system

Abstract

The system according to this embodiment aims to automatically identify problems in the office environment and propose improvements. [Solution] The system according to the embodiment comprises a collection unit, an acquisition unit, an analysis unit, a proposal unit, and an execution support unit. The collection unit collects sensing data from various sensors in the office. The acquisition unit acquires the calendar registration details of employees. The analysis unit analyzes the data collected by the collection unit and the acquisition unit to identify problems in the office environment. The proposal unit makes improvement proposals for the problems identified by the analysis unit. The execution support unit provides specific procedures for implementing the improvement measures proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for employees to directly report problems in the office environment, and there is room for improvement in providing a comfortable office environment.

[0005] The system according to the embodiment aims to automatically identify problems in the office environment and make improvement suggestions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a proposal unit, and an execution support unit. The collection unit collects sensing data from various sensors in the office. The acquisition unit acquires the calendar registration details of employees. The analysis unit analyzes the data collected by the collection unit and the acquisition unit to identify problems in the office environment. The proposal unit makes improvement suggestions for the problems identified by the analysis unit. The execution support unit provides specific procedures for implementing the improvement measures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically identify problems in the office environment and suggest improvements. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) An office environment improvement system according to an embodiment of the present invention is a system for providing an office environment in which employees can work comfortably. This system collects sensing data from various sensors in the office, obtains employees' calendar registrations, and analyzes them using a generating AI to identify problems in the office environment and propose improvements. For example, the office environment improvement system collects environmental data such as temperature, humidity, illuminance, and noise levels. Next, the office environment improvement system obtains employees' calendar registrations to understand meeting schedules and individual work hours. This data is input into the generating AI and analyzed. Based on the collected sensing data and calendar registrations, the generating AI identifies problems in the office environment. For example, this could be the case if the temperature in a conference room is too high at a particular time, or if noise is a problem in a particular area. This allows the system to automatically detect problems that employees might find difficult to talk about. Next, the generating AI makes improvement proposals for the identified problems. For example, this could be changing the settings of the air conditioner to appropriately adjust the temperature in the conference room, or installing partitions to reduce noise. These proposals are made using a RAG (Knowledge Database) based on past data and best practices. Furthermore, the system provides specific procedures for implementing the proposed improvements. For example, this could include scripts to automatically change air conditioner settings or diagrams indicating the placement of partitions. This ensures that proposed improvements are implemented quickly and effectively. This system provides employees with a comfortable office environment, improving corporate productivity. It also contributes to stress reduction by automatically resolving issues that employees might find difficult to discuss with others. In this way, the office environment improvement system can provide employees with a comfortable working environment and improve corporate productivity.

[0029] The office environment improvement system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a proposal unit, and an execution support unit. The collection unit collects sensing data from various sensors in the office. The collection unit collects environmental data such as temperature, humidity, illuminance, and noise level. For example, the collection unit measures the temperature in the office using a temperature sensor. The collection unit can also measure the humidity in the office using a humidity sensor. Furthermore, the collection unit can also measure the illuminance in the office using an illuminance sensor. For example, the collection unit places temperature sensors in various locations in the office and collects temperature data in real time. Humidity sensors periodically measure the humidity in the office and collect data. Illuminance sensors measure the illuminance in the office and collect data. The acquisition unit acquires the contents of employees' calendar registrations. For example, the acquisition unit acquires employee meeting schedules and individual work hours. For example, the acquisition unit acquires schedule data from employees' calendar applications. The acquisition unit can also acquire meeting schedules from employees' email applications. Furthermore, the acquisition unit can also acquire work hours from employee task management applications. For example, the acquisition unit can use the API of a calendar application to acquire schedule data. It can use the API of an email application to acquire meeting schedules. It can use the API of a task management application to acquire work hours. The analysis unit analyzes the data collected by the collection unit and the acquisition unit to identify problems in the office environment. For example, the analysis unit can analyze collected temperature data and meeting schedules to identify cases where the temperature in a meeting room is too high during a specific time period. The analysis unit can also analyze collected noise data and work hours to identify cases where noise is a problem in a specific area. The analysis unit can also analyze collected illuminance data and work hours to identify cases where there is insufficient illuminance in a specific area. For example, the analysis unit can compare temperature data and meeting schedules to identify cases where the temperature in a meeting room is too high during a specific time period. It can compare noise data and work hours to identify cases where noise is a problem in a specific area.The illuminance data and working hours are compared to identify areas where illuminance is insufficient. The proposal unit makes improvement suggestions for the problems identified by the analysis unit. For example, the proposal unit may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. The proposal unit may also suggest installing partitions to reduce noise. The proposal unit may also suggest installing additional lighting to improve illuminance. For example, the proposal unit may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. It may suggest installing partitions to reduce noise. It may suggest installing additional lighting to improve illuminance. The implementation support unit provides specific procedures for implementing the improvement measures proposed by the proposal unit. For example, the implementation support unit may provide a script to automatically change the settings of the air conditioner. The implementation support unit may also provide a drawing showing the installation location of the partitions. The implementation support unit may also provide specific procedures for installing additional lighting. For example, the implementation support unit may provide a script to automatically change the settings of the air conditioner to properly adjust the temperature of the conference room. It may provide a drawing showing the installation location of the partitions to reduce noise. It may provide specific procedures for installing additional lighting to improve illuminance. As a result, the office environment improvement system according to this embodiment can provide employees with a comfortable working environment and improve the productivity of the company.

[0030] The data collection unit collects sensing data from various sensors within the office. For example, it collects environmental data such as temperature, humidity, illuminance, and noise levels. Specifically, it measures the temperature in the office using temperature sensors, the humidity using humidity sensors, the illuminance using illuminance sensors, and the noise level using noise sensors. These sensors are placed throughout the office to collect data in real time. For example, temperature sensors are installed in each room and area of ​​the office to periodically collect temperature data. Humidity sensors periodically measure and collect humidity data. Illuminance sensors measure and collect illuminance data. Noise sensors measure and collect noise levels. This allows the data collection unit to collect environmental data within the office in real time and transmit it to a central database. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The data acquisition unit retrieves employee calendar entries. For example, it can retrieve employee meeting schedules and individual work hours. Specifically, it can retrieve schedule data from employee calendar applications and meeting schedules from email applications. Furthermore, the data acquisition unit can also retrieve work hours from employee task management applications. For example, it can use the calendar application's API to retrieve schedule data, the email application's API to retrieve meeting schedules, and the task management application's API to retrieve work hours. This allows the data acquisition unit to accurately understand employee schedules and work hours, enabling improvements to the office environment. Additionally, the data acquisition unit can centrally manage employee schedule data and integrate with other systems and departments as needed. For example, retrieved schedule data can be stored on a cloud server, allowing access by the analysis and proposal departments. Adjusting the data acquisition frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data acquisition unit to acquire data efficiently and effectively, improving the overall system performance.

[0032] The analysis unit analyzes data collected by the collection and acquisition units to identify problems in the office environment. For example, the analysis unit analyzes collected temperature data and meeting schedules to identify cases where the conference room temperature is too high during specific time periods. Specifically, it uses AI to analyze temperature data and schedule data to identify cases where the conference room temperature is too high during specific time periods. Furthermore, the analysis unit can also analyze collected noise data and work hours to identify cases where noise is a problem in specific areas. For example, it uses AI to analyze noise data and work hours to identify cases where noise is a problem in specific areas. In addition, the analysis unit can analyze collected illuminance data and work hours to identify cases where illuminance is insufficient in specific areas. For example, it uses AI to analyze illuminance data and work hours to identify cases where illuminance is insufficient in specific areas. This allows the analysis unit to quickly and accurately analyze collected data and identify problems in the office environment. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past temperature data, it can predict temperature fluctuations during specific time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0033] The proposal department makes improvement suggestions for problems identified by the analysis department. For example, the proposal department might suggest changing the air conditioning settings to properly regulate the temperature in a conference room. Specifically, it might use AI to optimize the air conditioning settings and adjust the conference room temperature appropriately. Furthermore, the proposal department could also suggest installing partitions to reduce noise. For example, it might use AI to identify the optimal location for partitions and reduce noise. In addition, the proposal department could suggest installing additional lighting to improve illumination. For example, it might use AI to identify the optimal location for lighting and improve illumination. In this way, the proposal department can make concrete improvement suggestions for problems identified by the analysis department and improve the office environment. Furthermore, the proposal department can collect employee feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after implementing an improvement suggestion, it can review the suggestion based on employee feedback and make further improvements. The proposal department can also simulate multiple improvement measures and identify the most effective one. In this way, the proposal department can provide a comfortable working environment for employees and improve the productivity of the company.

[0034] The Implementation Support Department provides specific procedures for implementing the improvement measures proposed by the Proposal Department. For example, the Implementation Support Department provides a script to automatically change the settings of an air conditioner. Specifically, it provides a script that uses AI to optimize the settings of an air conditioner and automatically changes them. Furthermore, the Implementation Support Department can also provide drawings showing the installation locations of partitions. For example, it uses AI to identify the optimal installation location for partitions and provides a drawing showing that location. Furthermore, the Implementation Support Department can also provide specific procedures for the additional installation of lighting. For example, it uses AI to identify the optimal installation location for lighting and provides a drawing showing that location. This allows the Implementation Support Department to quickly and effectively implement the improvement measures proposed by the Proposal Department. Furthermore, the Implementation Support Department can monitor the implementation status of the improvement measures and make adjustments as needed. For example, after changing the settings of an air conditioner, it can monitor temperature data and readjust the settings as needed. Also, after installing partitions, it can monitor noise data and readjust the installation location as needed. After additional lighting is installed, it can monitor illuminance data and readjust the lighting arrangement as needed. This allows the Implementation Support Department to effectively implement the improvement measures proposed by the Proposal Department and continuously improve the office environment.

[0035] The data collection unit can collect environmental data such as temperature, humidity, illuminance, and noise level. For example, the data collection unit can measure the temperature in the office using a temperature sensor. The data collection unit can also measure the humidity in the office using a humidity sensor. The data collection unit can also measure the illuminance in the office using an illuminance sensor. The data collection unit can also measure the noise level in the office using a noise sensor. For example, the data collection unit can place temperature sensors in various locations in the office and collect temperature data in real time. The data collection unit can place humidity sensors in various locations in the office and collect humidity data in real time. The data collection unit can place illuminance sensors in various locations in the office and collect illuminance data in real time. The data collection unit can place noise sensors in various locations in the office and collect noise data in real time. This allows the data collection unit to collect detailed environmental data in the office. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the temperature sensor into AI, which can analyze the data to detect temperature fluctuations.

[0036] The acquisition unit can acquire employee calendar entries. The acquisition unit can acquire, for example, employee meeting schedules and individual work hours. The acquisition unit can acquire schedule data from, for example, an employee's calendar application. The acquisition unit can acquire meeting schedules from, for example, an employee's email application. The acquisition unit can acquire work hours from, for example, an employee's task management application. For example, the acquisition unit can acquire schedule data using the calendar application's API. The acquisition unit can acquire meeting schedules using the email application's API. The acquisition unit can acquire work hours using the task management application's API. This allows the acquisition unit to collect and analyze environmental data based on employee schedules. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input schedule data acquired from the calendar application into AI, and the AI ​​can analyze the data to identify schedule patterns.

[0037] The analysis unit can identify problems in the office environment based on the collected data, such as when the conference room temperature is too high at a specific time or when noise is a problem in a specific area. For example, the analysis unit can analyze the collected temperature data and meeting schedules to identify when the conference room temperature is too high at a specific time. The analysis unit can also analyze the collected noise data and work hours to identify when noise is a problem in a specific area. The analysis unit can also analyze the collected illuminance data and work hours to identify when there is insufficient illumination in a specific area. For example, the analysis unit can compare temperature data with meeting schedules to identify when the conference room temperature is too high at a specific time. The analysis unit can compare noise data with work hours to identify when noise is a problem in a specific area. The analysis unit can compare illuminance data with work hours to identify when there is insufficient illumination in a specific area. In this way, the analysis unit can automatically identify problems in the office environment. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and identify problems.

[0038] The proposal department can make improvement suggestions for identified problems. For example, the proposal department may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. The proposal department may also suggest installing partitions to reduce noise. The proposal department may also suggest installing additional lighting to improve illumination. For example, the proposal department may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. The proposal department may suggest installing partitions to reduce noise. The proposal department may suggest installing additional lighting to improve illumination. In this way, the proposal department can make specific improvement suggestions for identified problems. Some or all of the above processing in the proposal department is performed using a generative AI. For example, the proposal department can input improvement suggestions for identified problems into the generative AI, and the generative AI can suggest the optimal improvement measures.

[0039] The implementation support unit can provide specific procedures for implementing the proposed improvements. For example, the implementation support unit can provide a script to automatically change the settings of an air conditioner. For example, the implementation support unit can provide a diagram showing the installation location of a partition. For example, the implementation support unit can provide specific procedures for the additional installation of lighting. For example, the implementation support unit can provide a script to automatically change the settings of an air conditioner to appropriately adjust the temperature of a conference room. The implementation support unit can provide a diagram showing the installation location of a partition to reduce noise. The implementation support unit can provide specific procedures for the additional installation of lighting to improve illumination. This allows the implementation support unit to ensure that the proposed improvements are implemented quickly and effectively. Some or all of the above processing in the implementation support unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the implementation support unit can input a script to automatically change the settings of an air conditioner into a generation AI, and the generation AI can generate the script.

[0040] The data collection unit can dynamically change the placement of sensors and focus on collecting environmental data from specific areas. For example, the data collection unit can move sensors to collect data according to the usage status of a conference room. For example, the data collection unit can concentrate sensors in an area if a problem occurs in that area. For example, the data collection unit can change the placement of sensors in accordance with the movement of users and collect data in real time. For example, the data collection unit can move sensors to collect data according to the usage status of a conference room. For example, the data collection unit can concentrate sensors in an area if a problem occurs in that area. For example, the data collection unit can change the placement of sensors in accordance with the movement of users and collect data in real time. This allows the data collection unit to focus on collecting environmental data from specific areas. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input instructions to a generating AI to dynamically change the placement of sensors, and the generating AI can propose an optimal placement.

[0041] The data collection unit can analyze sensor data in real time and immediately notify if an anomaly is detected. For example, the data collection unit will immediately notify if the temperature rises rapidly. The data collection unit can also issue an alert if the noise level exceeds a certain threshold. The data collection unit can also provide real-time notifications if the illuminance changes rapidly. For example, the data collection unit will immediately notify if the temperature rises rapidly. The data collection unit will issue an alert if the noise level exceeds a certain threshold. The data collection unit will provide real-time notifications if the illuminance changes rapidly. This allows the data collection unit to immediately notify if an anomaly is detected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input sensor data into a generating AI, which can then detect an anomaly and issue a notification.

[0042] The data collection unit can compare sensor data with data from other offices and perform benchmarking. For example, the data collection unit can compare temperature data from other offices and suggest optimal temperature settings. For example, the data collection unit can compare noise levels from other offices and suggest noise reduction measures. For example, the data collection unit can compare illuminance data from other offices and suggest lighting adjustments. For example, the data collection unit can compare temperature data from other offices and suggest optimal temperature settings. For example, the data collection unit can compare noise levels from other offices and suggest noise reduction measures. For example, the data collection unit can compare illuminance data from other offices and suggest lighting adjustments. This allows the data collection unit to create an optimal environment by comparing data from other offices. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from other offices into a generating AI, which can perform benchmarking and suggest optimal environment settings.

[0043] The data collection unit can predict environmental changes by linking sensor data with external weather data. For example, the data collection unit can predict temperature changes based on external weather data. The data collection unit can also predict humidity changes based on external weather data. The data collection unit can also predict illuminance changes based on external weather data. For example, the data collection unit can predict temperature changes based on external weather data. The data collection unit can predict humidity changes based on external weather data. The data collection unit can predict illuminance changes based on external weather data. In this way, the data collection unit can predict environmental changes and take appropriate action by linking with external weather data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input external weather data into a generating AI, and the generating AI can predict environmental changes.

[0044] The data acquisition unit can analyze the contents of the calendar and prioritize the acquisition of data for important meetings and events. For example, the data acquisition unit can prioritize the acquisition of conference room environmental data before an important meeting. For example, the data acquisition unit can prioritize the acquisition of event venue environmental data before a large-scale event. For example, the data acquisition unit can prioritize the acquisition of presentation room environmental data before an important presentation. For example, the data acquisition unit can prioritize the acquisition of conference room environmental data before an important meeting. For example, the data acquisition unit can prioritize the acquisition of event venue environmental data before a large-scale event. For example, the data acquisition unit can prioritize the acquisition of presentation room environmental data before an important presentation. In this way, the data acquisition unit can prioritize the acquisition of data for important meetings and events. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the contents of the calendar into a generating AI, which can identify important meetings and events and prioritize the acquisition of data for them.

[0045] The data acquisition unit can compare calendar data with the schedules of other employees to identify common problems. For example, if multiple employees use the same meeting room, the data acquisition unit can compare the environmental data of that meeting room. For example, if multiple employees hold meetings at the same time, the data acquisition unit can also compare the environmental data of that time slot. For example, if multiple employees work in the same area, the data acquisition unit can also compare the environmental data of that area. For example, if multiple employees use the same meeting room, the data acquisition unit can compare the environmental data of that meeting room. For example, if multiple employees hold meetings at the same time, the data acquisition unit can compare the environmental data of that time slot. For example, if multiple employees work in the same area, the data acquisition unit can compare the environmental data of that area. This allows the data acquisition unit to identify common problems by comparing with the schedules of other employees and to make appropriate improvements. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input calendar data into a generating AI, which can then compare it with the schedules of other employees to identify common problems.

[0046] The data acquisition unit can maintain data consistency by linking calendar data with an external schedule management tool. For example, the data acquisition unit can synchronize calendar data with an external schedule management tool. The data acquisition unit can also acquire data from an external schedule management tool and reflect it in the calendar. The data acquisition unit can also prevent data duplication by linking with an external schedule management tool. For example, the data acquisition unit can synchronize calendar data with an external schedule management tool. The data acquisition unit can acquire data from an external schedule management tool and reflect it in the calendar. The data acquisition unit can prevent data duplication by linking with an external schedule management tool. As a result, the data acquisition unit can maintain data consistency and enable proper data management by linking with an external schedule management tool. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data from an external schedule management tool into a generating AI, and the generating AI can perform processing to maintain data consistency.

[0047] The acquisition unit can compare calendar data with past data and identify patterns. For example, the acquisition unit can identify problems that occur during specific time periods based on past data. The acquisition unit can also identify problems that occur in specific areas based on past data. The acquisition unit can also identify problems related to specific events based on past data. For example, the acquisition unit can identify problems that occur during specific time periods based on past data. The acquisition unit can identify problems that occur in specific areas based on past data. The acquisition unit can identify problems related to specific events based on past data. This allows the acquisition unit to identify patterns by comparing with past data and enable appropriate improvements. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input calendar data into a generating AI, which can then compare it with past data and identify patterns.

[0048] The analysis unit can integrate environmental data and calendar data during analysis and identify correlations. For example, the analysis unit can integrate environmental data and calendar data to identify problems occurring during specific time periods. The analysis unit can also integrate environmental data and calendar data to identify problems occurring in specific areas. The analysis unit can also integrate environmental data and calendar data to identify problems related to specific events. For example, the analysis unit can integrate environmental data and calendar data to identify problems occurring during specific time periods. The analysis unit can integrate environmental data and calendar data to identify problems occurring in specific areas. The analysis unit can integrate environmental data and calendar data to identify problems related to specific events. This allows the analysis unit to identify correlations by integrating environmental data and calendar data, enabling more appropriate analysis. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs environmental data and calendar data into the generative AI, which can then identify correlations.

[0049] The analysis unit can visualize the analysis results, making it easier to intuitively understand the problems. For example, the analysis unit can display the analysis results in graphs and charts to make the problems intuitively understandable. The analysis unit can also display the analysis results in a heatmap to make the location of the problems intuitively understandable. The analysis unit can also display the analysis results on a dashboard to make the overall situation intuitively understandable. For example, the analysis unit can display the analysis results in graphs and charts to make the problems intuitively understandable. The analysis unit can display the analysis results in a heatmap to make the location of the problems intuitively understandable. The analysis unit can display the analysis results on a dashboard to make the overall situation intuitively understandable. This allows the analysis unit to visualize the analysis results, making it easier to intuitively understand the problems. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the analysis results into the generation AI, which then performs the visualization.

[0050] The analysis unit can identify best practices by comparing data from other offices during analysis. For example, the analysis unit can identify the optimal temperature setting by comparing data from other offices. The analysis unit can also identify the optimal noise reduction measures by comparing data from other offices. The analysis unit can also identify the optimal lighting settings by comparing data from other offices. For example, the analysis unit can identify the optimal temperature setting by comparing data from other offices. The analysis unit can identify the optimal noise reduction measures by comparing data from other offices. The analysis unit can identify the optimal lighting settings by comparing data from other offices. This allows the analysis unit to identify best practices and propose optimal improvement measures by comparing data from other offices. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs data from other offices into the generative AI, which can then identify best practices.

[0051] The analysis unit can share its analysis results with external experts and obtain feedback. For example, the analysis unit can share its analysis results with external experts and obtain feedback regarding temperature settings. The analysis unit can also share its analysis results with external experts and obtain feedback regarding noise reduction measures. The analysis unit can also share its analysis results with external experts and obtain feedback regarding lighting settings. For example, the analysis unit can share its analysis results with external experts and obtain feedback regarding temperature settings. The analysis unit can share its analysis results with external experts and obtain feedback regarding noise reduction measures. The analysis unit can share its analysis results with external experts and obtain feedback regarding lighting settings. This allows the analysis unit to improve the accuracy of its analysis results by obtaining feedback from external experts. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs its analysis results into the generating AI, which then shares them with external experts and obtains feedback.

[0052] The proposal department can refer to past successful cases when making a proposal and propose the optimal improvement measures. For example, the proposal department can propose the optimal method of temperature control based on past successful cases. The proposal department can also propose the optimal method of noise reduction based on past successful cases. The proposal department can also propose the optimal method of lighting adjustment based on past successful cases. For example, the proposal department can propose the optimal method of temperature control based on past successful cases. The proposal department can propose the optimal method of noise reduction based on past successful cases. The proposal department can propose the optimal method of lighting adjustment based on past successful cases. In this way, the proposal department can propose the optimal improvement measures by referring to past successful cases. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department can input past successful cases into the generation AI, and the generation AI can propose the optimal improvement measures.

[0053] The proposal department can simulate the proposed content and evaluate its effects in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of temperature control in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of noise reduction in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of lighting adjustment in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of temperature control in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of noise reduction in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of lighting adjustment in advance. This allows the proposal department to evaluate the effects in advance by simulating the proposed content and select the optimal improvement measures. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department can input the proposed content into the generation AI, which can perform a simulation and evaluate the effects in advance.

[0054] The proposal department can compare the proposed content with data from other offices and select the optimal improvement measures. For example, the proposal department can select the optimal temperature control measures by comparing them with data from other offices. The proposal department can also select the optimal noise reduction measures by comparing them with data from other offices. The proposal department can also select the optimal lighting adjustment measures by comparing them with data from other offices. For example, the proposal department can select the optimal temperature control measures by comparing them with data from other offices. The proposal department can select the optimal noise reduction measures by comparing them with data from other offices. The proposal department can select the optimal lighting adjustment measures by comparing them with data from other offices. In this way, the proposal department can select the optimal improvement measures by comparing them with data from other offices. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department can input data from other offices into the generation AI, and the generation AI can select the optimal improvement measures.

[0055] The proposal department can improve the accuracy of its proposals by having them reviewed by external experts. For example, the proposal department can improve the accuracy of temperature control by having the proposals reviewed by external experts. The proposal department can also improve the accuracy of noise reduction measures by having the proposals reviewed by external experts. The proposal department can also improve the accuracy of lighting adjustments by having the proposals reviewed by external experts. For example, the proposal department can improve the accuracy of temperature control by having the proposals reviewed by external experts. The proposal department can improve the accuracy of noise reduction measures by having the proposals reviewed by external experts. The proposal department can improve the accuracy of lighting adjustments by having the proposals reviewed by external experts. In this way, the proposal department can improve the accuracy of its proposals by having them reviewed by external experts. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department inputs the proposal content into the generation AI, and the generation AI can improve its accuracy by having it reviewed by external experts.

[0056] The execution support unit can automatically allocate the necessary resources during execution and execute efficiently. For example, the execution support unit can automatically allocate the necessary personnel during execution. The execution support unit can also automatically allocate the necessary equipment during execution. The execution support unit can also automatically allocate the necessary budget during execution. For example, the execution support unit can automatically allocate the necessary personnel during execution. The execution support unit can automatically allocate the necessary equipment during execution. The execution support unit can automatically allocate the necessary budget during execution. This enables efficient execution by automatically allocating the necessary resources. Some or all of the above processing in the execution support unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the execution support unit can input the necessary resources into a generation AI, and the generation AI can automatically allocate them.

[0057] The execution support unit can monitor the execution results in real time and make adjustments as needed. For example, the execution support unit can monitor the execution results in real time and confirm the effect of temperature adjustment. For example, the execution support unit can monitor the execution results in real time and confirm the effect of noise reduction measures. For example, the execution support unit can monitor the execution results in real time and confirm the effect of lighting adjustment. For example, the execution support unit can monitor the execution results in real time and confirm the effect of temperature adjustment. For example, the execution support unit can monitor the execution results in real time and confirm the effect of noise reduction measures. For example, the execution support unit can monitor the execution results in real time and confirm the effect of lighting adjustment. This allows the execution support unit to monitor the execution results in real time and make adjustments as needed. Some or all of the above processing in the execution support unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the execution support unit can input the execution results into a generation AI, which can monitor them in real time and make adjustments as needed.

[0058] The execution support unit can compare execution procedures with data from other offices and select the optimal procedure. For example, the execution support unit can select the optimal temperature control procedure by comparing it with data from other offices. For example, the execution support unit can also select the optimal noise reduction procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal lighting adjustment procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal temperature control procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal noise reduction procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal lighting adjustment procedure by comparing it with data from other offices. In this way, the execution support unit can select the optimal procedure by comparing it with data from other offices. Some or all of the above processing in the execution support unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the execution support unit can input data from other offices into a generation AI, and the generation AI can select the optimal procedure.

[0059] The execution support unit can improve the accuracy of the execution procedures by having them reviewed by external experts. For example, the execution support unit can improve the accuracy of temperature control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of noise control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of lighting adjustment by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of temperature control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of noise control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of lighting adjustment by having the execution procedures reviewed by external experts.

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

[0061] The office environment improvement system can also include a task management unit to further enhance employee work efficiency. This unit analyzes employee work content and assigns tasks efficiently. For example, it can automatically assign optimal tasks based on an employee's skill set and past work history. It can also monitor task progress in real time and reallocate resources as needed. This maximizes employee work efficiency and ensures smooth project progress.

[0062] The office environment improvement system can also include a skills management department that collects and analyzes employee skill data. This department collects data such as employees' technical skills, soft skills, and experience. This allows for an understanding of employees' skill sets and the provision of appropriate training programs. For example, if an employee lacks a specific skill, relevant training courses can be suggested. Furthermore, if skills improve, promotions and compensation can be reviewed. This ultimately improves employee skills and enhances overall company performance.

[0063] The office environment improvement system can also include a movement management unit that collects and analyzes employee movement data. For example, the movement management unit can analyze employee movement patterns within the office and propose an efficient layout. This can reduce employee travel time and improve work efficiency. For instance, it can identify frequently used areas and place necessary equipment in those areas. Alternatively, it can identify areas with less movement and utilize them as relaxation spaces. This optimizes the office layout and improves employee comfort.

[0064] The office environment improvement system can also include a communication management department that collects and analyzes employee communication data. This department can analyze data from emails and chats between employees, for example, and make suggestions to improve communication efficiency. This can streamline information sharing among employees and improve work efficiency. For instance, it can identify teams that communicate frequently and provide them with dedicated communication tools. It can also identify teams with insufficient communication and propose regular meetings. This optimizes communication among employees and improves work efficiency.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects sensing data from various sensors within the office. For example, it collects environmental data within the office using temperature sensors, humidity sensors, illuminance sensors, noise sensors, etc. This allows for the collection of data such as temperature, humidity, illuminance, and noise levels in real time. Step 2: The retrieval unit retrieves the employee's calendar entries. For example, it retrieves the employee's meeting schedule and individual work hours using the APIs of calendar applications, email applications, and task management applications. Step 3: The analysis unit analyzes the data collected by the collection and acquisition units to identify problems in the office environment. For example, it may compare temperature data with meeting schedules to identify cases where the temperature in meeting rooms is too high during specific times, or compare noise data with work hours to identify cases where noise is a problem in specific areas. Step 4: The proposal team makes improvement suggestions for the problems identified by the analysis team. For example, they might suggest changing the air conditioning settings to properly regulate the temperature in the conference room, installing partitions to reduce noise, or adding lighting to improve illumination. Step 5: The Implementation Support Department provides specific procedures for implementing the improvement measures proposed by the Proposal Department. For example, it provides a script to automatically change the settings of an air conditioner, a diagram showing the location of partitions, and specific procedures for installing additional lighting.

[0067] (Example of form 2) An office environment improvement system according to an embodiment of the present invention is a system for providing an office environment in which employees can work comfortably. This system collects sensing data from various sensors in the office, obtains employees' calendar registrations, and analyzes them using a generating AI to identify problems in the office environment and propose improvements. For example, the office environment improvement system collects environmental data such as temperature, humidity, illuminance, and noise levels. Next, the office environment improvement system obtains employees' calendar registrations to understand meeting schedules and individual work hours. This data is input into the generating AI and analyzed. Based on the collected sensing data and calendar registrations, the generating AI identifies problems in the office environment. For example, this could be the case if the temperature in a conference room is too high at a particular time, or if noise is a problem in a particular area. This allows the system to automatically detect problems that employees might find difficult to talk about. Next, the generating AI makes improvement proposals for the identified problems. For example, this could be changing the settings of the air conditioner to appropriately adjust the temperature in the conference room, or installing partitions to reduce noise. These proposals are made using a RAG (Knowledge Database) based on past data and best practices. Furthermore, using generational AI, the system provides specific steps for implementing the proposed improvements. Examples include scripts that automatically change air conditioner settings and diagrams indicating partition placement. This ensures that the proposed improvements are implemented quickly and effectively. This system provides a comfortable office environment for employees, improving corporate productivity. It also contributes to stress reduction by automatically resolving issues that employees might find difficult to discuss with others. Thus, the office environment improvement system can provide a comfortable working environment for employees and improve corporate productivity.

[0068] The office environment improvement system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a proposal unit, and an execution support unit. The collection unit collects sensing data from various sensors in the office. The collection unit collects environmental data such as temperature, humidity, illuminance, and noise level. For example, the collection unit measures the temperature in the office using a temperature sensor. The collection unit can also measure the humidity in the office using a humidity sensor. Furthermore, the collection unit can also measure the illuminance in the office using an illuminance sensor. For example, the collection unit places temperature sensors in various locations in the office and collects temperature data in real time. Humidity sensors periodically measure the humidity in the office and collect data. Illuminance sensors measure the illuminance in the office and collect data. The acquisition unit acquires the contents of employees' calendar registrations. For example, the acquisition unit acquires employee meeting schedules and individual work hours. For example, the acquisition unit acquires schedule data from employees' calendar applications. The acquisition unit can also acquire meeting schedules from employees' email applications. Furthermore, the acquisition unit can also acquire work hours from employee task management applications. For example, the acquisition unit can use the API of a calendar application to acquire schedule data. It can use the API of an email application to acquire meeting schedules. It can use the API of a task management application to acquire work hours. The analysis unit analyzes the data collected by the collection unit and the acquisition unit to identify problems in the office environment. For example, the analysis unit can analyze collected temperature data and meeting schedules to identify cases where the temperature in a meeting room is too high during a specific time period. The analysis unit can also analyze collected noise data and work hours to identify cases where noise is a problem in a specific area. The analysis unit can also analyze collected illuminance data and work hours to identify cases where there is insufficient illuminance in a specific area. For example, the analysis unit can compare temperature data and meeting schedules to identify cases where the temperature in a meeting room is too high during a specific time period. It can compare noise data and work hours to identify cases where noise is a problem in a specific area.The illuminance data and working hours are compared to identify areas where illuminance is insufficient. The proposal unit makes improvement suggestions for the problems identified by the analysis unit. For example, the proposal unit may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. The proposal unit may also suggest installing partitions to reduce noise. The proposal unit may also suggest installing additional lighting to improve illuminance. For example, the proposal unit may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. It may suggest installing partitions to reduce noise. It may suggest installing additional lighting to improve illuminance. The implementation support unit provides specific procedures for implementing the improvement measures proposed by the proposal unit. For example, the implementation support unit may provide a script to automatically change the settings of the air conditioner. The implementation support unit may also provide a drawing showing the installation location of the partitions. The implementation support unit may also provide specific procedures for installing additional lighting. For example, the implementation support unit may provide a script to automatically change the settings of the air conditioner to properly adjust the temperature of the conference room. It may provide a drawing showing the installation location of the partitions to reduce noise. It may provide specific procedures for installing additional lighting to improve illuminance. As a result, the office environment improvement system according to this embodiment can provide employees with a comfortable working environment and improve the productivity of the company.

[0069] The data collection unit collects sensing data from various sensors within the office. For example, it collects environmental data such as temperature, humidity, illuminance, and noise levels. Specifically, it measures the temperature in the office using temperature sensors, the humidity using humidity sensors, the illuminance using illuminance sensors, and the noise level using noise sensors. These sensors are placed throughout the office to collect data in real time. For example, temperature sensors are installed in each room and area of ​​the office to periodically collect temperature data. Humidity sensors periodically measure and collect humidity data. Illuminance sensors measure and collect illuminance data. Noise sensors measure and collect noise levels. This allows the data collection unit to collect environmental data within the office in real time and transmit it to a central database. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The data acquisition unit retrieves employee calendar entries. For example, it can retrieve employee meeting schedules and individual work hours. Specifically, it can retrieve schedule data from employee calendar applications and meeting schedules from email applications. Furthermore, the data acquisition unit can also retrieve work hours from employee task management applications. For example, it can use the calendar application's API to retrieve schedule data, the email application's API to retrieve meeting schedules, and the task management application's API to retrieve work hours. This allows the data acquisition unit to accurately understand employee schedules and work hours, enabling improvements to the office environment. Additionally, the data acquisition unit can centrally manage employee schedule data and integrate with other systems and departments as needed. For example, retrieved schedule data can be stored on a cloud server, allowing access by the analysis and proposal departments. Adjusting the data acquisition frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data acquisition unit to acquire data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes data collected by the collection and acquisition units to identify problems in the office environment. For example, the analysis unit analyzes collected temperature data and meeting schedules to identify cases where the conference room temperature is too high during specific time periods. Specifically, it uses AI to analyze temperature data and schedule data to identify cases where the conference room temperature is too high during specific time periods. Furthermore, the analysis unit can also analyze collected noise data and work hours to identify cases where noise is a problem in specific areas. For example, it uses AI to analyze noise data and work hours to identify cases where noise is a problem in specific areas. In addition, the analysis unit can analyze collected illuminance data and work hours to identify cases where illuminance is insufficient in specific areas. For example, it uses AI to analyze illuminance data and work hours to identify cases where illuminance is insufficient in specific areas. This allows the analysis unit to quickly and accurately analyze collected data and identify problems in the office environment. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past temperature data, it can predict temperature fluctuations during specific time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0072] The proposal department makes improvement suggestions for problems identified by the analysis department. For example, the proposal department might suggest changing the air conditioning settings to properly regulate the temperature in a conference room. Specifically, it might use AI to optimize the air conditioning settings and adjust the conference room temperature appropriately. Furthermore, the proposal department could also suggest installing partitions to reduce noise. For example, it might use AI to identify the optimal location for partitions and reduce noise. In addition, the proposal department could suggest installing additional lighting to improve illumination. For example, it might use AI to identify the optimal location for lighting and improve illumination. In this way, the proposal department can make concrete improvement suggestions for problems identified by the analysis department and improve the office environment. Furthermore, the proposal department can collect employee feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after implementing an improvement suggestion, it can review the suggestion based on employee feedback and make further improvements. The proposal department can also simulate multiple improvement measures and identify the most effective one. In this way, the proposal department can provide a comfortable working environment for employees and improve the productivity of the company.

[0073] The Implementation Support Department provides specific procedures for implementing the improvement measures proposed by the Proposal Department. For example, the Implementation Support Department provides a script to automatically change the settings of an air conditioner. Specifically, it provides a script that uses AI to optimize the settings of an air conditioner and automatically changes them. Furthermore, the Implementation Support Department can also provide drawings showing the installation locations of partitions. For example, it uses AI to identify the optimal installation location for partitions and provides a drawing showing that location. Furthermore, the Implementation Support Department can also provide specific procedures for the additional installation of lighting. For example, it uses AI to identify the optimal installation location for lighting and provides a drawing showing that location. This allows the Implementation Support Department to quickly and effectively implement the improvement measures proposed by the Proposal Department. Furthermore, the Implementation Support Department can monitor the implementation status of the improvement measures and make adjustments as needed. For example, after changing the settings of an air conditioner, it can monitor temperature data and readjust the settings as needed. Also, after installing partitions, it can monitor noise data and readjust the installation location as needed. After additional lighting is installed, it can monitor illuminance data and readjust the lighting arrangement as needed. This allows the Implementation Support Department to effectively implement the improvement measures proposed by the Proposal Department and continuously improve the office environment.

[0074] The data collection unit can collect environmental data such as temperature, humidity, illuminance, and noise level. For example, the data collection unit can measure the temperature in the office using a temperature sensor. The data collection unit can also measure the humidity in the office using a humidity sensor. The data collection unit can also measure the illuminance in the office using an illuminance sensor. The data collection unit can also measure the noise level in the office using a noise sensor. For example, the data collection unit can place temperature sensors in various locations in the office and collect temperature data in real time. The data collection unit can place humidity sensors in various locations in the office and collect humidity data in real time. The data collection unit can place illuminance sensors in various locations in the office and collect illuminance data in real time. The data collection unit can place noise sensors in various locations in the office and collect noise data in real time. This allows the data collection unit to collect detailed environmental data in the office. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from the temperature sensor into AI, which can analyze the data to detect temperature fluctuations.

[0075] The acquisition unit can acquire employee calendar entries. The acquisition unit can acquire, for example, employee meeting schedules and individual work hours. The acquisition unit can acquire schedule data from, for example, an employee's calendar application. The acquisition unit can acquire meeting schedules from, for example, an employee's email application. The acquisition unit can acquire work hours from, for example, an employee's task management application. For example, the acquisition unit can acquire schedule data using the calendar application's API. The acquisition unit can acquire meeting schedules using the email application's API. The acquisition unit can acquire work hours using the task management application's API. This allows the acquisition unit to collect and analyze environmental data based on employee schedules. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input schedule data acquired from the calendar application into AI, and the AI ​​can analyze the data to identify schedule patterns.

[0076] The analysis unit can identify problems in the office environment based on the collected data, such as when the conference room temperature is too high at a specific time or when noise is a problem in a specific area. For example, the analysis unit can analyze the collected temperature data and meeting schedules to identify when the conference room temperature is too high at a specific time. The analysis unit can also analyze the collected noise data and work hours to identify when noise is a problem in a specific area. The analysis unit can also analyze the collected illuminance data and work hours to identify when there is insufficient illumination in a specific area. For example, the analysis unit can compare temperature data with meeting schedules to identify when the conference room temperature is too high at a specific time. The analysis unit can compare noise data with work hours to identify when noise is a problem in a specific area. The analysis unit can compare illuminance data with work hours to identify when there is insufficient illumination in a specific area. In this way, the analysis unit can automatically identify problems in the office environment. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and identify problems.

[0077] The proposal department can make improvement suggestions for identified problems. For example, the proposal department may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. The proposal department may also suggest installing partitions to reduce noise. The proposal department may also suggest installing additional lighting to improve illumination. For example, the proposal department may suggest changing the settings of the air conditioner to properly adjust the temperature of the conference room. The proposal department may suggest installing partitions to reduce noise. The proposal department may suggest installing additional lighting to improve illumination. In this way, the proposal department can make specific improvement suggestions for identified problems. Some or all of the above processing in the proposal department is performed using a generative AI. For example, the proposal department can input improvement suggestions for identified problems into the generative AI, and the generative AI can suggest the optimal improvement measures.

[0078] The implementation support unit can provide specific procedures for implementing the proposed improvements. For example, the implementation support unit can provide a script to automatically change the settings of an air conditioner. For example, the implementation support unit can provide a diagram showing the installation location of a partition. For example, the implementation support unit can provide specific procedures for the additional installation of lighting. For example, the implementation support unit can provide a script to automatically change the settings of an air conditioner to appropriately adjust the temperature of a conference room. The implementation support unit can provide a diagram showing the installation location of a partition to reduce noise. The implementation support unit can provide specific procedures for the additional installation of lighting to improve illumination. This allows the implementation support unit to ensure that the proposed improvements are implemented quickly and effectively. Some or all of the above processing in the implementation support unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the implementation support unit can input a script to automatically change the settings of an air conditioner into a generation AI, and the generation AI can generate the script.

[0079] The data collection unit can estimate the user's emotions and adjust the frequency of environmental data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is relaxed, the data collection unit can decrease the collection frequency to collect only the minimum necessary data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only specific important data. In this way, the data collection unit can adjust the frequency of environmental data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the collection frequency.

[0080] The data collection unit can dynamically change the placement of sensors and focus on collecting environmental data from specific areas. For example, the data collection unit can move sensors to collect data according to the usage status of a conference room. For example, the data collection unit can concentrate sensors in an area if a problem occurs in that area. For example, the data collection unit can change the placement of sensors in accordance with the movement of users and collect data in real time. For example, the data collection unit can move sensors to collect data according to the usage status of a conference room. For example, the data collection unit can concentrate sensors in an area if a problem occurs in that area. For example, the data collection unit can change the placement of sensors in accordance with the movement of users and collect data in real time. This allows the data collection unit to focus on collecting environmental data from specific areas. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input instructions to a generating AI to dynamically change the placement of sensors, and the generating AI can propose an optimal placement.

[0081] The data collection unit can analyze sensor data in real time and immediately notify if an anomaly is detected. For example, the data collection unit will immediately notify if the temperature rises rapidly. The data collection unit can also issue an alert if the noise level exceeds a certain threshold. The data collection unit can also provide real-time notifications if the illuminance changes rapidly. For example, the data collection unit will immediately notify if the temperature rises rapidly. The data collection unit will issue an alert if the noise level exceeds a certain threshold. The data collection unit will provide real-time notifications if the illuminance changes rapidly. This allows the data collection unit to immediately notify if an anomaly is detected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input sensor data into a generating AI, which can then detect an anomaly and issue a notification.

[0082] The data collection unit can estimate the user's emotions and select the types of environmental data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will focus on collecting temperature and noise levels. For example, if the user is relaxed, the data collection unit may focus on collecting illuminance and humidity. For example, if the user is in a hurry, the data collection unit may focus on collecting temperature and illuminance. For example, the data collection unit estimates the user's emotions and, if they are stressed, focuses on collecting temperature and noise levels. If the user is relaxed, the data collection unit focuses on collecting illuminance and humidity. If the user is in a hurry, the data collection unit focuses on collecting temperature and illuminance. This allows the data collection unit to select the types of environmental data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and select the types of environmental data to collect.

[0083] The data collection unit can compare sensor data with data from other offices and perform benchmarking. For example, the data collection unit can compare temperature data from other offices and suggest optimal temperature settings. For example, the data collection unit can compare noise levels from other offices and suggest noise reduction measures. For example, the data collection unit can compare illuminance data from other offices and suggest lighting adjustments. For example, the data collection unit can compare temperature data from other offices and suggest optimal temperature settings. For example, the data collection unit can compare noise levels from other offices and suggest noise reduction measures. For example, the data collection unit can compare illuminance data from other offices and suggest lighting adjustments. This allows the data collection unit to create an optimal environment by comparing data from other offices. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from other offices into a generating AI, which can perform benchmarking and suggest optimal environment settings.

[0084] The data collection unit can predict environmental changes by linking sensor data with external weather data. For example, the data collection unit can predict temperature changes based on external weather data. The data collection unit can also predict humidity changes based on external weather data. The data collection unit can also predict illuminance changes based on external weather data. For example, the data collection unit can predict temperature changes based on external weather data. The data collection unit can predict humidity changes based on external weather data. The data collection unit can predict illuminance changes based on external weather data. In this way, the data collection unit can predict environmental changes and take appropriate action by linking with external weather data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input external weather data into a generating AI, and the generating AI can predict environmental changes.

[0085] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring calendar entries based on the estimated emotions. For example, if the user is stressed, the acquisition unit will acquire calendar entries more frequently. For example, if the user is relaxed, the acquisition unit can reduce the acquisition frequency. For example, if the user is in a hurry, the acquisition unit can prioritize acquiring only important events. For example, the acquisition unit estimates the user's emotions and acquires calendar entries more frequently if the user is stressed. The acquisition unit reduces the acquisition frequency if the user is relaxed. The acquisition unit prioritizes acquiring only important events if the user is in a hurry. In this way, the acquisition unit can adjust the timing of acquiring calendar entries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the acquisition timing.

[0086] The data acquisition unit can analyze the contents of the calendar and prioritize the acquisition of data for important meetings and events. For example, the data acquisition unit can prioritize the acquisition of conference room environmental data before an important meeting. For example, the data acquisition unit can prioritize the acquisition of event venue environmental data before a large-scale event. For example, the data acquisition unit can prioritize the acquisition of presentation room environmental data before an important presentation. For example, the data acquisition unit can prioritize the acquisition of conference room environmental data before an important meeting. For example, the data acquisition unit can prioritize the acquisition of event venue environmental data before a large-scale event. For example, the data acquisition unit can prioritize the acquisition of presentation room environmental data before an important presentation. In this way, the data acquisition unit can prioritize the acquisition of data for important meetings and events. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the contents of the calendar into a generating AI, which can identify important meetings and events and prioritize the acquisition of data for them.

[0087] The data acquisition unit can compare calendar data with the schedules of other employees to identify common problems. For example, if multiple employees use the same meeting room, the data acquisition unit can compare the environmental data of that meeting room. For example, if multiple employees hold meetings at the same time, the data acquisition unit can also compare the environmental data of that time slot. For example, if multiple employees work in the same area, the data acquisition unit can also compare the environmental data of that area. For example, if multiple employees use the same meeting room, the data acquisition unit can compare the environmental data of that meeting room. For example, if multiple employees hold meetings at the same time, the data acquisition unit can compare the environmental data of that time slot. For example, if multiple employees work in the same area, the data acquisition unit can compare the environmental data of that area. This allows the data acquisition unit to identify common problems by comparing with the schedules of other employees and to make appropriate improvements. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input calendar data into a generating AI, which can then compare it with the schedules of other employees to identify common problems.

[0088] The acquisition unit can estimate the user's emotions and filter the calendar content it acquires based on the estimated emotions. For example, if the user is stressed, the acquisition unit can acquire only important events. For example, if the user is relaxed, the acquisition unit can acquire all events. For example, if the user is in a hurry, the acquisition unit can acquire only the most recent events. For example, the acquisition unit estimates the user's emotions and acquires only important events if the user is stressed. The acquisition unit acquires all events if the user is relaxed. The acquisition unit acquires only the most recent events if the user is in a hurry. In this way, the acquisition unit can filter the calendar content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit can input user emotion data into a generative AI, and the generative AI can estimate the emotions and filter the calendar content.

[0089] The data acquisition unit can maintain data consistency by linking calendar data with an external schedule management tool. For example, the data acquisition unit can synchronize calendar data with an external schedule management tool. The data acquisition unit can also acquire data from an external schedule management tool and reflect it in the calendar. The data acquisition unit can also prevent data duplication by linking with an external schedule management tool. For example, the data acquisition unit can synchronize calendar data with an external schedule management tool. The data acquisition unit can acquire data from an external schedule management tool and reflect it in the calendar. The data acquisition unit can prevent data duplication by linking with an external schedule management tool. As a result, the data acquisition unit can maintain data consistency and enable proper data management by linking with an external schedule management tool. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data from an external schedule management tool into a generating AI, and the generating AI can perform processing to maintain data consistency.

[0090] The acquisition unit can compare calendar data with past data and identify patterns. For example, the acquisition unit can identify problems that occur during specific time periods based on past data. The acquisition unit can also identify problems that occur in specific areas based on past data. The acquisition unit can also identify problems related to specific events based on past data. For example, the acquisition unit can identify problems that occur during specific time periods based on past data. The acquisition unit can identify problems that occur in specific areas based on past data. The acquisition unit can identify problems related to specific events based on past data. This allows the acquisition unit to identify patterns by comparing with past data and enable appropriate improvements. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input calendar data into a generating AI, which can then compare it with past data and identify patterns.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a detailed analysis. For example, if the user is relaxed, the analysis unit can perform a simplified analysis. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis. For example, the analysis unit estimates the user's emotions and performs a detailed analysis if the user is stressed. For example, if the user is relaxed, the analysis unit performs a simplified analysis. For example, if the user is in a hurry, the analysis unit performs a rapid analysis. This allows the analysis unit to adjust the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the analysis algorithm.

[0092] The analysis unit can integrate environmental data and calendar data during analysis and identify correlations. For example, the analysis unit can integrate environmental data and calendar data to identify problems occurring during specific time periods. The analysis unit can also integrate environmental data and calendar data to identify problems occurring in specific areas. The analysis unit can also integrate environmental data and calendar data to identify problems related to specific events. For example, the analysis unit can integrate environmental data and calendar data to identify problems occurring during specific time periods. The analysis unit can integrate environmental data and calendar data to identify problems occurring in specific areas. The analysis unit can integrate environmental data and calendar data to identify problems related to specific events. This allows the analysis unit to identify correlations by integrating environmental data and calendar data, enabling more appropriate analysis. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs environmental data and calendar data into the generative AI, which can then identify correlations.

[0093] The analysis unit can visualize the analysis results, making it easier to intuitively understand the problems. For example, the analysis unit can display the analysis results in graphs and charts to make the problems intuitively understandable. The analysis unit can also display the analysis results in a heatmap to make the location of the problems intuitively understandable. The analysis unit can also display the analysis results on a dashboard to make the overall situation intuitively understandable. For example, the analysis unit can display the analysis results in graphs and charts to make the problems intuitively understandable. The analysis unit can display the analysis results in a heatmap to make the location of the problems intuitively understandable. The analysis unit can display the analysis results on a dashboard to make the overall situation intuitively understandable. This allows the analysis unit to visualize the analysis results, making it easier to intuitively understand the problems. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the analysis results into the generation AI, which then performs the visualization.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple display method. For example, if the user is relaxed, the analysis unit can also provide a detailed display method. For example, if the user is in a hurry, the analysis unit can also provide a concise display method. For example, the analysis unit estimates the user's emotions and provides a simple display method if the user is stressed. For example, if the user is relaxed, the analysis unit provides a detailed display method. For example, if the user is in a hurry, the analysis unit provides a concise display method. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the display method.

[0095] The analysis unit can identify best practices by comparing data from other offices during analysis. For example, the analysis unit can identify the optimal temperature setting by comparing data from other offices. The analysis unit can also identify the optimal noise reduction measures by comparing data from other offices. The analysis unit can also identify the optimal lighting settings by comparing data from other offices. For example, the analysis unit can identify the optimal temperature setting by comparing data from other offices. The analysis unit can identify the optimal noise reduction measures by comparing data from other offices. The analysis unit can identify the optimal lighting settings by comparing data from other offices. This allows the analysis unit to identify best practices and propose optimal improvement measures by comparing data from other offices. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs data from other offices into the generative AI, which can then identify best practices.

[0096] The analysis unit can share its analysis results with external experts and obtain feedback. For example, the analysis unit can share its analysis results with external experts and obtain feedback regarding temperature settings. The analysis unit can also share its analysis results with external experts and obtain feedback regarding noise reduction measures. The analysis unit can also share its analysis results with external experts and obtain feedback regarding lighting settings. For example, the analysis unit can share its analysis results with external experts and obtain feedback regarding temperature settings. The analysis unit can share its analysis results with external experts and obtain feedback regarding noise reduction measures. The analysis unit can share its analysis results with external experts and obtain feedback regarding lighting settings. This allows the analysis unit to improve the accuracy of its analysis results by obtaining feedback from external experts. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs its analysis results into the generating AI, which then shares them with external experts and obtains feedback.

[0097] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can offer more detailed suggestions. If the user is in a hurry, the suggestion unit can offer suggestions that can be implemented quickly. For example, the suggestion unit can estimate the user's emotions and offer simple and easy-to-implement suggestions if the user is stressed. If the user is relaxed, the suggestion unit can offer more detailed suggestions. If the user is in a hurry, the suggestion unit can offer suggestions that can be implemented quickly. This allows the suggestion unit to adjust the content of its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit are performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI, which can then estimate the emotions and adjust the content of its suggestions.

[0098] The proposal department can refer to past successful cases when making a proposal and propose the optimal improvement measures. For example, the proposal department can propose the optimal method of temperature control based on past successful cases. The proposal department can also propose the optimal method of noise reduction based on past successful cases. The proposal department can also propose the optimal method of lighting adjustment based on past successful cases. For example, the proposal department can propose the optimal method of temperature control based on past successful cases. The proposal department can propose the optimal method of noise reduction based on past successful cases. The proposal department can propose the optimal method of lighting adjustment based on past successful cases. In this way, the proposal department can propose the optimal improvement measures by referring to past successful cases. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department can input past successful cases into the generation AI, and the generation AI can propose the optimal improvement measures.

[0099] The proposal department can simulate the proposed content and evaluate its effects in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of temperature control in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of noise reduction in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of lighting adjustment in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of temperature control in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of noise reduction in advance. For example, the proposal department can simulate the proposed content and evaluate the effect of lighting adjustment in advance. This allows the proposal department to evaluate the effects in advance by simulating the proposed content and select the optimal improvement measures. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department can input the proposed content into the generation AI, which can perform a simulation and evaluate the effects in advance.

[0100] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize the most important suggestions. If the user is relaxed, the suggestion unit may also prioritize detailed suggestions. If the user is in a hurry, the suggestion unit may also prioritize suggestions that can be implemented quickly. For example, the suggestion unit estimates the user's emotions and prioritizes the most important suggestions if the user is stressed. If the user is relaxed, the suggestion unit prioritizes detailed suggestions. If the user is in a hurry, the suggestion unit prioritizes suggestions that can be implemented quickly. In this way, the suggestion unit can determine the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI, which can estimate the emotions and determine the priority of suggestions.

[0101] The proposal department can compare the proposed content with data from other offices and select the optimal improvement measures. For example, the proposal department can select the optimal temperature control measures by comparing them with data from other offices. The proposal department can also select the optimal noise reduction measures by comparing them with data from other offices. The proposal department can also select the optimal lighting adjustment measures by comparing them with data from other offices. For example, the proposal department can select the optimal temperature control measures by comparing them with data from other offices. The proposal department can select the optimal noise reduction measures by comparing them with data from other offices. The proposal department can select the optimal lighting adjustment measures by comparing them with data from other offices. In this way, the proposal department can select the optimal improvement measures by comparing them with data from other offices. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department can input data from other offices into the generation AI, and the generation AI can select the optimal improvement measures.

[0102] The proposal department can improve the accuracy of its proposals by having them reviewed by external experts. For example, the proposal department can improve the accuracy of temperature control by having the proposals reviewed by external experts. The proposal department can also improve the accuracy of noise reduction measures by having the proposals reviewed by external experts. The proposal department can also improve the accuracy of lighting adjustments by having the proposals reviewed by external experts. For example, the proposal department can improve the accuracy of temperature control by having the proposals reviewed by external experts. The proposal department can improve the accuracy of noise reduction measures by having the proposals reviewed by external experts. The proposal department can improve the accuracy of lighting adjustments by having the proposals reviewed by external experts. In this way, the proposal department can improve the accuracy of its proposals by having them reviewed by external experts. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department inputs the proposal content into the generation AI, and the generation AI can improve its accuracy by having it reviewed by external experts.

[0103] The execution support unit can estimate the user's emotions and adjust the execution procedure based on the estimated emotions. For example, if the user is stressed, the execution support unit can provide a simple and easy-to-follow procedure. For example, if the user is relaxed, the execution support unit can also provide a detailed procedure. For example, if the user is in a hurry, the execution support unit can also provide a procedure that can be executed quickly. For example, the execution support unit estimates the user's emotions and provides a simple and easy-to-follow procedure if the user is stressed. For example, if the user is relaxed, the execution support unit provides a detailed procedure. For example, if the user is in a hurry, the execution support unit provides a procedure that can be executed quickly. In this way, the execution support unit can adjust the execution procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the execution support unit is performed using the generative AI. For example, the execution support unit can input user emotion data into the generative AI, and the generative AI can estimate the emotions and adjust the execution procedure.

[0104] The execution support unit can automatically allocate the necessary resources during execution and execute efficiently. For example, the execution support unit can automatically allocate the necessary personnel during execution. The execution support unit can also automatically allocate the necessary equipment during execution. The execution support unit can also automatically allocate the necessary budget during execution. For example, the execution support unit can automatically allocate the necessary personnel during execution. The execution support unit can automatically allocate the necessary equipment during execution. The execution support unit can automatically allocate the necessary budget during execution. This enables efficient execution by automatically allocating the necessary resources. Some or all of the above processing in the execution support unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the execution support unit can input the necessary resources into a generation AI, and the generation AI can automatically allocate them.

[0105] The execution support unit can monitor the execution results in real time and make adjustments as needed. For example, the execution support unit can monitor the execution results in real time and confirm the effect of temperature adjustment. For example, the execution support unit can monitor the execution results in real time and confirm the effect of noise reduction measures. For example, the execution support unit can monitor the execution results in real time and confirm the effect of lighting adjustment. For example, the execution support unit can monitor the execution results in real time and confirm the effect of temperature adjustment. For example, the execution support unit can monitor the execution results in real time and confirm the effect of noise reduction measures. For example, the execution support unit can monitor the execution results in real time and confirm the effect of lighting adjustment. This allows the execution support unit to monitor the execution results in real time and make adjustments as needed. Some or all of the above processing in the execution support unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the execution support unit can input the execution results into a generation AI, which can monitor them in real time and make adjustments as needed.

[0106] The execution support unit can estimate the user's emotions and determine the priority of the execution steps based on the estimated user emotions. For example, if the user is stressed, the execution support unit will prioritize the most important steps. For example, if the user is relaxed, the execution support unit may also prioritize detailed steps. For example, if the user is in a hurry, the execution support unit may also prioritize steps that can be executed quickly. For example, the execution support unit estimates the user's emotions and prioritizes the most important steps if the user is stressed. If the user is relaxed, the execution support unit prioritizes detailed steps. If the user is in a hurry, the execution support unit prioritizes steps that can be executed quickly. In this way, the execution support unit can determine the priority of the execution steps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution support unit is performed using generative AI. For example, the execution support unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the execution steps.

[0107] The execution support unit can compare execution procedures with data from other offices and select the optimal procedure. For example, the execution support unit can select the optimal temperature control procedure by comparing it with data from other offices. For example, the execution support unit can also select the optimal noise reduction procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal lighting adjustment procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal temperature control procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal noise reduction procedure by comparing it with data from other offices. For example, the execution support unit can select the optimal lighting adjustment procedure by comparing it with data from other offices. In this way, the execution support unit can select the optimal procedure by comparing it with data from other offices. Some or all of the above processing in the execution support unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the execution support unit can input data from other offices into a generation AI, and the generation AI can select the optimal procedure.

[0108] The execution support unit can improve the accuracy of the execution procedures by having them reviewed by external experts. For example, the execution support unit can improve the accuracy of temperature control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of noise control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of lighting adjustment by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of temperature control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of noise control by having the execution procedures reviewed by external experts. For example, the execution support unit can improve the accuracy of lighting adjustment by having the execution procedures reviewed by external experts.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The office environment improvement system can also include a health management department that collects and analyzes employee health data. This department collects data such as employee heart rate, blood pressure, and stress levels. This allows for real-time monitoring of employee health and the implementation of appropriate measures as needed. For example, if an employee's heart rate is high, lighting and music can be adjusted to create a more relaxing environment. Similarly, if stress levels are high, notifications can be sent to encourage breaks. This helps maintain employee health and provides a comfortable working environment.

[0111] The office environment improvement system can also include a task management unit to further enhance employee work efficiency. This unit analyzes employee work content and assigns tasks efficiently. For example, it can automatically assign optimal tasks based on an employee's skill set and past work history. It can also monitor task progress in real time and reallocate resources as needed. This maximizes employee work efficiency and ensures smooth project progress.

[0112] The office environment improvement system can also include a communication support unit that estimates employees' emotions and adjusts communication methods based on those estimates. For example, if an employee is stressed, the communication support unit can provide concise and clear instructions. If they are relaxed, it can provide detailed explanations. If they are in a hurry, it can send short, to-the-point messages. This enables appropriate communication tailored to employees' emotions, improving work efficiency.

[0113] The office environment improvement system can also include a performance management unit that collects and analyzes employee performance data. This unit collects data such as employee work speed, accuracy, and creativity. This allows for real-time monitoring of employee performance and the provision of feedback as needed. For example, if an employee's work speed is slow, more efficient work methods can be suggested. Conversely, if an employee demonstrates high creativity, a message of praise can be sent. This can boost employee motivation and improve performance.

[0114] The office environment improvement system can also include a skills management department that collects and analyzes employee skill data. This department collects data such as employees' technical skills, soft skills, and experience. This allows for an understanding of employees' skill sets and the provision of appropriate training programs. For example, if an employee lacks a specific skill, relevant training courses can be suggested. Furthermore, if skills improve, promotions and compensation can be reviewed. This ultimately improves employee skills and enhances overall company performance.

[0115] The office environment improvement system can also include a refresh support unit that estimates employees' emotions and suggests refresh breaks based on those emotions. For example, if an employee is feeling stressed, the refresh support unit might suggest a short break. If they are relaxed, it could suggest a longer break. If they are in a hurry, it could suggest a quick way to refresh themselves. This allows for appropriate refresh breaks tailored to employees' emotions, thereby improving work efficiency.

[0116] The office environment improvement system can also include a movement management unit that collects and analyzes employee movement data. For example, the movement management unit can analyze employee movement patterns within the office and propose an efficient layout. This can reduce employee travel time and improve work efficiency. For instance, it can identify frequently used areas and place necessary equipment in those areas. Alternatively, it can identify areas with less movement and utilize them as relaxation spaces. This optimizes the office layout and improves employee comfort.

[0117] The office environment improvement system can also include a music support unit that estimates employees' emotions and provides music based on those emotions. For example, if an employee is feeling stressed, the music support unit can provide relaxing music. If they are relaxed, it can provide music that enhances concentration. If they are in a hurry, it can provide fast-paced music. This allows for the provision of appropriate music tailored to employees' emotions, thereby improving work efficiency.

[0118] The office environment improvement system can also include a communication management department that collects and analyzes employee communication data. This department can analyze data from emails and chats between employees, for example, and make suggestions to improve communication efficiency. This can streamline information sharing among employees and improve work efficiency. For instance, it can identify teams that communicate frequently and provide them with dedicated communication tools. It can also identify teams with insufficient communication and propose regular meetings. This optimizes communication among employees and improves work efficiency.

[0119] The office environment improvement system can also include a lighting control unit that estimates employees' emotions and adjusts the lighting based on those emotions. For example, the lighting control unit can provide soft lighting when an employee is stressed, brighter lighting when they are relaxed, and lighting that enhances concentration when they are in a hurry. This allows for appropriate lighting tailored to employees' emotions, thereby improving work efficiency.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects sensing data from various sensors within the office. For example, it collects environmental data within the office using temperature sensors, humidity sensors, illuminance sensors, noise sensors, etc. This allows for the collection of data such as temperature, humidity, illuminance, and noise levels in real time. Step 2: The retrieval unit retrieves the employee's calendar entries. For example, it retrieves the employee's meeting schedule and individual work hours using the APIs of calendar applications, email applications, and task management applications. Step 3: The analysis unit analyzes the data collected by the collection and acquisition units to identify problems in the office environment. For example, it may compare temperature data with meeting schedules to identify cases where the temperature in meeting rooms is too high during specific times, or compare noise data with work hours to identify cases where noise is a problem in specific areas. Step 4: The proposal team makes improvement suggestions for the problems identified by the analysis team. For example, they might suggest changing the air conditioning settings to properly regulate the temperature in the conference room, installing partitions to reduce noise, or adding lighting to improve illumination. Step 5: The Implementation Support Department provides specific procedures for implementing the improvement measures proposed by the Proposal Department. For example, it provides a script to automatically change the settings of an air conditioner, a diagram showing the location of partitions, and specific procedures for installing additional lighting.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and execution support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects environmental data such as temperature, humidity, illuminance, and noise level using the sensors of the smart device 14. The acquisition unit obtains employee calendar registration details through an application on the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to identify problems in the office environment. The proposal unit makes improvement suggestions by the specific processing unit 290 of the data processing unit 12. The execution support unit provides specific procedures for implementing the proposed improvement measures by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and execution support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects environmental data such as temperature, humidity, illuminance, and noise level using the sensors of the smart glasses 214. The acquisition unit acquires employee calendar registration information through the application of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to identify problems in the office environment. The proposal unit makes improvement suggestions by the specific processing unit 290 of the data processing unit 12. The execution support unit provides specific procedures for implementing the proposed improvement measures by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and execution support unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects environmental data such as temperature, humidity, illuminance, and noise level using the sensors of the headset terminal 314. The acquisition unit acquires employee calendar registration details through the application of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to identify problems in the office environment. The proposal unit makes improvement suggestions by the specific processing unit 290 of the data processing unit 12. The execution support unit provides specific procedures for implementing the proposed improvement measures by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the collection unit, acquisition unit, analysis unit, proposal unit, and execution support unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects environmental data such as temperature, humidity, illuminance, and noise level using the sensors of the robot 414. The acquisition unit acquires employee calendar registration information through the application of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to identify problems in the office environment. The proposal unit makes improvement suggestions by the specific processing unit 290 of the data processing unit 12. The execution support unit provides specific procedures for implementing the proposed improvement measures by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A data collection unit that collects sensing data from various sensors within the office, The acquisition unit retrieves the calendar registration details of employees, An analysis unit analyzes the data collected by the collection unit and the acquisition unit to identify problems in the office environment, A proposal unit that makes improvement suggestions for the problems identified by the analysis unit, The system includes an implementation support unit that provides specific procedures for putting into action the improvement measures proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect environmental data such as temperature, humidity, illuminance, and noise levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Retrieve employee calendar entries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on the collected data, we identify problems in the office environment, such as excessively high temperatures in meeting rooms during certain times or noise issues in specific areas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will make suggestions for improvement regarding the identified problems. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned execution support unit, Provide specific steps for implementing the proposed improvements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of environmental data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Dynamically change the placement of sensors to focus on collecting environmental data in specific areas. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system analyzes sensor data in real time and immediately notifies if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and selects the types of environmental data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The sensor data is compared with data from other offices to perform benchmarking. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Sensor data is linked with external weather data to predict environmental changes. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of calendar entry data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, The calendar content is analyzed to prioritize data acquisition for important meetings and events. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, Compare calendar data with other employees' schedules to identify common problems. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, It estimates the user's emotions and filters the calendar content retrieved based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, Integrate calendar data with external scheduling tools to maintain data consistency. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, Compare calendar data with historical data to identify patterns. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, environmental data and calendar data are integrated to identify correlations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, Visualize the analysis results to make the problems intuitively understandable. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we compare data from other offices to identify best practices. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, Share the analysis results with external experts and get their feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will refer to past success stories and propose the most suitable improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Simulate the proposed plan and evaluate its effectiveness in advance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We will compare the proposed solutions with data from other offices and select the most suitable improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, We will have external experts review the proposal to improve its accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned execution support unit, It estimates the user's emotions and adjusts the execution steps based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned execution support unit, At runtime, it automatically allocates the necessary resources and executes efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned execution support unit, The execution results are monitored in real time, and adjustments are made as needed. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned execution support unit, It estimates the user's emotions and determines the priority of the execution steps based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned execution support unit, Compare the execution procedures with data from other offices and select the optimal procedure. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned execution support unit, We have external experts review the execution procedures to improve accuracy. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects sensing data from various sensors within the office, The acquisition unit retrieves the calendar registration details of employees, An analysis unit analyzes the data collected by the collection unit and the acquisition unit to identify problems in the office environment, A proposal unit that makes improvement suggestions for the problems identified by the analysis unit, The system includes an implementation support unit that provides specific procedures for putting into action the improvement measures proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect environmental data such as temperature, humidity, illuminance, and noise levels. The system according to feature 1.

3. The acquisition unit is, Retrieve employee calendar entries. The system according to feature 1.

4. The aforementioned analysis unit, Based on the collected data, we identify problems in the office environment, such as excessively high temperatures in meeting rooms during certain times or noise issues in specific areas. The system according to feature 1.

5. The aforementioned proposal section is, We will make suggestions for improvement regarding the identified problems. The system according to feature 1.

6. The aforementioned execution support unit, Provide specific steps for implementing the proposed improvements. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of environmental data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Dynamically change the placement of sensors to focus on collecting environmental data in specific areas. The system according to feature 1.

9. The aforementioned collection unit is The system analyzes sensor data in real time and immediately notifies if an anomaly is detected. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the user's emotions and selects the types of environmental data to collect based on those estimated emotions. The system according to feature 1.

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