system

Abstract

The system according to this embodiment aims to support the reduction of environmental impact and the improvement of energy efficiency in urban planning and building design. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The execution unit executes the content proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in urban planning and building design, effectively supporting the reduction of environmental load and the improvement of energy efficiency has not been fully achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to support the reduction of environmental load and the improvement of energy efficiency in urban planning and building design.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The execution unit executes the proposal made by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can support the reduction of environmental impact and the improvement of energy efficiency in urban planning and building design. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Eco-City Planner, according to an embodiment of the present invention, is an AI-based service for supporting sustainable urban design. The Eco-City Planner provides concrete advice aimed at reducing environmental impact and improving energy efficiency in urban planning and building design. Utilizing AI algorithms, the Eco-City Planner supports the efficiency of urban transportation systems and public facilities, thereby reducing carbon dioxide emissions. Furthermore, the Eco-City Planner contributes to the optimization of waste management and improved recycling rates. The Eco-City Planner supports the realization of sustainable cities by having AI analyze vast amounts of data and propose improvements to waste management. For example, the Eco-City Planner reduces the overall environmental impact of a city through optimization of waste collection routes and identification of recyclable resources. In this way, the Eco-City Planner promotes environmentally friendly urban development and serves as a powerful tool for residents and governments to cooperate in building a sustainable future. The Eco-City Planner also aims to be an important partner for urban planners and policymakers in realizing smarter and more eco-friendly urban development. Through this, the Eco-City Planner can support sustainable urban design and achieve reduced environmental impact and improved energy efficiency.

[0029] The eco-city planner according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects data. For example, the data collection unit collects data on urban transportation systems and public facilities. The data collection unit can collect sensor data, user data, environmental data, etc. For example, the data collection unit collects traffic volume data to understand the state of traffic congestion. The data collection unit can also collect data on the usage status of public facilities to support the efficient operation of facilities. Furthermore, the data collection unit can collect environmental data to monitor the environmental conditions of the city. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms. The analysis unit can analyze traffic data to identify the causes of traffic congestion. Furthermore, the analysis unit can analyze data on the usage status of public facilities and propose efficient operation methods for facilities. Furthermore, the analysis unit can analyze environmental data and propose measures to improve the urban environment. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal department can propose, for example, road designs to alleviate traffic congestion or the placement of energy-efficient buildings. The proposal department can propose improvement measures and optimization plans. For example, the proposal department can propose the optimization of traffic signals or the optimization of public transport schedules. The proposal department can also propose the introduction of energy management systems or the monitoring of facility usage. The implementation department carries out the proposals made by the proposal department. For example, the implementation department can implement the proposed road design to alleviate traffic congestion. The implementation department can operate the system and collect feedback. For example, the implementation department can implement the placement of energy-efficient buildings to reduce energy consumption. The implementation department can also introduce the proposed energy management system to reduce the energy consumption of facilities. In this way, the eco-city planner according to the embodiment can support sustainable urban design by carrying out data collection, analysis, proposal, and implementation in a continuous flow.

[0030] The data collection unit collects data. For example, it collects data from urban transportation systems and public facilities. Specifically, it acquires real-time traffic volume data from cameras and sensors installed in transportation systems to understand traffic congestion. This includes detailed data such as vehicle speed, distance between vehicles, and traffic signal status. It also utilizes data from sensors installed within public facilities and from users' smartphones to collect data on the usage of public facilities. For example, it can collect data on entry and exit from libraries and sports facilities, user dwell time, and congestion levels within facilities. Furthermore, to collect environmental data, it acquires data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed throughout the city. This allows for real-time monitoring of the city's environmental conditions and early detection of abnormal environmental changes. The data collection unit centrally manages this diverse data and stores it in a database. The frequency and accuracy of data collection are adjusted according to the characteristics and needs of the city. For example, traffic volume data may be collected minute by minute, while environmental data may be collected hour by hour. This allows the data collection unit to comprehensively understand the diverse conditions of the city and provide high-quality data to the analysis and proposal units.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to analyze the data. Specifically, it analyzes traffic data, comparing past traffic patterns with current traffic conditions to identify the causes of traffic congestion. Using machine learning algorithms, it predicts the probability of congestion occurring at specific times and locations, and identifies the factors causing congestion. It also analyzes public facility usage data, analyzing user behavior patterns and facility utilization rates to propose efficient facility operation methods. For example, it can predict facility congestion at specific times and propose operational methods to distribute users. Furthermore, it analyzes environmental data, evaluating the environmental conditions of various areas of a city based on data acquired from environmental sensors to propose urban environmental improvement measures. For example, it can identify areas with high noise levels or high carbon dioxide concentrations and propose improvement measures for these areas. Based on these analysis results, the analysis unit makes concrete proposals to support the sustainable development of cities. The analysis unit is required to analyze data in real time and respond quickly. It can also utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. This allows the analysis unit to conduct comprehensive and effective analyses of diverse urban challenges and provide useful information to the proposal unit.

[0032] The proposal department makes proposals based on the analysis results obtained by the analysis department. For example, the proposal department proposes road designs to alleviate traffic congestion and the placement of energy-efficient buildings. Specifically, to propose the optimization of traffic signals and public transport schedules, it calculates optimal signal timings and bus / train schedules based on traffic data provided by the analysis department. It also analyzes energy consumption data of public facilities and proposes measures to improve energy efficiency in order to propose the introduction of energy management systems and monitoring of facility usage. For example, it can propose optimal operating methods for lighting and air conditioning systems within facilities to reduce energy consumption. Furthermore, to propose measures to improve the urban environment, it proposes the placement of green spaces and the implementation of noise reduction measures based on environmental data. For example, it can propose the installation of sound barriers in areas with high noise levels and the expansion of green spaces to improve the urban environment. The proposal department compiles these proposals into concrete plans and provides them to the implementation department in an actionable format. The proposal department is required to make comprehensive and feasible proposals to support the sustainable development of cities. The proposal department can also evaluate the effectiveness of the proposals and revise them as necessary. This will enable the proposal department to provide effective solutions to diverse urban challenges and support sustainable urban design.

[0033] The implementation department will carry out the proposals submitted by the proposal department. For example, the implementation department will implement the proposed road design to alleviate traffic congestion. Specifically, it will introduce a system to adjust the timing of traffic signals and smooth the flow of traffic. It will also optimize the design and placement of buildings to reduce energy consumption by implementing energy-efficient building layouts. For example, it can promote the introduction of solar power generation systems and the use of insulation materials to improve energy efficiency. Furthermore, it will implement the proposed energy management system and install and operate it to reduce the energy consumption of facilities. For example, it can monitor energy consumption within facilities in real time and implement operational methods to avoid peak energy consumption. In addition, the implementation department will implement the proposed environmental improvement measures to improve the urban environment, such as expanding green spaces and installing sound barriers. For example, it can install sound barriers in areas with high noise levels to solve urban noise problems. The implementation department is required to continuously monitor these implementations and evaluate their effectiveness. The implementation department can also provide feedback on the effectiveness of the implementations and modify them as needed. In this way, the implementation department can effectively implement the proposed content and support the sustainable development of the city.

[0034] The Waste Management Department can propose measures to improve waste management. For example, the Waste Management Department can propose methods for sorting waste. The Waste Management Department can propose ways to promote recycling and reduce waste volume. For example, the Waste Management Department can propose methods for identifying recyclable resources. The Waste Management Department can also propose optimizing waste collection routes. For example, the Waste Management Department can optimize the schedule of garbage trucks to achieve efficient waste collection. In this way, the Waste Management Department can optimize waste treatment. Some or all of the above-mentioned processes in the Waste Management Department may be carried out using AI, for example, or without AI. For example, in order to propose methods for sorting waste, the Waste Management Department can input waste data into a generating AI, and the generating AI can then propose sorting methods.

[0035] The Recycling Support Department can support improving the recycling rate. The Recycling Support Department can, for example, support recycling awareness campaigns. The Recycling Support Department can support the development of recycling facilities. The Recycling Support Department can, for example, propose methods for identifying recyclable resources. The Recycling Support Department can also support promoting recycling. The Recycling Support Department can, for example, communicate the importance of recycling to residents through recycling awareness campaigns. In this way, the Recycling Support Department can promote the effective use of resources. Some or all of the above processes in the Recycling Support Department may be carried out using AI, for example, or not using AI. For example, in order to propose methods for identifying recyclable resources, the Recycling Support Department can input recycling data into a generating AI, and the generating AI can propose identification methods.

[0036] The Traffic Support Department can assist in improving the efficiency of traffic systems. For example, the Traffic Support Department can assist in optimizing traffic signals. The Traffic Support Department can assist in promoting the use of public transport. For example, the Traffic Support Department can propose road designs to alleviate traffic congestion. The Traffic Support Department can also propose optimizations for public transport operating schedules. For example, the Traffic Support Department can alleviate traffic congestion by optimizing traffic signals. In this way, the Traffic Support Department can alleviate traffic congestion. Some or all of the above processes in the Traffic Support Department may be performed using AI, for example, or without AI. For example, the Traffic Support Department can input traffic data into a generating AI to propose road designs to alleviate traffic congestion, and the generating AI can then propose road designs.

[0037] The Public Facilities Support Department can support the efficiency of public facilities. For example, the Public Facilities Support Department can support the introduction of energy management systems. The Public Facilities Support Department can support the monitoring of facility usage. For example, the Public Facilities Support Department can propose facility layouts to reduce energy consumption. The Public Facilities Support Department can also propose the optimization of facility usage. For example, the Public Facilities Support Department can reduce energy consumption by introducing energy management systems. In this way, the Public Facilities Support Department can reduce energy consumption. Some or all of the above processes in the Public Facilities Support Department may be performed using AI, for example, or without AI. For example, in order to propose facility layouts to reduce energy consumption, the Public Facilities Support Department can input facility data into a generating AI, and the generating AI can make facility layout proposals.

[0038] The data collection unit can collect data on urban transportation systems and public facilities. For example, the data collection unit can collect traffic volume data to understand traffic congestion. The data collection unit can also collect data on the usage of public facilities to support the efficient operation of those facilities. The data collection unit can also collect environmental data to monitor the urban environmental conditions. This enables the data collection unit to operate efficiently. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, in order to collect traffic volume data, the data collection unit can input data from traffic sensors into a generating AI, and the generating AI can then collect the data.

[0039] The analysis unit can analyze collected data and propose design and operation methods. For example, the analysis unit can analyze traffic data to identify the causes of traffic congestion. The analysis unit can also analyze data on the usage of public facilities and propose efficient ways to operate those facilities. The analysis unit can also analyze environmental data and propose measures to improve the urban environment. This enables the analysis unit to carry out efficient urban planning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to analyze traffic data, the analysis unit can input traffic data into a generating AI, and the generating AI can perform the data analysis.

[0040] Based on the analysis results, the proposal unit can propose road designs to alleviate traffic congestion and energy-efficient building layouts. For example, the proposal unit can propose the optimization of traffic signals. The proposal unit can also propose the optimization of public transport schedules. The proposal unit can also propose the introduction of energy management systems. In this way, the proposal unit can improve the efficiency of cities. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, in order to propose road designs to alleviate traffic congestion, the proposal unit can input the analysis results into a generating AI, and the generating AI can then propose road designs.

[0041] The implementation unit can carry out the proposed actions and reduce carbon dioxide emissions. For example, the implementation unit can implement the proposed road design to alleviate traffic congestion. The implementation unit can also implement an energy-efficient building layout to reduce energy consumption. The implementation unit can also implement the proposed energy management system to reduce the energy consumption of facilities. In this way, the implementation unit can reduce carbon dioxide emissions. Some or all of the above processes in the implementation unit may be carried out using AI, for example, or without AI. For example, in order to carry out the proposed actions, the implementation unit can input the proposed actions into a generating AI, and the generating AI can propose a method of implementation.

[0042] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and reflect it in future collections. The data collection unit can also optimize collection frequency and timing based on past data collection history. The data collection unit can also analyze past data collection history and determine the priority of data to be collected. This allows the data collection unit to select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input collection history data into a generating AI to analyze past data collection history, and the generating AI can select the optimal collection method.

[0043] The data collection unit can filter data based on the environmental conditions of a specific area of ​​the city during data collection. For example, the data collection unit can filter data based on the air pollution levels of a specific area of ​​the city. The data collection unit can also filter data based on the noise levels of a specific area of ​​the city. The data collection unit can also filter data based on the traffic volume of a specific area of ​​the city. This enables the data collection unit to collect data efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to filter the environmental conditions of a specific area of ​​the city, the data collection unit can input environmental data into a generating AI and perform the filtering using the generating AI.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the city during data collection. For example, the data collection unit can prioritize the collection of data from the city center to help alleviate traffic congestion. The data collection unit can also prioritize the collection of data from residential areas of the city to help improve energy efficiency. The data collection unit can also prioritize the collection of data from commercial areas of the city to help improve the efficiency of public facilities. This enables the data collection unit to collect data efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to collect data while considering the geographical location information of the city, the data collection unit can input geographic data into a generating AI, and the generating AI can perform the data collection.

[0045] The data collection unit can analyze urban social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data from areas that are trending on social media. The data collection unit can also determine data collection targets based on user feedback on social media. The data collection unit can also adjust the timing of data collection based on event information on social media. This enables the data collection unit to collect data efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI to analyze social media activity, and the generating AI can then collect the data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables the analysis unit to perform efficient analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI to evaluate the importance of the data, and the generating AI can perform the importance evaluation.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a traffic analysis algorithm to traffic data. The analysis unit can also apply an energy analysis algorithm to energy data. The analysis unit can also apply a waste analysis algorithm to waste data. This enables the analysis unit to perform efficient analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to apply an analysis algorithm according to the data category, the analysis unit can input data into a generating AI, and the generating AI can apply the analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze long-term trends based on historical data. The analysis unit can also adjust the level of detail of the analysis according to the data collection timing. This allows the analysis unit to prioritize the analysis of the latest information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to determine the priority of analysis based on the data collection timing, the analysis unit can input the data collection timing data into a generating AI, and the generating AI can perform the priority determination.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to perform efficient analysis. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. This enables the analysis unit to perform efficient analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to adjust the order of analysis based on the relevance of the data, the analysis unit can input relevance data into a generating AI, and the generating AI can adjust the order.

[0050] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it can provide detailed explanations for high-importance proposals and simplified explanations for low-importance proposals. The proposal department can also prioritize proposals according to their importance. This enables the proposal department to make efficient proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input proposal data into a generating AI to evaluate the importance of the proposals, and the generating AI can perform the importance evaluation.

[0051] The proposal unit can apply different proposal algorithms depending on the category of the proposal content when making a proposal. For example, the proposal unit can apply a traffic analysis algorithm to proposals related to transportation systems. The proposal unit can also apply an energy analysis algorithm to proposals related to energy efficiency. The proposal unit can also apply a waste analysis algorithm to proposals related to waste management. This enables the proposal unit to make efficient proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, in order to apply a proposal algorithm according to the category of the proposal content, the proposal unit can input proposal data into a generating AI, and the generating AI can apply the proposal algorithm.

[0052] The proposal department can determine the priority of proposals based on the submission timing of the proposals. For example, the proposal department can prioritize proposals that are urgent. The proposal department can also adjust the level of detail of proposals according to the submission timing of the proposals. The proposal department can also determine the order of proposals based on the submission timing of the proposals. This allows the proposal department to prioritize proposals that are urgent. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input submission timing data into a generating AI to determine the priority of proposals based on the submission timing of the proposals, and the generating AI can perform the priority determination.

[0053] The proposal department can adjust the order of proposals based on the relevance of their content when submitting proposals. For example, the proposal department may prioritize highly relevant proposals. It can also postpone less relevant proposals and prioritize important ones. The proposal department can also adjust the level of detail of proposals according to their relevance. This enables the proposal department to submit proposals efficiently. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input relevance data into a generating AI to adjust the order of proposals based on their relevance, and the generating AI can then adjust the order.

[0054] The execution unit can analyze past execution history and select the optimal execution method during execution. For example, the execution unit can identify the most efficient execution method from past execution history and reflect it in future executions. The execution unit can also optimize execution frequency and timing based on past execution history. The execution unit can also analyze past execution history and determine the priority of execution targets. This allows the execution unit to select the optimal execution method. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input execution history data into a generating AI to analyze past execution history, and the generating AI can select the optimal execution method.

[0055] The execution unit can customize the means of execution based on the current conditions of the city during execution. For example, the execution unit can customize the means of execution based on the current traffic conditions of the city. If traffic congestion occurs, the execution unit can suggest an alternative route. The execution unit can also customize the means of execution based on the current energy consumption conditions of the city. During times of high energy consumption, the execution unit can suggest energy-efficient means of execution. The execution unit can also customize the means of execution based on the current weather conditions of the city. During rainy weather, the execution unit can suggest an indoor route. This enables the execution unit to perform the task efficiently. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input situational data into a generating AI to customize the means of execution considering the current conditions of the city, and the generating AI can customize the means of execution.

[0056] The execution unit can select the optimal execution method during execution, taking into account the geographical location information of the city. For example, the execution unit may prioritize execution methods in the city center to help alleviate traffic congestion. It may also prioritize execution methods in residential areas of the city to help improve energy efficiency. It may also prioritize execution methods in commercial areas of the city to help improve the efficiency of public facilities. This enables the execution unit to perform execution efficiently. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, in order to select an execution method that takes into account the geographical location information of the city, the execution unit may input geographic data into a generating AI, and the generating AI may select a method.

[0057] The execution unit can analyze the city's social media activity during execution and propose means of execution. For example, the execution unit may prioritize proposing methods of execution for areas that are trending on social media. The execution unit can also determine means of execution based on user feedback on social media. The execution unit can also adjust the timing of execution based on event information on social media. This allows the execution unit to propose efficient means of execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input social media data into a generating AI to analyze social media activity, and the generating AI can then propose means of execution.

[0058] The waste management department can analyze past waste data to select the optimal management method during waste management. For example, the waste management department can identify the most efficient management method from past waste data and reflect it in future management. The waste management department can also optimize the frequency and timing of management based on past waste data. The waste management department can also analyze past waste data to determine the priority of items to be managed. This allows the waste management department to select the optimal management method. 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 waste data into a generating AI to analyze past waste data, and the generating AI can select the optimal management method.

[0059] The Waste Management Department can select the optimal waste management method when managing waste, taking into account the geographical location information of the city. For example, the Waste Management Department can prioritize waste management methods in the city center to help alleviate traffic congestion. The Waste Management Department can also prioritize waste management methods in residential areas of the city to help improve energy efficiency. The Waste Management Department can also prioritize waste management methods in commercial areas of the city to help improve the efficiency of public facilities. This enables the Waste Management Department to manage waste efficiently. Some or all of the above processes in the Waste Management Department may be carried out using AI, for example, or without AI. For example, in order to select a management method that takes into account the geographical location information of the city, the Waste Management Department can input geographic data into a generating AI, and the generating AI can select a method.

[0060] The Recycling Support Department can analyze past recycling data to select the optimal support method when providing recycling support. For example, the Recycling Support Department can identify the most efficient support method from past recycling data and reflect it in future support. The Recycling Support Department can also optimize the frequency and timing of support based on past recycling data. The Recycling Support Department can also analyze past recycling data to determine the priority of support targets. This allows the Recycling Support Department to select the optimal support method. Some or all of the above processes in the Recycling Support Department may be performed using AI, for example, or without AI. For example, the Recycling Support Department can input recycling data into a generating AI to analyze past recycling data, and the generating AI can select the optimal support method.

[0061] The Recycling Support Department can select the optimal support method when providing recycling support, taking into account the geographical location information of the city. For example, the Recycling Support Department can prioritize recycling support methods in the city center to help alleviate traffic congestion. The Recycling Support Department can also prioritize recycling support methods in residential areas of the city to help improve energy efficiency. The Recycling Support Department can also prioritize recycling support methods in commercial areas of the city to help improve the efficiency of public facilities. This enables the Recycling Support Department to provide efficient recycling support. Some or all of the above processing in the Recycling Support Department may be performed using AI, for example, or without AI. For example, in order to select a support method that takes into account the geographical location information of the city, the Recycling Support Department can input geographic data into a generating AI, and the generating AI can select a method.

[0062] The traffic support department can analyze past traffic data to select the optimal support method when providing traffic support. For example, the traffic support department can identify the most efficient support method from past traffic data and reflect it in future support. The traffic support department can also optimize the frequency and timing of support based on past traffic data. The traffic support department can also analyze past traffic data to determine the priority of support targets. This allows the traffic support department to select the optimal support method. Some or all of the above processes in the traffic support department may be performed using AI, for example, or without AI. For example, the traffic support department can input traffic data into a generating AI to analyze past traffic data, and the generating AI can select the optimal support method.

[0063] The traffic support unit can select the optimal support method when providing traffic support, taking into account the geographical location information of the city. For example, the traffic support unit can prioritize selecting a traffic support method in the city center to help alleviate traffic congestion. The traffic support unit can also prioritize selecting a traffic support method in the city's residential areas to help improve energy efficiency. The traffic support unit can also prioritize selecting a traffic support method in the city's commercial areas to help improve the efficiency of public facilities. This enables the traffic support unit to provide efficient traffic support. Some or all of the above processing in the traffic support unit may be performed using AI, for example, or without AI. For example, in order to select a support method that takes into account the geographical location information of the city, the traffic support unit can input geographic data into a generating AI, and the generating AI can select a method.

[0064] The Public Facilities Support Department can analyze past public facilities data to select the optimal support method when providing support to public facilities. For example, the Public Facilities Support Department can identify the most efficient support method from past public facilities data and reflect it in future support. The Public Facilities Support Department can also optimize the frequency and timing of support based on past public facilities data. The Public Facilities Support Department can also analyze past public facilities data to determine the priority of support targets. This allows the Public Facilities Support Department to select the optimal support method. Some or all of the above processes in the Public Facilities Support Department may be performed using AI, for example, or without AI. For example, the Public Facilities Support Department can input public facilities data into a generating AI to analyze past public facilities data, and the generating AI can select the optimal support method.

[0065] The Public Facilities Support Department can select the optimal support method when providing support to public facilities, taking into account the geographical location information of the city. For example, the Public Facilities Support Department can prioritize selecting support methods for public facilities in the city center to help alleviate traffic congestion. The Public Facilities Support Department can also prioritize selecting support methods for public facilities in residential areas of the city to help improve energy efficiency. The Public Facilities Support Department can also prioritize selecting support methods for public facilities in commercial areas of the city to help improve the efficiency of public facilities. This enables the Public Facilities Support Department to provide efficient support to public facilities. Some or all of the above processing in the Public Facilities Support Department may be performed using AI, for example, or without AI. For example, in order to select a support method that takes into account the geographical location information of the city, the Public Facilities Support Department can input geographic data into a generating AI, and the generating AI can select a method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] Eco-city planners can further collect residents' health data and incorporate it into urban planning. For example, the data collection unit can collect residents' step count and heart rate data, and the analysis unit can analyze this data to understand the residents' health status. The proposal unit can propose the installation of pedestrian-only streets and the placement of parks based on the health data. The implementation unit can carry out the proposed health promotion measures to improve the health of residents. In this way, eco-city planners can support sustainable urban design that takes residents' health into consideration.

[0068] Eco-city planners can further collect meteorological data and incorporate it into urban planning. For example, the data collection unit collects meteorological data such as temperature, humidity, and precipitation, and the analysis unit analyzes this data to understand the city's climate patterns. The proposal unit can propose improvements to building insulation performance and the placement of green spaces based on the meteorological data. The implementation unit can carry out the proposed climate adaptation measures to help the city adapt to climate change. In this way, eco-city planners can support sustainable urban design that responds to climate change.

[0069] Eco-city planners can further collect energy consumption data and incorporate it into urban planning. For example, the data collection department collects energy consumption data from individual households and businesses, and the analysis department analyzes this data to understand energy consumption patterns. The proposal department can then propose energy-efficient building designs and the introduction of renewable energy based on the energy consumption data. The implementation department can carry out the proposed energy efficiency measures to reduce energy consumption throughout the city. In this way, eco-city planners can support sustainable urban design that takes energy efficiency into consideration.

[0070] Eco-city planners can further collect resident mobility data and incorporate it into urban planning. For example, the data collection unit collects data on residents' travel routes and modes of transportation, and the analysis unit analyzes this data to understand residents' mobility patterns. The proposal unit can then propose optimizations for public transportation and the installation of dedicated bicycle lanes based on the mobility data. The implementation unit can then implement the proposed mobility efficiency measures to improve the convenience of residents' mobility. In this way, eco-city planners can support sustainable urban design that takes resident mobility into consideration.

[0071] Eco-city planners can further collect residents' health data and incorporate it into urban planning. For example, the data collection unit can collect residents' step count and heart rate data, and the analysis unit can analyze this data to understand the residents' health status. The proposal unit can propose the installation of pedestrian-only streets and the placement of parks based on the health data. The implementation unit can carry out the proposed health promotion measures to improve the health of residents. In this way, eco-city planners can support sustainable urban design that takes residents' health into consideration.

[0072] Eco-city planners can further collect resident mobility data and incorporate it into urban planning. For example, the data collection unit collects data on residents' travel routes and modes of transportation, and the analysis unit analyzes this data to understand residents' mobility patterns. The proposal unit can then propose optimizations for public transportation and the installation of dedicated bicycle lanes based on the mobility data. The implementation unit can then implement the proposed mobility efficiency measures to improve the convenience of residents' mobility. In this way, eco-city planners can support sustainable urban design that takes resident mobility into consideration.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The data collection unit collects data. The data collection unit can collect data on urban transportation systems and public facilities, sensor data, user data, environmental data, etc. For example, it can collect traffic volume data to understand the state of traffic congestion. It can also collect data on the usage of public facilities to support the efficient operation of those facilities. Furthermore, it can collect environmental data to monitor the environmental conditions of the city. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the data, and can analyze traffic data to identify the causes of traffic congestion. It can also analyze data on the usage of public facilities and propose ways to operate those facilities more efficiently. Furthermore, it can analyze environmental data and propose measures to improve the urban environment. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department proposes road designs to alleviate traffic congestion and the placement of energy-efficient buildings. The proposal department can propose improvement measures and optimization plans, such as optimizing traffic signals or public transport schedules. They can also propose the introduction of energy management systems or monitoring of facility usage. Step 4: The implementation team carries out the proposals made by the proposal team. The implementation team implements the proposed road design to alleviate traffic congestion. The implementation team can operate the system and collect feedback. For example, they can implement an energy-efficient building layout to reduce energy consumption. They can also implement a proposed energy management system to reduce the energy consumption of facilities.

[0075] (Example of form 2) The Eco-City Planner, according to an embodiment of the present invention, is an AI-based service for supporting sustainable urban design. The Eco-City Planner provides concrete advice aimed at reducing environmental impact and improving energy efficiency in urban planning and building design. Utilizing AI algorithms, the Eco-City Planner supports the efficiency of urban transportation systems and public facilities, thereby reducing carbon dioxide emissions. Furthermore, the Eco-City Planner contributes to the optimization of waste management and improved recycling rates. The Eco-City Planner supports the realization of sustainable cities by having AI analyze vast amounts of data and propose improvements to waste management. For example, the Eco-City Planner reduces the overall environmental impact of a city through optimization of waste collection routes and identification of recyclable resources. In this way, the Eco-City Planner promotes environmentally friendly urban development and serves as a powerful tool for residents and governments to cooperate in building a sustainable future. The Eco-City Planner also aims to be an important partner for urban planners and policymakers in realizing smarter and more eco-friendly urban development. Through this, the Eco-City Planner can support sustainable urban design and achieve reduced environmental impact and improved energy efficiency.

[0076] The eco-city planner according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an execution unit. The data collection unit collects data. For example, the data collection unit collects data on urban transportation systems and public facilities. The data collection unit can collect sensor data, user data, environmental data, etc. For example, the data collection unit collects traffic volume data to understand the state of traffic congestion. The data collection unit can also collect data on the usage status of public facilities to support the efficient operation of facilities. Furthermore, the data collection unit can collect environmental data to monitor the environmental conditions of the city. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms. The analysis unit can analyze traffic data to identify the causes of traffic congestion. Furthermore, the analysis unit can analyze data on the usage status of public facilities and propose efficient operation methods for facilities. Furthermore, the analysis unit can analyze environmental data and propose measures to improve the urban environment. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal department can propose, for example, road designs to alleviate traffic congestion or the placement of energy-efficient buildings. The proposal department can propose improvement measures and optimization plans. For example, the proposal department can propose the optimization of traffic signals or the optimization of public transport schedules. The proposal department can also propose the introduction of energy management systems or the monitoring of facility usage. The implementation department carries out the proposals made by the proposal department. For example, the implementation department can implement the proposed road design to alleviate traffic congestion. The implementation department can operate the system and collect feedback. For example, the implementation department can implement the placement of energy-efficient buildings to reduce energy consumption. The implementation department can also introduce the proposed energy management system to reduce the energy consumption of facilities. In this way, the eco-city planner according to the embodiment can support sustainable urban design by carrying out data collection, analysis, proposal, and implementation in a continuous flow.

[0077] The data collection unit collects data. For example, it collects data from urban transportation systems and public facilities. Specifically, it acquires real-time traffic volume data from cameras and sensors installed in transportation systems to understand traffic congestion. This includes detailed data such as vehicle speed, distance between vehicles, and traffic signal status. It also utilizes data from sensors installed within public facilities and from users' smartphones to collect data on the usage of public facilities. For example, it can collect data on entry and exit from libraries and sports facilities, user dwell time, and congestion levels within facilities. Furthermore, to collect environmental data, it acquires data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed throughout the city. This allows for real-time monitoring of the city's environmental conditions and early detection of abnormal environmental changes. The data collection unit centrally manages this diverse data and stores it in a database. The frequency and accuracy of data collection are adjusted according to the characteristics and needs of the city. For example, traffic volume data may be collected minute by minute, while environmental data may be collected hour by hour. This allows the data collection unit to comprehensively understand the diverse conditions of the city and provide high-quality data to the analysis and proposal units.

[0078] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to analyze the data. Specifically, it analyzes traffic data, comparing past traffic patterns with current traffic conditions to identify the causes of traffic congestion. Using machine learning algorithms, it predicts the probability of congestion occurring at specific times and locations, and identifies the factors causing congestion. It also analyzes public facility usage data, analyzing user behavior patterns and facility utilization rates to propose efficient facility operation methods. For example, it can predict facility congestion at specific times and propose operational methods to distribute users. Furthermore, it analyzes environmental data, evaluating the environmental conditions of various areas of a city based on data acquired from environmental sensors to propose urban environmental improvement measures. For example, it can identify areas with high noise levels or high carbon dioxide concentrations and propose improvement measures for these areas. Based on these analysis results, the analysis unit makes concrete proposals to support the sustainable development of cities. The analysis unit is required to analyze data in real time and respond quickly. It can also utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. This allows the analysis unit to conduct comprehensive and effective analyses of diverse urban challenges and provide useful information to the proposal unit.

[0079] The proposal department makes proposals based on the analysis results obtained by the analysis department. For example, the proposal department proposes road designs to alleviate traffic congestion and the placement of energy-efficient buildings. Specifically, to propose the optimization of traffic signals and public transport schedules, it calculates optimal signal timings and bus / train schedules based on traffic data provided by the analysis department. It also analyzes energy consumption data of public facilities and proposes measures to improve energy efficiency in order to propose the introduction of energy management systems and monitoring of facility usage. For example, it can propose optimal operating methods for lighting and air conditioning systems within facilities to reduce energy consumption. Furthermore, to propose measures to improve the urban environment, it proposes the placement of green spaces and the implementation of noise reduction measures based on environmental data. For example, it can propose the installation of sound barriers in areas with high noise levels and the expansion of green spaces to improve the urban environment. The proposal department compiles these proposals into concrete plans and provides them to the implementation department in an actionable format. The proposal department is required to make comprehensive and feasible proposals to support the sustainable development of cities. The proposal department can also evaluate the effectiveness of the proposals and revise them as necessary. This will enable the proposal department to provide effective solutions to diverse urban challenges and support sustainable urban design.

[0080] The implementation department will carry out the proposals submitted by the proposal department. For example, the implementation department will implement the proposed road design to alleviate traffic congestion. Specifically, it will introduce a system to adjust the timing of traffic signals and smooth the flow of traffic. It will also optimize the design and placement of buildings to reduce energy consumption by implementing energy-efficient building layouts. For example, it can promote the introduction of solar power generation systems and the use of insulation materials to improve energy efficiency. Furthermore, it will implement the proposed energy management system and install and operate it to reduce the energy consumption of facilities. For example, it can monitor energy consumption within facilities in real time and implement operational methods to avoid peak energy consumption. In addition, the implementation department will implement the proposed environmental improvement measures to improve the urban environment, such as expanding green spaces and installing sound barriers. For example, it can install sound barriers in areas with high noise levels to solve urban noise problems. The implementation department is required to continuously monitor these implementations and evaluate their effectiveness. The implementation department can also provide feedback on the effectiveness of the implementations and modify them as needed. In this way, the implementation department can effectively implement the proposed content and support the sustainable development of the city.

[0081] The Waste Management Department can propose measures to improve waste management. For example, the Waste Management Department can propose methods for sorting waste. The Waste Management Department can propose ways to promote recycling and reduce waste volume. For example, the Waste Management Department can propose methods for identifying recyclable resources. The Waste Management Department can also propose optimizing waste collection routes. For example, the Waste Management Department can optimize the schedule of garbage trucks to achieve efficient waste collection. In this way, the Waste Management Department can optimize waste treatment. Some or all of the above-mentioned processes in the Waste Management Department may be carried out using AI, for example, or without AI. For example, in order to propose methods for sorting waste, the Waste Management Department can input waste data into a generating AI, and the generating AI can then propose sorting methods.

[0082] The Recycling Support Department can support improving the recycling rate. The Recycling Support Department can, for example, support recycling awareness campaigns. The Recycling Support Department can support the development of recycling facilities. The Recycling Support Department can, for example, propose methods for identifying recyclable resources. The Recycling Support Department can also support promoting recycling. The Recycling Support Department can, for example, communicate the importance of recycling to residents through recycling awareness campaigns. In this way, the Recycling Support Department can promote the effective use of resources. Some or all of the above processes in the Recycling Support Department may be carried out using AI, for example, or not using AI. For example, in order to propose methods for identifying recyclable resources, the Recycling Support Department can input recycling data into a generating AI, and the generating AI can propose identification methods.

[0083] The Traffic Support Department can assist in improving the efficiency of traffic systems. For example, the Traffic Support Department can assist in optimizing traffic signals. The Traffic Support Department can assist in promoting the use of public transport. For example, the Traffic Support Department can propose road designs to alleviate traffic congestion. The Traffic Support Department can also propose optimizations for public transport operating schedules. For example, the Traffic Support Department can alleviate traffic congestion by optimizing traffic signals. In this way, the Traffic Support Department can alleviate traffic congestion. Some or all of the above processes in the Traffic Support Department may be performed using AI, for example, or without AI. For example, the Traffic Support Department can input traffic data into a generating AI to propose road designs to alleviate traffic congestion, and the generating AI can then propose road designs.

[0084] The Public Facilities Support Department can support the efficiency of public facilities. For example, the Public Facilities Support Department can support the introduction of energy management systems. The Public Facilities Support Department can support the monitoring of facility usage. For example, the Public Facilities Support Department can propose facility layouts to reduce energy consumption. The Public Facilities Support Department can also propose the optimization of facility usage. For example, the Public Facilities Support Department can reduce energy consumption by introducing energy management systems. In this way, the Public Facilities Support Department can reduce energy consumption. Some or all of the above processes in the Public Facilities Support Department may be performed using AI, for example, or without AI. For example, in order to propose facility layouts to reduce energy consumption, the Public Facilities Support Department can input facility data into a generating AI, and the generating AI can make facility layout proposals.

[0085] The data collection unit can collect data on urban transportation systems and public facilities. For example, the data collection unit can collect traffic volume data to understand traffic congestion. The data collection unit can also collect data on the usage of public facilities to support the efficient operation of those facilities. The data collection unit can also collect environmental data to monitor the urban environmental conditions. This enables the data collection unit to operate efficiently. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, in order to collect traffic volume data, the data collection unit can input data from traffic sensors into a generating AI, and the generating AI can then collect the data.

[0086] The analysis unit can analyze collected data and propose design and operation methods. For example, the analysis unit can analyze traffic data to identify the causes of traffic congestion. The analysis unit can also analyze data on the usage of public facilities and propose efficient ways to operate those facilities. The analysis unit can also analyze environmental data and propose measures to improve the urban environment. This enables the analysis unit to carry out efficient urban planning. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to analyze traffic data, the analysis unit can input traffic data into a generating AI, and the generating AI can perform the data analysis.

[0087] Based on the analysis results, the proposal unit can propose road designs to alleviate traffic congestion and energy-efficient building layouts. For example, the proposal unit can propose the optimization of traffic signals. The proposal unit can also propose the optimization of public transport schedules. The proposal unit can also propose the introduction of energy management systems. In this way, the proposal unit can improve the efficiency of cities. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, in order to propose road designs to alleviate traffic congestion, the proposal unit can input the analysis results into a generating AI, and the generating AI can then propose road designs.

[0088] The implementation unit can carry out the proposed actions and reduce carbon dioxide emissions. For example, the implementation unit can implement the proposed road design to alleviate traffic congestion. The implementation unit can also implement an energy-efficient building layout to reduce energy consumption. The implementation unit can also implement the proposed energy management system to reduce the energy consumption of facilities. In this way, the implementation unit can reduce carbon dioxide emissions. Some or all of the above processes in the implementation unit may be carried out using AI, for example, or without AI. For example, in order to carry out the proposed actions, the implementation unit can input the proposed actions into a generating AI, and the generating AI can propose a method of implementation.

[0089] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can shorten the timing of data collection to quickly collect the necessary data. This allows the data collection unit to reduce the user's burden. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0090] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and reflect it in future collections. The data collection unit can also optimize collection frequency and timing based on past data collection history. The data collection unit can also analyze past data collection history and determine the priority of data to be collected. This allows the data collection unit to select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input collection history data into a generating AI to analyze past data collection history, and the generating AI can select the optimal collection method.

[0091] The data collection unit can filter data based on the environmental conditions of a specific area of ​​the city during data collection. For example, the data collection unit can filter data based on the air pollution levels of a specific area of ​​the city. The data collection unit can also filter data based on the noise levels of a specific area of ​​the city. The data collection unit can also filter data based on the traffic volume of a specific area of ​​the city. This enables the data collection unit to collect data efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to filter the environmental conditions of a specific area of ​​the city, the data collection unit can input environmental data into a generating AI and perform the filtering using the generating AI.

[0092] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone the collection of less important data. If the user is relaxed, the data collection unit may prioritize the collection of detailed data. If the user is in a hurry, the data collection unit may prioritize data that needs to be collected quickly. This enables the data collection unit to collect data efficiently. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0093] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the city during data collection. For example, the data collection unit can prioritize the collection of data from the city center to help alleviate traffic congestion. The data collection unit can also prioritize the collection of data from residential areas of the city to help improve energy efficiency. The data collection unit can also prioritize the collection of data from commercial areas of the city to help improve the efficiency of public facilities. This enables the data collection unit to collect data efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, in order to collect data while considering the geographical location information of the city, the data collection unit can input geographic data into a generating AI, and the generating AI can perform the data collection.

[0094] The data collection unit can analyze urban social media activity and collect relevant data during data collection. For example, the data collection unit can prioritize collecting data from areas that are trending on social media. The data collection unit can also determine data collection targets based on user feedback on social media. The data collection unit can also adjust the timing of data collection based on event information on social media. This enables the data collection unit to collect data efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI to analyze social media activity, and the generating AI can then collect the data.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows the analysis unit to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0096] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables the analysis unit to perform efficient analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data into a generating AI to evaluate the importance of the data, and the generating AI can perform the importance evaluation.

[0097] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a traffic analysis algorithm to traffic data. The analysis unit can also apply an energy analysis algorithm to energy data. The analysis unit can also apply a waste analysis algorithm to waste data. This enables the analysis unit to perform efficient analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to apply an analysis algorithm according to the data category, the analysis unit can input data into a generating AI, and the generating AI can apply the analysis algorithm.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the analysis unit to provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0099] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the latest data to provide real-time information. The analysis unit can also analyze long-term trends based on historical data. The analysis unit can also adjust the level of detail of the analysis according to the data collection timing. This allows the analysis unit to prioritize the analysis of the latest information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to determine the priority of analysis based on the data collection timing, the analysis unit can input the data collection timing data into a generating AI, and the generating AI can perform the priority determination.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to perform efficient analysis. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. This enables the analysis unit to perform efficient analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, in order to adjust the order of analysis based on the relevance of the data, the analysis unit can input relevance data into a generating AI, and the generating AI can adjust the order.

[0101] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0102] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it can provide detailed explanations for high-importance proposals and simplified explanations for low-importance proposals. The proposal department can also prioritize proposals according to their importance. This enables the proposal department to make efficient proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input proposal data into a generating AI to evaluate the importance of the proposals, and the generating AI can perform the importance evaluation.

[0103] The proposal unit can apply different proposal algorithms depending on the category of the proposal content when making a proposal. For example, the proposal unit can apply a traffic analysis algorithm to proposals related to transportation systems. The proposal unit can also apply an energy analysis algorithm to proposals related to energy efficiency. The proposal unit can also apply a waste analysis algorithm to proposals related to waste management. This enables the proposal unit to make efficient proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, in order to apply a proposal algorithm according to the category of the proposal content, the proposal unit can input proposal data into a generating AI, and the generating AI can apply the proposal algorithm.

[0104] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows the suggestion unit to provide the most suitable suggestions for the user. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0105] The proposal department can determine the priority of proposals based on the submission timing of the proposals. For example, the proposal department can prioritize proposals that are urgent. The proposal department can also adjust the level of detail of proposals according to the submission timing of the proposals. The proposal department can also determine the order of proposals based on the submission timing of the proposals. This allows the proposal department to prioritize proposals that are urgent. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input submission timing data into a generating AI to determine the priority of proposals based on the submission timing of the proposals, and the generating AI can perform the priority determination.

[0106] The proposal department can adjust the order of proposals based on the relevance of their content when submitting proposals. For example, the proposal department may prioritize highly relevant proposals. It can also postpone less relevant proposals and prioritize important ones. The proposal department can also adjust the level of detail of proposals according to their relevance. This enables the proposal department to submit proposals efficiently. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input relevance data into a generating AI to adjust the order of proposals based on their relevance, and the generating AI can then adjust the order.

[0107] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated user emotions. For example, if the user is nervous, the execution unit can provide a simple and highly visible execution method. If the user is relaxed, the execution unit can also provide a detailed execution method. If the user is in a hurry, the execution unit can also provide a concise execution method. In this way, the execution unit can provide the optimal execution method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, in order to estimate the user's emotions, the execution unit can input the user's facial expression data into a generative AI, and the generative AI can perform the emotion estimation.

[0108] The execution unit can analyze past execution history and select the optimal execution method during execution. For example, the execution unit can identify the most efficient execution method from past execution history and reflect it in future executions. The execution unit can also optimize execution frequency and timing based on past execution history. The execution unit can also analyze past execution history and determine the priority of execution targets. This allows the execution unit to select the optimal execution method. Some or all of the above processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input execution history data into a generating AI to analyze past execution history, and the generating AI can select the optimal execution method.

[0109] The execution unit can customize the means of execution based on the current conditions of the city during execution. For example, the execution unit can customize the means of execution based on the current traffic conditions of the city. If traffic congestion occurs, the execution unit can suggest an alternative route. The execution unit can also customize the means of execution based on the current energy consumption conditions of the city. During times of high energy consumption, the execution unit can suggest energy-efficient means of execution. The execution unit can also customize the means of execution based on the current weather conditions of the city. During rainy weather, the execution unit can suggest an indoor route. This enables the execution unit to perform the task efficiently. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input situational data into a generating AI to customize the means of execution considering the current conditions of the city, and the generating AI can customize the means of execution.

[0110] The execution unit can estimate the user's emotions and determine the priority of executions based on the estimated emotions. For example, if the user is stressed, the execution unit may postpone less important executions. If the user is relaxed, the execution unit may prioritize detailed executions. If the user is in a hurry, the execution unit may prioritize tasks that need to be executed quickly. This enables the execution unit to perform tasks efficiently. 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 execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0111] The execution unit can select the optimal execution method during execution, taking into account the geographical location information of the city. For example, the execution unit may prioritize execution methods in the city center to help alleviate traffic congestion. It may also prioritize execution methods in residential areas of the city to help improve energy efficiency. It may also prioritize execution methods in commercial areas of the city to help improve the efficiency of public facilities. This enables the execution unit to perform execution efficiently. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, in order to select an execution method that takes into account the geographical location information of the city, the execution unit may input geographic data into a generating AI, and the generating AI may select a method.

[0112] The execution unit can analyze the city's social media activity during execution and propose means of execution. For example, the execution unit may prioritize proposing methods of execution for areas that are trending on social media. The execution unit can also determine means of execution based on user feedback on social media. The execution unit can also adjust the timing of execution based on event information on social media. This allows the execution unit to propose efficient means of execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input social media data into a generating AI to analyze social media activity, and the generating AI can then propose means of execution.

[0113] The waste management department can estimate the user's emotions and adjust the waste management method based on the estimated emotions. For example, if the user is stressed, the waste management department can provide a simple and highly visible waste management method. If the user is relaxed, the waste management department can also provide a detailed waste management method. If the user is in a hurry, the waste management department can provide a concise waste management method. This enables efficient waste management. 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 facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0114] The waste management department can analyze past waste data to select the optimal management method during waste management. For example, the waste management department can identify the most efficient management method from past waste data and reflect it in future management. The waste management department can also optimize the frequency and timing of management based on past waste data. The waste management department can also analyze past waste data to determine the priority of items to be managed. This allows the waste management department to select the optimal management method. 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 waste data into a generating AI to analyze past waste data, and the generating AI can select the optimal management method.

[0115] The waste management department can estimate the user's emotions and determine the priority of waste management based on the estimated emotions. For example, if the user is stressed, the waste management department may postpone less important waste management tasks. If the user is relaxed, the waste management department may also prioritize detailed waste management tasks. If the user is in a hurry, the waste management department may also prioritize waste that requires immediate management. This enables efficient waste management by the waste management department. 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 waste management department may be performed using AI or not. For example, the waste management department can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0116] The Waste Management Department can select the optimal waste management method when managing waste, taking into account the geographical location information of the city. For example, the Waste Management Department can prioritize waste management methods in the city center to help alleviate traffic congestion. The Waste Management Department can also prioritize waste management methods in residential areas of the city to help improve energy efficiency. The Waste Management Department can also prioritize waste management methods in commercial areas of the city to help improve the efficiency of public facilities. This enables the Waste Management Department to manage waste efficiently. Some or all of the above processes in the Waste Management Department may be carried out using AI, for example, or without AI. For example, in order to select a management method that takes into account the geographical location information of the city, the Waste Management Department can input geographic data into a generating AI, and the generating AI can select a method.

[0117] The recycling support unit can estimate the user's emotions and adjust the recycling support method based on the estimated user emotions. For example, if the user is stressed, the recycling support unit can provide a simple and highly visible recycling support method. If the user is relaxed, the recycling support unit can also provide a detailed recycling support method. If the user is in a hurry, the recycling support unit can provide a concise recycling support method. This enables the recycling support unit to provide efficient recycling support. 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 recycling support unit may be performed using AI, for example, or not using AI. For example, in order to estimate the user's emotions, the recycling support unit can input the user's facial expression data into a generative AI, and the generative AI can perform the emotion estimation.

[0118] The Recycling Support Department can analyze past recycling data to select the optimal support method when providing recycling support. For example, the Recycling Support Department can identify the most efficient support method from past recycling data and reflect it in future support. The Recycling Support Department can also optimize the frequency and timing of support based on past recycling data. The Recycling Support Department can also analyze past recycling data to determine the priority of support targets. This allows the Recycling Support Department to select the optimal support method. Some or all of the above processes in the Recycling Support Department may be performed using AI, for example, or without AI. For example, the Recycling Support Department can input recycling data into a generating AI to analyze past recycling data, and the generating AI can select the optimal support method.

[0119] The recycling support unit can estimate the user's emotions and determine the priority of recycling support based on the estimated emotions. For example, if the user is stressed, the recycling support unit may postpone less important recycling support. If the user is relaxed, the recycling support unit may also prioritize detailed recycling support. If the user is in a hurry, the recycling support unit may also prioritize recycling that requires immediate assistance. This enables the recycling support unit to provide efficient recycling support. 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 recycling support unit may be performed using AI or not using AI. For example, the recycling support unit can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0120] The Recycling Support Department can select the optimal support method when providing recycling support, taking into account the geographical location information of the city. For example, the Recycling Support Department can prioritize recycling support methods in the city center to help alleviate traffic congestion. The Recycling Support Department can also prioritize recycling support methods in residential areas of the city to help improve energy efficiency. The Recycling Support Department can also prioritize recycling support methods in commercial areas of the city to help improve the efficiency of public facilities. This enables the Recycling Support Department to provide efficient recycling support. Some or all of the above processing in the Recycling Support Department may be performed using AI, for example, or without AI. For example, in order to select a support method that takes into account the geographical location information of the city, the Recycling Support Department can input geographic data into a generating AI, and the generating AI can select a method.

[0121] The traffic assistance unit can estimate the user's emotions and adjust the method of traffic assistance based on the estimated emotions. For example, if the user is stressed, the traffic assistance unit can provide simple and highly visible traffic assistance. If the user is relaxed, the traffic assistance unit can also provide detailed traffic assistance. If the user is in a hurry, the traffic assistance unit can provide concise traffic assistance. This enables the traffic assistance unit to provide efficient traffic assistance. 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 assistance unit may be performed using AI, for example, or not using AI. For example, in order to estimate the user's emotions, the traffic assistance unit can input the user's facial expression data into a generative AI, and the generative AI can perform the emotion estimation.

[0122] The traffic support department can analyze past traffic data to select the optimal support method when providing traffic support. For example, the traffic support department can identify the most efficient support method from past traffic data and reflect it in future support. The traffic support department can also optimize the frequency and timing of support based on past traffic data. The traffic support department can also analyze past traffic data to determine the priority of support targets. This allows the traffic support department to select the optimal support method. Some or all of the above processes in the traffic support department may be performed using AI, for example, or without AI. For example, the traffic support department can input traffic data into a generating AI to analyze past traffic data, and the generating AI can select the optimal support method.

[0123] The traffic assistance unit can estimate the user's emotions and determine the priority of traffic assistance based on the estimated emotions. For example, if the user is stressed, the traffic assistance unit may postpone less important traffic assistance. If the user is relaxed, the traffic assistance unit may also prioritize detailed traffic assistance. If the user is in a hurry, the traffic assistance unit may also prioritize traffic that requires immediate assistance. This enables the traffic assistance unit to provide efficient traffic assistance. 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 assistance unit may be performed using AI, for example, or not using AI. For example, in order to estimate the user's emotions, the traffic assistance unit can input the user's facial expression data into a generative AI, and the generative AI can perform the emotion estimation.

[0124] The traffic support unit can select the optimal support method when providing traffic support, taking into account the geographical location information of the city. For example, the traffic support unit can prioritize selecting a traffic support method in the city center to help alleviate traffic congestion. The traffic support unit can also prioritize selecting a traffic support method in the city's residential areas to help improve energy efficiency. The traffic support unit can also prioritize selecting a traffic support method in the city's commercial areas to help improve the efficiency of public facilities. This enables the traffic support unit to provide efficient traffic support. Some or all of the above processing in the traffic support unit may be performed using AI, for example, or without AI. For example, in order to select a support method that takes into account the geographical location information of the city, the traffic support unit can input geographic data into a generating AI, and the generating AI can select a method.

[0125] The Public Facility Support Department can estimate the user's emotions and adjust the method of public facility support based on the estimated user emotions. For example, if the user is stressed, the Public Facility Support Department can provide a simple and highly visible method of public facility support. If the user is relaxed, the Public Facility Support Department can also provide a detailed method of public facility support. If the user is in a hurry, the Public Facility Support Department can also provide a concise method of public facility support. This enables the Public Facility Support Department to provide efficient public facility support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Public Facility Support Department may be performed using AI, for example, or not using AI. For example, in order to estimate the user's emotions, the Public Facility Support Department can input the user's facial expression data into a generative AI, and the generative AI can perform the emotion estimation.

[0126] The Public Facilities Support Department can analyze past public facilities data to select the optimal support method when providing support to public facilities. For example, the Public Facilities Support Department can identify the most efficient support method from past public facilities data and reflect it in future support. The Public Facilities Support Department can also optimize the frequency and timing of support based on past public facilities data. The Public Facilities Support Department can also analyze past public facilities data to determine the priority of support targets. This allows the Public Facilities Support Department to select the optimal support method. Some or all of the above processes in the Public Facilities Support Department may be performed using AI, for example, or without AI. For example, the Public Facilities Support Department can input public facilities data into a generating AI to analyze past public facilities data, and the generating AI can select the optimal support method.

[0127] The Public Facilities Support Department can estimate the user's emotions and determine the priority of public facilities support based on the estimated emotions. For example, if the user is stressed, the Public Facilities Support Department may postpone less important public facilities support. If the user is relaxed, the Public Facilities Support Department may prioritize detailed public facilities support. If the user is in a hurry, the Public Facilities Support Department may prioritize public facilities that require immediate support. This enables the Public Facilities Support Department to provide efficient public facilities support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Public Facilities Support Department may be performed using AI or not using AI. For example, the Public Facilities Support Department can input user facial expression data into a generative AI to estimate the user's emotions, and the generative AI can perform the emotion estimation.

[0128] The Public Facilities Support Department can select the optimal support method when providing support to public facilities, taking into account the geographical location information of the city. For example, the Public Facilities Support Department can prioritize selecting support methods for public facilities in the city center to help alleviate traffic congestion. The Public Facilities Support Department can also prioritize selecting support methods for public facilities in residential areas of the city to help improve energy efficiency. The Public Facilities Support Department can also prioritize selecting support methods for public facilities in commercial areas of the city to help improve the efficiency of public facilities. This enables the Public Facilities Support Department to provide efficient support to public facilities. Some or all of the above processing in the Public Facilities Support Department may be performed using AI, for example, or without AI. For example, in order to select a support method that takes into account the geographical location information of the city, the Public Facilities Support Department can input geographic data into a generating AI, and the generating AI can select a method.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] Eco-city planners can further collect residents' health data and incorporate it into urban planning. For example, the data collection unit can collect residents' step count and heart rate data, and the analysis unit can analyze this data to understand the residents' health status. The proposal unit can propose the installation of pedestrian-only streets and the placement of parks based on the health data. The implementation unit can carry out the proposed health promotion measures to improve the health of residents. In this way, eco-city planners can support sustainable urban design that takes residents' health into consideration.

[0131] Eco-city planners can further collect meteorological data and incorporate it into urban planning. For example, the data collection unit collects meteorological data such as temperature, humidity, and precipitation, and the analysis unit analyzes this data to understand the city's climate patterns. The proposal unit can propose improvements to building insulation performance and the placement of green spaces based on the meteorological data. The implementation unit can carry out the proposed climate adaptation measures to help the city adapt to climate change. In this way, eco-city planners can support sustainable urban design that responds to climate change.

[0132] Eco-city planners can further collect residents' emotional data and incorporate it into urban planning. For example, the data collection department can gather emotional data from residents' social media posts and survey results, and the analysis department can analyze this data to understand residents' emotional states. Based on the emotional data, the proposal department can suggest the installation of public spaces where residents can relax or improvements to transportation systems to reduce stress. The implementation department can then implement the proposed emotional care measures to improve residents' mental health. In this way, eco-city planners can support sustainable urban design that takes residents' emotions into consideration.

[0133] Eco-city planners can further collect energy consumption data and incorporate it into urban planning. For example, the data collection department collects energy consumption data from individual households and businesses, and the analysis department analyzes this data to understand energy consumption patterns. The proposal department can then propose energy-efficient building designs and the introduction of renewable energy based on the energy consumption data. The implementation department can carry out the proposed energy efficiency measures to reduce energy consumption throughout the city. In this way, eco-city planners can support sustainable urban design that takes energy efficiency into consideration.

[0134] Eco-city planners can further collect resident mobility data and incorporate it into urban planning. For example, the data collection unit collects data on residents' travel routes and modes of transportation, and the analysis unit analyzes this data to understand residents' mobility patterns. The proposal unit can propose optimizations for public transportation and the installation of dedicated bicycle lanes based on the mobility data. The implementation unit can implement the proposed mobility efficiency measures to improve the convenience of residents' mobility. In this way, eco-city planners can support sustainable urban design that takes resident mobility into consideration.

[0135] Eco-city planners can further collect residents' emotional data and incorporate it into urban planning. For example, the data collection department can gather emotional data from residents' social media posts and survey results, and the analysis department can analyze this data to understand residents' emotional states. Based on the emotional data, the proposal department can suggest the installation of public spaces where residents can relax or improvements to transportation systems to reduce stress. The implementation department can then implement the proposed emotional care measures to improve residents' mental health. In this way, eco-city planners can support sustainable urban design that takes residents' emotions into consideration.

[0136] Eco-city planners can further collect residents' health data and incorporate it into urban planning. For example, the data collection unit can collect residents' step count and heart rate data, and the analysis unit can analyze this data to understand the residents' health status. The proposal unit can propose the installation of pedestrian-only streets and the placement of parks based on the health data. The implementation unit can carry out the proposed health promotion measures to improve the health of residents. In this way, eco-city planners can support sustainable urban design that takes residents' health into consideration.

[0137] Eco-city planners can further collect residents' emotional data and incorporate it into urban planning. For example, the data collection department can gather emotional data from residents' social media posts and survey results, and the analysis department can analyze this data to understand residents' emotional states. Based on the emotional data, the proposal department can suggest the installation of public spaces where residents can relax or improvements to transportation systems to reduce stress. The implementation department can then implement the proposed emotional care measures to improve residents' mental health. In this way, eco-city planners can support sustainable urban design that takes residents' emotions into consideration.

[0138] Eco-city planners can further collect resident mobility data and incorporate it into urban planning. For example, the data collection unit collects data on residents' travel routes and modes of transportation, and the analysis unit analyzes this data to understand residents' mobility patterns. The proposal unit can propose optimizations for public transportation and the installation of dedicated bicycle lanes based on the mobility data. The implementation unit can implement the proposed mobility efficiency measures to improve the convenience of residents' mobility. In this way, eco-city planners can support sustainable urban design that takes resident mobility into consideration.

[0139] Eco-city planners can further collect residents' emotional data and incorporate it into urban planning. For example, the data collection department can gather emotional data from residents' social media posts and survey results, and the analysis department can analyze this data to understand residents' emotional states. Based on the emotional data, the proposal department can suggest the installation of public spaces where residents can relax or improvements to transportation systems to reduce stress. The implementation department can then implement the proposed emotional care measures to improve residents' mental health. In this way, eco-city planners can support sustainable urban design that takes residents' emotions into consideration.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The data collection unit collects data. The data collection unit can collect data on urban transportation systems and public facilities, sensor data, user data, environmental data, etc. For example, it can collect traffic volume data to understand the state of traffic congestion. It can also collect data on the usage of public facilities to support the efficient operation of those facilities. Furthermore, it can collect environmental data to monitor the environmental conditions of the city. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the data, and can analyze traffic data to identify the causes of traffic congestion. It can also analyze data on the usage of public facilities and propose ways to operate those facilities more efficiently. Furthermore, it can analyze environmental data and propose measures to improve the urban environment. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department proposes road designs to alleviate traffic congestion and the placement of energy-efficient buildings. The proposal department can propose improvement measures and optimization plans, such as optimizing traffic signals or public transport schedules. They can also propose the introduction of energy management systems or monitoring of facility usage. Step 4: The implementation team carries out the proposals made by the proposal team. The implementation team implements the proposed road design to alleviate traffic congestion. The implementation team can operate the system and collect feedback. For example, they can implement an energy-efficient building layout to reduce energy consumption. They can also implement a proposed energy management system to reduce the energy consumption of facilities.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, execution unit, waste management unit, recycling support unit, transportation support unit, and public facility support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects sensor data and user data from the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the proposed content. The waste management unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes measures to improve waste management. The recycling support unit is implemented by the control unit 46A of the smart device 14 and supports the improvement of the recycling rate. The transportation support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the efficiency of the transportation system. The public facility support unit is implemented by the control unit 46A of the smart device 14 and supports the efficiency of public facilities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, execution unit, waste management unit, recycling support unit, transportation support unit, and public facility support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects sensor data and user data from the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the proposed content. The waste management unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes measures to improve waste management. The recycling support unit is implemented by the control unit 46A of the smart glasses 214 and supports the improvement of the recycling rate. The transportation support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the efficiency of the transportation system. The public facility support unit is implemented by the control unit 46A of the smart glasses 214 and supports the efficiency of public facilities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, execution unit, waste management unit, recycling support unit, traffic support unit, and public facility support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects sensor data and user data from the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the proposed content. The waste management unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes measures to improve waste management. The recycling support unit is implemented by the control unit 46A of the headset terminal 314 and supports the improvement of the recycling rate. The traffic support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the efficiency of the traffic system. The public facility support unit is implemented by the control unit 46A of the headset terminal 314 and supports the efficiency of public facilities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, execution unit, waste management unit, recycling support unit, traffic support unit, and public facility support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects sensor data and user data from the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the analysis results. The execution unit is implemented by the control unit 46A of the robot 414 and executes the proposed content. The waste management unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes measures to improve waste management. The recycling support unit is implemented by the control unit 46A of the robot 414 and supports the improvement of the recycling rate. The traffic support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports the efficiency of the traffic system. The public facility support unit is implemented by the control unit 46A of the robot 414 and supports the efficiency of public facilities. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, A system comprising: an execution unit that executes the content proposed by the aforementioned proposal unit. (Note 2) We have a waste management department that proposes measures to improve waste management. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a recycling support department to help improve the recycling rate. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a transportation support department that assists in improving the efficiency of the transportation system. The system described in Appendix 1, characterized by the features described herein. (Note 5) The facility includes a Public Facilities Support Department that assists in improving the efficiency of public facilities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system described in Appendix 1, characterized by collecting data on urban transportation systems and public facilities. (Note 7) The aforementioned analysis unit, The system described in Appendix 1, characterized by analyzing collected data and proposing design and operation methods. (Note 8) The aforementioned proposal section is, The system described in Appendix 1, characterized by proposing road designs to alleviate traffic congestion and energy-efficient building layouts based on the analysis results. (Note 9) The execution unit is, The system described in Appendix 1, characterized by implementing the proposed measures and reducing carbon dioxide emissions. (Note 10) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, filtering is performed based on the environmental conditions of specific areas within the city. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the geographical location information of cities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, analyze urban social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the proposed content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting a proposal, the priority of the proposals will be determined based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When submitting proposals, adjust the order of the proposals based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, During execution, the system analyzes past execution history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, At runtime, the means of execution are customized based on the current state of the city. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The execution unit is, During execution, the optimal execution method is selected considering the geographical location information of the city. The system described in Appendix 1, characterized by the features described herein. (Note 33) The execution unit is, During implementation, we will analyze urban social media activity and propose implementation methods. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned waste management department, The system estimates user emotions and adjusts waste management methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned waste management department, When managing waste, we analyze past waste data to select the optimal management method. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned waste management department, It estimates user emotions and determines waste management priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned waste management department, When managing waste, the optimal management method should be selected considering the geographical location information of the city. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned Recycling Support Department The system estimates the user's emotions and adjusts the recycling support method based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned Recycling Support Department When providing recycling support, we analyze past recycling data to select the most suitable support method. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned Recycling Support Department The system estimates user sentiment and prioritizes recycling support based on the estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned Recycling Support Department When providing recycling support, the most suitable support method will be selected considering the geographical location information of the city. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned traffic support department, The system estimates the user's emotions and adjusts the traffic assistance methods based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned traffic support department, When providing traffic assistance, past traffic data is analyzed to select the optimal assistance method. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned traffic support department, The system estimates the user's emotions and determines the priority of traffic assistance based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned traffic support department, When providing transportation assistance, the optimal assistance method is selected by considering the geographical location information of the city. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned Public Facilities Support Department, We estimate the user's emotions and adjust the method of supporting public facilities based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned Public Facilities Support Department, When providing support to public facilities, we analyze past public facility data to select the most suitable support method. The system described in Appendix 5, characterized by the features described herein. (Note 48) The aforementioned Public Facilities Support Department, The system estimates user sentiment and determines the priority of public facility support based on the estimated user sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 49) The aforementioned Public Facilities Support Department, When providing support to public facilities, the optimal support method will be selected considering the geographical location information of the city. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, A system comprising: an execution unit that executes the content proposed by the aforementioned proposal unit.

2. We have a waste management department that proposes measures to improve waste management. The system according to feature 1.

3. We have a recycling support department to help improve the recycling rate. The system according to feature 1.

4. It is equipped with a transportation support department that assists in improving the efficiency of the transportation system. The system according to feature 1.

5. The facility includes a Public Facilities Support Department that assists in improving the efficiency of public facilities. The system according to feature 1.

6. The aforementioned collection unit is The system according to claim 1, characterized by collecting data on urban transportation systems and public facilities.

7. The aforementioned analysis unit, The system according to claim 1, characterized by analyzing collected data and proposing design and operation methods.

8. The aforementioned proposal section is, The system according to claim 1, characterized in that it proposes road designs and energy-efficient building layouts to alleviate traffic congestion based on the analysis results.

9. The execution unit is, The system according to claim 1, characterized in that it implements the proposed measures and reduces carbon dioxide emissions.

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

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