system

Abstract

The system according to this embodiment aims to improve urban efficiency, sustainability, and the quality of life for citizens. [Solution] The system according to the embodiment comprises a traffic management unit, an energy management unit, a waste management unit, a safety management unit, and a citizen services unit. The traffic management unit has functions for traffic management. The energy management unit optimizes energy consumption based on the traffic flow optimized by the traffic management unit. The waste management unit optimizes waste collection routes based on the energy consumption optimized by the energy management unit. The safety management unit conducts predictive police activities based on the waste collection routes optimized by the waste management unit. The citizen services unit provides services that meet the needs of citizens based on the predictive police activities provided by the safety management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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] Conventional technologies do not sufficiently provide an integrated platform for improving the efficiency, sustainability, and quality of life of citizens in cities, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency, sustainability, and quality of life of citizens in cities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a traffic management unit, an energy management unit, a waste management unit, a safety management unit, and a citizen services unit. The traffic management unit has functions for traffic management. The energy management unit optimizes energy consumption based on the traffic flow optimized by the traffic management unit. The waste management unit optimizes waste collection routes based on the energy consumption optimized by the energy management unit. The safety management unit conducts predictive police activities based on the waste collection routes optimized by the waste management unit. The citizen services unit provides services that meet the needs of citizens based on the predictive police activities provided by the safety management unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve urban efficiency, sustainability, and the quality of life for citizens. [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 controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides a smart city ecosystem that uses AI to improve urban efficiency, sustainability, and the quality of life for residents. This smart city ecosystem provides functions such as traffic management, energy management, waste management, safety enhancement, and personalized citizen services. These functions are optimized in real time using AI and IoT, enabling predictive maintenance and personalized service delivery. For example, in traffic management, AI optimizes traffic flow; in energy management, AI optimizes energy consumption; in waste management, AI optimizes waste collection routes; and in safety enhancement, AI performs predictive police activities. In personalized citizen services, AI provides services tailored to the needs of citizens. This improves urban efficiency, sustainability, and the quality of life for residents. Thus, the smart city ecosystem can improve urban efficiency, sustainability, and the quality of life for residents.

[0029] The smart city ecosystem according to this embodiment comprises a traffic management unit, an energy management unit, a waste management unit, a safety management unit, and a citizen services unit. The traffic management unit is established for traffic management and optimizes traffic flow using AI. For example, the traffic management unit adjusts the timing of traffic signals to alleviate traffic congestion. The traffic management unit can also collect real-time traffic data and analyze traffic flow patterns. Furthermore, the traffic management unit can assess the risk of traffic accidents and issue warnings in high-risk areas. The energy management unit optimizes energy consumption based on the traffic flow optimized by the traffic management unit. For example, the energy management unit assesses the stability of energy supply and takes preventive measures when supply risks are high. Furthermore, the energy management unit can monitor the use of renewable energy and select the optimal energy source. Furthermore, the energy management unit can analyze energy consumption patterns and propose optimal consumption patterns. The waste management unit optimizes waste collection routes based on the energy consumption optimized by the energy management unit. For example, the waste management unit proposes the optimal collection method for each type of waste to improve the recycling rate. Furthermore, the Waste Management Department can monitor the operation of waste collection vehicles and propose optimal routes. It can also grasp the quantity and type of waste in real time and optimize collection routes. The Safety Management Department conducts predictive police activities based on the waste collection routes optimized by the Waste Management Department. For example, the Safety Management Department analyzes past crime data to predict crime risks in specific areas and time periods. It can also detect emergencies in real time and enable a rapid response. Additionally, the Safety Management Department can analyze security camera footage to detect anomalies and issue warnings. The Citizen Services Department provides services tailored to citizens' needs based on the predictive police activities provided by the Safety Management Department. For example, the Citizen Services Department collects citizen feedback in real time to improve service quality. It can also analyze past service usage data to propose optimal services tailored to specific needs.Furthermore, the Citizen Services Department can utilize chatbots to enable rapid responses. This allows the smart city ecosystem, as embodied in this embodiment, to improve urban efficiency, sustainability, and the quality of life for residents.

[0030] The Traffic Management Department is established for traffic management and uses AI to optimize traffic flow. Specifically, the Traffic Management Department collects real-time traffic data from sensors and cameras installed at major intersections and roads within the city. This data includes vehicle speed, number of vehicles, and traffic signal status. The collected data is sent to a database in the cloud and analyzed by AI algorithms. The AI ​​learns traffic flow patterns and calculates optimal signal timing. For example, it can predict increases in traffic volume during specific times and adjust signal timing to alleviate traffic congestion. The Traffic Management Department can also assess the risk of traffic accidents and issue warnings in high-risk areas. The AI ​​analyzes past traffic accident data and predicts the risk of accidents under specific conditions. This allows the Traffic Management Department to issue warnings in high-risk areas and alert drivers. Furthermore, the Traffic Management Department can monitor the operation status of public transport and provide real-time information on delays and service disruptions. This enables the Traffic Management Department to efficiently manage traffic flow throughout the city and ensure smooth movement for residents.

[0031] The Energy Management Department optimizes energy consumption based on traffic flow optimized by the Traffic Management Department. Specifically, the Energy Management Department collects and analyzes energy consumption data within the city in real time. This includes electricity consumption, gas consumption, and water usage. The collected data is analyzed by AI algorithms to assess the stability of the energy supply. For example, if the energy supply is unstable, the Energy Management Department reduces supply risks by taking preventative measures. The Energy Management Department can also monitor the use of renewable energy and select the optimal energy source. For example, by monitoring the use of solar and wind power in real time and selecting the optimal energy source, it can achieve efficient energy use. Furthermore, the Energy Management Department can analyze energy consumption patterns and propose optimal consumption patterns. The AI ​​learns from past energy consumption data and proposes optimal consumption patterns for specific times of day and under specific conditions. This allows the Energy Management Department to promote efficient energy use and optimize energy consumption across the entire city.

[0032] The Waste Management Department optimizes waste collection routes based on energy consumption optimized by the Energy Management Department. Specifically, the Waste Management Department collects and analyzes waste collection data within the city in real time. This includes the amount, type, and frequency of waste collection. The collected data is analyzed by an AI algorithm to calculate the optimal collection route. For example, it proposes the optimal collection method depending on the amount and type of waste, thereby improving the recycling rate. The Waste Management Department can also monitor the operation status of waste collection vehicles and propose the optimal route. The AI ​​learns from past collection data and calculates the optimal collection route under specific conditions. This allows the Waste Management Department to improve collection efficiency and increase the waste recycling rate. Furthermore, the Waste Management Department can grasp the amount and type of waste in real time and optimize collection routes. This allows the Waste Management Department to efficiently manage waste throughout the city and reduce its environmental impact.

[0033] The Security Management Department conducts predictive police activities based on waste collection routes optimized by the Waste Management Department. Specifically, the Security Management Department collects and analyzes crime data within the city in real time. This includes historical crime data, security camera footage, and emergency call data. The collected data is analyzed by AI algorithms to predict crime risk in specific areas and time periods. For example, it evaluates crime risk in specific areas and time periods based on historical crime data and optimizes police activities. The Security Management Department can also detect emergencies in real time and enable a rapid response. The AI ​​analyzes security camera footage, detects anomalies, and issues warnings. This allows the Security Management Department to improve overall city safety and ensure the peace of mind of residents. Furthermore, based on the results of predictive police activities, the Security Management Department can also propose the optimal allocation of police resources. This enables the Security Management Department to achieve efficient police activities and maintain public safety throughout the city.

[0034] The Citizen Services Department provides services tailored to citizens' needs based on predictive police activities provided by the Safety Management Department. Specifically, the Citizen Services Department collects citizen feedback in real time to improve the quality of services. This includes feedback collected through online surveys, chatbots, and social media. The collected feedback is analyzed by AI algorithms to identify citizens' needs and complaints. This allows the Citizen Services Department to propose optimal services tailored to specific needs. For example, it can analyze past service usage data to predict service demand in specific areas and time periods. The Citizen Services Department can also utilize chatbots to enable rapid responses. Chatbots use natural language processing technology to respond to citizens' inquiries and provide necessary information. This allows the Citizen Services Department to respond to citizens' needs quickly and efficiently. Furthermore, the Citizen Services Department can collaborate with other departments to provide integrated services. For example, it can collaborate with the Traffic Management Department and the Energy Management Department to provide services based on traffic information and energy consumption information. This allows the Citizen Services Department to improve the overall efficiency, sustainability, and quality of life of residents in the city.

[0035] The traffic management department can optimize traffic flow using AI. For example, it can adjust the timing of traffic signals to alleviate traffic congestion. It can also collect real-time traffic data and analyze traffic flow patterns. Furthermore, it can assess the risk of traffic accidents and issue warnings in high-risk areas. This improves traffic efficiency through traffic flow optimization. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can use an AI model to analyze traffic data and calculate the optimal timing for adjusting traffic signal timing.

[0036] The energy management department can optimize energy consumption using AI. For example, the energy management department can evaluate the stability of energy supply and take preventive measures if supply risks are high. It can also monitor the use of renewable energy and select the optimal energy source. Furthermore, the energy management department can analyze energy consumption patterns and propose optimal consumption patterns. This improves energy efficiency through the optimization of energy consumption. Some or all of the above processes in the energy management department may be performed using AI, for example, or without AI. For example, the energy management department can input energy consumption data into an AI model and calculate the optimal consumption pattern.

[0037] The waste management department can use AI to optimize waste collection routes. For example, the waste management department can propose the optimal collection method for each type of waste, thereby improving the recycling rate. It can also monitor the operation status of waste collection vehicles and propose the optimal route. Furthermore, the waste management department can grasp the quantity and type of waste in real time and optimize collection routes. This optimization of waste collection routes improves the efficiency of waste management. Some or all of the above processes in the waste management department may be performed using AI, or not. For example, the waste management department can use an AI model to analyze waste data and calculate the optimal route for waste collection.

[0038] The Security Management Department can use AI to conduct predictive police activities. For example, it can analyze past crime data to predict crime risk in specific areas and time periods. It can also detect emergencies in real time, enabling a rapid response. Furthermore, it can analyze security camera footage to detect anomalies and issue warnings. This improves security through predictive police activities. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not. For example, the Security Management Department can use an AI model to analyze crime data and calculate risk in order to predict crime risk.

[0039] The Citizen Services Department can use AI to provide services tailored to the needs of citizens. For example, the Citizen Services Department can collect citizen feedback in real time to improve the quality of services. It can also analyze past service usage data to suggest optimal services tailored to specific needs. Furthermore, the Citizen Services Department can utilize chatbots to enable rapid responses. This will improve the quality of life for residents by providing services that meet their needs. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or not. For example, the Citizen Services Department can input citizen feedback into an AI model to make suggestions for improving the quality of services.

[0040] The traffic management department can analyze historical traffic data to predict and optimize traffic flow patterns during specific events and time periods. For example, it can predict patterns of increased traffic volume during specific events based on historical data and adjust traffic signals in advance. It can also predict traffic flow during rush hour and set optimal signal timings. Furthermore, it can analyze holiday traffic patterns and adjust signals to avoid congestion. This enables the optimization of traffic flow through predictions based on historical data. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can input historical traffic data into an AI model to predict traffic flow patterns.

[0041] The traffic management department can assess the risk of traffic accidents in real time and issue warnings in high-risk areas. For example, the traffic management department can analyze real-time traffic data to identify areas with a high accident risk. It can also display warning signs in high-risk areas. Furthermore, the traffic management department can pre-position police and ambulances in high-risk areas. This improves safety through traffic accident risk assessment. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can input real-time traffic data into an AI model to assess accident risk.

[0042] The traffic management department can monitor the operation status of public transport in real time, predict delays and congestion, and propose the optimal route. For example, the traffic management department can identify routes experiencing delays based on real-time operation data. It can also propose alternative routes to avoid routes where congestion is predicted. Furthermore, the traffic management department can suggest optimal transfer points depending on the operation status. This makes it possible to propose the optimal route by monitoring the operation status of public transport. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can input real-time operation data into an AI model and calculate the optimal route.

[0043] The traffic management department can utilize drones to optimize traffic flow, collecting aerial monitoring data and incorporating it into traffic management. For example, the traffic management department can use drones to monitor traffic conditions in real time. Furthermore, the traffic management department can adjust the timing of traffic signals based on data collected from drones. In addition, the traffic management department can use drones to quickly detect traffic accidents and congestion. This improves the accuracy of traffic management through the use of drones. Some or all of the above processes in the traffic management department may be performed using AI, for example, or without AI. For example, the traffic management department can input monitoring data collected from drones into an AI model to analyze traffic conditions.

[0044] The energy management department can analyze past energy consumption data and propose optimal energy consumption patterns according to the season and time of day. For example, the energy management department can analyze seasonal energy consumption patterns from past data. It can also analyze energy consumption patterns by time of day and propose optimal consumption patterns. Furthermore, the energy management department can predict energy consumption patterns during specific events and propose optimal consumption patterns. This enables the optimization of energy consumption through proposals based on past data. Some or all of the above processes in the energy management department may be performed using AI, for example, or without AI. For example, the energy management department can input past energy consumption data into an AI model and calculate optimal consumption patterns.

[0045] The Energy Management Department can assess the stability of energy supply in real time and take preventive measures if supply risk is high. For example, the Energy Management Department assesses the stability of energy supply based on real-time data. Furthermore, if supply risk is high, the Energy Management Department can adjust energy supply as a preventive measure. In addition, the Energy Management Department can proactively strengthen energy supply to areas with high supply risk. This allows for the reduction of supply risk through assessment of energy supply stability. Some or all of the above processes in the Energy Management Department may be performed using AI, for example, or without AI. For example, the Energy Management Department can input real-time energy data into an AI model to assess supply risk.

[0046] The Energy Management Department can monitor the use of renewable energy in real time and select the optimal energy source. For example, the Energy Management Department monitors the use of renewable energy based on real-time data. It can also select the optimal energy source according to the supply status of renewable energy. Furthermore, the Energy Management Department can analyze the use of renewable energy and propose the optimal energy consumption pattern. This makes it possible to select the optimal energy source by monitoring the use of renewable energy. Some or all of the above processes in the Energy Management Department may be performed using AI, for example, or without AI. For example, the Energy Management Department can input renewable energy use data into an AI model and select the optimal energy source.

[0047] The Energy Management Department can utilize smart meters to optimize energy consumption and collect and analyze detailed consumption data. For example, the Energy Management Department can collect energy consumption data in real time using smart meters. The Energy Management Department can also analyze energy consumption patterns based on the collected data. Furthermore, the Energy Management Department can propose optimal energy consumption patterns based on the analysis results. This enables the optimization of energy consumption through the use of smart meters. Some or all of the above processes in the Energy Management Department may be performed using AI, for example, or without AI. For example, the Energy Management Department can input data collected from smart meters into an AI model and analyze energy consumption patterns.

[0048] The waste management department can analyze historical waste data to predict waste volume during specific seasons or events and optimize collection routes. For example, the waste management department can predict waste volume during specific seasons based on historical data. It can also predict waste volume during specific events and propose optimal collection routes. Furthermore, the waste management department can analyze seasonal waste volume and optimize collection routes. This enables the optimization of waste collection routes through predictions based on historical data. Some or all of the above processes in the waste management department may be performed using AI, for example, or without AI. For example, the waste management department can input historical waste data into an AI model to predict waste volume.

[0049] The waste management department can propose the optimal collection method for each type of waste, thereby improving the recycling rate. For example, the waste management department can propose the optimal collection method for each type of waste. Furthermore, the waste management department can prioritize the collection of recyclable waste. In addition, the waste management department can propose the optimal collection route for each type of waste. This improves the recycling rate by proposing the optimal collection method for each type of waste. Some or all of the above processes in the waste management department may be performed using AI, for example, or without AI. For example, the waste management department can input data for each type of waste into an AI model and propose the optimal collection method.

[0050] The waste management department can monitor the operation status of waste collection vehicles in real time and propose the optimal route. For example, the waste management department monitors the operation status of waste collection vehicles based on real-time data. The waste management department can also propose the optimal route according to the operation status of the collection vehicles. Furthermore, the waste management department can analyze the operation status of the collection vehicles and propose the optimal collection route. This makes it possible to propose the optimal route by monitoring the operation status of waste collection vehicles. Some or all of the above processes in the waste management department may be performed using AI, for example, or not using AI. For example, the waste management department can input real-time operation data into an AI model and calculate the optimal route.

[0051] The waste management department can utilize sensors for waste management to understand the quantity and type of waste in real time. For example, the waste management department can use sensors to understand the quantity of waste in real time. It can also use sensors to understand the type of waste in real time. Furthermore, the waste management department can propose the optimal collection route based on the data collected from the sensors. In this way, by utilizing sensors, the quantity and type of waste can be understood in real time. Some or all of the above processes in the waste management department may be performed using AI, for example, or not using AI. For example, the waste management department can input data collected from sensors into an AI model to analyze the quantity and type of waste.

[0052] The Security Management Department can analyze past crime data to predict crime risk in specific areas and time periods and optimize police activities. For example, the Security Management Department can predict crime risk in specific areas from past data. It can also predict crime risk in specific time periods and optimize police activities. Furthermore, the Security Management Department can proactively strengthen police activities in areas with high crime risk. This makes it possible to optimize police activities through predictions based on past data. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not. For example, the Security Management Department can input past crime data into an AI model to predict crime risk.

[0053] The Safety Management Department can detect emergencies in real time and enable a rapid response. For example, the Safety Management Department can detect emergencies based on real-time data. Furthermore, in the event of an emergency, the Safety Management Department can quickly dispatch police and ambulances. In addition, when an emergency is detected, the Safety Management Department can quickly notify citizens. This enables a rapid response through real-time detection of emergencies. Some or all of the above processes in the Safety Management Department may be performed using AI, for example, or without AI. For example, the Safety Management Department can input real-time data into an AI model to detect emergencies.

[0054] The Security Management Department can analyze security camera footage in real time, detect anomalies, and issue warnings. For example, the Security Management Department can analyze security camera footage in real time to detect abnormal behavior. Furthermore, if an anomaly is detected, the Security Management Department can issue a warning. In addition, the Security Management Department can quickly carry out police activities based on security camera footage. This enables early detection of anomalies through the analysis of security camera footage. Some or all of the above processes performed by the Security Management Department may be carried out using AI, for example, or without AI. For example, the Security Management Department can input security camera footage data into an AI model to detect anomalies.

[0055] The Security Management Department can utilize drones for security management, collecting aerial surveillance data and using it to improve police operations. For example, the Security Management Department can use drones to collect surveillance data in real time. Furthermore, the Security Management Department can optimize police operations based on the data collected from drones. In addition, the Security Management Department can use drones to quickly detect emergencies. This improves the accuracy of security management through the use of drones. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not. For example, the Security Management Department can input surveillance data collected from drones into an AI model to optimize police operations.

[0056] The Citizen Services Department can analyze past service usage data and propose optimal services tailored to specific needs. For example, it can propose services based on past data to meet specific needs. It can also analyze service usage patterns during specific time periods and propose optimal services. Furthermore, it can propose optimal service delivery methods based on service usage data. This enables the provision of optimal services through proposals based on past data. Some or all of the above processes performed by the Citizen Services Department may be carried out using AI, for example, or without AI. For example, the Citizen Services Department can input past service usage data into an AI model and propose optimal services.

[0057] The Citizen Services Department can collect citizen feedback in real time and improve the quality of its services. For example, the Citizen Services Department can collect citizen feedback in real time. Furthermore, the Citizen Services Department can improve the quality of its services based on the collected feedback. In addition, the Citizen Services Department can analyze the feedback and propose the optimal way to deliver services. This improves the quality of services through real-time feedback collection. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or without AI. For example, the Citizen Services Department can input citizen feedback data into an AI model to make suggestions for improving the quality of its services.

[0058] The Citizen Services Department can understand citizens' location information in real time and provide optimal services. For example, the Citizen Services Department can understand citizens' location information in real time. Furthermore, the Citizen Services Department can provide optimal services based on location information. In addition, the Citizen Services Department can analyze citizens' location information and propose the optimal service delivery method. This enables the provision of optimal services through real-time location information. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or without AI. For example, the Citizen Services Department can input citizens' location data into an AI model and propose the optimal service.

[0059] The Citizen Services Department can utilize chatbots for citizen services to enable faster responses. For example, the Citizen Services Department can use chatbots to respond to citizen inquiries in real time. Furthermore, the Citizen Services Department can use chatbots to provide services tailored to citizens' needs. This enables faster service delivery through the use of chatbots. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or without AI. For example, the Citizen Services Department can input chatbot dialogue data into an AI model to generate optimal responses.

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

[0061] The smart city ecosystem can also be equipped with an environmental monitoring unit. This unit can monitor environmental data such as air quality, water quality, and noise levels within the city in real time. For example, it can use air quality sensors to measure the concentration of harmful substances in the air and issue a warning if the levels exceed the standard. It can also use water quality sensors to monitor the water quality of rivers and lakes, prompting a rapid response if pollution is detected. Furthermore, it can use noise sensors to monitor noise levels within the city, enabling countermeasures to be taken if noise levels exceed a certain threshold. Thus, adding an environmental monitoring unit improves urban environmental protection and the health of residents.

[0062] The smart city ecosystem can also include a health management department. This department can collect residents' health data and provide personalized health advice. For example, it can use wearable devices to collect data such as residents' heart rate, steps taken, and sleep patterns to monitor their health. The health management department can also assess health risks based on the collected data and propose preventative measures. Furthermore, it can analyze residents' health data and take measures to address specific health problems. Thus, adding a health management department improves residents' health and quality of life.

[0063] The smart city ecosystem can also include an education support department. This department can collect residents' educational data and provide personalized educational programs. For example, it can monitor residents' learning progress using an online learning platform and suggest appropriate learning content. Furthermore, the education support department can assess learning risks based on the collected data and provide learning support. It can also analyze residents' educational data and provide programs tailored to specific educational needs. Thus, adding an education support department improves residents' educational opportunities and learning outcomes.

[0064] The smart city ecosystem can also include a Cultural Activities Support Department. This department can collect data on residents' cultural activities and propose personalized cultural events and activities. For example, it can suggest appropriate cultural events and workshops based on residents' interests and preferences. Furthermore, the Cultural Activities Support Department can monitor participation in cultural activities based on the collected data and implement measures to promote participation. It can also analyze residents' cultural activity data and plan events tailored to specific cultural needs. Thus, adding a Cultural Activities Support Department enriches residents' cultural lives and strengthens community cohesion.

[0065] A smart city ecosystem can also include a disaster response unit. This unit can monitor disaster risks within the city in real time and enable a rapid response. For example, it can use earthquake sensors to detect earthquakes and issue warnings to residents. It can also use flood sensors to monitor river levels and issue evacuation orders when the risk of flooding increases. Furthermore, the disaster response unit can analyze past disaster data and take proactive measures in areas with high disaster risk. In this way, adding a disaster response unit improves the city's disaster response capabilities and ensures the safety of residents.

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

[0067] Step 1: The Traffic Management Department is established for traffic management and uses AI to optimize traffic flow. It adjusts the timing of traffic signals to alleviate traffic congestion. It also collects real-time traffic data and analyzes traffic flow patterns. Furthermore, it assesses the risk of traffic accidents and issues warnings in high-risk areas. Step 2: The Energy Management Department optimizes energy consumption based on traffic flow optimized by the Traffic Management Department. It assesses the stability of the energy supply and takes preventative measures if the supply risk is high. It also monitors the use of renewable energy and selects the optimal energy source. Furthermore, it analyzes energy consumption patterns and proposes the optimal consumption pattern. Step 3: The Waste Management Department optimizes waste collection routes based on energy consumption optimized by the Energy Management Department. It proposes the optimal collection method for each type of waste and improves the recycling rate. It also monitors the operation status of waste collection vehicles and proposes the optimal route. Furthermore, it grasps the quantity and type of waste in real time and optimizes collection routes. Step 4: The Security Management Department conducts predictive police activities based on waste collection routes optimized by the Waste Management Department. It analyzes past crime data to predict crime risks in specific areas and time periods. It also detects emergencies in real time, enabling a rapid response. Furthermore, it analyzes security camera footage to detect anomalies and issue warnings. Step 5: The Citizen Services Department will provide services tailored to citizens' needs based on the predictive police activities provided by the Safety Management Department. It will collect citizen feedback in real time to improve service quality. It will also analyze past service usage data to propose optimal services tailored to specific needs. Furthermore, it will utilize chatbots to enable rapid responses.

[0068] (Example of form 2) An embodiment of the present invention provides a smart city ecosystem that uses AI to improve urban efficiency, sustainability, and the quality of life for residents. This smart city ecosystem provides functions such as traffic management, energy management, waste management, safety enhancement, and personalized citizen services. These functions are optimized in real time using AI and IoT, enabling predictive maintenance and personalized service delivery. For example, in traffic management, AI optimizes traffic flow; in energy management, AI optimizes energy consumption; in waste management, AI optimizes waste collection routes; and in safety enhancement, AI performs predictive police activities. In personalized citizen services, AI provides services tailored to the needs of citizens. This improves urban efficiency, sustainability, and the quality of life for residents. Thus, the smart city ecosystem can improve urban efficiency, sustainability, and the quality of life for residents.

[0069] The smart city ecosystem according to this embodiment comprises a traffic management unit, an energy management unit, a waste management unit, a safety management unit, and a citizen services unit. The traffic management unit is established for traffic management and optimizes traffic flow using AI. For example, the traffic management unit adjusts the timing of traffic signals to alleviate traffic congestion. The traffic management unit can also collect real-time traffic data and analyze traffic flow patterns. Furthermore, the traffic management unit can assess the risk of traffic accidents and issue warnings in high-risk areas. The energy management unit optimizes energy consumption based on the traffic flow optimized by the traffic management unit. For example, the energy management unit assesses the stability of energy supply and takes preventive measures when supply risks are high. Furthermore, the energy management unit can monitor the use of renewable energy and select the optimal energy source. Furthermore, the energy management unit can analyze energy consumption patterns and propose optimal consumption patterns. The waste management unit optimizes waste collection routes based on the energy consumption optimized by the energy management unit. For example, the waste management unit proposes the optimal collection method for each type of waste to improve the recycling rate. Furthermore, the Waste Management Department can monitor the operation of waste collection vehicles and propose optimal routes. It can also grasp the quantity and type of waste in real time and optimize collection routes. The Safety Management Department conducts predictive police activities based on the waste collection routes optimized by the Waste Management Department. For example, the Safety Management Department analyzes past crime data to predict crime risks in specific areas and time periods. It can also detect emergencies in real time and enable a rapid response. Additionally, the Safety Management Department can analyze security camera footage to detect anomalies and issue warnings. The Citizen Services Department provides services tailored to citizens' needs based on the predictive police activities provided by the Safety Management Department. For example, the Citizen Services Department collects citizen feedback in real time to improve service quality. It can also analyze past service usage data to propose optimal services tailored to specific needs.Furthermore, the Citizen Services Department can utilize chatbots to enable rapid responses. This allows the smart city ecosystem, as embodied in this embodiment, to improve urban efficiency, sustainability, and the quality of life for residents.

[0070] The Traffic Management Department is established for traffic management and uses AI to optimize traffic flow. Specifically, the Traffic Management Department collects real-time traffic data from sensors and cameras installed at major intersections and roads within the city. This data includes vehicle speed, number of vehicles, and traffic signal status. The collected data is sent to a database in the cloud and analyzed by AI algorithms. The AI ​​learns traffic flow patterns and calculates optimal signal timing. For example, it can predict increases in traffic volume during specific times and adjust signal timing to alleviate traffic congestion. The Traffic Management Department can also assess the risk of traffic accidents and issue warnings in high-risk areas. The AI ​​analyzes past traffic accident data and predicts the risk of accidents under specific conditions. This allows the Traffic Management Department to issue warnings in high-risk areas and alert drivers. Furthermore, the Traffic Management Department can monitor the operation status of public transport and provide real-time information on delays and service disruptions. This enables the Traffic Management Department to efficiently manage traffic flow throughout the city and ensure smooth movement for residents.

[0071] The Energy Management Department optimizes energy consumption based on traffic flow optimized by the Traffic Management Department. Specifically, the Energy Management Department collects and analyzes energy consumption data within the city in real time. This includes electricity consumption, gas consumption, and water usage. The collected data is analyzed by AI algorithms to assess the stability of the energy supply. For example, if the energy supply is unstable, the Energy Management Department reduces supply risks by taking preventative measures. The Energy Management Department can also monitor the use of renewable energy and select the optimal energy source. For example, by monitoring the use of solar and wind power in real time and selecting the optimal energy source, it can achieve efficient energy use. Furthermore, the Energy Management Department can analyze energy consumption patterns and propose optimal consumption patterns. The AI ​​learns from past energy consumption data and proposes optimal consumption patterns for specific times of day and under specific conditions. This allows the Energy Management Department to promote efficient energy use and optimize energy consumption across the entire city.

[0072] The Waste Management Department optimizes waste collection routes based on energy consumption optimized by the Energy Management Department. Specifically, the Waste Management Department collects and analyzes waste collection data within the city in real time. This includes the amount, type, and frequency of waste collection. The collected data is analyzed by an AI algorithm to calculate the optimal collection route. For example, it proposes the optimal collection method depending on the amount and type of waste, thereby improving the recycling rate. The Waste Management Department can also monitor the operation status of waste collection vehicles and propose the optimal route. The AI ​​learns from past collection data and calculates the optimal collection route under specific conditions. This allows the Waste Management Department to improve collection efficiency and increase the waste recycling rate. Furthermore, the Waste Management Department can grasp the amount and type of waste in real time and optimize collection routes. This allows the Waste Management Department to efficiently manage waste throughout the city and reduce its environmental impact.

[0073] The Security Management Department conducts predictive police activities based on waste collection routes optimized by the Waste Management Department. Specifically, the Security Management Department collects and analyzes crime data within the city in real time. This includes historical crime data, security camera footage, and emergency call data. The collected data is analyzed by AI algorithms to predict crime risk in specific areas and time periods. For example, it evaluates crime risk in specific areas and time periods based on historical crime data and optimizes police activities. The Security Management Department can also detect emergencies in real time and enable a rapid response. The AI ​​analyzes security camera footage, detects anomalies, and issues warnings. This allows the Security Management Department to improve overall city safety and ensure the peace of mind of residents. Furthermore, based on the results of predictive police activities, the Security Management Department can also propose the optimal allocation of police resources. This enables the Security Management Department to achieve efficient police activities and maintain public safety throughout the city.

[0074] The Citizen Services Department provides services tailored to citizens' needs based on predictive police activities provided by the Safety Management Department. Specifically, the Citizen Services Department collects citizen feedback in real time to improve the quality of services. This includes feedback collected through online surveys, chatbots, and social media. The collected feedback is analyzed by AI algorithms to identify citizens' needs and complaints. This allows the Citizen Services Department to propose optimal services tailored to specific needs. For example, it can analyze past service usage data to predict service demand in specific areas and time periods. The Citizen Services Department can also utilize chatbots to enable rapid responses. Chatbots use natural language processing technology to respond to citizens' inquiries and provide necessary information. This allows the Citizen Services Department to respond to citizens' needs quickly and efficiently. Furthermore, the Citizen Services Department can collaborate with other departments to provide integrated services. For example, it can collaborate with the Traffic Management Department and the Energy Management Department to provide services based on traffic information and energy consumption information. This allows the Citizen Services Department to improve the overall efficiency, sustainability, and quality of life of residents in the city.

[0075] The traffic management department can optimize traffic flow using AI. For example, it can adjust the timing of traffic signals to alleviate traffic congestion. It can also collect real-time traffic data and analyze traffic flow patterns. Furthermore, it can assess the risk of traffic accidents and issue warnings in high-risk areas. This improves traffic efficiency through traffic flow optimization. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can use an AI model to analyze traffic data and calculate the optimal timing for adjusting traffic signal timing.

[0076] The energy management department can optimize energy consumption using AI. For example, the energy management department can evaluate the stability of energy supply and take preventive measures if supply risks are high. It can also monitor the use of renewable energy and select the optimal energy source. Furthermore, the energy management department can analyze energy consumption patterns and propose optimal consumption patterns. This improves energy efficiency through the optimization of energy consumption. Some or all of the above processes in the energy management department may be performed using AI, for example, or without AI. For example, the energy management department can input energy consumption data into an AI model and calculate the optimal consumption pattern.

[0077] The waste management department can use AI to optimize waste collection routes. For example, the waste management department can propose the optimal collection method for each type of waste, thereby improving the recycling rate. It can also monitor the operation status of waste collection vehicles and propose the optimal route. Furthermore, the waste management department can grasp the quantity and type of waste in real time and optimize collection routes. This optimization of waste collection routes improves the efficiency of waste management. Some or all of the above processes in the waste management department may be performed using AI, or not. For example, the waste management department can use an AI model to analyze waste data and calculate the optimal route for waste collection.

[0078] The Security Management Department can use AI to conduct predictive police activities. For example, it can analyze past crime data to predict crime risk in specific areas and time periods. It can also detect emergencies in real time, enabling a rapid response. Furthermore, it can analyze security camera footage to detect anomalies and issue warnings. This improves security through predictive police activities. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not. For example, the Security Management Department can use an AI model to analyze crime data and calculate risk in order to predict crime risk.

[0079] The Citizen Services Department can use AI to provide services tailored to the needs of citizens. For example, the Citizen Services Department can collect citizen feedback in real time to improve the quality of services. It can also analyze past service usage data to suggest optimal services tailored to specific needs. Furthermore, the Citizen Services Department can utilize chatbots to enable rapid responses. This will improve the quality of life for residents by providing services that meet their needs. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or not. For example, the Citizen Services Department can input citizen feedback into an AI model to make suggestions for improving the quality of services.

[0080] The traffic management unit can estimate a user's emotions and adjust the timing of traffic signals based on those emotions. For example, if a user is stressed, the traffic management unit can shorten the waiting time at traffic signals. If a user is relaxed, the traffic management unit can maintain the normal timing of traffic signals. Furthermore, if a user is in a hurry, the traffic management unit can adjust the timing of traffic signals to allow for faster passage. This improves traffic efficiency by adjusting traffic signals according to the user's emotions. 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 AI, for example, or not using AI. For example, the traffic management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The traffic management department can analyze historical traffic data to predict and optimize traffic flow patterns during specific events and time periods. For example, it can predict patterns of increased traffic volume during specific events based on historical data and adjust traffic signals in advance. It can also predict traffic flow during rush hour and set optimal signal timings. Furthermore, it can analyze holiday traffic patterns and adjust signals to avoid congestion. This enables the optimization of traffic flow through predictions based on historical data. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can input historical traffic data into an AI model to predict traffic flow patterns.

[0082] The traffic management department can assess the risk of traffic accidents in real time and issue warnings in high-risk areas. For example, the traffic management department can analyze real-time traffic data to identify areas with a high accident risk. It can also display warning signs in high-risk areas. Furthermore, the traffic management department can pre-position police and ambulances in high-risk areas. This improves safety through traffic accident risk assessment. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can input real-time traffic data into an AI model to assess accident risk.

[0083] The traffic management unit can estimate the user's emotions and adjust how traffic information is provided based on the estimated emotions. For example, if the user is stressed, the traffic management unit can provide concise 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 prioritize providing the most important information. This enhances the user's understanding of the information by providing traffic information tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 AI or not. For example, the traffic management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The traffic management department can monitor the operation status of public transport in real time, predict delays and congestion, and propose the optimal route. For example, the traffic management department can identify routes experiencing delays based on real-time operation data. It can also propose alternative routes to avoid routes where congestion is predicted. Furthermore, the traffic management department can suggest optimal transfer points depending on the operation status. This makes it possible to propose the optimal route by monitoring the operation status of public transport. Some or all of the above processes in the traffic management department may be performed using AI, for example, or not. For example, the traffic management department can input real-time operation data into an AI model and calculate the optimal route.

[0085] The traffic management department can utilize drones to optimize traffic flow, collecting aerial monitoring data and incorporating it into traffic management. For example, the traffic management department can use drones to monitor traffic conditions in real time. Furthermore, the traffic management department can adjust the timing of traffic signals based on data collected from drones. In addition, the traffic management department can use drones to quickly detect traffic accidents and congestion. This improves the accuracy of traffic management through the use of drones. Some or all of the above processes in the traffic management department may be performed using AI, for example, or without AI. For example, the traffic management department can input monitoring data collected from drones into an AI model to analyze traffic conditions.

[0086] The energy management unit can estimate the user's emotions and determine energy consumption priorities based on those emotions. For example, if the user is stressed, the energy management unit will prioritize a comfortable temperature setting. It can also prioritize energy efficiency if the user is relaxed. Furthermore, if the user is in a hurry, the energy management unit can prioritize rapid energy supply. This improves energy efficiency by prioritizing energy consumption according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 AI, or not. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The energy management department can analyze past energy consumption data and propose optimal energy consumption patterns according to the season and time of day. For example, the energy management department can analyze seasonal energy consumption patterns from past data. It can also analyze energy consumption patterns by time of day and propose optimal consumption patterns. Furthermore, the energy management department can predict energy consumption patterns during specific events and propose optimal consumption patterns. This enables the optimization of energy consumption through proposals based on past data. Some or all of the above processes in the energy management department may be performed using AI, for example, or without AI. For example, the energy management department can input past energy consumption data into an AI model and calculate optimal consumption patterns.

[0088] The Energy Management Department can assess the stability of energy supply in real time and take preventive measures if supply risk is high. For example, the Energy Management Department assesses the stability of energy supply based on real-time data. Furthermore, if supply risk is high, the Energy Management Department can adjust energy supply as a preventive measure. In addition, the Energy Management Department can proactively strengthen energy supply to areas with high supply risk. This allows for the reduction of supply risk through assessment of energy supply stability. Some or all of the above processes in the Energy Management Department may be performed using AI, for example, or without AI. For example, the Energy Management Department can input real-time energy data into an AI model to assess supply risk.

[0089] The energy management unit can estimate the user's emotions and adjust the method of notifying the user of energy consumption based on the estimated emotions. For example, if the user is stressed, the energy management unit can provide a concise and easy-to-understand notification. It can also provide a detailed notification if the user is relaxed. Furthermore, if the user is in a hurry, the energy management unit can prioritize notifying the user of the most important information. This allows for a deeper understanding of the information by adjusting the notification method according to the user's emotions. 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 energy management unit may be performed using AI or not using AI. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The Energy Management Department can monitor the use of renewable energy in real time and select the optimal energy source. For example, the Energy Management Department monitors the use of renewable energy based on real-time data. It can also select the optimal energy source according to the supply status of renewable energy. Furthermore, the Energy Management Department can analyze the use of renewable energy and propose the optimal energy consumption pattern. This makes it possible to select the optimal energy source by monitoring the use of renewable energy. Some or all of the above processes in the Energy Management Department may be performed using AI, for example, or without AI. For example, the Energy Management Department can input renewable energy use data into an AI model and select the optimal energy source.

[0091] The Energy Management Department can utilize smart meters to optimize energy consumption and collect and analyze detailed consumption data. For example, the Energy Management Department can collect energy consumption data in real time using smart meters. The Energy Management Department can also analyze energy consumption patterns based on the collected data. Furthermore, the Energy Management Department can propose optimal energy consumption patterns based on the analysis results. This enables the optimization of energy consumption through the use of smart meters. Some or all of the above processes in the Energy Management Department may be performed using AI, for example, or without AI. For example, the Energy Management Department can input data collected from smart meters into an AI model and analyze energy consumption patterns.

[0092] The waste management unit can estimate the user's emotions and adjust the frequency of waste collection based on the estimated emotions. For example, if the user is stressed, the waste management unit can increase the frequency of waste collection. Conversely, if the user is relaxed, the waste management unit can maintain the normal collection frequency. Furthermore, if the user is in a hurry, the waste management unit can perform expedited waste collection. This improves the efficiency of waste management by adjusting the waste collection frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 waste management unit may be performed using AI, or not. For example, the waste management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The waste management department can analyze historical waste data to predict waste volume during specific seasons or events and optimize collection routes. For example, the waste management department can predict waste volume during specific seasons based on historical data. It can also predict waste volume during specific events and propose optimal collection routes. Furthermore, the waste management department can analyze seasonal waste volume and optimize collection routes. This enables the optimization of waste collection routes through predictions based on historical data. Some or all of the above processes in the waste management department may be performed using AI, for example, or without AI. For example, the waste management department can input historical waste data into an AI model to predict waste volume.

[0094] The waste management department can propose the optimal collection method for each type of waste, thereby improving the recycling rate. For example, the waste management department can propose the optimal collection method for each type of waste. Furthermore, the waste management department can prioritize the collection of recyclable waste. In addition, the waste management department can propose the optimal collection route for each type of waste. This improves the recycling rate by proposing the optimal collection method for each type of waste. Some or all of the above processes in the waste management department may be performed using AI, for example, or without AI. For example, the waste management department can input data for each type of waste into an AI model and propose the optimal collection method.

[0095] The waste management department can estimate the user's emotions and adjust the waste collection notification method based on the estimated emotions. For example, if the user is stressed, the waste management department can provide a concise and easy-to-understand notification. If the user is relaxed, the waste management department can also provide a detailed notification. Furthermore, if the user is in a hurry, the waste management department can prioritize notifying them of the most important information. This allows for a deeper understanding of the information by adjusting the notification method according to the user's emotions. 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 waste management department may be performed using AI or not. For example, the waste management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The waste management department can monitor the operation status of waste collection vehicles in real time and propose the optimal route. For example, the waste management department monitors the operation status of waste collection vehicles based on real-time data. The waste management department can also propose the optimal route according to the operation status of the collection vehicles. Furthermore, the waste management department can analyze the operation status of the collection vehicles and propose the optimal collection route. This makes it possible to propose the optimal route by monitoring the operation status of waste collection vehicles. Some or all of the above processes in the waste management department may be performed using AI, for example, or not using AI. For example, the waste management department can input real-time operation data into an AI model and calculate the optimal route.

[0097] The waste management department can utilize sensors for waste management to understand the quantity and type of waste in real time. For example, the waste management department can use sensors to understand the quantity of waste in real time. It can also use sensors to understand the type of waste in real time. Furthermore, the waste management department can propose the optimal collection route based on the data collected from the sensors. In this way, by utilizing sensors, the quantity and type of waste can be understood in real time. Some or all of the above processes in the waste management department may be performed using AI, for example, or not using AI. For example, the waste management department can input data collected from sensors into an AI model to analyze the quantity and type of waste.

[0098] The Security Management Department can estimate a user's emotions and prioritize police activities based on those emotions. For example, if a user is stressed, the Security Management Department will prioritize rapid police action. If a user is relaxed, the Security Management Department can maintain normal police activities. Furthermore, if a user is in a hurry, the Security Management Department can prioritize a quick response. This improves security by prioritizing police activities according to the user's emotions. 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 Security Management Department may be performed using AI or not. For example, the Security Management Department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The Security Management Department can analyze past crime data to predict crime risk in specific areas and time periods and optimize police activities. For example, the Security Management Department can predict crime risk in specific areas from past data. It can also predict crime risk in specific time periods and optimize police activities. Furthermore, the Security Management Department can proactively strengthen police activities in areas with high crime risk. This makes it possible to optimize police activities through predictions based on past data. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not. For example, the Security Management Department can input past crime data into an AI model to predict crime risk.

[0100] The Safety Management Department can detect emergencies in real time and enable a rapid response. For example, the Safety Management Department can detect emergencies based on real-time data. Furthermore, in the event of an emergency, the Safety Management Department can quickly dispatch police and ambulances. In addition, when an emergency is detected, the Safety Management Department can quickly notify citizens. This enables a rapid response through real-time detection of emergencies. Some or all of the above processes in the Safety Management Department may be performed using AI, for example, or without AI. For example, the Safety Management Department can input real-time data into an AI model to detect emergencies.

[0101] The safety management department can estimate the user's emotions and adjust the method of providing safety information based on the estimated emotions. For example, if the user is stressed, the safety management department can provide concise and easy-to-understand safety information. If the user is relaxed, the safety management department can also provide detailed safety information. Furthermore, if the user is in a hurry, the safety management department can prioritize providing the most important information. This enhances the understanding of the information by providing safety information tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 safety management department may be performed using AI, or not using AI. For example, the safety management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The Security Management Department can analyze security camera footage in real time, detect anomalies, and issue warnings. For example, the Security Management Department can analyze security camera footage in real time to detect abnormal behavior. Furthermore, if an anomaly is detected, the Security Management Department can issue a warning. In addition, the Security Management Department can quickly carry out police activities based on security camera footage. This enables early detection of anomalies through the analysis of security camera footage. Some or all of the above processes performed by the Security Management Department may be carried out using AI, for example, or without AI. For example, the Security Management Department can input security camera footage data into an AI model to detect anomalies.

[0103] The Security Management Department can utilize drones for security management, collecting aerial surveillance data and using it to improve police operations. For example, the Security Management Department can use drones to collect surveillance data in real time. Furthermore, the Security Management Department can optimize police operations based on the data collected from drones. In addition, the Security Management Department can use drones to quickly detect emergencies. This improves the accuracy of security management through the use of drones. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not. For example, the Security Management Department can input surveillance data collected from drones into an AI model to optimize police operations.

[0104] The Citizen Services Department can estimate a user's emotions and prioritize service delivery based on those emotions. For example, if a user is stressed, the Citizen Services Department will prioritize prompt service delivery. Conversely, if a user is relaxed, the Citizen Services Department can maintain normal service delivery. Furthermore, if a user is in a hurry, the Citizen Services Department can prioritize a quick response. This improves resident satisfaction by prioritizing service delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 Citizen Services Department may be performed using AI or not. For example, the Citizen Services Department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The Citizen Services Department can analyze past service usage data and propose optimal services tailored to specific needs. For example, it can propose services based on past data to meet specific needs. It can also analyze service usage patterns during specific time periods and propose optimal services. Furthermore, it can propose optimal service delivery methods based on service usage data. This enables the provision of optimal services through proposals based on past data. Some or all of the above processes performed by the Citizen Services Department may be carried out using AI, for example, or without AI. For example, the Citizen Services Department can input past service usage data into an AI model and propose optimal services.

[0106] The Citizen Services Department can collect citizen feedback in real time and improve the quality of its services. For example, the Citizen Services Department can collect citizen feedback in real time. Furthermore, the Citizen Services Department can improve the quality of its services based on the collected feedback. In addition, the Citizen Services Department can analyze the feedback and propose the optimal way to deliver services. This improves the quality of services through real-time feedback collection. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or without AI. For example, the Citizen Services Department can input citizen feedback data into an AI model to make suggestions for improving the quality of its services.

[0107] The Citizen Services Department can estimate a user's emotions and adjust its service delivery methods based on those emotions. For example, if a user is stressed, the Citizen Services Department can provide concise and easy-to-understand services. If a user is relaxed, the Citizen Services Department can provide detailed services. Furthermore, if a user is in a hurry, the Citizen Services Department can provide a quick response. This adjustment of service delivery methods according to the user's emotions improves resident satisfaction. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Citizen Services Department may be performed using AI or not. For example, the Citizen Services Department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The Citizen Services Department can understand citizens' location information in real time and provide optimal services. For example, the Citizen Services Department can understand citizens' location information in real time. Furthermore, the Citizen Services Department can provide optimal services based on location information. In addition, the Citizen Services Department can analyze citizens' location information and propose the optimal service delivery method. This enables the provision of optimal services through real-time location information. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or without AI. For example, the Citizen Services Department can input citizens' location data into an AI model and propose the optimal service.

[0109] The Citizen Services Department can utilize chatbots for citizen services to enable faster responses. For example, the Citizen Services Department can use chatbots to respond to citizen inquiries in real time. Furthermore, the Citizen Services Department can use chatbots to provide services tailored to citizens' needs. This enables faster service delivery through the use of chatbots. Some or all of the above processes in the Citizen Services Department may be performed using AI, for example, or without AI. For example, the Citizen Services Department can input chatbot dialogue data into an AI model to generate optimal responses.

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

[0111] The smart city ecosystem can also be equipped with an environmental monitoring unit. This unit can monitor environmental data such as air quality, water quality, and noise levels within the city in real time. For example, it can use air quality sensors to measure the concentration of harmful substances in the air and issue a warning if the levels exceed the standard. It can also use water quality sensors to monitor the water quality of rivers and lakes, prompting a rapid response if pollution is detected. Furthermore, it can use noise sensors to monitor noise levels within the city, enabling countermeasures to be taken if noise levels exceed a certain threshold. Thus, adding an environmental monitoring unit improves urban environmental protection and the health of residents.

[0112] The smart city ecosystem can also include a health management department. This department can collect residents' health data and provide personalized health advice. For example, it can use wearable devices to collect data such as residents' heart rate, steps taken, and sleep patterns to monitor their health. The health management department can also assess health risks based on the collected data and propose preventative measures. Furthermore, it can analyze residents' health data and take measures to address specific health problems. Thus, adding a health management department improves residents' health and quality of life.

[0113] The smart city ecosystem can also include an education support department. This department can collect residents' educational data and provide personalized educational programs. For example, it can monitor residents' learning progress using an online learning platform and suggest appropriate learning content. Furthermore, the education support department can assess learning risks based on the collected data and provide learning support. It can also analyze residents' educational data and provide programs tailored to specific educational needs. Thus, adding an education support department improves residents' educational opportunities and learning outcomes.

[0114] The smart city ecosystem can also include a Cultural Activities Support Department. This department can collect data on residents' cultural activities and propose personalized cultural events and activities. For example, it can suggest appropriate cultural events and workshops based on residents' interests and preferences. Furthermore, the Cultural Activities Support Department can monitor participation in cultural activities based on the collected data and implement measures to promote participation. It can also analyze residents' cultural activity data and plan events tailored to specific cultural needs. Thus, adding a Cultural Activities Support Department enriches residents' cultural lives and strengthens community cohesion.

[0115] A smart city ecosystem can also include a disaster response unit. This unit can monitor disaster risks within the city in real time and enable a rapid response. For example, it can use earthquake sensors to detect earthquakes and issue warnings to residents. It can also use flood sensors to monitor river levels and issue evacuation orders when the risk of flooding increases. Furthermore, the disaster response unit can analyze past disaster data and take proactive measures in areas with high disaster risk. In this way, adding a disaster response unit improves the city's disaster response capabilities and ensures the safety of residents.

[0116] The traffic management department can estimate a user's emotions and adjust the public transport schedule based on the estimated emotions. For example, if a user is stressed, the schedule can be adjusted to reduce waiting times. If a user is relaxed, the normal schedule can be maintained. Furthermore, if a user is in a hurry, the schedule can be adjusted to enable faster travel. This improves the efficiency of public transport use by adjusting the schedule according to the user's emotions. 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 department may be performed using AI, for example, or not using AI. For example, the traffic management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The energy management unit can estimate the user's emotions and optimize energy consumption based on those emotions. For example, if the user is stressed, it can prioritize a comfortable temperature setting. If the user is relaxed, it can prioritize energy efficiency. Furthermore, if the user is in a hurry, it can prioritize rapid energy supply. This improves energy efficiency by optimizing energy consumption according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 AI, or not using AI. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The waste management department can estimate the user's emotions and adjust the waste collection route based on the estimated emotions. For example, if the user is stressed, the collection route can be adjusted for faster collection. If the user is relaxed, the normal collection route can be maintained. Furthermore, if the user is in a hurry, the collection route can be adjusted for faster collection. This improves the efficiency of waste management by adjusting the collection route according to the user's emotions. 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 waste management department may be performed using AI or not. For example, the waste management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The safety management department can estimate the user's emotions and adjust emergency response methods based on the estimated emotions. For example, if the user is stressed, a rapid response will be prioritized. If the user is relaxed, a normal response can be maintained. Furthermore, if the user is in a hurry, a rapid response can also be prioritized. This improves safety by adjusting emergency response methods according to the user's emotions. 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 safety management department may be performed using AI, for example, or not using AI. For example, the safety management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The Citizen Services Department can estimate a user's emotions and adjust the method of service delivery based on the estimated emotions. For example, if a user is stressed, the service can be provided in a concise and easy-to-understand manner. If the user is relaxed, a more detailed service can be provided. Furthermore, if the user is in a hurry, a quick response can be provided. This adjustment of service delivery methods according to the user's emotions improves resident satisfaction. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Citizen Services Department may be performed using AI, or not using AI. For example, the Citizen Services Department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

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

[0122] Step 1: The Traffic Management Department is established for traffic management and uses AI to optimize traffic flow. It adjusts the timing of traffic signals to alleviate traffic congestion. It also collects real-time traffic data and analyzes traffic flow patterns. Furthermore, it assesses the risk of traffic accidents and issues warnings in high-risk areas. Step 2: The Energy Management Department optimizes energy consumption based on traffic flow optimized by the Traffic Management Department. It assesses the stability of the energy supply and takes preventative measures if the supply risk is high. It also monitors the use of renewable energy and selects the optimal energy source. Furthermore, it analyzes energy consumption patterns and proposes the optimal consumption pattern. Step 3: The Waste Management Department optimizes waste collection routes based on energy consumption optimized by the Energy Management Department. It proposes the optimal collection method for each type of waste and improves the recycling rate. It also monitors the operation status of waste collection vehicles and proposes the optimal route. Furthermore, it grasps the quantity and type of waste in real time and optimizes collection routes. Step 4: The Security Management Department conducts predictive police activities based on waste collection routes optimized by the Waste Management Department. It analyzes past crime data to predict crime risks in specific areas and time periods. It also detects emergencies in real time, enabling a rapid response. Furthermore, it analyzes security camera footage to detect anomalies and issue warnings. Step 5: The Citizen Services Department will provide services tailored to citizens' needs based on the predictive police activities provided by the Safety Management Department. It will collect citizen feedback in real time to improve service quality. It will also analyze past service usage data to propose optimal services tailored to specific needs. Furthermore, it will utilize chatbots to enable rapid responses.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements mentioned above, including the traffic management department, energy management department, waste management department, safety management department, and citizen services department, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the traffic management department is implemented by the control unit 46A of the smart device 14, which adjusts the timing of traffic signals and alleviates traffic congestion. The energy management department is implemented by the specific processing unit 290 of the data processing device 12, which optimizes energy consumption. The waste management department is implemented by the control unit 46A of the smart device 14, which optimizes waste collection routes. The safety management department is implemented by the specific processing unit 290 of the data processing device 12, which performs predictive police activities. The citizen services department is implemented by the control unit 46A of the smart device 14, which provides services that meet the needs of citizens. The correspondence between each department and the devices and control units is not limited to the examples given above, and various changes are possible.

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

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements mentioned above, including the traffic management unit, energy management unit, waste management unit, safety management unit, and citizen services unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the traffic management unit is implemented by the control unit 46A of the smart glasses 214, which adjusts the timing of traffic signals and alleviates traffic congestion. The energy management unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes energy consumption. The waste management unit is implemented by the control unit 46A of the smart glasses 214, which optimizes waste collection routes. The safety management unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs predictive police activities. The citizen services unit is implemented by the control unit 46A of the smart glasses 214, which provides services that meet the needs of citizens. The correspondence between each unit and the devices and control units is not limited to the examples given above, and various changes are possible.

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

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements mentioned above, including the traffic management department, energy management department, waste management department, safety management department, and citizen services department, is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the traffic management department is implemented by the control unit 46A of the headset terminal 314, which adjusts the timing of traffic signals and alleviates traffic congestion. The energy management department is implemented by the specific processing unit 290 of the data processing device 12, which optimizes energy consumption. The waste management department is implemented by the control unit 46A of the headset terminal 314, which optimizes waste collection routes. The safety management department is implemented by the specific processing unit 290 of the data processing device 12, which performs predictive police activities. The citizen services department is implemented by the control unit 46A of the headset terminal 314, which provides services that meet the needs of citizens. The correspondence between each department and the devices and control units is not limited to the examples given above, and various changes are possible.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements mentioned above, including the traffic management unit, energy management unit, waste management unit, safety management unit, and citizen services unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the traffic management unit is implemented by the control unit 46A of the robot 414, which adjusts the timing of traffic signals and alleviates traffic congestion. The energy management unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes energy consumption. The waste management unit is implemented by the control unit 46A of the robot 414, which optimizes waste collection routes. The safety management unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs predictive police activities. The citizen services unit is implemented by the control unit 46A of the robot 414, which provides services that meet the needs of citizens. The correspondence between each unit and the devices and control units is not limited to the examples given above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The traffic management department for traffic management, An energy management unit that optimizes energy consumption based on the traffic flow optimized by the aforementioned traffic management unit, A waste management unit optimizes waste collection routes based on energy consumption optimized by the aforementioned energy management unit, The Safety Management Department conducts predictive police activities based on waste collection routes optimized by the aforementioned Waste Management Department, The system comprises a Citizen Services Department that provides services tailored to the needs of citizens based on predictive police activities provided by the aforementioned Safety Management Department. A system characterized by the following features. (Note 2) The aforementioned traffic management department, Optimizing traffic flow using AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned energy management unit, Optimizing energy consumption using AI The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned waste management department, Optimizing waste collection routes using AI The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned safety management department, Using AI to conduct predictive police activities The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Citizen Services Department, Using AI to provide services tailored to the needs of citizens The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned traffic management department, The system estimates the user's emotions and adjusts the timing of traffic signals based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned traffic management department, By analyzing historical traffic data, we predict and optimize traffic flow patterns during specific events and time periods. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned traffic management department, It assesses the risk of traffic accidents in real time and issues warnings in high-risk areas. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned traffic management department, The system estimates the user's emotions and adjusts how traffic information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned traffic management department, It monitors the status of public transportation in real time, predicts delays and congestion, and suggests the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned traffic management department, Drones are used to optimize traffic flow, collecting aerial monitoring data and incorporating it into traffic management. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned energy management unit, It estimates the user's emotions and determines energy consumption priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned energy management unit, By analyzing past energy consumption data, we propose optimal energy consumption patterns tailored to the season and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned energy management unit, The stability of energy supply is assessed in real time, and preventive measures are taken when supply risks are high. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned energy management unit, It estimates the user's emotions and adjusts how energy consumption notifications are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned energy management unit, We monitor the use of renewable energy in real time and select the optimal energy source. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned energy management unit, Smart meters are used to optimize energy consumption, and detailed consumption data is collected and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned waste management department, The system estimates the user's emotions and adjusts the frequency of waste collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned waste management department, By analyzing historical waste data, we predict waste volume during specific seasons and events and optimize collection routes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned waste management department, We propose the optimal collection method for each type of waste, thereby improving the recycling rate. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned waste management department, The system estimates the user's emotions and adjusts the waste collection notification method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned waste management department, We monitor the operation status of waste collection vehicles in real time and suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned waste management department, Using sensors for waste management allows for real-time tracking of waste quantity and type. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned safety management department, The system estimates user sentiment and prioritizes police actions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned safety management department, By analyzing past crime data, police can predict crime risks in specific areas and time periods to optimize police operations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned safety management department, It enables real-time detection of emergencies and a rapid response. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned safety management department, The system estimates the user's emotions and adjusts how safety information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned safety management department, It analyzes security camera footage in real time, detects anomalies, and issues warnings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned safety management department, Drones are being used for safety management to collect aerial surveillance data and use it to inform police operations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Citizen Services Department, The system estimates user emotions and prioritizes service delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Citizen Services Department, We analyze past service usage data and propose the optimal service tailored to specific needs. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Citizen Services Department, Collect citizen feedback in real time to improve the quality of services. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Citizen Services Department, We estimate the user's emotions and adjust the way we deliver services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Citizen Services Department, We track citizens' locations in real time and provide optimal services. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Citizen Services Department, Using chatbots for citizen services enables faster responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The traffic management department for traffic management, An energy management unit that optimizes energy consumption based on the traffic flow optimized by the aforementioned traffic management unit, A waste management unit optimizes waste collection routes based on energy consumption optimized by the aforementioned energy management unit, The Safety Management Department conducts predictive police activities based on waste collection routes optimized by the aforementioned Waste Management Department, The system comprises a Citizen Services Department that provides services tailored to the needs of citizens based on predictive police activities provided by the aforementioned Safety Management Department. A system characterized by the following features.

2. The aforementioned traffic management department, Optimizing traffic flow using AI The system according to feature 1.

3. The aforementioned energy management unit, Optimizing energy consumption using AI The system according to feature 1.

4. The aforementioned waste management department, Using AI to optimize waste collection routes The system according to feature 1.

5. The aforementioned safety management department, Using AI to conduct predictive police activities The system according to feature 1.

6. The aforementioned Citizen Services Department, Using AI to provide services that meet the needs of citizens. The system according to feature 1.

7. The aforementioned traffic management department, The system estimates the user's emotions and adjusts the timing of traffic signals based on those emotions. The system according to feature 1.

8. The aforementioned traffic management department, By analyzing historical traffic data, we predict and optimize traffic flow patterns during specific events and time periods. The system according to feature 1.

9. The aforementioned traffic management department, It assesses the risk of traffic accidents in real time and issues warnings in high-risk areas. The system according to feature 1.

10. The aforementioned traffic management department, The system estimates the user's emotions and adjusts how traffic information is provided based on those estimated emotions. The system according to feature 1.

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