical details. The energy monitoring unit cooperates with an external energy management system to optimize energy consumption. For example, the energy monitoring unit cooperates with an external energy management system to optimize energy consumption. For example, the energy monitoring unit cooperates with an external energy management system to grasp energy consumption data in real time. The energy monitoring unit can also cooperate with an external energy management system to propose an efficient energy management method. For example, the energy monitoring unit cooperates with an external energy management system to propose an optimal energy management procedure based on the energy consumption data. Furthermore, the energy monitoring unit can also cooperate with an external energy management system to propose an optimal energy management procedure based on the energy consumption data. For example, the energy monitoring unit cooperates with an external energy management system to propose an optimal energy management procedure based on the energy consumption data. In this way, by cooperating with an external energy management system, energy consumption can be optimized and energy efficiency can be improved. Some or all of the above-described processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input energy consumption data into the generation AI and have the generation AI execute a proposal for optimal energy management procedures.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] Smart office systems can also be equipped with a health management unit that monitors employees' health. The health management unit collects employees' vital signs in real time and monitors their health. For example, the health management unit can monitor employees' heart rate and blood pressure and issue alerts if any abnormalities are detected. The health management unit can also analyze employees' stress levels and suggest appropriate breaks. Furthermore, the health management unit can record employees' exercise levels and recommend activities to maintain their health. This allows for real-time monitoring of employees' health and appropriate responses, thereby providing a healthy work environment.

[0062] The smart office system can further include a productivity improvement unit to improve employee productivity. The productivity improvement unit analyzes employees' work patterns and suggests optimal work methods. For example, the productivity improvement unit analyzes employees' work history and suggests efficient work procedures. The productivity improvement unit can also make suggestions to optimize employees' work environments. Furthermore, the productivity improvement unit can monitor employees' work efficiency in real time and make suggestions for improvement as needed. This can improve employee productivity and support efficient work execution.

[0063] The smart office system can also be equipped with a task management unit to improve employee work efficiency. The task management unit monitors employee tasks in real time and performs efficient task management. For example, the task management unit grasps the progress of employee tasks in real time and proposes a recovery plan if a delay occurs. The task management unit can also automatically adjust the priority of employee tasks to support efficient task management. Furthermore, the task management unit can make suggestions to optimize the allocation of employee tasks. This improves employee work efficiency and supports efficient business execution.

[0064] The smart office system can further include an environment optimization unit for optimizing employees' work environments. The environment optimization unit collects employees' work environment data in real time and proposes an optimal work environment. For example, the environment optimization unit can propose optimal temperature and lighting based on the employees' work environment data. The environment optimization unit can also analyze the employees' work environment data and make proposals for providing an efficient work environment. Furthermore, the environment optimization unit can propose optimal device placement based on the employees' work environment data. This optimizes employees' work environments and supports efficient work performance.

[0065] The smart office system can further include a performance improvement unit for improving employee work performance. The performance improvement unit collects employee work performance data in real time and proposes optimal work methods. For example, the performance improvement unit can propose efficient work procedures based on employee work performance data. The performance improvement unit can also analyze employee work performance data and make proposals for improving work efficiency. Furthermore, the performance improvement unit can propose an optimal work environment based on employee work performance data. This can improve employee work performance and support efficient work execution.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The communications department provides a high-speed, stable communications environment. For example, 5G technology can be used to achieve high-speed, stable communications, allowing remote workers to access devices in the office and share data in real time. Remote access protocols can also be used to allow employees to access office servers from home and retrieve the necessary data. Data encryption technology can also be used to ensure secure communications. Step 2: The Business Efficiency Department uses AI technology to streamline business processes. For example, AI can automatically adjust employee schedules and suggest optimal meeting times. Furthermore, AI can analyze employees' calendar information and suggest optimal meeting times. The Business Efficiency Department can also use AI to analyze business data and grasp the progress of work in real time. For example, AI can link with project management tools to monitor the progress of work in real time. Step 3: The device integration unit uses IoT technology to integrate devices in the office. For example, IoT sensors automatically adjust the temperature and lighting in the office to provide a comfortable working environment. Furthermore, IoT sensors use temperature and illuminance sensors to monitor the office environment in real time and automatically adjust the temperature and lighting to the optimum level. The device integration unit also uses IoT devices to monitor energy consumption in the office, creating an eco-friendly office space. For example, IoT devices collect energy consumption data in real time and suggest optimal ways to improve energy efficiency.

[0068] (Example 2) A smart office system according to an embodiment of the present invention utilizes 5G technology to provide a high-speed, stable communication environment and combines AI and IoT technologies to achieve efficient business processing. This smart office system provides a flexible office environment that accommodates both remote and office work, realizing a sustainable office space that is both ecological and efficient. It also optimizes the office environment to enable the provision of high-quality services to customers. For example, by utilizing 5G technology to provide a high-speed, stable communication environment, remote employees can access in-office devices and share data in real time. Next, AI technology is used to improve the efficiency of business processing. For example, AI can automatically adjust employee schedules and suggest optimal meeting times. AI can also analyze business data and grasp the progress of work in real time. This improves business efficiency. Furthermore, IoT technology is used to connect in-office devices. For example, IoT sensors can automatically adjust the temperature and lighting in the office to provide a comfortable working environment. IoT devices can also monitor energy consumption in the office to create an eco-friendly office space. Finally, the office environment is optimized to enable the provision of high-quality services to customers. For example, AI can analyze customer needs and propose optimal services. IoT devices can also detect customer visits and respond quickly, improving customer satisfaction. In this way, the combination of 5G, AI, and IoT technologies can create an efficient and sustainable smart office space, enabling the provision of high-quality services to customers. As a result, smart office systems can provide efficient business processes and sustainable office spaces, enabling the provision of high-quality services to customers.

[0069] A smart office system according to an embodiment includes a communication unit, a business efficiency improvement unit, and a device linkage unit. The communication unit provides a high-speed and stable communication environment. The communication unit, for example, uses 5G technology to achieve high-speed and stable communication. The communication unit enables employees working remotely to access devices in the office and share data in real time. For example, the communication unit uses a remote access protocol to allow employees to access an office server from home and obtain necessary data. The communication unit can also achieve secure communication using data encryption technology. The business efficiency improvement unit uses AI technology to improve the efficiency of business processing. For example, the business efficiency improvement unit uses AI to automatically adjust employees' schedules and suggest optimal meeting times. For example, the AI ​​analyzes employees' calendar information and suggests optimal meeting times. The business efficiency improvement unit can also analyze business data and grasp the progress of work in real time. For example, the AI ​​links with a project management tool to monitor the progress of work in real time. The device linkage unit uses IoT technology to link devices in the office. The device linking unit, for example, uses IoT sensors to automatically adjust the temperature and lighting in the office, providing a comfortable working environment. For example, the IoT sensors use temperature sensors and illuminance sensors to monitor the office environment in real time and automatically adjust the temperature and lighting to the optimum level. The device linking unit also uses IoT devices to monitor energy consumption in the office, creating an ecological office space. For example, the IoT devices collect energy consumption data in real time and propose optimal methods for improving energy efficiency. As a result, the smart office system according to the embodiment can provide a high-speed and stable communication environment, streamline business processing, and link devices in the office.

[0070] The smart office system includes an environmental adjustment unit that uses IoT sensors to adjust the temperature or lighting in the office based on specific technical details. The environmental adjustment unit automatically adjusts the temperature and lighting in the office using the IoT sensors. For example, the environmental adjustment unit uses temperature sensors to monitor the temperature in the office in real time and adjust it to an optimal temperature. For example, temperature sensors are installed in each area of ​​the office and collect temperature data for each area. The environmental adjustment unit controls air conditioners and heaters based on the collected temperature data to adjust the temperature to an optimal level. The environmental adjustment unit also automatically adjusts the lighting in the office using illuminance sensors. For example, illuminance sensors are installed in each area of ​​the office and collect illuminance data for each area. The environmental adjustment unit adjusts the brightness of the lighting based on the collected illuminance data to provide a comfortable working environment. This allows the temperature and lighting in the office to be automatically adjusted and a comfortable working environment to be provided. Some or all of the above-described processing in the environmental adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental adjustment unit can input data obtained from a temperature sensor or illuminance sensor into the generation AI and have the generation AI adjust the temperature and lighting to the optimum level.

[0071] The smart office system includes a service proposal unit that uses AI to analyze customer needs and propose services based on specific technical details. The service proposal unit uses AI to analyze customer needs and propose optimal services. For example, the service proposal unit analyzes the customer's past usage history to understand the customer's needs. For example, the AI ​​analyzes the customer's past purchase history and inquiry history to identify the customer's needs. The service proposal unit also understands the customer's current needs in real time and proposes optimal services. For example, the AI ​​analyzes the customer's current behavioral data and real-time feedback to understand the customer's needs. Furthermore, the service proposal unit proposes optimal services taking into account the customer's geographic location information. For example, the AI ​​proposes nearby stores and services based on the customer's location information. This allows for analyzing customer needs and proposing optimal services, thereby improving customer satisfaction. Some or all of the above-described processing in the service proposal unit may be performed using AI, for example, or without AI. For example, the service proposal unit may input customer usage history data into a generation AI and have the generation AI propose optimal services.

[0072] The smart office system includes an energy monitoring unit in which IoT devices monitor energy consumption based on specific technical details within the office. The energy monitoring unit monitors energy consumption within the office using the IoT devices. For example, the energy monitoring unit collects energy consumption data in real time and proposes optimal methods for improving energy efficiency. For example, the IoT devices collect energy consumption data from each device and send it to the energy monitoring unit. The energy monitoring unit analyzes energy consumption patterns based on the collected data and proposes optimal methods for improving energy efficiency. The energy monitoring unit also analyzes past energy consumption data and automatically proposes optimal methods for improving energy efficiency. For example, the energy monitoring unit proposes optimal energy management methods based on the past energy consumption data, depending on the season and time of day. This allows energy consumption within the office to be monitored, resulting in an eco-friendly office space. Some or all of the above-described processing in the energy monitoring unit may be performed using, or without, AI. For example, the energy monitoring unit may input energy consumption data into a generation AI and have the generation AI propose optimal methods for improving energy efficiency.

[0073] The communications unit enables remote working employees to access in-office devices and share data based on specific technical content. The communications unit enables remote working employees to access in-office devices and share data in real time. For example, the communications unit uses a remote access protocol to allow employees to access in-office servers from home and obtain necessary data. The communications unit can also achieve secure communications using data encryption technology. For example, the communications unit uses a virtual private network (VPN) to allow remote working employees to securely access in-office devices. Furthermore, the communications unit uses remote desktop technology to allow employees to operate in-office computers from home. For example, the communications unit uses the remote desktop protocol (RDP) to allow employees to remotely connect to in-office computers from home and perform their work. This allows remote working employees to access in-office devices and share data in real time.

[0074] In the business efficiency improvement unit, AI adjusts employee schedules based on specific technical details and suggests meeting times. The business efficiency improvement unit automatically adjusts employee schedules using AI and suggests optimal meeting times. For example, the business efficiency improvement unit analyzes employee calendar information and suggests optimal meeting times. For example, the AI ​​identifies each employee's free time based on the employee calendar information and suggests optimal meeting times. The business efficiency improvement unit can also analyze employee workloads and adjust work priorities. For example, the AI ​​automatically adjusts work priorities based on employee work progress data to support efficient work execution. Furthermore, the business efficiency improvement unit can also consider the importance of meetings and the roles of participants when adjusting employee schedules. For example, the AI ​​suggests optimal meeting times based on the importance of meetings and the roles of participants. As a result, the AI ​​automatically adjusts employee schedules and suggests optimal meeting times, thereby improving business efficiency. Some or all of the above-described processing in the business efficiency improvement unit may be performed using AI, for example, or without AI. For example, the business efficiency improvement department can input employee calendar information into the generation AI and have the generation AI suggest optimal meeting times.

[0075] The device linking unit links devices in the office based on specific technical content. The device linking unit links devices in the office using IoT technology. For example, the device linking unit monitors devices in the office in real time using IoT sensors and proposes an optimal linking method. For example, the IoT sensors monitor the operating status of each device in real time and send the data to the device linking unit. The device linking unit optimizes the linkage between devices based on the collected data. The device linking unit can also apply an algorithm to optimize the data transfer speed between devices. For example, the device linking unit monitors the data transfer speed between devices in real time and applies an optimal algorithm. Furthermore, the device linking unit can set an optimal linking method taking into account device location information. For example, the device linking unit sets an optimal linking method based on device location information. This enables efficient business processing by linking devices in the office using IoT technology. Some or all of the above-mentioned processing in the device linking unit may be performed using AI, for example, or without AI. For example, the device linking unit can input device operating status data to a generation AI and have the generation AI propose an optimal linking method.

[0076] The communication unit estimates the user's emotion and adjusts the priority of communications based on the estimated user emotion and specific technical content. The communication unit estimates the user emotion and adjusts the priority of communications based on the estimated user emotion. For example, the communication unit analyzes the user's facial expressions and voice to estimate the emotion. For example, the communication unit uses a camera to analyze the user's facial expressions in real time to estimate the emotion. The communication unit can also analyze the user's voice using a microphone to estimate the emotion. For example, the communication unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the communication unit adjusts the priority of communications based on the estimated emotion. For example, if the user is feeling stressed, important communications are prioritized and unnecessary notifications are suppressed. Also, if the user is relaxed, normal communication priority can be maintained. In this way, by adjusting the priority of communications according to the user's emotion, important communications can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0077] The communication unit monitors communication stability based on specific technical content, and selects a communication path based on the specific technical content when communication quality deteriorates. The communication unit monitors communication stability in real time, and automatically selects an optimal communication path when communication quality deteriorates. For example, the communication unit monitors communication quality in real time and detects communication delays and packet loss. For example, the communication unit monitors communication quality in real time using a network monitoring tool. Furthermore, the communication unit automatically selects an optimal communication path when communication quality deteriorates. For example, the communication unit automatically selects an optimal Wi-Fi network when communication quality deteriorates. Furthermore, the communication unit can switch to an optimal mobile data communication when communication is interrupted. Furthermore, the communication unit can select an optimal communication protocol when communication delays occur. As a result, a stable communication environment can be maintained by automatically selecting an optimal communication path when communication quality deteriorates. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input communication quality data into the generation AI and have the generation AI select the optimal communication route.

[0078] The communication unit selects a communication protocol based on specific technical details according to the type of communication data. The communication unit automatically selects the optimal communication protocol according to the type of communication data. For example, the communication unit selects a low-latency communication protocol for a video conference. For example, the communication unit automatically selects a low-latency communication protocol during a video conference to achieve smooth communication. The communication unit can also select a high-speed communication protocol for a file transfer. For example, the communication unit automatically selects a high-speed communication protocol during a large-capacity file transfer to achieve rapid file transfer. The communication unit can also select a lightweight communication protocol for messaging. For example, the communication unit automatically selects a lightweight communication protocol when sending and receiving text messages to achieve efficient communication. This enables efficient communication by selecting the optimal communication protocol according to the type of communication data. Some or all of the above-described processing in the communication unit may be performed using, or without, AI. For example, the communication unit may input the type of communication data to a generation AI and cause the generation AI to select the optimal communication protocol.

[0079] The communication unit estimates the user's emotion and adjusts the communication bandwidth based on the estimated user emotion and specific technical content. The communication unit estimates the user emotion and dynamically adjusts the communication bandwidth based on the estimated user emotion. For example, the communication unit analyzes the user's facial expressions and voice to estimate the emotion. For example, the communication unit uses a camera to analyze the user's facial expressions in real time to estimate the emotion. The communication unit can also analyze the user's voice using a microphone to estimate the emotion. For example, the communication unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the communication unit dynamically adjusts the communication bandwidth based on the estimated emotion. For example, if the user is stressed, the communication unit can prioritize bandwidth allocation to important communications. Alternatively, if the user is relaxed, the communication unit can maintain normal bandwidth allocation. In this way, by dynamically adjusting the communication bandwidth according to the user's emotion, the communication bandwidth can be prioritized and allocated to important communications. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0080] The communication unit sets a communication route based on specific technical details, taking into account the location information of the device in the office. The communication unit sets an optimal communication route, taking into account the location information of the device in the office. For example, the communication unit selects an optimal Wi-Fi access point based on the device's location information. For example, the communication unit acquires device location information in real time and automatically selects an optimal Wi-Fi access point. The communication unit can also set an optimal Bluetooth connection based on the device's location information. For example, the communication unit automatically sets an optimal Bluetooth connection based on the device's location information. The communication unit can also set an optimal wired connection based on the device's location information. For example, the communication unit automatically sets an optimal wired connection based on the device's location information. This enables efficient communication by setting an optimal communication route based on the device's location information. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit may input device location information data to a generation AI and cause the generation AI to set an optimal communication route.

[0081] The communication unit cooperates with an external cloud service to back up data based on specific technical details. The communication unit cooperates with the external cloud service to automatically back up data. For example, the communication unit periodically backs up data to the cloud service. For example, the communication unit backs up data to the cloud service at a fixed time every day. The communication unit can also immediately back up important data to the cloud service when it is updated. For example, the communication unit backs up data to the cloud service in real time when the data is updated. Furthermore, the communication unit can also back up data to the cloud service when communication quality is stable. For example, the communication unit selects a time period when communication quality is stable and backs up data to the cloud service during that time period. This improves data security by automatically backing up data in cooperation with an external cloud service. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input a data backup schedule to the generation AI and have the generation AI suggest the optimal backup timing.

[0082] The business efficiency improvement unit estimates the user's emotions and adjusts the priority of tasks based on the estimated user emotions and specific technical content. The business efficiency improvement unit estimates the user's emotions and adjusts the priority of tasks based on the estimated user emotions. For example, the business efficiency improvement unit analyzes the user's facial expressions and voice to estimate emotions. For example, the business efficiency improvement unit uses a camera to analyze the user's facial expressions in real time to estimate emotions. The business efficiency improvement unit can also analyze the user's voice using a microphone to estimate emotions. For example, the business efficiency improvement unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the business efficiency improvement unit adjusts the priority of tasks based on the estimated emotions. For example, if the user is feeling stressed, important tasks can be prioritized and unnecessary tasks can be postponed. Also, if the user is relaxed, normal business priorities can be maintained. In this way, by adjusting the priority of tasks according to the user's emotions, important tasks can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the business efficiency improvement unit may be performed using AI, or may be performed without using AI. For example, the business efficiency improvement unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0083] The business efficiency improvement unit analyzes employees' past work histories and proposes business processes based on specific technical content. The business efficiency improvement unit analyzes employees' past work histories and proposes optimal business processes. For example, the business efficiency improvement unit proposes an optimal task order based on the past work histories. For example, the business efficiency improvement unit analyzes employees' past work histories and proposes an efficient task order. The business efficiency improvement unit can also propose efficient business procedures based on the past work histories. For example, the business efficiency improvement unit analyzes employees' past work histories and proposes optimal business procedures. Furthermore, the business efficiency improvement unit can also propose optimal task allocation based on the past work histories. For example, the business efficiency improvement unit analyzes employees' past work histories and proposes efficient task allocation. In this way, by analyzing employees' past work histories, optimal business processes are proposed and business efficiency is improved. Some or all of the above-mentioned processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the business efficiency improvement unit can input employees' work history data into a generation AI and have the generation AI execute a proposal for an optimal business process.

[0084] The business efficiency improvement unit monitors the progress of work based on specific technical details, and when a delay occurs, proposes a recovery plan based on the specific technical details. The business efficiency improvement unit monitors the progress of work in real time, and automatically proposes a recovery plan when a delay occurs. For example, the business efficiency improvement unit monitors the progress of work in real time, and proposes an optimal recovery plan when a delay occurs. For example, the business efficiency improvement unit works in conjunction with a project management tool to monitor the progress of work in real time. The business efficiency improvement unit can also propose resource reallocation when a delay occurs. For example, the business efficiency improvement unit proposes resource reallocation when a delay occurs, thereby minimizing work delays. Furthermore, the business efficiency improvement unit can also propose schedule readjustment when a delay occurs. For example, the business efficiency improvement unit proposes schedule readjustment when a delay occurs, thereby smoothly progressing work progress. In this way, by monitoring the progress of work in real time and automatically proposing a recovery plan when a delay occurs, it is possible to minimize work delays. Some or all of the above-described processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the business efficiency improvement department can input business progress data into the generation AI and have the generation AI propose an optimal recovery plan.

[0085] The work efficiency improvement unit estimates the user's emotions and adjusts task allocation based on the estimated user emotions and specific technical content. The work efficiency improvement unit estimates the user's emotions and adjusts task allocation based on the estimated user emotions. For example, the work efficiency improvement unit analyzes the user's facial expressions and voice to estimate emotions. For example, the work efficiency improvement unit uses a camera to analyze the user's facial expressions in real time to estimate emotions. The work efficiency improvement unit can also analyze the user's voice using a microphone to estimate emotions. For example, the work efficiency improvement unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the work efficiency improvement unit adjusts task allocation based on the estimated emotions. For example, if the user is feeling stressed, important tasks can be prioritized and unnecessary tasks can be postponed. Also, if the user is relaxed, normal task allocation can be maintained. In this way, by adjusting task allocation according to the user's emotions, important tasks can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the business efficiency improvement unit may be performed using AI, or may be performed without using AI. For example, the business efficiency improvement unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0086] The business efficiency improvement unit monitors device usage in the office and allocates devices based on specific technical details. The business efficiency improvement unit monitors device usage in the office and allocates optimal devices. For example, the business efficiency improvement unit monitors device usage in real time and allocates optimal devices. For example, the business efficiency improvement unit monitors the usage of each device in real time and understands the frequency of use and operating status. The business efficiency improvement unit can also analyze device usage and propose efficient device allocation. For example, the business efficiency improvement unit proposes optimal device placement based on device usage. Furthermore, the business efficiency improvement unit can propose optimal device placement based on device usage. For example, the business efficiency improvement unit proposes optimal device placement based on device usage. In this way, by monitoring device usage in the office, optimal device allocation is achieved, thereby improving business efficiency. Some or all of the above-mentioned processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without AI. For example, the business efficiency improvement unit may input device usage data into a generation AI and have the generation AI execute optimal device allocation.

[0087] The Business Efficiency Improvement Department works in conjunction with an external project management tool to manage the progress of work based on specific technical content. The Business Efficiency Improvement Department works in conjunction with an external project management tool to centrally manage the progress of work. For example, the Business Efficiency Improvement Department works in conjunction with an external project management tool to grasp the progress of work in real time. For example, the Business Efficiency Improvement Department works in conjunction with a project management tool to monitor the progress of each project in real time. The Business Efficiency Improvement Department can also work in conjunction with an external project management tool to centrally manage the progress of work. For example, the Business Efficiency Improvement Department works in conjunction with a project management tool to centrally manage the progress of each project. Furthermore, the Business Efficiency Improvement Department can also work in conjunction with an external project management tool to visualize the progress of work. For example, the Business Efficiency Improvement Department works in conjunction with a project management tool to display the progress of each project as graphs and charts, providing it in a visually easy-to-understand format. In this way, by working in conjunction with an external project management tool, business progress can be centrally managed and efficient business management is possible. Some or all of the above-described processing in the business efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the business efficiency improvement unit may input data from a project management tool into the generation AI and have the generation AI perform business progress management.

[0088] The device collaboration unit estimates the user's emotion and adjusts the device collaboration method based on the estimated user emotion and specific technical content. The device collaboration unit estimates the user's emotion and adjusts the device collaboration method based on the estimated user emotion. For example, the device collaboration unit analyzes the user's facial expressions and voice to estimate the emotion. For example, the device collaboration unit uses a camera to analyze the user's facial expressions in real time to estimate the emotion. The device collaboration unit can also analyze the user's voice using a microphone to estimate the emotion. For example, the device collaboration unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the device collaboration unit adjusts the device collaboration method based on the estimated emotion. For example, if the user is stressed, the device collaboration unit prioritizes collaboration of important devices and suppresses unnecessary collaboration. Alternatively, if the user is relaxed, the device collaboration unit can maintain normal device collaboration. In this way, by adjusting the device collaboration method according to the user's emotion, the device collaboration unit can prioritize collaboration of important devices. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the device cooperation unit may be performed using AI, or may be performed without using AI. For example, the device cooperation unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0089] The device collaboration unit monitors the operating status of devices in the office based on specific technical details and proposes an integration method. The device collaboration unit monitors the operating status of devices in the office in real time and proposes an optimal integration method. For example, the device collaboration unit monitors the operating status of devices in real time and proposes an optimal integration method. For example, the device collaboration unit monitors the operating status of each device in real time and understands the frequency of use and operating status. The device collaboration unit can also analyze the operating status of devices and propose an efficient integration method. For example, the device collaboration unit proposes an optimal integration procedure based on the operating status of the devices. Furthermore, the device collaboration unit can propose an optimal integration procedure based on the operating status of the devices. For example, the device collaboration unit proposes an optimal integration procedure based on the operating status of the devices. In this way, by monitoring the operating status of devices in real time, an optimal integration method is proposed and efficient device collaboration is possible. Some or all of the above-mentioned processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit can input device operating status data to a generation AI and have the generation AI propose an optimal integration method.

[0090] The device collaboration unit applies an algorithm for adjusting the data transfer rate between devices based on specific technical content. The device collaboration unit applies an algorithm for optimizing the data transfer rate between devices. For example, the device collaboration unit monitors the data transfer rate between devices in real time and applies an optimal algorithm. For example, the device collaboration unit monitors the data transfer rate between devices in real time and applies an optimal algorithm. The device collaboration unit can also analyze the data transfer rate between devices and apply an efficient algorithm. For example, the device collaboration unit proposes an optimal data transfer procedure based on the data transfer rate between devices. Furthermore, the device collaboration unit can propose an optimal data transfer procedure based on the data transfer rate between devices. For example, the device collaboration unit proposes an optimal data transfer procedure based on the data transfer rate between devices. This enables efficient data transfer by optimizing the data transfer rate between devices. Some or all of the above-described processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit may input data on the data transfer rate between devices to a generation AI and cause the generation AI to propose an optimal data transfer procedure.

[0091] The device linking unit estimates a user's emotion and adjusts the device linking order based on the estimated user emotion and specific technical content. The device linking unit estimates a user's emotion and adjusts the device linking order based on the estimated user emotion. For example, the device linking unit analyzes the user's facial expressions and voice to estimate the emotion. For example, the device linking unit uses a camera to analyze the user's facial expressions in real time to estimate the emotion. The device linking unit can also analyze the user's voice using a microphone to estimate the emotion. For example, the device linking unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the device linking unit adjusts the device linking order based on the estimated emotion. For example, if the user is stressed, the device linking unit prioritizes the linking of important devices and suppresses unnecessary linking. Alternatively, if the user is relaxed, the device linking unit can maintain the normal device linking order. In this way, by adjusting the device linking order according to the user's emotion, the device linking order can prioritize the linking of important devices. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the device cooperation unit may be performed using AI, or may be performed without using AI. For example, the device cooperation unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0092] The device collaboration unit sets an collaboration method based on specific technical details, taking into account the location information of devices in the office. The device collaboration unit sets an optimal collaboration method, taking into account the location information of devices in the office. For example, the device collaboration unit sets an optimal collaboration method based on the device location information. For example, the device collaboration unit acquires device location information in real time and automatically sets an optimal collaboration method. The device collaboration unit can also analyze device location information and set an efficient collaboration method. For example, the device collaboration unit sets an optimal collaboration procedure based on the device location information. Furthermore, the device collaboration unit can also set an optimal collaboration procedure based on the device location information. For example, the device collaboration unit sets an optimal collaboration procedure based on the device location information. This enables efficient device collaboration by setting an optimal collaboration method based on the device location information. Some or all of the above-described processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit may input device location information data to a generation AI and cause the generation AI to propose an optimal collaboration procedure.

[0093] The device collaboration unit collaborates with an external IoT platform to manage devices based on specific technical details. The device collaboration unit collaborates with an external IoT platform to centralize device management. For example, the device collaboration unit collaborates with an external IoT platform to centralize device management. For example, the device collaboration unit collaborates with an external IoT platform to grasp the operating status of each device in real time. The device collaboration unit can also collaborate with an external IoT platform to centrally manage device settings. For example, the device collaboration unit collaborates with an external IoT platform to centrally manage the settings of each device. Furthermore, the device collaboration unit can collaborate with an external IoT platform to monitor the operating status of devices in real time and propose an optimal management method. For example, the device collaboration unit collaborates with an external IoT platform to propose an optimal management method based on the operating status of each device. As a result, collaboration with an external IoT platform centralizes device management and enables efficient device management. Some or all of the above-described processing in the device collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the device collaboration unit can input device operating status data into the generation AI and have the generation AI propose the optimal management method.

[0094] The environment adjustment unit estimates the user's emotion and adjusts the temperature or lighting based on the estimated user emotion. The environment adjustment unit estimates the user's emotion and adjusts the temperature or lighting based on the estimated user emotion. For example, the environment adjustment unit analyzes the user's facial expression and voice to estimate the emotion. For example, the environment adjustment unit uses a camera to analyze the user's facial expression in real time to estimate the emotion. The environment adjustment unit can also analyze the user's voice using a microphone to estimate the emotion. For example, the environment adjustment unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the environment adjustment unit adjusts the temperature and lighting based on the estimated emotion. For example, if the user is feeling stressed, the temperature and lighting can be adjusted to a relaxing level. Alternatively, if the user is relaxed, the temperature and lighting can be maintained at normal levels. This allows for a comfortable work environment by adjusting the temperature and lighting according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the environment adjustment unit may be performed using AI, or may be performed without using AI. For example, the environment adjustment unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0095] The environmental adjustment unit monitors environmental data within the office in real time and proposes environmental settings based on specific technical details. The environmental adjustment unit monitors environmental data within the office in real time and proposes optimal environmental settings. For example, the environmental adjustment unit monitors environmental data in real time and proposes optimal temperature and lighting. For example, the environmental adjustment unit uses a temperature sensor and an illuminance sensor to collect environmental data within the office in real time and proposes optimal temperature and lighting. The environmental adjustment unit can also analyze the environmental data and propose efficient environmental settings. For example, the environmental adjustment unit proposes optimal environmental settings based on the collected environmental data. Furthermore, the environmental adjustment unit can propose optimal environmental settings based on the environmental data. For example, the environmental adjustment unit proposes optimal environmental settings based on the collected environmental data. In this way, by monitoring environmental data within the office in real time, optimal environmental settings can be proposed and a comfortable work environment can be provided. Some or all of the above-mentioned processing in the environmental adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the environmental adjustment unit can input environmental data to a generation AI and have the generation AI propose optimal environmental settings.

[0096] The environment adjustment unit analyzes past environmental data and sets the environment based on specific technical details corresponding to the season and time of day. The environment adjustment unit analyzes past environmental data and automatically sets the optimal environment setting according to the season and time of day. For example, the environment adjustment unit sets the optimal temperature and lighting according to the season based on the past environmental data. For example, the environment adjustment unit analyzes past environmental data and sets the optimal temperature and lighting according to the season. The environment adjustment unit can also set the optimal environment setting according to the time of day based on the past environmental data. For example, the environment adjustment unit analyzes past environmental data and sets the optimal environment setting according to the time of day. Furthermore, the environment adjustment unit can automatically set an efficient environment setting based on the past environmental data. For example, the environment adjustment unit automatically sets an efficient environment setting based on the past environmental data. In this way, by analyzing past environmental data, the optimal environment setting according to the season and time of day can be automatically set, thereby providing a comfortable working environment. Some or all of the above-mentioned processing in the environment adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the environmental adjustment unit can input past environmental data into the generation AI and have the generation AI propose optimal environmental settings.

[0097] The environment adjustment unit estimates the user's emotions and determines the priority of environment settings based on the estimated user emotions and specific technical content. The environment adjustment unit estimates the user's emotions and determines the priority of environment settings based on the estimated user emotions. For example, the environment adjustment unit analyzes the user's facial expressions and voice to estimate emotions. For example, the environment adjustment unit uses a camera to analyze the user's facial expressions in real time to estimate emotions. The environment adjustment unit can also analyze the user's voice using a microphone to estimate emotions. For example, the environment adjustment unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the environment adjustment unit determines the priority of environment settings based on the estimated emotions. For example, if the user is feeling stressed, the environment adjustment unit can prioritize relaxing environment settings. Also, if the user is relaxed, the normal environment settings can be maintained. In this way, by determining the priority of environment settings according to the user's emotions, important environment settings can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the environment adjustment unit may be performed using AI, or may be performed without using AI. For example, the environment adjustment unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0098] The environment adjustment unit sets the environment based on specific technical details, taking into account the device usage status in the office. The environment adjustment unit sets the optimal environment setting, taking into account the device usage status in the office. For example, the environment adjustment unit monitors device usage status in real time and sets the optimal temperature and lighting. For example, the environment adjustment unit monitors the usage status of each device in real time and sets the optimal temperature and lighting. The environment adjustment unit can also analyze device usage status and propose efficient environment settings. For example, the environment adjustment unit proposes optimal environment settings based on device usage status. Furthermore, the environment adjustment unit can also set the optimal environment setting based on device usage status. For example, the environment adjustment unit sets the optimal environment setting based on device usage status. This enables efficient environment management by setting the optimal environment setting, taking into account the device usage status. Some or all of the above-mentioned processing in the environment adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the environment adjustment unit can input device usage status data to a generation AI and have the generation AI propose optimal environment settings.

[0099] The environmental adjustment unit cooperates with external environmental data to adjust the environmental settings within the office based on specific technical details. The environmental adjustment unit cooperates with external environmental data to optimize the environmental settings within the office. For example, the environmental adjustment unit sets optimal temperature and lighting based on external weather data. For example, the environmental adjustment unit acquires external weather data in real time and sets optimal temperature and lighting. The environmental adjustment unit can also propose efficient environmental settings based on external air quality data. For example, the environmental adjustment unit proposes optimal environmental settings based on external air quality data. Furthermore, the environmental adjustment unit can also set optimal environmental settings based on external environmental data. For example, the environmental adjustment unit sets optimal environmental settings based on external environmental data. In this way, by cooperating with external environmental data, the environmental settings within the office can be optimized and a comfortable working environment can be provided. Some or all of the above-mentioned processing in the environmental adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the environmental adjustment unit can input external environmental data into a generation AI and have the generation AI execute a proposal for optimal environmental settings.

[0100] The service proposal unit estimates the user's emotions and adjusts the service proposal method based on the estimated user emotions and specific technical content. The service proposal unit estimates the user's emotions and adjusts the service proposal method based on the estimated user emotions. For example, the service proposal unit analyzes the user's facial expressions and voice to estimate the emotions. For example, the service proposal unit uses a camera to analyze the user's facial expressions in real time to estimate the emotions. The service proposal unit can also analyze the user's voice using a microphone to estimate the emotions. For example, the service proposal unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the service proposal unit adjusts the service proposal method based on the estimated emotions. For example, if the user is feeling stressed, the service proposal unit can make simple and intuitive service proposals. On the other hand, if the user is relaxed, the service proposal unit can make detailed service proposals. This enables more appropriate service proposals by adjusting the service proposal method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the service suggestion unit may be performed using AI, or may be performed without using AI. For example, the service suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0101] The service proposal unit analyzes the customer's past usage history and proposes a service based on specific technical content. The service proposal unit analyzes the customer's past usage history and proposes an optimal service. For example, the service proposal unit proposes an optimal service based on the customer's past usage history. For example, the service proposal unit analyzes the customer's past purchase history and inquiry history and proposes an optimal service. The service proposal unit can also analyze the customer's past usage history and make efficient service proposals. For example, the service proposal unit proposes efficient services based on the customer's past usage history. Furthermore, the service proposal unit can also propose customized services based on the customer's past usage history. For example, the service proposal unit proposes customized services based on the customer's past usage history. In this way, by analyzing the customer's past usage history, optimal services can be proposed and customer satisfaction can be improved. Some or all of the above-mentioned processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit can input the customer's usage history data into a generation AI and cause the generation AI to propose an optimal service.

[0102] The service proposal unit grasps the customer's current needs in real time and proposes a service based on specific technical content. The service proposal unit grasps the customer's current needs in real time and proposes an optimal service. For example, the service proposal unit grasps the customer's current needs in real time and proposes an optimal service. For example, the service proposal unit analyzes the customer's current behavioral data and real-time feedback to propose an optimal service. The service proposal unit can also analyze the customer's current needs and make an efficient service proposal. For example, the service proposal unit makes an efficient service proposal based on the customer's current needs. Furthermore, the service proposal unit can also propose a customized service based on the customer's current needs. For example, the service proposal unit proposes a customized service based on the customer's current needs. In this way, by grasping the customer's current needs in real time, the optimal service can be proposed and customer satisfaction can be improved. Some or all of the above-mentioned processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit can input the customer's current needs data into a generation AI and cause the generation AI to propose an optimal service.

[0103] The service proposal unit estimates the user's emotions and determines the priority of services based on the estimated user emotions and specific technical content. The service proposal unit estimates the user's emotions and determines the priority of services based on the estimated user emotions. For example, the service proposal unit analyzes the user's facial expressions and voice to estimate the emotions. For example, the service proposal unit uses a camera to analyze the user's facial expressions in real time to estimate the emotions. The service proposal unit can also analyze the user's voice using a microphone to estimate the emotions. For example, the service proposal unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the service proposal unit determines the priority of services based on the estimated emotions. For example, if the user is feeling stressed, important services can be prioritized and suggested. Also, if the user is relaxed, normal service suggestions can be maintained. In this way, by determining the priority of services according to the user's emotions, important services can be prioritized and suggested. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the service suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the service suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] The service proposal unit proposes a service based on specific technical content, taking into account the customer's geographical location information. The service proposal unit proposes an optimal service, taking into account the customer's geographical location information. For example, the service proposal unit proposes an optimal service based on the customer's geographical location information. For example, the service proposal unit acquires the customer's geographical location information in real time and proposes an optimal service. The service proposal unit can also analyze the customer's geographical location information and make efficient service proposals. For example, the service proposal unit proposes efficient services based on the customer's geographical location information. Furthermore, the service proposal unit can also propose customized services based on the customer's geographical location information. For example, the service proposal unit proposes customized services based on the customer's geographical location information. This makes it possible to propose optimal services and improve customer satisfaction by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit may input the customer's geographical location information data into a generation AI and cause the generation AI to propose an optimal service.

[0105] The service proposal unit cooperates with an external customer management system to adjust service proposals based on specific technical content. The service proposal unit cooperates with an external customer management system to optimize service proposals. For example, the service proposal unit cooperates with an external customer management system to grasp customer information in real time. For example, the service proposal unit cooperates with an external customer management system to acquire customer information in real time and propose optimal services. The service proposal unit can also cooperate with an external customer management system to make efficient service proposals. For example, the service proposal unit cooperates with an external customer management system to make efficient service proposals based on customer information. Furthermore, the service proposal unit can also cooperate with an external customer management system to propose customized services. For example, the service proposal unit cooperates with an external customer management system to propose customized services based on customer information. By cooperating with an external customer management system, service proposals can be optimized and customer satisfaction can be improved. Some or all of the above-described processing in the service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the service proposal unit can input data from a customer management system into the generation AI and have the generation AI propose the most appropriate service.

[0106] The energy monitoring unit estimates the user's emotions and adjusts the energy consumption monitoring method based on the estimated user emotions and specific technical content. The energy monitoring unit estimates the user's emotions and adjusts the energy consumption monitoring method based on the estimated user emotions. For example, the energy monitoring unit analyzes the user's facial expressions and voice to estimate emotions. For example, the energy monitoring unit uses a camera to analyze the user's facial expressions in real time to estimate emotions. The energy monitoring unit can also analyze the user's voice using a microphone to estimate emotions. For example, the energy monitoring unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the energy monitoring unit adjusts the energy consumption monitoring method based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize monitoring important energy consumption. Alternatively, if the user is relaxed, it can maintain normal energy consumption monitoring. In this way, by adjusting the energy consumption monitoring method according to the user's emotions, it is possible to prioritize monitoring important energy consumption. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the energy monitoring unit may be performed using AI, or may be performed without using AI. For example, the energy monitoring unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0107] The energy monitoring unit monitors energy consumption data within the office in real time and proposes an energy management method based on specific technical details. The energy monitoring unit monitors energy consumption data within the office in real time and proposes an optimal energy management method. For example, the energy monitoring unit monitors energy consumption data in real time and proposes an optimal energy management method. For example, the energy monitoring unit monitors energy consumption data of each device in real time and proposes an optimal energy management method. The energy monitoring unit can also analyze the energy consumption data and propose an efficient energy management method. For example, the energy monitoring unit proposes an optimal energy management procedure based on the energy consumption data. Furthermore, the energy monitoring unit can propose an optimal energy management procedure based on the energy consumption data. For example, the energy monitoring unit proposes an optimal energy management procedure based on the energy consumption data. In this way, by monitoring energy consumption data within the office in real time, an optimal energy management method can be proposed and energy efficiency can be improved. Some or all of the above-mentioned processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input energy consumption data to a generation AI and have the generation AI propose an optimal energy management procedure.

[0108] The energy monitoring unit analyzes past energy consumption data and proposes a method based on specific technical details for improving energy efficiency. The energy monitoring unit analyzes past energy consumption data and automatically proposes an optimal method for improving energy efficiency. For example, the energy monitoring unit proposes an optimal energy management method based on past energy consumption data. For example, the energy monitoring unit analyzes past energy consumption data and proposes an optimal energy management method. The energy monitoring unit can also propose an efficient energy management method based on past energy consumption data. For example, the energy monitoring unit proposes an efficient energy management method based on past energy consumption data. Furthermore, the energy monitoring unit can also propose an optimal energy management procedure based on past energy consumption data. For example, the energy monitoring unit proposes an optimal energy management procedure based on past energy consumption data. In this way, by analyzing past energy consumption data, an optimal method for improving energy efficiency can be automatically proposed and energy consumption can be optimized. Some or all of the above-described processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input past energy consumption data into a generation AI and have the generation AI execute a proposal for an optimal energy management procedure.

[0109] The energy monitoring unit estimates the user's emotions and determines the priorities of energy consumption based on the estimated user emotions and specific technical details. The energy monitoring unit estimates the user's emotions and determines the priorities of energy consumption based on the estimated user emotions. For example, the energy monitoring unit analyzes the user's facial expressions and voice to estimate emotions. For example, the energy monitoring unit uses a camera to analyze the user's facial expressions in real time to estimate emotions. The energy monitoring unit can also analyze the user's voice using a microphone to estimate emotions. For example, the energy monitoring unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the energy monitoring unit determines the priorities of energy consumption based on the estimated emotions. For example, if the user is feeling stressed, important energy consumption is prioritized for monitoring. Also, if the user is relaxed, normal energy consumption monitoring can be maintained. In this way, by determining the priorities of energy consumption according to the user's emotions, important energy consumption can be prioritized for monitoring. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the energy monitoring unit may be performed using AI, or may be performed without using AI. For example, the energy monitoring unit may input user facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0110] The energy monitoring unit sets an energy management method based on specific technical details, taking into account the device usage status in the office. The energy monitoring unit sets an optimal energy management method, taking into account the device usage status in the office. For example, the energy monitoring unit monitors device usage status in real time and sets an optimal energy management method. For example, the energy monitoring unit monitors the usage status of each device in real time and sets an optimal energy management method. The energy monitoring unit can also analyze device usage status and set an efficient energy management method. For example, the energy monitoring unit sets an optimal energy management procedure based on the device usage status. Furthermore, the energy monitoring unit can also set an optimal energy management procedure based on the device usage status. For example, the energy monitoring unit sets an optimal energy management procedure based on the device usage status. This enables efficient energy management by setting an optimal energy management method, taking into account the device usage status. Some or all of the above-described processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input device usage status data to a generation AI and have the generation AI propose an optimal energy management procedure.

[0111] The energy monitoring unit cooperates with an external energy management system to adjust energy consumption based on specific technical details. The energy monitoring unit cooperates with an external energy management system to optimize energy consumption. For example, the energy monitoring unit cooperates with an external energy management system to optimize energy consumption. For example, the energy monitoring unit cooperates with an external energy management system to grasp energy consumption data in real time. The energy monitoring unit can also cooperate with an external energy management system to propose an efficient energy management method. For example, the energy monitoring unit cooperates with an external energy management system to propose an optimal energy management procedure based on the energy consumption data. Furthermore, the energy monitoring unit can also cooperate with an external energy management system to propose an optimal energy management procedure based on the energy consumption data. For example, the energy monitoring unit cooperates with an external energy management system to propose an optimal energy management procedure based on the energy consumption data. In this way, by cooperating with an external energy management system, energy consumption can be optimized and energy efficiency can be improved. Some or all of the above-described processing in the energy monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the energy monitoring unit can input energy consumption data into the generation AI and have the generation AI execute a proposal for optimal energy management procedures. === Hard Collateral 1-1 === Each of the multiple elements including the communication unit, business efficiency improvement unit, device cooperation unit, environment adjustment unit, service proposal unit, and energy monitoring unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the communication unit is realized by the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing device 12. The business efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12. The device cooperation unit is realized by the control unit 46A of the smart device 14. The environment adjustment unit is realized by the sensor and control unit 46A of the smart device 14. The service proposal unit is realized by the specific processing unit 290 of the data processing device 12. The energy monitoring unit is realized by the sensor and control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned communication unit, business efficiency improvement unit, device cooperation unit, environment adjustment unit, service proposal unit, and energy monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the communication unit is realized by the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing device 12. The business efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12. The device cooperation unit is realized by the control unit 46A of the smart glasses 214. The environment adjustment unit is realized by the sensor and control unit 46A of the smart glasses 214. The service proposal unit is realized by the specific processing unit 290 of the data processing device 12. The energy monitoring unit is realized by the sensor and control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned communication unit, business efficiency improvement unit, device cooperation unit, environment adjustment unit, service proposal unit, and energy monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the communication unit is realized by the communication I / F 44 of the headset type terminal 314 and the communication I / F 26 of the data processing device 12. The business efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12. The device cooperation unit is realized by the control unit 46A of the headset type terminal 314. The environment adjustment unit is realized by the sensor and control unit 46A of the headset type terminal 314. The service proposal unit is realized by the specific processing unit 290 of the data processing device 12. The energy monitoring unit is realized by the sensor and control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned communication unit, business efficiency improvement unit, device cooperation unit, environment adjustment unit, service proposal unit, and energy monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the communication unit is realized by the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing device 12. The business efficiency improvement unit is realized by the specific processing unit 290 of the data processing device 12. The device cooperation unit is realized by the control unit 46A of the robot 414. The environment adjustment unit is realized by the sensor and control unit 46A of the robot 414. The service proposal unit is realized by the specific processing unit 290 of the data processing device 12. The energy monitoring unit is realized by the sensor and control unit 46A of the robot 414.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] Smart office systems can also be equipped with a health management unit that monitors employees' health. The health management unit collects employees' vital signs in real time and monitors their health. For example, the health management unit can monitor employees' heart rate and blood pressure and issue alerts if any abnormalities are detected. The health management unit can also analyze employees' stress levels and suggest appropriate breaks. Furthermore, the health management unit can record employees' exercise levels and recommend activities to maintain their health. This allows for real-time monitoring of employees' health and appropriate responses, thereby providing a healthy work environment.

[0114] The smart office system can further include a music adjustment unit that estimates the emotions of employees and adjusts the music in the office based on the estimated emotions. The music adjustment unit analyzes the facial expressions and voices of employees to estimate their emotions. For example, if an employee is feeling stressed, it can play relaxing music. Alternatively, if an employee is relaxed, it can play music that helps employees concentrate. Furthermore, the music adjustment unit can adjust the volume and sound quality according to the employee's emotions. This makes it possible to create a comfortable working environment by providing a music environment that suits the employee's emotions.

[0115] The smart office system can further include a productivity improvement unit to improve employee productivity. The productivity improvement unit analyzes employees' work patterns and suggests optimal work methods. For example, the productivity improvement unit analyzes employees' work history and suggests efficient work procedures. The productivity improvement unit can also make suggestions to optimize employees' work environments. Furthermore, the productivity improvement unit can monitor employees' work efficiency in real time and make suggestions for improvement as needed. This can improve employee productivity and support efficient work execution.

[0116] The smart office system can further include a scent adjustment unit that estimates the emotions of employees and adjusts the scent in the office based on the estimated emotions. The scent adjustment unit analyzes the facial expressions and voice of employees to estimate their emotions. For example, if an employee is feeling stressed, it can emit a relaxing scent. Alternatively, if an employee is relaxed, it can emit a scent that improves concentration. Furthermore, the scent adjustment unit can adjust the strength of the scent according to the employee's emotions. This makes it possible to create a comfortable working environment by providing a scent environment that suits the employee's emotions.

[0117] The smart office system can further include a lighting color adjustment unit that estimates the emotions of employees and adjusts the lighting color in the office based on the estimated emotions. The lighting color adjustment unit analyzes the facial expressions and voice of employees to estimate their emotions. For example, if an employee is feeling stressed, the lighting can be adjusted to warm colors that help them relax. Alternatively, if an employee is relaxed, the lighting can be adjusted to cool colors that help them concentrate. Furthermore, the lighting color adjustment unit can adjust the brightness of the lighting according to the emotions of the employee. This makes it possible to provide a lighting environment that suits the emotions of employees, thereby creating a comfortable working environment.

[0118] The smart office system can also be equipped with a task management unit to improve employee work efficiency. The task management unit monitors employee tasks in real time and performs efficient task management. For example, the task management unit grasps the progress of employee tasks in real time and proposes a recovery plan if a delay occurs. The task management unit can also automatically adjust the priority of employee tasks to support efficient task management. Furthermore, the task management unit can make suggestions to optimize the allocation of employee tasks. This improves employee work efficiency and supports efficient business execution.

[0119] The smart office system can further include a device priority adjustment unit that estimates an employee's emotions and adjusts the priorities for device usage in the office based on the estimated emotions. The device priority adjustment unit analyzes the employee's facial expressions and voice to estimate emotions. For example, if an employee is feeling stressed, it can prioritize the use of important devices and suppress the use of unnecessary devices. Also, if the employee is relaxed, it can maintain normal device usage priorities. This allows the device usage priorities to be adjusted according to the employee's emotions, giving priority to the use of important devices.

[0120] The smart office system can further include an environment optimization unit for optimizing employees' work environments. The environment optimization unit collects employees' work environment data in real time and proposes an optimal work environment. For example, the environment optimization unit can propose optimal temperature and lighting based on the employees' work environment data. The environment optimization unit can also analyze the employees' work environment data and make proposals for providing an efficient work environment. Furthermore, the environment optimization unit can propose optimal device placement based on the employees' work environment data. This optimizes employees' work environments and supports efficient work performance.

[0121] The smart office system can further include a device integration adjustment unit that estimates the employee's emotions and adjusts the integration method of devices in the office based on the estimated emotions. The device integration adjustment unit analyzes the employee's facial expressions and voice to estimate emotions. For example, if the employee is feeling stressed, it can prioritize the integration of important devices and suppress unnecessary integration. Also, if the employee is relaxed, it can maintain normal device integration. In this way, by adjusting the device integration method according to the employee's emotions, it is possible to prioritize the integration of important devices.

[0122] The smart office system can further include a performance improvement unit for improving employee work performance. The performance improvement unit collects employee work performance data in real time and proposes optimal work methods. For example, the performance improvement unit can propose efficient work procedures based on employee work performance data. The performance improvement unit can also analyze employee work performance data and make proposals for improving work efficiency. Furthermore, the performance improvement unit can propose an optimal work environment based on employee work performance data. This can improve employee work performance and support efficient work execution.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The communications department provides a high-speed, stable communications environment. For example, 5G technology can be used to achieve high-speed, stable communications, allowing remote workers to access devices in the office and share data in real time. Remote access protocols can also be used to allow employees to access office servers from home and retrieve the necessary data. Data encryption technology can also be used to ensure secure communications. Step 2: The Business Efficiency Department uses AI technology to streamline business processes. For example, AI can automatically adjust employee schedules and suggest optimal meeting times. Furthermore, AI can analyze employees' calendar information and suggest optimal meeting times. The Business Efficiency Department can also use AI to analyze business data and grasp the progress of work in real time. For example, AI can link with project management tools to monitor the progress of work in real time. Step 3: The device integration unit uses IoT technology to integrate devices in the office. For example, IoT sensors automatically adjust the temperature and lighting in the office to provide a comfortable working environment. Furthermore, IoT sensors use temperature and illuminance sensors to monitor the office environment in real time and automatically adjust the temperature and lighting to the optimum level. The device integration unit also uses IoT devices to monitor energy consumption in the office, creating an eco-friendly office space. For example, IoT devices collect energy consumption data in real time and suggest optimal ways to improve energy efficiency.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] 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.

[0174] 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.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 communication section that describes specific technical details; a business efficiency improvement unit that improves the efficiency of business processing based on the communication environment provided by the communication unit; a device linking unit that links devices in the office based on the business process made more efficient by the business efficiency improving unit; Equipped with A system characterized by:

2. IoT sensors provide an environmental control unit that adjusts the temperature or lighting in the office based on specific technical requirements The system of claim 1 .

3. Equipped with a service proposal department that uses AI to analyze customer needs and propose services based on specific technical content. The system of claim 1 .

4. The IoT device will have an energy monitoring unit that monitors energy consumption based on specific technical content within the office. The system of claim 1 .

5. The communication unit Allowing remote employees to access in-office devices and share data based on specific technical context The system of claim 1 .

6. The business efficiency department AI adjusts employee schedules based on specific technical details and suggests meeting times The system of claim 1 .

7. The device cooperation unit Connecting office devices based on specific technical requirements The system of claim 1 .

8. The communication unit Estimate user emotions and adjust communication priorities based on specific technical content based on the estimated user emotions. The system of claim 1 .

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