system

Abstract

The system according to this embodiment aims to optimize urban transportation, public services, and energy management. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an efficiency improvement unit, and a provision unit. The collection unit collects urban traffic data, public service data, and energy data. The analysis unit analyzes the data collected by the collection unit. The efficiency improvement unit optimizes urban functions based on the analysis results obtained by the analysis unit. The provision unit provides the information optimized by the efficiency improvement unit.

Description

Technical Field

[0004] ,

[0006] , , ,

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[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, the optimization of urban traffic, public services, and energy management has not been sufficiently carried out, and there is room for improvement.

[0005] ​​​​​​​​The system according to this embodiment comprises a data collection unit, an analysis unit, an efficiency improvement unit, and a data provision unit. The data collection unit collects urban traffic data, public service data, and energy data. The analysis unit analyzes the data collected by the data collection unit. The efficiency improvement unit optimizes urban functions based on the analysis results obtained by the analysis unit. The data provision unit provides the information optimized by the efficiency improvement unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize urban transportation, public services, and energy management. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 reception 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 reception 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) A smart city agent according to an embodiment of the present invention is a system that integrates a next-generation communication network and AI technology to optimize urban traffic, public services, and energy management. This smart city agent utilizes real-time data processing and AI analysis to improve the efficiency and sustainability of urban functions. For example, the smart city agent collects real-time data such as urban traffic data, public service data, and energy data. This data is collected through sensors and IoT devices and transmitted to the AI ​​system via a next-generation communication network. The AI ​​system then analyzes the collected data to improve the efficiency of urban traffic management, public service optimization, and energy management. For example, the AI ​​uses advanced machine learning algorithms to analyze traffic flow and perform optimal signal control to alleviate traffic congestion. It also analyzes public service data in real time to optimize service provision. Furthermore, it analyzes energy data to automatically adjust sustainable energy distribution. This system is expected to have concrete effects such as a 20% improvement in the average rate of improvement of urban traffic flow, a 30% reduction in the efficiency of public energy use, and a 40% reduction in emergency response time. Furthermore, this system is intended for use by experts, businesses, and government agencies of all ages involved in urban development, including urban planners, policymakers, public transport and energy management departments, and local governments that prioritize environmental sustainability. In this way, pioneering the integration of AI and IoT technologies, it will be possible to build sustainable smart urban infrastructure and implement data-driven urban management processes. This will solve challenges such as traffic congestion, increased energy consumption, and delays in public services caused by urban overcrowding, thereby improving the efficiency and sustainability of urban functions. This will enable smart city agents to efficiently optimize urban transport, public services, and energy management.

[0029] The smart city agent according to this embodiment comprises a data collection unit, an analysis unit, an efficiency improvement unit, and a data provision unit. The data collection unit collects urban traffic data, public service data, and energy data. The data collection unit collects traffic volume, traffic speed, and traffic accident data, for example, using sensors and IoT devices. The data collection unit can also collect public service data such as garbage collection data and public transportation operation data. Furthermore, the data collection unit can also collect energy data such as electricity consumption data and gas consumption data. For example, the data collection unit measures traffic volume in real time and detects the occurrence of traffic congestion. The data collection unit can also monitor the operation status of public transportation and collect delay information. Furthermore, the data collection unit can collect energy consumption data and understand energy usage patterns. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze traffic data and identify traffic flow patterns. The analysis unit can also analyze public service data to improve the efficiency of service provision. Furthermore, the analysis unit can analyze energy data and optimize energy use. For example, the analysis unit identifies the causes of traffic congestion based on traffic data and proposes optimal signal control. The analysis unit can also identify bottlenecks in service provision based on public service data and propose improvement measures. Furthermore, the analysis unit can identify energy waste based on energy data and propose efficiency improvements. The efficiency unit optimizes urban functions based on the analysis results obtained by the analysis unit. For example, the efficiency unit optimizes traffic signal control to alleviate traffic congestion. It can also optimize the provision of public services to improve service quality. Furthermore, the efficiency unit can optimize energy allocation to improve energy efficiency. For example, the efficiency unit adjusts the timing of traffic signals to smooth traffic flow. It can also optimize the frequency of public service provision to improve service quality. Furthermore, the efficiency unit can adjust energy allocation in real time to reduce energy waste. The provision unit provides the information optimized by the efficiency unit.The service provider can, for example, provide real-time traffic information and guide drivers to the optimal route. It can also provide real-time information on the availability of public services and inform citizens of service usage. Furthermore, it can provide real-time information on energy usage to support the optimization of energy use. For example, the service provider can provide traffic information via a smartphone app and guide drivers to the optimal route. It can also provide information on the availability of public services via a website and inform citizens of service usage. Furthermore, it can provide energy usage information via a dashboard to support the optimization of energy use. As a result, the smart city agent according to this embodiment can efficiently optimize urban traffic, public services, and energy management.

[0030] The data collection unit collects urban traffic data, public service data, and energy data. For example, the unit uses sensors and IoT devices to collect traffic volume, traffic speed, and traffic accident data. Specifically, cameras and sensors installed on roads detect the passage of vehicles and collect this data in real time. This allows for a detailed understanding of fluctuations in traffic volume and speed at specific times and locations. In the event of a traffic accident, the information is also collected urgently, enabling a rapid response to changes in traffic conditions. Furthermore, the data collection unit can also collect public service data, such as garbage collection data and public transportation operation data. Garbage collection data can be used to monitor the amount of garbage generated and collected from each household and business using sensors, helping to optimize the routes and schedules of garbage collection vehicles. Public transportation operation data, such as the operating status of buses and trains, delay information, and passenger numbers, can be collected in real time and used for operation management and service improvement. Furthermore, the data collection unit can also collect energy data, such as electricity consumption data and gas consumption data. For example, smart meters installed in homes and businesses measure electricity and gas consumption in real time and collect this data. This allows for a detailed understanding of energy usage patterns and helps improve the efficiency of energy management. The data collection unit centrally manages this data and can link with other systems and departments as needed. For instance, collected data can be stored on a cloud server and made accessible to the analysis and efficiency departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the collection unit. For example, the analysis unit uses AI to analyze traffic data and identify traffic flow patterns. Specifically, the AI ​​uses machine learning algorithms to learn from past traffic data and build a model that predicts current traffic conditions. Using this model, it is possible to analyze real-time collected traffic data and identify the locations and causes of traffic congestion. The analysis unit can also analyze public service data to improve the efficiency of service provision. For example, by analyzing garbage collection data, it is possible to improve collection efficiency by optimizing the routes and schedules of collection vehicles. Furthermore, the analysis unit can analyze energy data to optimize energy use. For example, by analyzing electricity consumption data, it is possible to propose measures to reduce electricity use during peak hours. The analysis unit can comprehensively analyze this data and provide information to improve the efficiency of the entire city. In addition, the analysis unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, based on historical traffic data, it can predict the tendency for traffic congestion to occur during specific times of day or seasons and formulate future countermeasures. 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 Efficiency Improvement Unit optimizes urban functions based on the analysis results obtained by the Analysis Unit. For example, the Efficiency Improvement Unit optimizes traffic signal control to alleviate traffic congestion. Specifically, it adjusts the timing of traffic signals in real time based on the causes and patterns of traffic congestion identified by the Analysis Unit. This makes traffic flow smoother and reduces the occurrence of congestion. The Efficiency Improvement Unit can also optimize the provision of public services and improve the quality of services. For example, by optimizing the routes of garbage trucks and improving collection efficiency, the frequency of garbage collection can be reduced, and costs can be lowered. Furthermore, the Efficiency Improvement Unit can optimize energy allocation and improve the efficiency of energy use. For example, based on power consumption data, measures can be taken to suppress power use during peak hours, thereby reducing energy costs. To implement these measures, the Efficiency Improvement Unit can monitor data in real time and make adjustments as needed. For example, to adjust the timing of traffic signals, it monitors traffic data in real time and changes the control of signals according to traffic conditions. Also, to optimize energy allocation, it monitors energy data in real time and implements measures to reduce wasteful energy use. In this way, the Efficiency Improvement Unit can improve the efficiency of urban functions and improve the performance of the entire system.

[0033] The Service Provider provides information that has been streamlined by the Efficiency Optimization Department. For example, the Service Provider provides real-time traffic information and guides drivers to the optimal route. Specifically, it provides drivers with current traffic conditions and the best route through smartphone apps and car navigation systems. This allows drivers to avoid congestion and reach their destination smoothly. The Service Provider can also provide real-time information on the availability of public services and inform citizens about the status of service use. For example, it provides citizens with garbage collection schedules and public transportation operating status through websites and smartphone apps. This allows citizens to understand the availability of services and use them efficiently. Furthermore, the Service Provider can provide real-time information on energy usage and support the optimization of energy use. For example, it provides energy usage information through a dashboard and provides information to reduce energy waste. This optimizes energy use and reduces energy costs. The Service Provider centrally manages this information and can link with other systems and departments as needed. For example, it stores traffic information and public service availability on a cloud server, making it accessible to other systems and departments. It also allows for flexible responses to specific situations and conditions by adjusting the frequency and accuracy of information provision. This allows the information provider to deliver information efficiently and effectively, improving the overall performance of the system.

[0034] The traffic management department can collect traffic data and analyze traffic flow. For example, the traffic management department collects traffic volume, traffic speed, and traffic accident data. For example, the traffic management department can measure traffic volume in real time and detect the occurrence of traffic congestion. The traffic management department can also monitor traffic speed and evaluate the smoothness of traffic flow. Furthermore, the traffic management department can collect traffic accident data and understand the circumstances of accident occurrence. In this way, traffic management can be optimized by collecting traffic data and analyzing traffic flow. Some or all of the above processing in the traffic management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management department can input traffic data into a generative AI and have the generative AI perform traffic flow analysis.

[0035] The Public Service Management Department can collect public service data and optimize service provision. For example, the Public Service Management Department collects garbage collection data and public transportation operation data. For example, the Public Service Management Department can collect garbage collection data in real time to improve the efficiency of garbage collection. The Public Service Management Department can also collect public transportation operation data and monitor its operation status. Furthermore, the Public Service Management Department can identify bottlenecks in service provision and propose improvement measures. In this way, by collecting public service data and optimizing service provision, the efficiency of public services can be improved. Some or all of the above processing in the Public Service Management Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Public Service Management Department can input public service data into a generative AI and have the generative AI perform the optimization of service provision.

[0036] The energy management unit can collect energy data and automatically adjust for a sustainable energy allocation. For example, the energy management unit collects electricity consumption data and gas consumption data. For example, the energy management unit collects electricity consumption data in real time to understand energy usage patterns. The energy management unit can also collect gas consumption data to improve the efficiency of energy use. Furthermore, the energy management unit can automatically adjust for a sustainable energy allocation and reduce energy waste. In this way, energy management can be optimized by collecting energy data and automatically adjusting for a sustainable energy allocation. Some or all of the above processes in the energy management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the energy management unit can input energy data into a generative AI and have the generative AI perform the energy allocation adjustment.

[0037] The traffic management unit can perform signal control to alleviate traffic congestion. For example, the traffic management unit can adjust the timing of traffic signals to smooth traffic flow. For example, the traffic management unit can adjust the timing of traffic signals in real time to alleviate traffic congestion. The traffic management unit can also change the signal control pattern to distribute traffic flow. Furthermore, the traffic management unit can predict the occurrence of traffic congestion and adjust signal control in advance. This allows for improved traffic flow by performing optimal signal control to alleviate traffic congestion. Some or all of the above processes in the traffic management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management unit can input traffic data into a generative AI and have the generative AI perform the optimization of signal control.

[0038] The Public Service Management Department can analyze public service data in real time and optimize service delivery. For example, it can analyze garbage collection data in real time to improve the efficiency of garbage collection. For example, it can analyze public transportation operation data in real time to monitor operation status. Furthermore, the Public Service Management Department can identify bottlenecks in service delivery in real time and propose improvement measures. In addition, the Public Service Management Department can adjust the data collection frequency and analysis algorithms in real time to improve the efficiency of service delivery. This allows for the optimization of public services by analyzing public service data in real time and optimizing service delivery. Some or all of the above processes in the Public Service Management Department may be performed using, for example, generative AI, or not. For example, the Public Service Management Department can input public service data collected in real time into a generative AI and have the generative AI perform the optimization of service delivery.

[0039] The energy management unit can analyze energy data and automatically adjust for a sustainable energy allocation. For example, the energy management unit can analyze energy data in real time to understand energy usage patterns. For example, the energy management unit can identify energy waste based on energy data and propose efficiency improvements. The energy management unit can also automatically adjust for a sustainable energy allocation based on energy data. Furthermore, the energy management unit can predict energy consumption based on energy data and adjust the allocation algorithm. This allows for more efficient energy management by analyzing energy data and automatically adjusting for a sustainable energy allocation. Some or all of the above processes in the energy management unit may be performed using, for example, a generative AI, or not. For example, the energy management unit can input energy data into a generative AI and have the generative AI perform the energy allocation adjustment.

[0040] The data collection unit can analyze past data collection history and select a collection method. For example, the data collection unit can identify the most efficient collection time period from past data collection history. The data collection unit can also determine the optimal sensor placement based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and optimize the frequency of data collection. This allows for more efficient data collection by analyzing past data collection history and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0041] The data collection unit can filter data based on specific areas or time periods within a city. For example, the collection unit can prioritize data collection in specific areas of a city to collect important data. The collection unit can also concentrate data collection during specific time periods to collect data efficiently. Furthermore, the collection unit can consider urban event information and collect data in relevant areas. This allows for efficient collection of important data by filtering based on specific areas and time periods within a city. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not. For example, the collection unit can input information on specific areas and time periods within a city into a generative AI and have the generative AI perform the data collection filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on urban event information during data collection. For example, when an event is held in the city, the data collection unit prioritizes the collection of data in the relevant area. The data collection unit can also select and collect the necessary data depending on the type of event. Furthermore, the data collection unit can adjust the scope of data collection according to the scale of the event. This improves the efficiency of data collection by prioritizing the collection of highly relevant data while considering urban event information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input urban event information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze social media trends and collect relevant data during data collection. For example, the data collection unit can analyze social media trends in real time and collect relevant data. The data collection unit can also select the target of data collection based on trends. Furthermore, the data collection unit can adjust the data collection method in response to changes in trends. This improves the accuracy of data collection by analyzing social media trends and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input social media trend data into a generative AI and have the generative AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the priority of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply a traffic flow analysis algorithm to traffic data. For example, the analysis unit can also apply a service optimization algorithm to public service data. Furthermore, the analysis unit can apply an energy management algorithm to energy data. By applying different analysis algorithms depending on the type of data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the type of data into the generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection date and time during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can allocate analysis resources according to the data collection period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection date and time into the generative AI and have the generative AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relationships between the data. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, it may postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relationships between the data. This allows for efficient data analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relationships between the data into a generative AI and have the generative AI adjust the order of analysis.

[0048] The optimization unit can improve the accuracy of optimization based on the interrelationships of data during the optimization process. For example, the optimization unit performs optimization considering the interrelationships between traffic data and energy data. The optimization unit can also perform optimization considering the interrelationships between public service data and energy data. Furthermore, the optimization unit can also perform optimization considering the interrelationships between traffic data and public service data. By improving the accuracy of optimization by considering the interrelationships of data, more accurate optimization becomes possible. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the optimization unit can input the interrelationships of data into a generative AI and have the generative AI perform the optimization accuracy improvement.

[0049] The optimization unit can perform optimization based on specific areas and time periods within a city. For example, the optimization unit may prioritize optimization in specific areas of a city. The optimization unit can also concentrate optimization during specific time periods. Furthermore, the optimization unit can perform optimization while considering city event information. This enables efficient optimization by performing optimization based on specific areas and time periods within a city. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input information on specific areas and time periods within a city into a generative AI and have the generative AI perform the optimization.

[0050] The optimization unit can perform optimization based on urban event information. For example, the optimization unit prioritizes the optimization of relevant areas when an event is being held in the city. The optimization unit can also select the target of optimization according to the type of event. Furthermore, the optimization unit can adjust the scope of optimization according to the scale of the event. This makes efficient optimization possible by considering urban event information. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input urban event information into a generative AI and have the generative AI perform the optimization.

[0051] The optimization unit can improve the accuracy of optimization by referring to relevant literature and data during the optimization process. For example, the optimization unit performs optimization by referring to relevant research papers. The optimization unit can also improve the accuracy of optimization based on past data. Furthermore, the optimization unit can perform optimization by referring to optimization examples from other cities. By improving the accuracy of optimization by referring to relevant literature and data, more accurate optimization becomes possible. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input relevant literature and data into a generative AI and have the generative AI perform the optimization accuracy improvement.

[0052] The information delivery unit can select the method of information delivery by referring to the user's past usage history at the time of delivery. For example, the information delivery unit can select the optimal method of information delivery from the user's past usage history. For example, the information delivery unit can also customize the content of the information delivery based on the user's past usage history. Furthermore, the information delivery unit can analyze the user's past usage history and deliver the information at the optimal timing. This makes it possible to provide the user with the most optimal information by selecting the optimal method of information delivery by referring to the user's past usage history. Some or all of the above processing in the information delivery unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the information delivery unit can input the user's past usage history into a generation AI and have the generation AI select the method of information delivery.

[0053] The information provider can customize the content of the information provided based on the user's attribute information at the time of provision. For example, the information provider can customize the content of the information provided according to the user's age. For example, the information provider can also customize the content of the information provided according to the user's occupation. Furthermore, the information provider can also customize the content of the information provided according to the user's interests. By customizing the content of the information provided based on the user's attribute information, it becomes possible to provide information that is optimal for the user. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the user's attribute information into a generative AI and have the generative AI perform the customization of the content of the information provided.

[0054] The information provider can select the method of providing information based on the user's geographical location information at the time of provision. For example, the information provider can provide relevant information based on the user's current location. The information provider can also select the optimal method of providing information by referring to the user's travel history. Furthermore, the information provider can adjust the timing of information provision based on the user's geographical location information. This makes it possible to provide the user with the most optimal information by selecting the optimal method of providing information while considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the user's geographical location information into a generative AI and have the generative AI perform the selection of the information provision method.

[0055] The information provider can analyze the user's social media activity and adjust the content of the information provided at the time of delivery. For example, the information provider can analyze the user's social media activity and adjust the content of the information provided based on their interests. For example, the information provider can also customize the content of the information provided by referring to the user's statements on social media. Furthermore, the information provider can determine the priority of information provision according to the number of followers the user has on social media. This makes it possible to provide the user with the most optimal information by analyzing the user's social media activity and adjusting the content of the information provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the user's social media activity data into a generative AI and have the generative AI perform the adjustment of the content of the information provided.

[0056] The traffic management unit can select a signal control method by referring to past traffic data during traffic management. For example, the traffic management unit can select the optimal signal control pattern based on past traffic data. For example, the traffic management unit can also analyze past traffic data and select a signal control method that avoids congestion. Furthermore, the traffic management unit can adjust the timing of signal control by referring to past traffic data. This improves the accuracy of traffic management by selecting the optimal signal control method by referring to past traffic data. Some or all of the above processes in the traffic management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the traffic management unit can input past traffic data into a generation AI and have the generation AI perform the selection of a signal control method.

[0057] The traffic management unit can control traffic signals based on specific events or time periods during traffic management. For example, the traffic management unit can prioritize traffic signal control in relevant areas during urban events. The traffic management unit can also efficiently manage traffic by concentrating traffic signal control during specific time periods. Furthermore, the traffic management unit can adjust the method of traffic signal control depending on the type of event. This enables efficient traffic management by controlling traffic signals based on specific events or time periods. Some or all of the above processes in the traffic management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management unit can input information about specific events or time periods into a generative AI and have the generative AI execute the traffic signal control.

[0058] The traffic management unit can control traffic signals based on the city's geographical characteristics during traffic management. For example, the traffic management unit can select the optimal signal control pattern considering the city's geographical characteristics. The traffic management unit can also adjust the signal control method based on, for example, the terrain and road layout. Furthermore, the traffic management unit can adjust the timing of signal control by referring to the city's geographical characteristics. This enables efficient traffic management by controlling traffic signals while considering the city's geographical characteristics. Some or all of the above processes in the traffic management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the traffic management unit can input information on the city's geographical characteristics into a generative AI and have the generative AI perform the signal control.

[0059] The traffic management department can improve the accuracy of signal control by referring to relevant traffic research during traffic management. For example, the traffic management department can improve the accuracy of signal control by referring to relevant traffic research. The traffic management department can also select the optimal signal control method based on past traffic research. Furthermore, the traffic management department can improve the accuracy of signal control by referring to traffic management examples from other cities. This makes more accurate traffic management possible by improving the accuracy of signal control by referring to relevant traffic research. Some or all of the above processes in the traffic management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management department can input data from relevant traffic research into a generative AI and have the generative AI perform the improvement of signal control accuracy.

[0060] The Public Service Management Department can select a service delivery method by referring to past service data when managing public services. For example, the Public Service Management Department can select the optimal service delivery method based on past service data. The Public Service Management Department can also select an efficient service delivery method by analyzing past service data. Furthermore, the Public Service Management Department can adjust the timing of service delivery by referring to past service data. This allows for increased efficiency of public services by selecting the optimal delivery method by referring to past service data. Some or all of the above processes in the Public Service Management Department may be performed using, for example, a generating AI, or without using a generating AI. For example, the Public Service Management Department can input past service data into a generating AI and have the generating AI select a service delivery method.

[0061] The Public Service Management Department can provide services based on specific areas and time periods when managing public services. For example, the Public Service Management Department can prioritize service provision in specific areas of a city. For example, the Public Service Management Department can concentrate service provision during specific time periods to provide services efficiently. Furthermore, the Public Service Management Department can consider urban event information and provide services in relevant areas. This enables the efficient provision of public services by providing services based on specific areas and time periods. Some or all of the above processes in the Public Service Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Public Service Management Department can input information on specific areas and time periods into a generative AI and have the generative AI perform the service provision.

[0062] The Public Service Management Department can provide services based on urban event information when managing public services. For example, when an urban event is being held, the Public Service Management Department can prioritize service provision in the relevant area. The Public Service Management Department can also select and provide necessary services according to the type of event. Furthermore, the Public Service Management Department can adjust the scope of service provision according to the scale of the event. This makes it possible to provide public services efficiently by providing services while taking urban event information into consideration. Some or all of the above processing in the Public Service Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Public Service Management Department can input urban event information into a generative AI and have the generative AI perform the service provision.

[0063] The Public Service Management Department can improve the accuracy of service provision by referring to relevant research when managing public services. For example, the Public Service Management Department may provide services by referring to relevant research papers. For example, the Public Service Management Department may also improve the accuracy of service provision based on past data. Furthermore, the Public Service Management Department may provide services by referring to service provision examples from other cities. By improving the accuracy of service provision by referring to relevant research, it becomes possible to provide more accurate public services. Some or all of the above processes in the Public Service Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Public Service Management Department may input data from relevant research into a generative AI and have the generative AI perform the task of improving the accuracy of service provision.

[0064] The energy management unit can select an energy allocation method by referring to past energy data during energy management. For example, the energy management unit can select the optimal energy allocation method based on past energy data. The energy management unit can also select an efficient energy allocation method by analyzing past energy data. Furthermore, the energy management unit can adjust the timing of energy allocation by referring to past energy data. This allows for more efficient energy management by selecting the optimal allocation method by referring to past energy data. Some or all of the above processes in the energy management unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the energy management unit can input past energy data into a generation AI and have the generation AI select an allocation method.

[0065] The energy management unit can allocate energy based on specific areas or time periods during energy management. For example, the energy management unit can prioritize energy allocation in specific areas of a city. The energy management unit can also efficiently manage energy by concentrating energy allocation during specific time periods. Furthermore, the energy management unit can consider urban event information and allocate energy in relevant areas. This enables efficient energy management by allocating energy based on specific areas or time periods. Some or all of the above processes in the energy management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the energy management unit can input information on specific areas or time periods into a generative AI and have the generative AI perform the energy allocation.

[0066] The energy management unit can allocate energy while considering urban event information. For example, when an urban event is being held, the energy management unit can prioritize energy allocation to the relevant areas. The energy management unit can also select and allocate the necessary energy according to the type of event. Furthermore, the energy management unit can adjust the scope of energy allocation according to the scale of the event. This enables efficient energy management by considering urban event information when allocating energy. Some or all of the above processing in the energy management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the energy management unit can input urban event information into a generative AI and have the generative AI perform the energy allocation.

[0067] The Energy Management Department can improve the accuracy of energy allocation by referring to relevant energy research during energy management. For example, the Energy Management Department can improve the accuracy of energy allocation by referring to relevant energy research. The Energy Management Department can also select the optimal energy allocation method based on past energy research. Furthermore, the Energy Management Department can improve the accuracy of energy allocation by referring to energy management examples from other cities. This makes more accurate energy management possible by improving the accuracy of allocation by referring to relevant energy research. Some or all of the above processes in the Energy Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Energy Management Department can input data from relevant energy research into a generative AI and have the generative AI perform the task of improving the accuracy of allocation.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] A smart city agent can be equipped with a green space management department to optimize urban green space management. This department collects urban green space data and analyzes the health of the green spaces. For example, the green space management department can use sensors to monitor soil moisture and nutrient levels in real time and perform irrigation and fertilization as needed. It can also use drones to take aerial photographs of trees to detect and address pests and diseases early. Furthermore, the green space management department can collaborate with urban planning departments to propose the creation of new green spaces or the expansion of existing ones in order to optimize the area of ​​urban green space. This will streamline urban green space management and improve the urban environment and the quality of life for residents.

[0070] Smart city agents can be equipped with a disaster management department to enhance urban disaster management. This department collects disaster data such as earthquakes, floods, and fires, and analyzes disaster risks. For example, it can monitor earthquakes and floods in real time using seismometers and water level gauges, and activate early warning systems. It can also detect fires using fire sensors and support rapid firefighting efforts. Furthermore, it can optimize evacuation routes during disasters and provide residents with quick and appropriate evacuation instructions. This strengthens urban disaster management and minimizes damage from disasters.

[0071] A smart city agent can be equipped with a health management department to improve urban health management. This department collects urban health data and analyzes the health status of residents. For example, it can use wearable devices to monitor residents' heart rate and body temperature in real time and notify medical institutions if abnormalities are detected. It can also monitor urban air quality using air quality sensors and assess health risks. Furthermore, based on residents' health data, the health management department can propose health promotion programs and raise residents' health awareness. This can improve urban health management and promote the health and well-being of residents.

[0072] A smart city agent can be equipped with an education management department to optimize urban education management. This department collects urban education data and analyzes the educational environment. For example, it can collect school attendance and performance data to evaluate the quality of education. It can also monitor students' learning progress in real time through online learning platforms and provide individualized instruction as needed. Furthermore, it can analyze the utilization of educational facilities and propose optimal facility placement and resource allocation. This optimizes urban education management, leading to improved educational quality and enhanced student learning outcomes.

[0073] A smart city agent can be equipped with a cultural management department to promote urban cultural management. This department collects urban cultural data and analyzes cultural activities. For example, it can collect urban event data and participant data to evaluate the effectiveness of cultural events. It can also analyze residents' cultural preferences and propose cultural events suitable for them. Furthermore, it can analyze the usage of urban cultural facilities and propose optimal operating methods. This can promote urban cultural management and improve the quality of cultural life for residents.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit collects urban traffic data, public service data, and energy data. For example, it collects traffic volume, traffic speed, and traffic accident data using sensors and IoT devices. It can also collect garbage collection data, public transport operation data, electricity consumption data, and gas consumption data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze traffic data and identify traffic flow patterns. It can also analyze public service data to improve the efficiency of service delivery. Furthermore, it can analyze energy data to optimize energy use. Step 3: The efficiency unit optimizes urban functions based on the analysis results obtained by the analysis unit. For example, it optimizes traffic signal control to alleviate traffic congestion. It can also optimize the provision of public services and improve the quality of services. Furthermore, it can optimize energy allocation to improve energy efficiency. Step 4: The provisioning unit provides information that has been optimized by the efficiency unit. For example, it can provide real-time traffic information and guide drivers to the best route. It can also provide real-time information on the availability of public services and inform citizens about the status of service use. Furthermore, it can provide real-time information on energy usage to help optimize energy use.

[0076] (Example of form 2) A smart city agent according to an embodiment of the present invention is a system that integrates a next-generation communication network and AI technology to optimize urban traffic, public services, and energy management. This smart city agent utilizes real-time data processing and AI analysis to improve the efficiency and sustainability of urban functions. For example, the smart city agent collects real-time data such as urban traffic data, public service data, and energy data. This data is collected through sensors and IoT devices and transmitted to the AI ​​system via a next-generation communication network. The AI ​​system then analyzes the collected data to improve the efficiency of urban traffic management, public service optimization, and energy management. For example, the AI ​​uses advanced machine learning algorithms to analyze traffic flow and perform optimal signal control to alleviate traffic congestion. It also analyzes public service data in real time to optimize service provision. Furthermore, it analyzes energy data to automatically adjust sustainable energy distribution. This system is expected to have concrete effects such as a 20% improvement in the average rate of improvement of urban traffic flow, a 30% reduction in the efficiency of public energy use, and a 40% reduction in emergency response time. Furthermore, this system is intended for use by experts, businesses, and government agencies of all ages involved in urban development, including urban planners, policymakers, public transport and energy management departments, and local governments that prioritize environmental sustainability. In this way, pioneering the integration of AI and IoT technologies, it will be possible to build sustainable smart urban infrastructure and implement data-driven urban management processes. This will solve challenges such as traffic congestion, increased energy consumption, and delays in public services caused by urban overcrowding, thereby improving the efficiency and sustainability of urban functions. This will enable smart city agents to efficiently optimize urban transport, public services, and energy management.

[0077] The smart city agent according to this embodiment comprises a data collection unit, an analysis unit, an efficiency improvement unit, and a data provision unit. The data collection unit collects urban traffic data, public service data, and energy data. The data collection unit collects traffic volume, traffic speed, and traffic accident data, for example, using sensors and IoT devices. The data collection unit can also collect public service data such as garbage collection data and public transportation operation data. Furthermore, the data collection unit can also collect energy data such as electricity consumption data and gas consumption data. For example, the data collection unit measures traffic volume in real time and detects the occurrence of traffic congestion. The data collection unit can also monitor the operation status of public transportation and collect delay information. Furthermore, the data collection unit can collect energy consumption data and understand energy usage patterns. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze traffic data and identify traffic flow patterns. The analysis unit can also analyze public service data to improve the efficiency of service provision. Furthermore, the analysis unit can analyze energy data and optimize energy use. For example, the analysis unit identifies the causes of traffic congestion based on traffic data and proposes optimal signal control. The analysis unit can also identify bottlenecks in service provision based on public service data and propose improvement measures. Furthermore, the analysis unit can identify energy waste based on energy data and propose efficiency improvements. The efficiency unit optimizes urban functions based on the analysis results obtained by the analysis unit. For example, the efficiency unit optimizes traffic signal control to alleviate traffic congestion. It can also optimize the provision of public services to improve service quality. Furthermore, the efficiency unit can optimize energy allocation to improve energy efficiency. For example, the efficiency unit adjusts the timing of traffic signals to smooth traffic flow. It can also optimize the frequency of public service provision to improve service quality. Furthermore, the efficiency unit can adjust energy allocation in real time to reduce energy waste. The provision unit provides the information optimized by the efficiency unit.The service provider can, for example, provide real-time traffic information and guide drivers to the optimal route. It can also provide real-time information on the availability of public services and inform citizens of service usage. Furthermore, it can provide real-time information on energy usage to support the optimization of energy use. For example, the service provider can provide traffic information via a smartphone app and guide drivers to the optimal route. It can also provide information on the availability of public services via a website and inform citizens of service usage. Furthermore, it can provide energy usage information via a dashboard to support the optimization of energy use. As a result, the smart city agent according to this embodiment can efficiently optimize urban traffic, public services, and energy management.

[0078] The data collection unit collects urban traffic data, public service data, and energy data. For example, the unit uses sensors and IoT devices to collect traffic volume, traffic speed, and traffic accident data. Specifically, cameras and sensors installed on roads detect the passage of vehicles and collect this data in real time. This allows for a detailed understanding of fluctuations in traffic volume and speed at specific times and locations. In the event of a traffic accident, the information is also collected urgently, enabling a rapid response to changes in traffic conditions. Furthermore, the data collection unit can also collect public service data, such as garbage collection data and public transportation operation data. Garbage collection data can be used to monitor the amount of garbage generated and collected from each household and business using sensors, helping to optimize the routes and schedules of garbage collection vehicles. Public transportation operation data, such as the operating status of buses and trains, delay information, and passenger numbers, can be collected in real time and used for operation management and service improvement. Furthermore, the data collection unit can also collect energy data, such as electricity consumption data and gas consumption data. For example, smart meters installed in homes and businesses measure electricity and gas consumption in real time and collect this data. This allows for a detailed understanding of energy usage patterns and helps improve the efficiency of energy management. The data collection unit centrally manages this data and can link with other systems and departments as needed. For instance, collected data can be stored on a cloud server and made accessible to the analysis and efficiency departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0079] The analysis unit analyzes data collected by the collection unit. For example, the analysis unit uses AI to analyze traffic data and identify traffic flow patterns. Specifically, the AI ​​uses machine learning algorithms to learn from past traffic data and build a model that predicts current traffic conditions. Using this model, it is possible to analyze real-time collected traffic data and identify the locations and causes of traffic congestion. The analysis unit can also analyze public service data to improve the efficiency of service provision. For example, by analyzing garbage collection data, it is possible to improve collection efficiency by optimizing the routes and schedules of collection vehicles. Furthermore, the analysis unit can analyze energy data to optimize energy use. For example, by analyzing electricity consumption data, it is possible to propose measures to reduce electricity use during peak hours. The analysis unit can comprehensively analyze this data and provide information to improve the efficiency of the entire city. In addition, the analysis unit can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, based on historical traffic data, it can predict the tendency for traffic congestion to occur during specific times of day or seasons and formulate future countermeasures. 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.

[0080] The Efficiency Improvement Unit optimizes urban functions based on the analysis results obtained by the Analysis Unit. For example, the Efficiency Improvement Unit optimizes traffic signal control to alleviate traffic congestion. Specifically, it adjusts the timing of traffic signals in real time based on the causes and patterns of traffic congestion identified by the Analysis Unit. This makes traffic flow smoother and reduces the occurrence of congestion. The Efficiency Improvement Unit can also optimize the provision of public services and improve the quality of services. For example, by optimizing the routes of garbage trucks and improving collection efficiency, the frequency of garbage collection can be reduced, and costs can be lowered. Furthermore, the Efficiency Improvement Unit can optimize energy allocation and improve the efficiency of energy use. For example, based on power consumption data, measures can be taken to suppress power use during peak hours, thereby reducing energy costs. To implement these measures, the Efficiency Improvement Unit can monitor data in real time and make adjustments as needed. For example, to adjust the timing of traffic signals, it monitors traffic data in real time and changes the control of signals according to traffic conditions. Also, to optimize energy allocation, it monitors energy data in real time and implements measures to reduce wasteful energy use. In this way, the Efficiency Improvement Unit can improve the efficiency of urban functions and improve the performance of the entire system.

[0081] The Service Provider provides information that has been streamlined by the Efficiency Optimization Department. For example, the Service Provider provides real-time traffic information and guides drivers to the optimal route. Specifically, it provides drivers with current traffic conditions and the best route through smartphone apps and car navigation systems. This allows drivers to avoid congestion and reach their destination smoothly. The Service Provider can also provide real-time information on the availability of public services and inform citizens about the status of service use. For example, it provides citizens with garbage collection schedules and public transportation operating status through websites and smartphone apps. This allows citizens to understand the availability of services and use them efficiently. Furthermore, the Service Provider can provide real-time information on energy usage and support the optimization of energy use. For example, it provides energy usage information through a dashboard and provides information to reduce energy waste. This optimizes energy use and reduces energy costs. The Service Provider centrally manages this information and can link with other systems and departments as needed. For example, it stores traffic information and public service availability on a cloud server, making it accessible to other systems and departments. It also allows for flexible responses to specific situations and conditions by adjusting the frequency and accuracy of information provision. This allows the information provider to deliver information efficiently and effectively, improving the overall performance of the system.

[0082] The traffic management department can collect traffic data and analyze traffic flow. For example, the traffic management department collects traffic volume, traffic speed, and traffic accident data. For example, the traffic management department can measure traffic volume in real time and detect the occurrence of traffic congestion. The traffic management department can also monitor traffic speed and evaluate the smoothness of traffic flow. Furthermore, the traffic management department can collect traffic accident data and understand the circumstances of accident occurrence. In this way, traffic management can be optimized by collecting traffic data and analyzing traffic flow. Some or all of the above processing in the traffic management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management department can input traffic data into a generative AI and have the generative AI perform traffic flow analysis.

[0083] The Public Service Management Department can collect public service data and optimize service provision. For example, the Public Service Management Department collects garbage collection data and public transportation operation data. For example, the Public Service Management Department can collect garbage collection data in real time to improve the efficiency of garbage collection. The Public Service Management Department can also collect public transportation operation data and monitor its operation status. Furthermore, the Public Service Management Department can identify bottlenecks in service provision and propose improvement measures. In this way, by collecting public service data and optimizing service provision, the efficiency of public services can be improved. Some or all of the above processing in the Public Service Management Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Public Service Management Department can input public service data into a generative AI and have the generative AI perform the optimization of service provision.

[0084] The energy management unit can collect energy data and automatically adjust for a sustainable energy allocation. For example, the energy management unit collects electricity consumption data and gas consumption data. For example, the energy management unit collects electricity consumption data in real time to understand energy usage patterns. The energy management unit can also collect gas consumption data to improve the efficiency of energy use. Furthermore, the energy management unit can automatically adjust for a sustainable energy allocation and reduce energy waste. In this way, energy management can be optimized by collecting energy data and automatically adjusting for a sustainable energy allocation. Some or all of the above processes in the energy management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the energy management unit can input energy data into a generative AI and have the generative AI perform the energy allocation adjustment.

[0085] The traffic management unit can perform signal control to alleviate traffic congestion. For example, the traffic management unit can adjust the timing of traffic signals to smooth traffic flow. For example, the traffic management unit can adjust the timing of traffic signals in real time to alleviate traffic congestion. The traffic management unit can also change the signal control pattern to distribute traffic flow. Furthermore, the traffic management unit can predict the occurrence of traffic congestion and adjust signal control in advance. This allows for improved traffic flow by performing optimal signal control to alleviate traffic congestion. Some or all of the above processes in the traffic management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management unit can input traffic data into a generative AI and have the generative AI perform the optimization of signal control.

[0086] The Public Service Management Department can analyze public service data in real time and optimize service delivery. For example, it can analyze garbage collection data in real time to improve the efficiency of garbage collection. For example, it can analyze public transportation operation data in real time to monitor operation status. Furthermore, the Public Service Management Department can identify bottlenecks in service delivery in real time and propose improvement measures. In addition, the Public Service Management Department can adjust the data collection frequency and analysis algorithms in real time to improve the efficiency of service delivery. This allows for the optimization of public services by analyzing public service data in real time and optimizing service delivery. Some or all of the above processes in the Public Service Management Department may be performed using, for example, generative AI, or not. For example, the Public Service Management Department can input public service data collected in real time into a generative AI and have the generative AI perform the optimization of service delivery.

[0087] The energy management unit can analyze energy data and automatically adjust for a sustainable energy allocation. For example, the energy management unit can analyze energy data in real time to understand energy usage patterns. For example, the energy management unit can identify energy waste based on energy data and propose efficiency improvements. The energy management unit can also automatically adjust for a sustainable energy allocation based on energy data. Furthermore, the energy management unit can predict energy consumption based on energy data and adjust the allocation algorithm. This allows for more efficient energy management by analyzing energy data and automatically adjusting for a sustainable energy allocation. Some or all of the above processes in the energy management unit may be performed using, for example, a generative AI, or not. For example, the energy management unit can input energy data into a generative AI and have the generative AI perform the energy allocation adjustment.

[0088] 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 data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also temporarily stop data collection if the user is in a hurry and resume it later. This reduces the burden on the user by adjusting the timing of data collection based on their emotions. 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. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0089] The data collection unit can analyze past data collection history and select a collection method. For example, the data collection unit can identify the most efficient collection time period from past data collection history. The data collection unit can also determine the optimal sensor placement based on past data collection history. Furthermore, the data collection unit can analyze past data collection history and optimize the frequency of data collection. This allows for more efficient data collection by analyzing past data collection history and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0090] The data collection unit can filter data based on specific areas or time periods within a city. For example, the collection unit can prioritize data collection in specific areas of a city to collect important data. The collection unit can also concentrate data collection during specific time periods to collect data efficiently. Furthermore, the collection unit can consider urban event information and collect data in relevant areas. This allows for efficient collection of important data by filtering based on specific areas and time periods within a city. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not. For example, the collection unit can input information on specific areas and time periods within a city into a generative AI and have the generative AI perform the data collection filtering.

[0091] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on those emotions. For example, if the user is stressed, the data collection unit may postpone the collection of less important data. If the user is relaxed, for example, the data collection unit may prioritize the collection of detailed data. Also, if the user is in a hurry, the data collection unit may prioritize the collection of only important data. This ensures that important data is collected preferentially by determining the priority of data to collect based on the user's emotions. 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. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data collection.

[0092] The data collection unit can prioritize the collection of highly relevant data based on urban event information during data collection. For example, when an event is held in the city, the data collection unit prioritizes the collection of data in the relevant area. The data collection unit can also select and collect the necessary data depending on the type of event. Furthermore, the data collection unit can adjust the scope of data collection according to the scale of the event. This improves the efficiency of data collection by prioritizing the collection of highly relevant data while considering urban event information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input urban event information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0093] The data collection unit can analyze social media trends and collect relevant data during data collection. For example, the data collection unit can analyze social media trends in real time and collect relevant data. The data collection unit can also select the target of data collection based on trends. Furthermore, the data collection unit can adjust the data collection method in response to changes in trends. This improves the accuracy of data collection by analyzing social media trends and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input social media trend data into a generative AI and have the generative AI perform the collection of relevant data.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the user's emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0095] The analysis unit can adjust the level of detail of the analysis based on the priority of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply a traffic flow analysis algorithm to traffic data. For example, the analysis unit can also apply a service optimization algorithm to public service data. Furthermore, the analysis unit can apply an energy management algorithm to energy data. By applying different analysis algorithms depending on the type of data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the type of data into the generative AI and have the generative AI execute the application of an appropriate analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the user's emotions. For example, if the user is tense, the analysis unit may provide a display method with calm colors. For example, if the user is relaxed, the analysis unit may also provide a display method with bright colors. Furthermore, if the user is in a hurry, the analysis unit may provide a concise and highly visible display method. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0098] The analysis unit can determine the priority of analysis based on the data collection date and time during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can allocate analysis resources according to the data collection period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection date and time into the generative AI and have the generative AI determine the analysis priority.

[0099] The analysis unit can adjust the order of analysis based on the relationships between the data. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, it may postpone the analysis of less relevant data. The analysis unit can also optimize the order of analysis according to the relationships between the data. This allows for efficient data analysis by adjusting the order of analysis based on the relationships between the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relationships between the data into a generative AI and have the generative AI adjust the order of analysis.

[0100] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on those emotions. For example, if the user is tense, the optimization unit can apply simple optimization criteria. If the user is relaxed, for example, the optimization unit can also apply detailed optimization criteria. Furthermore, if the user is in a hurry, the optimization unit can apply rapid optimization criteria. This allows the system to apply the most optimal criteria for the user by adjusting the optimization criteria based on their emotions. 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. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the optimization criteria.

[0101] The optimization unit can improve the accuracy of optimization based on the interrelationships of data during the optimization process. For example, the optimization unit performs optimization considering the interrelationships between traffic data and energy data. The optimization unit can also perform optimization considering the interrelationships between public service data and energy data. Furthermore, the optimization unit can also perform optimization considering the interrelationships between traffic data and public service data. By improving the accuracy of optimization by considering the interrelationships of data, more accurate optimization becomes possible. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the optimization unit can input the interrelationships of data into a generative AI and have the generative AI perform the optimization accuracy improvement.

[0102] The optimization unit can perform optimization based on specific areas and time periods within a city. For example, the optimization unit may prioritize optimization in specific areas of a city. The optimization unit can also concentrate optimization during specific time periods. Furthermore, the optimization unit can perform optimization while considering city event information. This enables efficient optimization by performing optimization based on specific areas and time periods within a city. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input information on specific areas and time periods within a city into a generative AI and have the generative AI perform the optimization.

[0103] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the user's emotions. For example, if the user is tense, the optimization unit may provide a display method with calm colors. For example, if the user is relaxed, the optimization unit may also provide a display method with bright colors. Furthermore, if the user is in a hurry, the optimization unit may provide a concise and highly visible display method. By adjusting the display method of the optimization results based on the user's emotions, it becomes possible to provide a display that is easy for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using a generative AI, for example, or without a generative AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the optimization results.

[0104] The optimization unit can perform optimization based on urban event information. For example, the optimization unit prioritizes the optimization of relevant areas when an event is being held in the city. The optimization unit can also select the target of optimization according to the type of event. Furthermore, the optimization unit can adjust the scope of optimization according to the scale of the event. This makes efficient optimization possible by considering urban event information. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input urban event information into a generative AI and have the generative AI perform the optimization.

[0105] The optimization unit can improve the accuracy of optimization by referring to relevant literature and data during the optimization process. For example, the optimization unit performs optimization by referring to relevant research papers. The optimization unit can also improve the accuracy of optimization based on past data. Furthermore, the optimization unit can perform optimization by referring to optimization examples from other cities. By improving the accuracy of optimization by referring to relevant literature and data, more accurate optimization becomes possible. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input relevant literature and data into a generative AI and have the generative AI perform the optimization accuracy improvement.

[0106] The information provider can estimate the user's emotions and adjust the method of information delivery based on those emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a detailed method of information delivery. Furthermore, if the user is in a hurry, the information provider can provide a concise method of information delivery. By adjusting the method of information delivery based on the user's emotions, it becomes possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using a generative AI, or not using a generative AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the method of information delivery.

[0107] The information delivery unit can select the method of information delivery by referring to the user's past usage history at the time of delivery. For example, the information delivery unit can select the optimal method of information delivery from the user's past usage history. For example, the information delivery unit can also customize the content of the information delivery based on the user's past usage history. Furthermore, the information delivery unit can analyze the user's past usage history and deliver the information at the optimal timing. This makes it possible to provide the user with the most optimal information by selecting the optimal method of information delivery by referring to the user's past usage history. Some or all of the above processing in the information delivery unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the information delivery unit can input the user's past usage history into a generation AI and have the generation AI select the method of information delivery.

[0108] The information provider can customize the content of the information provided based on the user's attribute information at the time of provision. For example, the information provider can customize the content of the information provided according to the user's age. For example, the information provider can also customize the content of the information provided according to the user's occupation. Furthermore, the information provider can also customize the content of the information provided according to the user's interests. By customizing the content of the information provided based on the user's attribute information, it becomes possible to provide information that is optimal for the user. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the user's attribute information into a generative AI and have the generative AI perform the customization of the content of the information provided.

[0109] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on those emotions. For example, if the user is stressed, the information delivery unit may postpone providing less important information. For example, if the user is relaxed, the information delivery unit may prioritize providing detailed information. Also, if the user is in a hurry, the information delivery unit may prioritize providing only important information. In this way, by determining the priority of information delivery based on the user's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using a generative AI, or not using a generative AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI determine the priority of information delivery.

[0110] The information provider can select the method of providing information based on the user's geographical location information at the time of provision. For example, the information provider can provide relevant information based on the user's current location. The information provider can also select the optimal method of providing information by referring to the user's travel history. Furthermore, the information provider can adjust the timing of information provision based on the user's geographical location information. This makes it possible to provide the user with the most optimal information by selecting the optimal method of providing information while considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the user's geographical location information into a generative AI and have the generative AI perform the selection of the information provision method.

[0111] The information provider can analyze the user's social media activity and adjust the content of the information provided at the time of delivery. For example, the information provider can analyze the user's social media activity and adjust the content of the information provided based on their interests. For example, the information provider can also customize the content of the information provided by referring to the user's statements on social media. Furthermore, the information provider can determine the priority of information provision according to the number of followers the user has on social media. This makes it possible to provide the user with the most optimal information by analyzing the user's social media activity and adjusting the content of the information provided. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the user's social media activity data into a generative AI and have the generative AI perform the adjustment of the content of the information provided.

[0112] The traffic management unit can estimate a user's emotions and adjust the traffic signal control method based on those emotions. For example, if a user is stressed, the traffic management unit may shorten the waiting time at traffic lights. If a user is relaxed, the traffic management unit may also perform normal signal control. Furthermore, if a user is in a hurry, the traffic management unit may prioritize signal control. By adjusting the traffic signal control method based on the user's emotions, optimal signal control for the user becomes possible. 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. Some or all of the above processing in the traffic management unit may be performed using, for example, generative AI, or not using generative AI. For example, the traffic management unit can input user emotion data into a generative AI and have the generative AI adjust the traffic signal control method.

[0113] The traffic management unit can select a signal control method by referring to past traffic data during traffic management. For example, the traffic management unit can select the optimal signal control pattern based on past traffic data. For example, the traffic management unit can also analyze past traffic data and select a signal control method that avoids congestion. Furthermore, the traffic management unit can adjust the timing of signal control by referring to past traffic data. This improves the accuracy of traffic management by selecting the optimal signal control method by referring to past traffic data. Some or all of the above processes in the traffic management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the traffic management unit can input past traffic data into a generation AI and have the generation AI perform the selection of a signal control method.

[0114] The traffic management unit can control traffic signals based on specific events or time periods during traffic management. For example, the traffic management unit can prioritize traffic signal control in relevant areas during urban events. The traffic management unit can also efficiently manage traffic by concentrating traffic signal control during specific time periods. Furthermore, the traffic management unit can adjust the method of traffic signal control depending on the type of event. This enables efficient traffic management by controlling traffic signals based on specific events or time periods. Some or all of the above processes in the traffic management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management unit can input information about specific events or time periods into a generative AI and have the generative AI execute the traffic signal control.

[0115] The traffic management unit can estimate the user's emotions and adjust the way traffic information is provided based on those emotions. For example, if the user is stressed, the traffic management unit can provide simple and easy-to-understand traffic information. If the user is relaxed, the traffic management unit can also provide detailed traffic information. Furthermore, if the user is in a hurry, the traffic management unit can provide concise traffic information. By adjusting the way traffic information is provided based on the user's emotions, it is possible to provide traffic information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the traffic management unit may be performed using, for example, generative AI, or not using generative AI. For example, the traffic management unit can input user emotion data into a generative AI and have the generative AI adjust the way traffic information is provided.

[0116] The traffic management unit can control traffic signals based on the city's geographical characteristics during traffic management. For example, the traffic management unit can select the optimal signal control pattern considering the city's geographical characteristics. The traffic management unit can also adjust the signal control method based on, for example, the terrain and road layout. Furthermore, the traffic management unit can adjust the timing of signal control by referring to the city's geographical characteristics. This enables efficient traffic management by controlling traffic signals while considering the city's geographical characteristics. Some or all of the above processes in the traffic management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the traffic management unit can input information on the city's geographical characteristics into a generative AI and have the generative AI perform the signal control.

[0117] The traffic management department can improve the accuracy of signal control by referring to relevant traffic research during traffic management. For example, the traffic management department can improve the accuracy of signal control by referring to relevant traffic research. The traffic management department can also select the optimal signal control method based on past traffic research. Furthermore, the traffic management department can improve the accuracy of signal control by referring to traffic management examples from other cities. This makes more accurate traffic management possible by improving the accuracy of signal control by referring to relevant traffic research. Some or all of the above processes in the traffic management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the traffic management department can input data from relevant traffic research into a generative AI and have the generative AI perform the improvement of signal control accuracy.

[0118] The Public Service Management Department can estimate a user's emotions and adjust the delivery method of public services based on those emotions. For example, if a user is stressed, the Public Service Management Department can provide a simple and highly visible service delivery method. If a user is relaxed, the Public Service Management Department can also provide a detailed service delivery method. Furthermore, if a user is in a hurry, the Public Service Management Department can provide a concise service delivery method. By adjusting the delivery method of public services based on the user's emotions, it becomes possible to provide the optimal service for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Public Service Management Department may be performed using, for example, generative AI, or not using generative AI. For example, the Public Service Management Department can input user emotion data into a generative AI and have the generative AI perform the adjustment of the delivery method of public services.

[0119] The Public Service Management Department can select a service delivery method by referring to past service data when managing public services. For example, the Public Service Management Department can select the optimal service delivery method based on past service data. The Public Service Management Department can also select an efficient service delivery method by analyzing past service data. Furthermore, the Public Service Management Department can adjust the timing of service delivery by referring to past service data. This allows for increased efficiency of public services by selecting the optimal delivery method by referring to past service data. Some or all of the above processes in the Public Service Management Department may be performed using, for example, a generating AI, or without using a generating AI. For example, the Public Service Management Department can input past service data into a generating AI and have the generating AI select a service delivery method.

[0120] The Public Service Management Department can provide services based on specific areas and time periods when managing public services. For example, the Public Service Management Department can prioritize service provision in specific areas of a city. For example, the Public Service Management Department can concentrate service provision during specific time periods to provide services efficiently. Furthermore, the Public Service Management Department can consider urban event information and provide services in relevant areas. This enables the efficient provision of public services by providing services based on specific areas and time periods. Some or all of the above processes in the Public Service Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Public Service Management Department can input information on specific areas and time periods into a generative AI and have the generative AI perform the service provision.

[0121] The Public Service Management Department can estimate a user's emotions and prioritize service delivery based on those emotions. For example, if a user is stressed, the Public Service Management Department may postpone providing less important services. If a user is relaxed, the Public Service Management Department may prioritize providing detailed services. Furthermore, if a user is in a hurry, the Public Service Management Department may prioritize providing only essential services. This ensures that important services are prioritized by prioritizing service delivery based on the user's emotions. 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. Some or all of the above processing in the Public Service Management Department may be performed using or without generative AI. For example, the Public Service Management Department can input user emotion data into a generative AI and have the generative AI determine the priority of service delivery.

[0122] The Public Service Management Department can provide services based on urban event information when managing public services. For example, when an urban event is being held, the Public Service Management Department can prioritize service provision in the relevant area. The Public Service Management Department can also select and provide necessary services according to the type of event. Furthermore, the Public Service Management Department can adjust the scope of service provision according to the scale of the event. This makes it possible to provide public services efficiently by providing services while taking urban event information into consideration. Some or all of the above processing in the Public Service Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Public Service Management Department can input urban event information into a generative AI and have the generative AI perform the service provision.

[0123] The Public Service Management Department can improve the accuracy of service provision by referring to relevant research when managing public services. For example, the Public Service Management Department may provide services by referring to relevant research papers. For example, the Public Service Management Department may also improve the accuracy of service provision based on past data. Furthermore, the Public Service Management Department may provide services by referring to service provision examples from other cities. By improving the accuracy of service provision by referring to relevant research, it becomes possible to provide more accurate public services. Some or all of the above processes in the Public Service Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Public Service Management Department may input data from relevant research into a generative AI and have the generative AI perform the task of improving the accuracy of service provision.

[0124] The energy management unit can estimate the user's emotions and adjust the energy allocation method based on those emotions. For example, if the user is stressed, the energy management unit will reduce the burden of energy allocation. For example, if the user is relaxed, the energy management unit can also perform detailed energy allocation. Furthermore, if the user is in a hurry, the energy management unit can perform rapid energy allocation. This allows for optimal energy allocation for the user by adjusting the energy allocation method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the energy management unit may be performed using a generative AI, or not. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI adjust the energy allocation method.

[0125] The energy management unit can select an energy allocation method by referring to past energy data during energy management. For example, the energy management unit can select the optimal energy allocation method based on past energy data. The energy management unit can also select an efficient energy allocation method by analyzing past energy data. Furthermore, the energy management unit can adjust the timing of energy allocation by referring to past energy data. This allows for more efficient energy management by selecting the optimal allocation method by referring to past energy data. Some or all of the above processes in the energy management unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the energy management unit can input past energy data into a generation AI and have the generation AI select an allocation method.

[0126] The energy management unit can allocate energy based on specific areas or time periods during energy management. For example, the energy management unit can prioritize energy allocation in specific areas of a city. The energy management unit can also efficiently manage energy by concentrating energy allocation during specific time periods. Furthermore, the energy management unit can consider urban event information and allocate energy in relevant areas. This enables efficient energy management by allocating energy based on specific areas or time periods. Some or all of the above processes in the energy management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the energy management unit can input information on specific areas or time periods into a generative AI and have the generative AI perform the energy allocation.

[0127] The energy management unit can estimate the user's emotions and determine the priority of energy allocation based on those emotions. For example, if the user is stressed, the energy management unit will postpone less important energy allocations. For example, if the user is relaxed, the energy management unit can prioritize detailed energy allocations. Furthermore, if the user is in a hurry, the energy management unit can prioritize only important energy allocations. This allows for prioritizing important energy allocations by determining the priority of energy allocation based on the user's emotions. 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. Some or all of the above processing in the energy management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI determine the priority of energy allocation.

[0128] The energy management unit can allocate energy while considering urban event information. For example, when an urban event is being held, the energy management unit can prioritize energy allocation to the relevant areas. The energy management unit can also select and allocate the necessary energy according to the type of event. Furthermore, the energy management unit can adjust the scope of energy allocation according to the scale of the event. This enables efficient energy management by considering urban event information when allocating energy. Some or all of the above processing in the energy management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the energy management unit can input urban event information into a generative AI and have the generative AI perform the energy allocation.

[0129] The Energy Management Department can improve the accuracy of energy allocation by referring to relevant energy research during energy management. For example, the Energy Management Department can improve the accuracy of energy allocation by referring to relevant energy research. The Energy Management Department can also select the optimal energy allocation method based on past energy research. Furthermore, the Energy Management Department can improve the accuracy of energy allocation by referring to energy management examples from other cities. This makes more accurate energy management possible by improving the accuracy of allocation by referring to relevant energy research. Some or all of the above processes in the Energy Management Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Energy Management Department can input data from relevant energy research into a generative AI and have the generative AI perform the task of improving the accuracy of allocation.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] A smart city agent can be equipped with a green space management department to optimize urban green space management. This department collects urban green space data and analyzes the health of the green spaces. For example, the green space management department can use sensors to monitor soil moisture and nutrient levels in real time and perform irrigation and fertilization as needed. It can also use drones to take aerial photographs of trees to detect and address pests and diseases early. Furthermore, the green space management department can collaborate with urban planning departments to propose the creation of new green spaces or the expansion of existing ones in order to optimize the area of ​​urban green space. This will streamline urban green space management and improve the urban environment and the quality of life for residents.

[0132] Smart city agents can be equipped with a disaster management department to enhance urban disaster management. This department collects disaster data such as earthquakes, floods, and fires, and analyzes disaster risks. For example, it can monitor earthquakes and floods in real time using seismometers and water level gauges, and activate early warning systems. It can also detect fires using fire sensors and support rapid firefighting efforts. Furthermore, it can optimize evacuation routes during disasters and provide residents with quick and appropriate evacuation instructions. This strengthens urban disaster management and minimizes damage from disasters.

[0133] A smart city agent can be equipped with a health management department to improve urban health management. This department collects urban health data and analyzes the health status of residents. For example, it can use wearable devices to monitor residents' heart rate and body temperature in real time and notify medical institutions if abnormalities are detected. It can also monitor urban air quality using air quality sensors and assess health risks. Furthermore, based on residents' health data, the health management department can propose health promotion programs and raise residents' health awareness. This can improve urban health management and promote the health and well-being of residents.

[0134] A smart city agent can be equipped with an education management department to optimize urban education management. This department collects urban education data and analyzes the educational environment. For example, it can collect school attendance and performance data to evaluate the quality of education. It can also monitor students' learning progress in real time through online learning platforms and provide individualized instruction as needed. Furthermore, it can analyze the utilization of educational facilities and propose optimal facility placement and resource allocation. This optimizes urban education management, leading to improved educational quality and enhanced student learning outcomes.

[0135] A smart city agent can be equipped with a cultural management department to promote urban cultural management. This department collects urban cultural data and analyzes cultural activities. For example, it can collect urban event data and participant data to evaluate the effectiveness of cultural events. It can also analyze residents' cultural preferences and propose cultural events suitable for them. Furthermore, it can analyze the usage of urban cultural facilities and propose optimal operating methods. This can promote urban cultural management and improve the quality of cultural life for residents.

[0136] Smart city agents can estimate a user's emotions and adjust how traffic information is provided based on those emotions. For example, if a user is stressed, traffic information can be presented concisely to reduce their burden. If a user is relaxed, detailed traffic information can be provided, allowing them to access more information. Furthermore, if a user is in a hurry, the most important traffic information can be prioritized to enable them to act quickly. This enables the provision of optimal traffic information tailored to the user's emotions, thereby improving user satisfaction.

[0137] Smart city agents can estimate users' emotions and adjust how public services are delivered based on those estimates. For example, if a user is stressed, the agent can simplify the procedures for public services to reduce their burden. If a user is relaxed, the agent can provide detailed procedural information to ensure they feel comfortable proceeding. Furthermore, if a user is in a hurry, the agent can prioritize the most important procedures to allow them to complete them quickly. This enables the provision of optimal public services tailored to the user's emotions, thereby improving user satisfaction.

[0138] The smart city agent can estimate a user's emotions and optimize energy use based on those emotions. For example, if a user is stressed, it can automatically optimize energy use to reduce their burden. If a user is relaxed, it can provide detailed information about energy use, allowing them to manage their energy consumption. Furthermore, if a user is in a hurry, it can quickly optimize energy use, eliminating worries about energy consumption. This enables optimal energy use management tailored to the user's emotions, thereby improving user satisfaction.

[0139] Smart city agents can estimate users' emotions and enhance urban safety management based on those estimated emotions. For example, if a user is feeling anxious, they can quickly provide urban safety information to increase the user's sense of security. If a user is relaxed, they can provide detailed safety information to help them live with peace of mind. Furthermore, if a user is in a hurry, they can prioritize providing the most important safety information to enable them to act quickly. This allows for optimal safety management tailored to the user's emotions, thereby improving their sense of security.

[0140] A smart city agent can estimate a user's emotions and optimize urban environmental management based on those emotions. For example, if a user is stressed, it can provide concise environmental data to reduce their burden. If a user is relaxed, it can provide detailed environmental data to encourage active participation in environmental management. Furthermore, if a user is in a hurry, it can prioritize providing the most important environmental data to enable them to act quickly. This allows for optimal environmental management tailored to the user's emotions, thereby improving user satisfaction.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The data collection unit collects urban traffic data, public service data, and energy data. For example, it collects traffic volume, traffic speed, and traffic accident data using sensors and IoT devices. It can also collect garbage collection data, public transport operation data, electricity consumption data, and gas consumption data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use AI to analyze traffic data and identify traffic flow patterns. It can also analyze public service data to improve the efficiency of service delivery. Furthermore, it can analyze energy data to optimize energy use. Step 3: The efficiency unit optimizes urban functions based on the analysis results obtained by the analysis unit. For example, it optimizes traffic signal control to alleviate traffic congestion. It can also optimize the provision of public services and improve the quality of services. Furthermore, it can optimize energy allocation to improve energy efficiency. Step 4: The provisioning unit provides information that has been optimized by the efficiency unit. For example, it can provide real-time traffic information and guide drivers to the best route. It can also provide real-time information on the availability of public services and inform citizens about the status of service use. Furthermore, it can provide real-time information on energy usage to help optimize energy use.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, efficiency improvement unit, and provision 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 traffic data, public service data, and energy data using sensors and IoT devices of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The efficiency improvement unit is implemented in the specific processing unit 290 of the data processing unit 12 and optimizes urban functions based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the optimized information to the user. 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.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, efficiency improvement unit, and provision 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 traffic data, public service data, and energy data using sensors and IoT devices of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The efficiency improvement unit is implemented in the specific processing unit 290 of the data processing unit 12 and optimizes urban functions based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the optimized information to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the data collection unit, analysis unit, efficiency improvement unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects traffic data, public service data, and energy data using sensors and IoT devices of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The efficiency improvement unit is implemented in the specific processing unit 290 of the data processing unit 12 and optimizes urban functions based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the optimized information to the user. 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.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] Each of the multiple elements described above, including the data collection unit, analysis unit, efficiency improvement unit, and provision 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 traffic data, public service data, and energy data using the sensors and IoT devices of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The efficiency improvement unit is implemented in the specific processing unit 290 of the data processing unit 12 and optimizes urban functions based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides the optimized information to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] (Note 1) A data collection unit that collects urban transportation data, public service data, and energy data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, an efficiency improvement unit is provided to optimize urban functions, The system includes a providing unit that provides information that has been made more efficient by the efficiency improvement unit. A system characterized by the following features. (Note 2) It includes a traffic management unit that collects traffic data and analyzes traffic flow. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a public service management department that collects public service data and optimizes the provision of services. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an energy management unit that collects energy data and automatically adjusts for sustainable energy allocation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned traffic management department, Traffic signal control is implemented to alleviate traffic congestion. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned Public Services Management Department, Analyze public service data in real time to optimize service delivery. The system described in Appendix 3, characterized by the features described herein. (Note 7) It includes an energy management unit that collects energy data and automatically adjusts energy distribution. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past data collection history and select a data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on specific areas or time periods within the city. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user emotions and prioritizes the data to collect based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, prioritize the collection of highly relevant data based on urban event information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze social media trends and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis is presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the priority of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the date and time the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, It estimates user sentiment and adjusts optimization criteria based on user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, improve the accuracy of the optimization based on the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the optimization process is performed based on specific areas or time periods within a city. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, It estimates the user's emotions and adjusts how the optimization results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, the optimization process is based on city event information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, we refer to literature and data to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, the method of providing information is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the content of the information provided will be adjusted based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of information provision based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the method of providing information will be selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and adjust the content of the information provided. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned traffic management department, It estimates the user's emotions and adjusts the traffic signal control method based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned traffic management department, During traffic management, the signal control method is selected by referring to past traffic data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned traffic management department, During traffic management, signal control is performed based on specific events or time periods. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned traffic management department, We estimate the user's emotions and adjust how traffic information is provided based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned traffic management department, Traffic management involves controlling traffic signals based on the geographical characteristics of the city. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned traffic management department, When managing traffic, refer to traffic research to improve the accuracy of signal control. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned Public Services Management Department, Estimate user sentiment and adjust the delivery of public services based on user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned Public Services Management Department, When managing public services, the method of delivery is selected by referring to past service data. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned Public Services Management Department, When managing public services, services are provided based on specific areas or time periods. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned Public Services Management Department, It estimates user emotions and determines service delivery priorities based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned Public Services Management Department, When managing public services, services are provided based on urban event information. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned Public Services Management Department, When managing public services, we refer to research to improve the accuracy of service delivery. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned energy management unit, It estimates the user's emotions and adjusts the energy allocation method based on the user's emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned energy management unit, When managing energy, the allocation method is selected by referring to past energy data. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned energy management unit, When managing energy, energy allocation is performed based on specific areas or time periods. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned energy management unit, It estimates the user's emotions and determines energy allocation priorities based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned energy management unit, During energy management, energy allocation is performed based on urban event information. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned energy management unit, When managing energy, refer to energy research to improve the accuracy of allocation. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0215] 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 urban transportation data, public service data, and energy data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, an efficiency improvement unit is provided to optimize urban functions, The system includes a providing unit that provides information that has been made more efficient by the efficiency improvement unit. A system characterized by the following features.

2. It includes a traffic management unit that collects traffic data and analyzes traffic flow. The system according to feature 1.

3. It includes a public service management department that collects public service data and optimizes the provision of services. The system according to feature 1.

4. It includes an energy management unit that collects energy data and automatically adjusts for sustainable energy allocation. The system according to feature 1.

5. The aforementioned traffic management department, Traffic signal control is implemented to alleviate traffic congestion. The system according to feature 2.

6. The aforementioned Public Services Management Department, Analyze public service data in real time to optimize service delivery. The system according to claim 3.

7. It includes an energy management unit that collects energy data and automatically adjusts energy distribution. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of data collection based on those emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze past data collection history and select a data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on specific areas or time periods within the city. The system according to feature 1.

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