system

Abstract

The system according to this embodiment aims to comprehensively perform tasks ranging from data collection and analysis to the proposal and execution of actions in order to optimize facility operations. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a visualization unit, and an execution unit. The data collection unit collects data from IoT sensors and cameras. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis unit. The visualization unit visualizes the actions proposed by the proposal unit in a chat format. The execution unit executes the actions visualized by the visualization unit.

Description

Technical Field

[0005]

[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 to comprehensively perform from data collection and analysis to action proposal and execution in the optimization of facility operation, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively perform from data collection and analysis to action proposal and execution in order to optimize facility operation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a visualization unit, and an execution unit. The data collection unit collects data from IoT sensors and cameras. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis unit. The visualization unit visualizes the actions proposed by the proposal unit in a chat format. The execution unit executes the actions visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively perform tasks ranging from data collection and analysis to the proposal and execution of actions in order to optimize facility operations. [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) The smart building platform according to an embodiment of the present invention is a system for comprehensive and highly flexible facility management to optimize facility operations. This smart building platform uses a generating AI to utilize data collected from IoT sensors and cameras, as well as information inside and outside the facility, as input. Based on this information, the generating AI proposes actions for equipment, robots, and employees, and visualizes them in a chat format. Furthermore, it automatically generates outputs such as robot movements and equipment adjustments. For example, the smart building platform inputs data collected from IoT sensors and cameras into the generating AI. This includes temperature, humidity, lighting conditions, equipment operating status, employee movements, and visitor behavior patterns inside the facility. Information from outside the facility, such as weather, road and public transportation information, and social media trends, is also collected. Next, the generating AI analyzes this information and proposes optimal equipment and robot movements and employee actions. For example, if the temperature inside the facility becomes high, it may propose adjusting the air conditioning system, or it may propose operating cleaning robots during peak visitor times. It also proposes actions for employees, such as enhancing services in specific areas or guiding visitors. These suggestions are visualized in a chat format, allowing employees and managers to review them. Furthermore, the generating AI updates its suggestions through consultations with other agents and employees, and executes the optimal actions. This system improves the efficiency of facility operations and provides a comfortable environment. For example, optimizing temperature and lighting adjustments, cleaning robot operation, and employee actions improves visitor satisfaction and stimulates the facility's economic activity. In addition, automation by the generating AI reduces the burden on employees and improves operational efficiency. Moreover, because the generating AI collects and analyzes information inside and outside the facility in real time, it can flexibly respond to changing situations. For example, it can adjust the environment and services inside the facility in response to changes in weather or traffic conditions. This improves the sustainability of facility operations and ensures long-term business stability. In this way, a smart building platform utilizing generating AI optimizes facility operations and provides a comfortable environment and efficient management.This allows the smart building platform to optimize facility operations and provide a comfortable environment and efficient management.

[0029] The smart building platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a visualization unit, and an execution unit. The data collection unit collects data from IoT sensors and cameras. For example, the data collection unit can collect temperature data within a facility using a temperature sensor. The data collection unit can also collect humidity data within a facility using a humidity sensor. Furthermore, the data collection unit can collect video data within a facility using surveillance cameras. For example, the data collection unit can collect temperature data within a facility in real time using a temperature sensor. The data collection unit can also periodically collect humidity data within a facility using a humidity sensor. The data collection unit can also collect video data within a facility 24 hours a day using surveillance cameras. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using data mining technology. Furthermore, the analysis unit can analyze the collected data using machine learning algorithms. Furthermore, the analysis unit can analyze the collected data using generative AI. For example, the analysis unit can extract patterns from the collected data using data mining technology. The analysis unit can also classify the collected data using machine learning algorithms. The analysis unit can also build predictive models based on data collected using generative AI. The proposal unit proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose how to operate equipment. It can also propose operation instructions for robots. Furthermore, the proposal unit can propose action plans for employees. For example, the proposal unit can specifically propose how to operate equipment. It can also propose detailed operation instructions for robots. It can also efficiently propose action plans for employees. The visualization unit visualizes the actions proposed by the proposal unit in a chat format. For example, the visualization unit can visualize proposed actions using text chat. It can also visualize proposed actions using voice chat. Furthermore, the visualization unit can visualize proposed actions using a chatbot.For example, the visualization unit can visualize actions proposed using text chat in real time. The visualization unit can also visualize actions proposed using voice chat in audio format. The visualization unit can also automatically visualize actions proposed using a chatbot. The execution unit executes the actions visualized by the visualization unit. The execution unit can, for example, operate equipment. The execution unit can also execute robot actions. Furthermore, the execution unit can execute employee actions. For example, the execution unit can specifically operate equipment. The execution unit can also execute robot actions in detail. The execution unit can also efficiently execute employee actions. As a result, the smart building platform according to this embodiment enables comprehensive and flexible facility management to optimize facility operations.

[0030] The data collection unit collects data from IoT sensors and cameras. For example, the data collection unit can collect temperature data within a facility using temperature sensors. Specifically, temperature sensors are installed in each room or area of ​​the facility, collecting temperature data in real time and transmitting it to a central database. This allows for a unified understanding of the temperature situation throughout the entire facility. The data collection unit can also collect humidity data within the facility using humidity sensors. Humidity sensors are installed in each room or area, similar to temperature sensors, collecting humidity data periodically and transmitting it to a central database. This allows for detailed monitoring of the humidity situation within the facility. Furthermore, the data collection unit can also collect video data within the facility using surveillance cameras. Surveillance cameras are installed in important areas and entrances within the facility, collecting video data 24 hours a day and transmitting it to a central database. This allows for real-time monitoring of the security situation within the facility. For example, the data collection unit can collect temperature data within a facility in real time using temperature sensors. The data collection unit can also periodically collect humidity data within the facility using humidity sensors. The data collection unit can also collect video data within the facility 24 hours a day using surveillance cameras. This allows the data collection unit to efficiently collect environmental and security data within the facility, contributing to the optimization of facility operations.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze collected data using data mining techniques. By using data mining techniques, useful patterns and trends can be extracted from large amounts of collected data. The analysis unit can also analyze collected data using machine learning algorithms. By using machine learning algorithms, data classification and prediction can be performed, providing insights into facility management. Furthermore, the analysis unit can analyze collected data using generative AI. By using generative AI, predictive models can be built based on collected data to predict future situations. For example, the analysis unit can extract patterns from data collected using data mining techniques. The analysis unit can also classify collected data using machine learning algorithms. The analysis unit can also build predictive models based on collected data using generative AI. This allows the analysis unit to analyze collected data from multiple perspectives and provide information necessary for optimizing facility management. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical temperature data, temperature fluctuations in specific seasons or time periods can be predicted, and future countermeasures can be planned. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The proposal department proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis department. For example, the proposal department can propose how to operate equipment. Specifically, it can propose optimal settings for the air conditioning system based on data from temperature and humidity sensors. The proposal department can also propose robot operation instructions. For example, it can propose the optimal cleaning route and timing for cleaning robots to achieve efficient cleaning work. Furthermore, the proposal department can propose employee action plans. For example, it can propose optimizing employee work schedules and assignments in response to fluctuations in temperature and humidity within the facility. The proposal department can propose specific methods for operating equipment. The proposal department can also propose detailed robot operation instructions. The proposal department can also efficiently propose employee action plans. In this way, the proposal department can propose specific and practical actions based on the analysis results, supporting the efficiency and optimization of facility operations. Furthermore, the proposal department can evaluate the effectiveness of the proposals and continuously improve them. For example, by collecting the results of the execution of proposed actions as feedback and improving the proposal algorithm, it can make more accurate proposals. In this way, the proposal department can contribute to the optimization of facility operations and improve the overall system performance.

[0033] The visualization unit visualizes actions proposed by the proposal unit in a chat format. For example, the visualization unit can visualize actions proposed using text chat. Specifically, it displays proposed actions in text format so that users can easily understand them. The visualization unit can also visualize actions proposed using voice chat. By using voice chat, users can confirm the proposed content by voice and respond quickly. Furthermore, the visualization unit can also visualize actions proposed using a chatbot. By using a chatbot, the proposed content is displayed automatically, allowing users to obtain information interactively. For example, the visualization unit can visualize actions proposed using text chat in real time. The visualization unit can also visualize actions proposed using voice chat by voice. The visualization unit can also automatically visualize actions proposed using a chatbot. This allows the visualization unit to communicate proposed actions to users in an easy-to-understand manner and support quick responses. Furthermore, the visualization unit can collect user feedback and improve the visualization method. For example, based on user reactions and opinions, it can improve the display method of text chat and voice chat to provide a more user-friendly interface. This allows the visualization unit to provide users with effective information and contribute to optimizing facility operations.

[0034] The execution unit executes actions visualized by the visualization unit. For example, the execution unit can perform equipment operations. Specifically, it can automatically apply proposed air conditioning system settings to optimize temperature and humidity within the facility. The execution unit can also perform robot operations. For example, it can instruct cleaning robots on proposed cleaning routes to achieve efficient cleaning. Furthermore, the execution unit can perform employee actions. For example, it can notify employees of proposed work schedules and assignments to support efficient work. The execution unit can perform equipment operations in detail. The execution unit can also perform robot operations in detail. The execution unit can also efficiently perform employee actions. This allows the execution unit to quickly and accurately execute proposed actions, optimizing facility operations. Furthermore, the execution unit can collect execution results as feedback and provide it to the proposal and analysis units. For example, by evaluating the effectiveness of the executed actions and reflecting this in future proposals and analyses, the overall system performance can be improved. This allows the execution unit to contribute to the optimization of facility operations and improve the reliability and efficiency of the entire system.

[0035] The data collection unit can collect data such as temperature, humidity, lighting conditions, equipment operating status, employee movements, and visitor behavior patterns within the facility. For example, the data collection unit can collect temperature data within the facility using a temperature sensor. The data collection unit can also collect humidity data within the facility using a humidity sensor. The data collection unit can also collect lighting conditions within the facility using a lighting sensor. The data collection unit can also collect equipment operating status using an equipment operating status sensor. The data collection unit can also collect employee movements using an employee movement sensor. The data collection unit can also collect visitor behavior patterns using a visitor behavior pattern sensor. For example, the data collection unit can collect temperature data within the facility in real time using a temperature sensor. The data collection unit can also periodically collect humidity data within the facility using a humidity sensor. The data collection unit can also continuously collect lighting conditions within the facility using a lighting sensor. The data collection unit can also collect detailed equipment operating status using an equipment operating status sensor. The data collection unit can also accurately collect employee movements using an employee movement sensor. The data collection unit can also collect detailed visitor behavior patterns using a visitor behavior pattern sensor. This allows for the collection of detailed data within the facility, thereby optimizing facility operations.

[0036] The data collection unit can collect information such as weather outside the facility, road and public transport information, and social media trends. For example, the data collection unit can collect weather forecast data. The data collection unit can also collect road information data. The data collection unit can also collect public transport information. The data collection unit can also collect social media trend data. For example, the data collection unit can collect weather forecast data in real time. The data collection unit can also collect road information data periodically. The data collection unit can also continuously collect public transport information. The data collection unit can also collect social media trend data in detail. By collecting information outside the facility, facility operations can be optimized.

[0037] The analysis unit can analyze collected data and use generative AI to propose optimal equipment and robot operations, as well as employee actions. For example, the analysis unit can analyze collected data using data mining techniques. It can also analyze collected data using machine learning algorithms. Furthermore, it can analyze collected data using generative AI. For instance, the analysis unit can extract patterns from collected data using data mining techniques. It can also classify collected data using machine learning algorithms. Finally, it can build predictive models based on collected data using generative AI. This enables the proposal of optimal actions through the use of generative AI.

[0038] The proposal department can visualize actions suggested by the generation AI in a chat format. For example, the proposal department can visualize suggested actions using text chat. The proposal department can also visualize suggested actions using voice chat. The proposal department can also visualize suggested actions using a chatbot. For example, the proposal department can visualize suggested actions in real time using text chat. The proposal department can also visualize suggested actions using voice chat in audio format. The proposal department can also automatically visualize suggested actions using a chatbot. This makes it easier for employees and managers to review suggested actions by visualizing them in a chat format.

[0039] The execution unit can perform visualized actions. For example, the execution unit can operate equipment. The execution unit can also perform robot actions. The execution unit can also perform employee actions. For example, the execution unit can perform specific equipment operations. The execution unit can also perform robot actions in detail. The execution unit can also efficiently perform employee actions. This allows for increased efficiency in facility operations by performing visualized actions.

[0040] The data collection unit can dynamically change the frequency of data collection based on the usage frequency of specific areas within the facility. For example, in areas with high usage frequency, the data collection unit increases the frequency of data collection to collect more detailed data. In areas with low usage frequency, the data collection unit can also reduce the frequency of data collection to conserve resources. The data collection unit can also adjust the data collection frequency in real time in response to fluctuations in usage frequency. This allows for efficient use of resources by adjusting the data collection frequency according to usage frequency.

[0041] The data collection unit can detect anomalies within the facility during data collection and collect additional data if an anomaly occurs. For example, in areas where an anomaly is detected, the data collection unit increases the frequency of data collection to collect more detailed data. The data collection unit can also collect additional data to identify the cause of an anomaly if one occurs. The data collection unit can also maintain the frequency of data collection and monitor the situation until the anomaly is resolved. This makes it easier to identify the cause of an anomaly by collecting additional data when one occurs.

[0042] The data collection unit can prioritize the collection of data related to specific events, taking into account the event schedule within the facility. For example, during an event, the unit prioritizes the collection of event-related data. The unit can also collect data related to event preparation and cleanup before and after an event. Depending on the type of event, the unit can select and collect only the necessary data. This allows for more efficient event preparation and operation by prioritizing the collection of event-related data.

[0043] The data collection unit can select an energy-efficient data collection method, taking into account the energy consumption situation within the facility. For example, the unit can collect data during periods of low energy consumption. The unit can also collect data using energy-efficient sensors and equipment. The unit can also adjust the frequency and method of data collection according to the energy consumption situation. By selecting an energy-efficient data collection method, it becomes possible to collect data while suppressing energy consumption.

[0044] The analysis unit can detect anomalies by comparing current data with past data during analysis and propose countermeasures for those anomalies. For example, the analysis unit can detect anomalies by comparing current data with past data and identify the cause of the anomaly. If an anomaly is detected, the analysis unit can also propose countermeasures for that anomaly. The analysis unit can also analyze the frequency and patterns of anomalies and propose preventive measures. This allows for the rapid proposal of countermeasures for anomalies by detecting them by comparing current data with past data.

[0045] The analysis unit can apply different analysis algorithms to different areas within the facility during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the characteristics of each area. The analysis unit can also analyze data for each area individually and provide detailed results. The analysis unit can also integrate the analysis results from each area to optimize the overall process. This allows for the provision of detailed analysis results by applying the optimal analysis algorithm for each area.

[0046] The analysis unit can provide analysis results that contribute to improving energy efficiency by considering energy consumption data within the facility during the analysis. For example, the analysis unit can provide analysis results that contribute to improving energy efficiency based on energy consumption data. The analysis unit can also propose efficient operating methods during peak energy consumption periods. The analysis unit can also propose specific measures to reduce energy consumption. In this way, by considering energy consumption data, it can provide analysis results that contribute to improving energy efficiency.

[0047] The analysis unit can provide analysis results that contribute to enhanced security by considering security data within the facility during the analysis. For example, the analysis unit can provide analysis results that contribute to enhanced security based on security data. The analysis unit can also detect security threats and propose countermeasures. The analysis unit can also propose improvements to security measures based on the analysis results of security data. In this way, by considering security data, it can provide analysis results that contribute to enhanced security.

[0048] The proposal department can suggest optimal actions based on the usage status of specific areas within the facility. For example, in areas with high usage, the proposal department might suggest increasing the frequency of cleaning and maintenance. In areas with low usage, the proposal department might suggest reducing energy consumption. The proposal department can also suggest optimal actions according to usage conditions. This allows for more efficient facility management by suggesting optimal actions based on usage.

[0049] The proposal department can propose countermeasures for anomalies based on the results of anomaly detection within the facility. For example, if an anomaly is detected, the proposal department can identify the cause of the anomaly and propose countermeasures. The proposal department can also analyze the frequency and patterns of anomalies and propose preventive measures. The proposal department can also propose continuous monitoring and countermeasures until the anomaly is resolved. This enables a rapid response to anomalies by proposing countermeasures based on the anomaly detection results.

[0050] The proposal department can propose actions that contribute to improving energy efficiency, taking into account the energy consumption situation within the facility. For example, the proposal department can propose actions that contribute to improving energy efficiency based on energy consumption data. The proposal department can also propose efficient operating methods during peak energy consumption periods. The proposal department can also propose specific measures to reduce energy consumption. In this way, by considering the energy consumption situation, it is possible to propose actions that contribute to improving energy efficiency.

[0051] The proposal department can propose actions that contribute to strengthening security, taking into account the security situation within the facility. For example, the proposal department can propose actions that contribute to strengthening security based on security data. The proposal department can also detect security threats and propose countermeasures. The proposal department can also propose improvements to security measures based on the results of security data analysis. In this way, by considering the security situation, it is possible to propose actions that contribute to strengthening security.

[0052] The visualization unit can highlight important information based on the usage status of specific areas within the facility during visualization. For example, the visualization unit can highlight information about frequently used areas to draw the attention of administrators. The visualization unit can also display information about infrequently used areas concisely, saving resources. The visualization unit can also highlight important information in real time according to usage status. This allows it to draw the attention of administrators by highlighting important information according to usage status.

[0053] The visualization unit can display the results of anomaly detection within the facility in real time during visualization. For example, if an anomaly is detected, the visualization unit will display detailed information about the anomaly in real time. The visualization unit can also display the frequency and patterns of anomalies in real time and notify administrators. The visualization unit can also continuously display anomaly information until the anomaly is resolved. This enables rapid response to anomalies by displaying anomaly detection results in real time.

[0054] The visualization unit can display information that contributes to improving energy efficiency by considering the energy consumption situation within the facility during visualization. For example, the visualization unit can display information that contributes to improving energy efficiency based on energy consumption data. The visualization unit can also display efficient operating methods during peak energy consumption periods. The visualization unit can also display specific measures aimed at reducing energy consumption. In this way, by considering the energy consumption situation, it can display information that contributes to improving energy efficiency.

[0055] The visualization unit can display information that contributes to strengthening security, taking into account the security situation within the facility during visualization. For example, the visualization unit can display information that contributes to strengthening security based on security data. The visualization unit can also display security threats in real time and notify administrators. The visualization unit can also display areas for improvement in security measures based on the analysis results of security data. In this way, by taking the security situation into consideration, it can display information that contributes to strengthening security.

[0056] The execution unit can perform actions at the optimal time based on the usage status of specific areas within the facility during execution. For example, in areas with high usage frequency, the execution unit can frequently adjust the timing of actions. In areas with low usage frequency, the execution unit can also reduce the timing of actions. The execution unit can also perform actions at the optimal time according to the usage status. This improves the efficiency of facility operations by performing actions at the optimal time according to the usage status.

[0057] The execution unit can implement countermeasures against anomalies based on the anomaly detection results within the facility during execution. For example, if an anomaly is detected, the execution unit will quickly implement countermeasures. The execution unit can also identify the cause of the anomaly and implement appropriate countermeasures. The execution unit can also continuously implement countermeasures until the anomaly is resolved. This enables rapid response to anomalies by implementing countermeasures based on the anomaly detection results.

[0058] The execution unit can take actions that contribute to improving energy efficiency by considering the energy consumption situation within the facility during execution. For example, the execution unit can take actions that contribute to improving energy efficiency based on energy consumption data. The execution unit can also implement efficient operating methods during peak energy consumption periods. The execution unit can also implement specific measures to reduce energy consumption. In this way, by considering the energy consumption situation, it can take actions that contribute to improving energy efficiency.

[0059] The execution unit can take actions that contribute to strengthening security, taking into account the security situation within the facility during execution. For example, the execution unit can take actions that contribute to strengthening security based on security data. The execution unit can also detect security threats and take swift countermeasures. The execution unit can also implement improvements to security measures based on the results of security data analysis. In this way, by taking the security situation into consideration, it can take actions that contribute to strengthening security.

[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 proposal department can suggest optimal actions based on the usage patterns of specific areas within the facility. For example, in areas with high usage, the proposal department might suggest increasing the frequency of cleaning and maintenance. In areas with low usage, the proposal department might suggest reducing energy consumption. The proposal department can also suggest optimal actions according to usage patterns. This allows for more efficient facility management by suggesting the most appropriate actions based on usage patterns.

[0062] The analysis unit can apply different analysis algorithms to different areas within the facility. For example, the analysis unit can select the optimal analysis algorithm according to the characteristics of each area. The analysis unit can also analyze data for each area individually and provide detailed results. The analysis unit can also integrate the analysis results from each area to optimize the overall process. This allows for the provision of detailed analysis results by applying the optimal analysis algorithm for each area.

[0063] The data collection unit can dynamically change the frequency of data collection based on the usage frequency of specific areas within the facility. For example, in areas with high usage frequency, the data collection unit increases the frequency of data collection to collect more detailed data. In areas with low usage frequency, the data collection unit can also reduce the frequency of data collection to conserve resources. The data collection unit can also adjust the data collection frequency in real time in response to fluctuations in usage frequency. This allows for efficient use of resources by adjusting the data collection frequency according to usage frequency.

[0064] The analysis unit can provide analysis results that contribute to improving energy efficiency by considering energy consumption data within the facility during the analysis. For example, the analysis unit can provide analysis results that contribute to improving energy efficiency based on energy consumption data. The analysis unit can also propose efficient operating methods during peak energy consumption periods. The analysis unit can also propose specific measures to reduce energy consumption. In this way, by considering energy consumption data, it can provide analysis results that contribute to improving energy efficiency.

[0065] The execution unit can implement countermeasures against anomalies based on the anomaly detection results within the facility during execution. For example, if an anomaly is detected, the execution unit will quickly implement countermeasures. The execution unit can also identify the cause of the anomaly and implement appropriate countermeasures. The execution unit can also continuously implement countermeasures until the anomaly is resolved. This enables rapid response to anomalies by implementing countermeasures based on the anomaly detection results.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data from IoT sensors and cameras. For example, it can collect temperature data within the facility in real time using a temperature sensor, collect humidity data within the facility periodically using a humidity sensor, and collect video data within the facility 24 hours a day using surveillance cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can extract patterns from the collected data using data mining techniques, classify the collected data using machine learning algorithms, and build a predictive model based on the collected data using generative AI. Step 3: The proposal department proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis department. For example, it can propose specific and efficient methods for operating equipment, instructions for robot operation, and action plans for employees. Step 4: The visualization unit visualizes the actions proposed by the proposal unit in a chat format. For example, actions proposed using text chat, voice chat, or a chatbot can be visualized in real time. Step 5: The execution unit executes the actions visualized by the visualization unit. For example, it can perform specific and efficient actions such as operating equipment, operating robots, and taking employee actions.

[0068] (Example of form 2) The smart building platform according to an embodiment of the present invention is a system for comprehensive and highly flexible facility management to optimize facility operations. This smart building platform uses a generating AI to utilize data collected from IoT sensors and cameras, as well as information inside and outside the facility, as input. Based on this information, the generating AI proposes actions for equipment, robots, and employees, and visualizes them in a chat format. Furthermore, it automatically generates outputs such as robot movements and equipment adjustments. For example, the smart building platform inputs data collected from IoT sensors and cameras into the generating AI. This includes temperature, humidity, lighting conditions, equipment operating status, employee movements, and visitor behavior patterns inside the facility. Information from outside the facility, such as weather, road and public transportation information, and social media trends, is also collected. Next, the generating AI analyzes this information and proposes optimal equipment and robot movements and employee actions. For example, if the temperature inside the facility becomes high, it may propose adjusting the air conditioning system, or it may propose operating cleaning robots during peak visitor times. It also proposes actions for employees, such as enhancing services in specific areas or guiding visitors. These suggestions are visualized in a chat format, allowing employees and managers to review them. Furthermore, the generating AI updates its suggestions through consultations with other agents and employees, and executes the optimal actions. This system improves the efficiency of facility operations and provides a comfortable environment. For example, optimizing temperature and lighting adjustments, cleaning robot operation, and employee actions improves visitor satisfaction and stimulates the facility's economic activity. In addition, automation by the generating AI reduces the burden on employees and improves operational efficiency. Moreover, because the generating AI collects and analyzes information inside and outside the facility in real time, it can flexibly respond to changing situations. For example, it can adjust the environment and services inside the facility in response to changes in weather or traffic conditions. This improves the sustainability of facility operations and ensures long-term business stability. In this way, a smart building platform utilizing generating AI optimizes facility operations and provides a comfortable environment and efficient management.This allows the smart building platform to optimize facility operations and provide a comfortable environment and efficient management.

[0069] The smart building platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a visualization unit, and an execution unit. The data collection unit collects data from IoT sensors and cameras. For example, the data collection unit can collect temperature data within a facility using a temperature sensor. The data collection unit can also collect humidity data within a facility using a humidity sensor. Furthermore, the data collection unit can collect video data within a facility using surveillance cameras. For example, the data collection unit can collect temperature data within a facility in real time using a temperature sensor. The data collection unit can also periodically collect humidity data within a facility using a humidity sensor. The data collection unit can also collect video data within a facility 24 hours a day using surveillance cameras. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data using data mining technology. Furthermore, the analysis unit can analyze the collected data using machine learning algorithms. Furthermore, the analysis unit can analyze the collected data using generative AI. For example, the analysis unit can extract patterns from the collected data using data mining technology. The analysis unit can also classify the collected data using machine learning algorithms. The analysis unit can also build predictive models based on data collected using generative AI. The proposal unit proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose how to operate equipment. It can also propose operation instructions for robots. Furthermore, the proposal unit can propose action plans for employees. For example, the proposal unit can specifically propose how to operate equipment. It can also propose detailed operation instructions for robots. It can also efficiently propose action plans for employees. The visualization unit visualizes the actions proposed by the proposal unit in a chat format. For example, the visualization unit can visualize proposed actions using text chat. It can also visualize proposed actions using voice chat. Furthermore, the visualization unit can visualize proposed actions using a chatbot.For example, the visualization unit can visualize actions proposed using text chat in real time. The visualization unit can also visualize actions proposed using voice chat in audio format. The visualization unit can also automatically visualize actions proposed using a chatbot. The execution unit executes the actions visualized by the visualization unit. The execution unit can, for example, operate equipment. The execution unit can also execute robot actions. Furthermore, the execution unit can execute employee actions. For example, the execution unit can specifically operate equipment. The execution unit can also execute robot actions in detail. The execution unit can also efficiently execute employee actions. As a result, the smart building platform according to this embodiment enables comprehensive and flexible facility management to optimize facility operations.

[0070] The data collection unit collects data from IoT sensors and cameras. For example, the data collection unit can collect temperature data within a facility using temperature sensors. Specifically, temperature sensors are installed in each room or area of ​​the facility, collecting temperature data in real time and transmitting it to a central database. This allows for a unified understanding of the temperature situation throughout the entire facility. The data collection unit can also collect humidity data within the facility using humidity sensors. Humidity sensors are installed in each room or area, similar to temperature sensors, collecting humidity data periodically and transmitting it to a central database. This allows for detailed monitoring of the humidity situation within the facility. Furthermore, the data collection unit can also collect video data within the facility using surveillance cameras. Surveillance cameras are installed in important areas and entrances within the facility, collecting video data 24 hours a day and transmitting it to a central database. This allows for real-time monitoring of the security situation within the facility. For example, the data collection unit can collect temperature data within a facility in real time using temperature sensors. The data collection unit can also periodically collect humidity data within the facility using humidity sensors. The data collection unit can also collect video data within the facility 24 hours a day using surveillance cameras. This allows the data collection unit to efficiently collect environmental and security data within the facility, contributing to the optimization of facility operations.

[0071] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze collected data using data mining techniques. By using data mining techniques, useful patterns and trends can be extracted from large amounts of collected data. The analysis unit can also analyze collected data using machine learning algorithms. By using machine learning algorithms, data classification and prediction can be performed, providing insights into facility management. Furthermore, the analysis unit can analyze collected data using generative AI. By using generative AI, predictive models can be built based on collected data to predict future situations. For example, the analysis unit can extract patterns from data collected using data mining techniques. The analysis unit can also classify collected data using machine learning algorithms. The analysis unit can also build predictive models based on collected data using generative AI. This allows the analysis unit to analyze collected data from multiple perspectives and provide information necessary for optimizing facility management. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical temperature data, temperature fluctuations in specific seasons or time periods can be predicted, and future countermeasures can be planned. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The proposal department proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis department. For example, the proposal department can propose how to operate equipment. Specifically, it can propose optimal settings for the air conditioning system based on data from temperature and humidity sensors. The proposal department can also propose robot operation instructions. For example, it can propose the optimal cleaning route and timing for cleaning robots to achieve efficient cleaning work. Furthermore, the proposal department can propose employee action plans. For example, it can propose optimizing employee work schedules and assignments in response to fluctuations in temperature and humidity within the facility. The proposal department can propose specific methods for operating equipment. The proposal department can also propose detailed robot operation instructions. The proposal department can also efficiently propose employee action plans. In this way, the proposal department can propose specific and practical actions based on the analysis results, supporting the efficiency and optimization of facility operations. Furthermore, the proposal department can evaluate the effectiveness of the proposals and continuously improve them. For example, by collecting the results of the execution of proposed actions as feedback and improving the proposal algorithm, it can make more accurate proposals. In this way, the proposal department can contribute to the optimization of facility operations and improve the overall system performance.

[0073] The visualization unit visualizes actions proposed by the proposal unit in a chat format. For example, the visualization unit can visualize actions proposed using text chat. Specifically, it displays proposed actions in text format so that users can easily understand them. The visualization unit can also visualize actions proposed using voice chat. By using voice chat, users can confirm the proposed content by voice and respond quickly. Furthermore, the visualization unit can also visualize actions proposed using a chatbot. By using a chatbot, the proposed content is displayed automatically, allowing users to obtain information interactively. For example, the visualization unit can visualize actions proposed using text chat in real time. The visualization unit can also visualize actions proposed using voice chat by voice. The visualization unit can also automatically visualize actions proposed using a chatbot. This allows the visualization unit to communicate proposed actions to users in an easy-to-understand manner and support quick responses. Furthermore, the visualization unit can collect user feedback and improve the visualization method. For example, based on user reactions and opinions, it can improve the display method of text chat and voice chat to provide a more user-friendly interface. This allows the visualization unit to provide users with effective information and contribute to optimizing facility operations.

[0074] The execution unit executes actions visualized by the visualization unit. For example, the execution unit can perform equipment operations. Specifically, it can automatically apply proposed air conditioning system settings to optimize temperature and humidity within the facility. The execution unit can also perform robot operations. For example, it can instruct cleaning robots on proposed cleaning routes to achieve efficient cleaning. Furthermore, the execution unit can perform employee actions. For example, it can notify employees of proposed work schedules and assignments to support efficient work. The execution unit can perform equipment operations in detail. The execution unit can also perform robot operations in detail. The execution unit can also efficiently perform employee actions. This allows the execution unit to quickly and accurately execute proposed actions, optimizing facility operations. Furthermore, the execution unit can collect execution results as feedback and provide it to the proposal and analysis units. For example, by evaluating the effectiveness of the executed actions and reflecting this in future proposals and analyses, the overall system performance can be improved. This allows the execution unit to contribute to the optimization of facility operations and improve the reliability and efficiency of the entire system.

[0075] The data collection unit can collect data such as temperature, humidity, lighting conditions, equipment operating status, employee movements, and visitor behavior patterns within the facility. For example, the data collection unit can collect temperature data within the facility using a temperature sensor. The data collection unit can also collect humidity data within the facility using a humidity sensor. The data collection unit can also collect lighting conditions within the facility using a lighting sensor. The data collection unit can also collect equipment operating status using an equipment operating status sensor. The data collection unit can also collect employee movements using an employee movement sensor. The data collection unit can also collect visitor behavior patterns using a visitor behavior pattern sensor. For example, the data collection unit can collect temperature data within the facility in real time using a temperature sensor. The data collection unit can also periodically collect humidity data within the facility using a humidity sensor. The data collection unit can also continuously collect lighting conditions within the facility using a lighting sensor. The data collection unit can also collect detailed equipment operating status using an equipment operating status sensor. The data collection unit can also accurately collect employee movements using an employee movement sensor. The data collection unit can also collect detailed visitor behavior patterns using a visitor behavior pattern sensor. This allows for the collection of detailed data within the facility, thereby optimizing facility operations.

[0076] The data collection unit can collect information such as weather outside the facility, road and public transport information, and social media trends. For example, the data collection unit can collect weather forecast data. The data collection unit can also collect road information data. The data collection unit can also collect public transport information. The data collection unit can also collect social media trend data. For example, the data collection unit can collect weather forecast data in real time. The data collection unit can also collect road information data periodically. The data collection unit can also continuously collect public transport information. The data collection unit can also collect social media trend data in detail. By collecting information outside the facility, facility operations can be optimized.

[0077] The analysis unit can analyze collected data and use generative AI to propose optimal equipment and robot operations, as well as employee actions. For example, the analysis unit can analyze collected data using data mining techniques. It can also analyze collected data using machine learning algorithms. Furthermore, it can analyze collected data using generative AI. For instance, the analysis unit can extract patterns from collected data using data mining techniques. It can also classify collected data using machine learning algorithms. Finally, it can build predictive models based on collected data using generative AI. This enables the proposal of optimal actions through the use of generative AI.

[0078] The proposal department can visualize actions suggested by the generation AI in a chat format. For example, the proposal department can visualize suggested actions using text chat. The proposal department can also visualize suggested actions using voice chat. The proposal department can also visualize suggested actions using a chatbot. For example, the proposal department can visualize suggested actions in real time using text chat. The proposal department can also visualize suggested actions using voice chat in audio format. The proposal department can also automatically visualize suggested actions using a chatbot. This makes it easier for employees and managers to review suggested actions by visualizing them in a chat format.

[0079] The execution unit can perform visualized actions. For example, the execution unit can operate equipment. The execution unit can also perform robot actions. The execution unit can also perform employee actions. For example, the execution unit can perform specific equipment operations. The execution unit can also perform robot actions in detail. The execution unit can also efficiently perform employee actions. This allows for increased efficiency in facility operations by performing visualized actions.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection and collect more detailed data. If the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This reduces the user's burden by adjusting the timing of data collection according to their 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.

[0081] The data collection unit can dynamically change the frequency of data collection based on the usage frequency of specific areas within the facility. For example, in areas with high usage frequency, the data collection unit increases the frequency of data collection to collect more detailed data. In areas with low usage frequency, the data collection unit can also reduce the frequency of data collection to conserve resources. The data collection unit can also adjust the data collection frequency in real time in response to fluctuations in usage frequency. This allows for efficient use of resources by adjusting the data collection frequency according to usage frequency.

[0082] The data collection unit can detect anomalies within the facility during data collection and collect additional data if an anomaly occurs. For example, in areas where an anomaly is detected, the data collection unit increases the frequency of data collection to collect more detailed data. The data collection unit can also collect additional data to identify the cause of an anomaly if one occurs. The data collection unit can also maintain the frequency of data collection and monitor the situation until the anomaly is resolved. This makes it easier to identify the cause of an anomaly by collecting additional data when one occurs.

[0083] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. If the user is in a hurry, the data collection unit can also prioritize data that can be collected quickly. This allows for the priority collection of important data by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit can prioritize the collection of data related to specific events, taking into account the event schedule within the facility. For example, during an event, the unit prioritizes the collection of event-related data. The unit can also collect data related to event preparation and cleanup before and after an event. Depending on the type of event, the unit can select and collect only the necessary data. This allows for more efficient event preparation and operation by prioritizing the collection of event-related data.

[0085] The data collection unit can select an energy-efficient data collection method, taking into account the energy consumption situation within the facility. For example, the unit can collect data during periods of low energy consumption. The unit can also collect data using energy-efficient sensors and equipment. The unit can also adjust the frequency and method of data collection according to the energy consumption situation. By selecting an energy-efficient data collection method, it becomes possible to collect data while suppressing energy consumption.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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.

[0087] The analysis unit can detect anomalies by comparing current data with past data during analysis and propose countermeasures for those anomalies. For example, the analysis unit can detect anomalies by comparing current data with past data and identify the cause of the anomaly. If an anomaly is detected, the analysis unit can also propose countermeasures for that anomaly. The analysis unit can also analyze the frequency and patterns of anomalies and propose preventive measures. This allows for the rapid proposal of countermeasures for anomalies by detecting them by comparing current data with past data.

[0088] The analysis unit can apply different analysis algorithms to different areas within the facility during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the characteristics of each area. The analysis unit can also analyze data for each area individually and provide detailed results. The analysis unit can also integrate the analysis results from each area to optimize the overall process. This allows for the provision of detailed analysis results by applying the optimal analysis algorithm for each area.

[0089] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed results. If the user is in a hurry, the analysis unit can also provide concise results that get straight to the point. If the user is stressed, the analysis unit can also provide visually easy-to-understand results. In this way, by adjusting the level of detail in the analysis results according to the user's emotions, the system can provide the user with the most optimal analysis results. 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.

[0090] The analysis unit can provide analysis results that contribute to improving energy efficiency by considering energy consumption data within the facility during the analysis. For example, the analysis unit can provide analysis results that contribute to improving energy efficiency based on energy consumption data. The analysis unit can also propose efficient operating methods during peak energy consumption periods. The analysis unit can also propose specific measures to reduce energy consumption. In this way, by considering energy consumption data, it can provide analysis results that contribute to improving energy efficiency.

[0091] The analysis unit can provide analysis results that contribute to enhanced security by considering security data within the facility during the analysis. For example, the analysis unit can provide analysis results that contribute to enhanced security based on security data. The analysis unit can also detect security threats and propose countermeasures. The analysis unit can also propose improvements to security measures based on the analysis results of security data. In this way, by considering security data, it can provide analysis results that contribute to enhanced security.

[0092] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. If the user is stressed, it can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand. 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.

[0093] The proposal department can suggest optimal actions based on the usage status of specific areas within the facility. For example, in areas with high usage, the proposal department might suggest increasing the frequency of cleaning and maintenance. In areas with low usage, the proposal department might suggest reducing energy consumption. The proposal department can also suggest optimal actions according to usage conditions. This allows for more efficient facility management by suggesting optimal actions based on usage.

[0094] The proposal department can propose countermeasures for anomalies based on the results of anomaly detection within the facility. For example, if an anomaly is detected, the proposal department can identify the cause of the anomaly and propose countermeasures. The proposal department can also analyze the frequency and patterns of anomalies and propose preventive measures. The proposal department can also propose continuous monitoring and countermeasures until the anomaly is resolved. This enables a rapid response to anomalies by proposing countermeasures based on the anomaly detection results.

[0095] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize only important suggestions. If the user is relaxed, the suggestion function may also prioritize detailed suggestions. If the user is in a hurry, the suggestion function may also prioritize suggestions that can be acted upon quickly. This allows for prioritizing important suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The proposal department can propose actions that contribute to improving energy efficiency, taking into account the energy consumption situation within the facility. For example, the proposal department can propose actions that contribute to improving energy efficiency based on energy consumption data. The proposal department can also propose efficient operating methods during peak energy consumption periods. The proposal department can also propose specific measures to reduce energy consumption. In this way, by considering the energy consumption situation, it is possible to propose actions that contribute to improving energy efficiency.

[0097] The proposal department can propose actions that contribute to strengthening security, taking into account the security situation within the facility. For example, the proposal department can propose actions that contribute to strengthening security based on security data. The proposal department can also detect security threats and propose countermeasures. The proposal department can also propose improvements to security measures based on the results of security data analysis. In this way, by considering the security situation, it is possible to propose actions that contribute to strengthening security.

[0098] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on the estimated emotions. For example, if the user is relaxed, the visualization unit can provide detailed information. If the user is in a hurry, the visualization unit can also provide concise information that gets straight to the point. If the user is stressed, the visualization unit can also provide visually easy-to-understand information. In this way, by adjusting the visualization's presentation according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The visualization unit can highlight important information based on the usage status of specific areas within the facility during visualization. For example, the visualization unit can highlight information about frequently used areas to draw the attention of administrators. The visualization unit can also display information about infrequently used areas concisely, saving resources. The visualization unit can also highlight important information in real time according to usage status. This allows it to draw the attention of administrators by highlighting important information according to usage status.

[0100] The visualization unit can display the results of anomaly detection within the facility in real time during visualization. For example, if an anomaly is detected, the visualization unit will display detailed information about the anomaly in real time. The visualization unit can also display the frequency and patterns of anomalies in real time and notify administrators. The visualization unit can also continuously display anomaly information until the anomaly is resolved. This enables rapid response to anomalies by displaying anomaly detection results in real time.

[0101] The visualization unit can estimate the user's emotions and determine the visualization priority based on the estimated emotions. For example, if the user is stressed, the visualization unit will prioritize displaying only important information. If the user is relaxed, the visualization unit can also prioritize displaying detailed information. If the user is in a hurry, the visualization unit can also prioritize information that can be quickly viewed. In this way, by determining the visualization priority according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The visualization unit can display information that contributes to improving energy efficiency by considering the energy consumption situation within the facility during visualization. For example, the visualization unit can display information that contributes to improving energy efficiency based on energy consumption data. The visualization unit can also display efficient operating methods during peak energy consumption periods. The visualization unit can also display specific measures aimed at reducing energy consumption. In this way, by considering the energy consumption situation, it can display information that contributes to improving energy efficiency.

[0103] The visualization unit can display information that contributes to strengthening security, taking into account the security situation within the facility during visualization. For example, the visualization unit can display information that contributes to strengthening security based on security data. The visualization unit can also display security threats in real time and notify administrators. The visualization unit can also display areas for improvement in security measures based on the analysis results of security data. In this way, by taking the security situation into consideration, it can display information that contributes to strengthening security.

[0104] The execution unit can estimate the user's emotions and adjust the timing of actions based on those emotions. For example, if the user is relaxed, the execution unit can flexibly adjust the timing of actions. If the user is in a hurry, the execution unit can also perform actions quickly. If the user is stressed, the execution unit can also carefully adjust the timing of actions. This allows actions to be performed at the optimal time for the user by adjusting the timing according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The execution unit can perform actions at the optimal time based on the usage status of specific areas within the facility during execution. For example, in areas with high usage frequency, the execution unit can frequently adjust the timing of actions. In areas with low usage frequency, the execution unit can also reduce the timing of actions. The execution unit can also perform actions at the optimal time according to the usage status. This improves the efficiency of facility operations by performing actions at the optimal time according to the usage status.

[0106] The execution unit can implement countermeasures against anomalies based on the anomaly detection results within the facility during execution. For example, if an anomaly is detected, the execution unit will quickly implement countermeasures. The execution unit can also identify the cause of the anomaly and implement appropriate countermeasures. The execution unit can also continuously implement countermeasures until the anomaly is resolved. This enables rapid response to anomalies by implementing countermeasures based on the anomaly detection results.

[0107] The execution unit can estimate the user's emotions and determine the priority of actions to perform based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize only important actions. If the user is relaxed, the execution unit may also prioritize detailed actions. If the user is in a hurry, the execution unit may also prioritize actions that can be performed quickly. This allows for prioritizing important actions by determining the priority of actions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The execution unit can take actions that contribute to improving energy efficiency by considering the energy consumption situation within the facility during execution. For example, the execution unit can take actions that contribute to improving energy efficiency based on energy consumption data. The execution unit can also implement efficient operating methods during peak energy consumption periods. The execution unit can also implement specific measures to reduce energy consumption. In this way, by considering the energy consumption situation, it can take actions that contribute to improving energy efficiency.

[0109] The execution unit can take actions that contribute to strengthening security, taking into account the security situation within the facility during execution. For example, the execution unit can take actions that contribute to strengthening security based on security data. The execution unit can also detect security threats and take swift countermeasures. The execution unit can also implement improvements to security measures based on the results of security data analysis. In this way, by taking the security situation into consideration, it can take actions that contribute to strengthening security.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The proposal department can suggest optimal actions based on the usage patterns of specific areas within the facility. For example, in areas with high usage, the proposal department might suggest increasing the frequency of cleaning and maintenance. In areas with low usage, the proposal department might suggest reducing energy consumption. The proposal department can also suggest optimal actions according to usage patterns. This allows for more efficient facility management by suggesting the most appropriate actions based on usage patterns.

[0112] The analysis unit can apply different analysis algorithms to different areas within the facility. For example, the analysis unit can select the optimal analysis algorithm according to the characteristics of each area. The analysis unit can also analyze data for each area individually and provide detailed results. The analysis unit can also integrate the analysis results from each area to optimize the overall process. This allows for the provision of detailed analysis results by applying the optimal analysis algorithm for each area.

[0113] The data collection unit can dynamically change the frequency of data collection based on the usage frequency of specific areas within the facility. For example, in areas with high usage frequency, the data collection unit increases the frequency of data collection to collect more detailed data. In areas with low usage frequency, the data collection unit can also reduce the frequency of data collection to conserve resources. The data collection unit can also adjust the data collection frequency in real time in response to fluctuations in usage frequency. This allows for efficient use of resources by adjusting the data collection frequency according to usage frequency.

[0114] The analysis unit can provide analysis results that contribute to improving energy efficiency by considering energy consumption data within the facility during the analysis. For example, the analysis unit can provide analysis results that contribute to improving energy efficiency based on energy consumption data. The analysis unit can also propose efficient operating methods during peak energy consumption periods. The analysis unit can also propose specific measures to reduce energy consumption. In this way, by considering energy consumption data, it can provide analysis results that contribute to improving energy efficiency.

[0115] The execution unit can implement countermeasures against anomalies based on the anomaly detection results within the facility during execution. For example, if an anomaly is detected, the execution unit will quickly implement countermeasures. The execution unit can also identify the cause of the anomaly and implement appropriate countermeasures. The execution unit can also continuously implement countermeasures until the anomaly is resolved. This enables rapid response to anomalies by implementing countermeasures based on the anomaly detection results.

[0116] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the unit can increase the frequency of data collection and collect more detailed data. If the user is in a hurry, the unit can prioritize collecting only the most important data. This reduces the user's burden by adjusting the timing of data collection according to their emotions.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0118] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions that get straight to the point. If the user is stressed, it can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand.

[0119] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on those emotions. For example, if the user is relaxed, the visualization unit can provide detailed information. If the user is in a hurry, the visualization unit can provide concise information that gets straight to the point. If the user is stressed, the visualization unit can provide visually easy-to-understand information. In this way, by adjusting the visualization's presentation according to the user's emotions, information that is easy for the user to understand can be provided.

[0120] The execution unit can estimate the user's emotions and adjust the timing of actions based on those emotions. For example, if the user is relaxed, the execution unit can flexibly adjust the timing of actions. If the user is in a hurry, the execution unit can also perform actions quickly. If the user is stressed, the execution unit can also carefully adjust the timing of actions. This allows actions to be performed at the optimal time for the user by adjusting the timing according to their emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects data from IoT sensors and cameras. For example, it can collect temperature data within the facility in real time using a temperature sensor, collect humidity data within the facility periodically using a humidity sensor, and collect video data within the facility 24 hours a day using surveillance cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can extract patterns from the collected data using data mining techniques, classify the collected data using machine learning algorithms, and build a predictive model based on the collected data using generative AI. Step 3: The proposal department proposes actions for equipment, robots, and employees based on the analysis results obtained by the analysis department. For example, it can propose specific and efficient methods for operating equipment, instructions for robot operation, and action plans for employees. Step 4: The visualization unit visualizes the actions proposed by the proposal unit in a chat format. For example, actions proposed using text chat, voice chat, or a chatbot can be visualized in real time. Step 5: The execution unit executes the actions visualized by the visualization unit. For example, it can perform specific and efficient actions such as operating equipment, operating robots, and taking employee actions.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data within the facility using the camera 42, temperature sensor, and humidity sensor of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes actions based on the analysis results using the specific processing unit 290 of the data processing unit 12. The visualization unit visualizes the proposed actions in a chat format using the control unit 46A of the smart device 14. The execution unit executes the visualized actions using 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data within the facility using the camera 42, temperature sensor, and humidity sensor of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes actions based on the analysis results using the specific processing unit 290 of the data processing unit 12. The visualization unit visualizes the proposed actions in a chat format using the control unit 46A of the smart glasses 214. The execution unit executes the visualized actions using 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 can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, and execution unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data within the facility using the camera 42, temperature sensor, and humidity sensor of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes actions based on the analysis results using the specific processing unit 290 of the data processing unit 12. The visualization unit visualizes the proposed actions in a chat format using the control unit 46A of the headset terminal 314. The execution unit executes the visualized actions using 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, visualization unit, and execution unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data within the facility using the camera 42, temperature sensor, and humidity sensor of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes actions based on the analysis results using the specific processing unit 290 of the data processing unit 12. The visualization unit visualizes the proposed actions in a chat format using the control unit 46A of the robot 414. The execution unit executes the visualized actions using 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects data from IoT sensors and cameras, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes actions for equipment, robots, and employees. A visualization unit that visualizes the actions proposed by the aforementioned proposal unit in a chat format, The system comprises an execution unit that performs the actions visualized by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The facility collects data such as temperature, humidity, lighting conditions, equipment operation status, employee movements, and visitor behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect information such as weather outside the facility, road and public transport information, and social media trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is analyzed, and the AI ​​generates suggestions for optimal equipment and robot operation, as well as employee actions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Visualize actions suggested by the generation AI in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Execute the visualized actions The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The frequency of data collection is dynamically changed based on the usage frequency of specific areas within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, anomalies are detected within the facility, and if an anomaly occurs, additional data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the 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 When collecting data, the facility's event schedule is taken into consideration, and data related to specific events is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, we will select an energy-efficient data collection method, taking into account the energy consumption situation within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the system detects anomalies by comparing them with past data and proposes countermeasures for those anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied to different areas within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, we take into account the energy consumption data within the facility to provide analysis results that contribute to improving energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, we take into account the security data within the facility and provide analysis results that contribute to strengthening security. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we suggest the most appropriate action based on the usage status of specific areas within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will propose countermeasures for any anomalies detected within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned proposal section is, When making a proposal, consider the energy consumption situation within the facility and propose actions that will contribute to improving energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, consider the security situation within the facility and propose actions that will contribute to strengthening security. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, When visualization is performed, important information is highlighted based on the usage status of specific areas within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, During visualization, the results of anomaly detection within the facility are displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, It estimates the user's emotions and determines the visualization priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, When visualization is performed, information that contributes to improving energy efficiency is displayed, taking into account the energy consumption status within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, When visualization is performed, information that contributes to strengthening security will be displayed, taking into account the security situation within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, It estimates the user's emotions and adjusts the timing of actions taken based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, At runtime, actions are taken at the optimal time based on the usage status of specific areas within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 33) The execution unit is, During execution, countermeasures against anomalies are implemented based on the results of anomaly detection within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 34) The execution unit is, It estimates the user's emotions and determines the priority of actions to take based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The execution unit is, During execution, the system takes into account the energy consumption status within the facility and performs actions that contribute to improving energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 36) The execution unit is, During execution, the system will take into account the security situation within the facility and perform actions that contribute to strengthening security. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 data from IoT sensors and cameras, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes actions for equipment, robots, and employees. A visualization unit that visualizes the actions proposed by the aforementioned proposal unit in a chat format, The system comprises an execution unit that performs the actions visualized by the visualization unit. A system characterized by the following features.

2. The aforementioned collection unit is The facility collects data such as temperature, humidity, lighting conditions, equipment operation status, employee movements, and visitor behavior patterns. The system according to feature 1.

3. The aforementioned collection unit is We collect information such as weather outside the facility, road and public transport information, and social media trends. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is analyzed, and the AI ​​generates suggestions for the optimal operation of equipment and robots, as well as the actions of employees. The system according to feature 1.

5. The aforementioned proposal section is, Visualize the actions suggested by the AI ​​generator in a chat format. The system according to feature 1.

6. The execution unit is, Execute the visualized actions The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The frequency of data collection is dynamically changed based on the usage frequency of specific areas within the facility. The system according to feature 1.

9. The aforementioned collection unit is During data collection, anomalies are detected within the facility, and if an anomaly occurs, additional data is collected. The system according to feature 1.

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

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