System

Abstract

An object of a system according to an embodiment is to analyze behavior data of residents and propose a city plan according to individual preferences.SOLUTION: A system includes a collection unit, an analysis unit, an avatar generation unit, a simulation unit, and a proposal unit. The collection unit collects behavior data of residents. The analysis unit analyzes the data collected by the collection unit and specifies a behavior pattern or a preference of the resident. The avatar generation unit generates an avatar on the digital twin based on the analysis result obtained by the analysis unit. The simulation unit performs a simulation using the avatar generated by the avatar generation unit. The proposal part proposes city planning on the basis of the simulation result obtained by the simulation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately propose urban planning based on resident behavior data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze behavioral data of residents and propose urban planning that matches individual preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an avatar generation unit, a simulation unit, and a proposal unit. The collection unit collects behavioral data of residents. The analysis unit analyzes the data collected by the collection unit and identifies the behavioral patterns and preferences of residents. The avatar generation unit generates avatars on the digital twin based on the analysis results obtained by the analysis unit. The simulation unit performs a simulation using the avatars generated by the avatar generation unit. The proposal unit proposes urban planning based on the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze behavioral data of residents and propose urban planning that is tailored to their individual preferences. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An urban planning proposal system according to an embodiment of the present invention collects resident behavioral data, generates avatars on a digital twin, and performs simulations to propose urban plans tailored to individual preferences. The urban planning proposal system collects resident behavioral data, generates avatars on a digital twin, and performs simulations to propose urban plans tailored to individual preferences. For example, the urban planning proposal system collects resident behavioral data and social media posts. For example, data such as places visited by residents and content posted by residents is collected and analyzed by an analysis unit to identify residents' interests. Next, the urban planning proposal system uses a generation AI to analyze the collected data and identify resident behavioral patterns and preferences. The generation AI receives inputs such as resident behavioral data and social media posts, and performs analysis based on the inputs. For example, the generation AI receives a prompt such as "Please analyze the resident's behavioral patterns" and identifies the behavioral patterns. Next, in the urban planning proposal system, the generation AI generates avatars on a digital twin based on the analysis results. The generation AI generates avatars based on the resident's behavioral patterns and preferences. For example, the generation AI receives a prompt saying, "Please generate an avatar based on the behavioral patterns of residents," and generates an avatar. Then, the urban planning proposal system performs a simulation using the generated avatar. The generation AI performs a simulation using the avatar and proposes an urban plan tailored to individual preferences. For example, the generation AI receives a prompt saying, "Please perform a simulation using an avatar and propose an optimal urban plan," and performs a simulation. Then, the urban planning proposal system proposes an urban plan based on the simulation results. The generation AI proposes an urban plan based on the simulation results. For example, the generation AI receives a prompt saying, "Please propose an urban plan based on the simulation results," and proposes an urban plan. This allows the urban planning proposal system to automatically collect and analyze resident behavioral data, generate avatars, perform simulations, and propose urban plans.This allows the urban planning proposal system to automatically collect and analyze resident behavior data, generate avatars, perform simulations, and propose urban plans. For example, it can propose optimal urban plans based on residents' behavioral patterns and preferences. This allows it to propose urban plans that are tailored to residents' preferences.

[0029] The urban planning proposal system according to the embodiment includes a collection unit, an analysis unit, an avatar generation unit, a simulation unit, and a proposal unit. The collection unit collects behavioral data of residents. The behavioral data of residents includes, but is not limited to, travel history, purchase history, and social media posts. The collection unit collects, for example, data on places visited by residents and content posted by residents. The collection unit can also collect behavioral data of residents using an IoT sensor. For example, the collection unit collects GPS data of places visited by residents. The collection unit can also collect social media posts. For example, the collection unit collects content posted by residents on social media. The analysis unit analyzes the data collected by the collection unit to identify behavioral patterns and preferences of residents. The analysis is performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit uses data mining technology to identify behavioral patterns of residents. The analysis unit can also identify preferences of residents using a machine learning algorithm. For example, the analysis unit uses a machine learning algorithm to identify resident interests. The avatar generation unit generates avatars on the digital twin based on the analysis results obtained by the analysis unit. The avatars are generated based on, for example, the behavioral patterns and preferences of residents, but are not limited to such examples. For example, the avatar generation unit generates avatars based on the behavioral patterns of residents. The avatar generation unit can also generate avatars based on the preferences of residents. For example, the avatar generation unit generates avatars based on the interests and concerns of residents. The simulation unit performs a simulation using the avatars generated by the avatar generation unit. The simulation is performed using, for example, a scenario and an algorithm, but is not limited to such examples. For example, the simulation unit sets a scenario and performs the simulation. The simulation unit can also perform the simulation using an algorithm. For example, the simulation unit uses an algorithm to simulate an optimal urban plan. The proposal unit proposes an urban plan based on the simulation results obtained by the simulation unit.The proposals include, for example, infrastructure development, transportation planning, and living environment improvement, but are not limited to these examples. For example, the proposal unit proposes infrastructure development based on simulation results. The proposal unit can also propose transportation plans. For example, the proposal unit proposes transportation plans based on simulation results. As a result, the urban plan proposal system according to the embodiment can automatically collect and analyze resident behavior data, generate avatars, perform simulations, and propose urban plans. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose urban plans using an AI model that inputs simulation results obtained by the simulation unit and outputs urban plans.

[0030] The collection unit can collect data on places visited by residents or content posted by residents. The collection unit, for example, collects GPS data of places visited by residents. For example, the collection unit collects data using location information services of places visited by residents. The collection unit can also collect content posted by residents on social media. For example, the collection unit collects content posted by residents on social media. By collecting data such as places visited by residents and content posted by them, it is possible to more accurately understand the behavioral patterns and preferences of residents. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input GPS data of places visited by residents to the generation AI and have the generation AI analyze the location information.

[0031] The analysis unit can analyze the collected data and identify the interests and concerns of residents. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can use data mining technology to identify the behavioral patterns of residents. The analysis unit can also use a machine learning algorithm to identify the interests and concerns of residents. For example, the analysis unit can use a machine learning algorithm to identify the interests and concerns of residents. By analyzing the collected data and identifying the interests and concerns of residents, it is possible to more accurately understand the behavioral patterns and preferences of residents. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI identify the interests and concerns.

[0032] The avatar generation unit can generate avatars based on the behavioral patterns and preferences of residents. The avatar generation unit generates avatars based on, for example, the behavioral patterns of residents. For example, the avatar generation unit analyzes the behavioral patterns of residents and generates avatars based on the results. The avatar generation unit can also generate avatars based on the preferences of residents. For example, the avatar generation unit generates avatars based on the interests and concerns of residents. In this way, by generating avatars based on the behavioral patterns and preferences of residents, avatars that reflect the behavioral patterns and preferences of residents can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, or without, AI, for example. For example, the avatar generation unit inputs the analysis results into a generation AI and causes the generation AI to generate avatars.

[0033] The simulation unit can perform a simulation using the generated avatar and propose an urban plan tailored to individual preferences. The simulation unit, for example, sets a scenario and performs a simulation. For example, the simulation unit sets a scenario based on the behavior patterns and preferences of residents and performs a simulation. The simulation unit can also perform a simulation using an algorithm. For example, the simulation unit uses an algorithm to simulate an optimal urban plan. In this way, by performing a simulation using the generated avatar and proposing an urban plan tailored to individual preferences, it is possible to propose an urban plan tailored to the preferences of residents. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the generated avatar into a generation AI and have the generation AI execute a simulation.

[0034] The proposal unit can propose an urban plan based on the simulation results. The proposal unit, for example, proposes infrastructure development based on the simulation results. For example, the proposal unit proposes infrastructure development based on the simulation results. The proposal unit can also propose a transportation plan. For example, the proposal unit proposes a transportation plan based on the simulation results. By proposing an urban plan based on the simulation results, it is possible to propose an urban plan that suits the preferences of residents. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the simulation results to a generation AI and have the generation AI execute a proposed urban plan.

[0035] The collection unit can analyze the resident's past behavioral data and select the optimal data collection method. The collection unit can, for example, optimize the timing and location of data collection based on places the resident has frequently visited in the past. For example, the collection unit can optimize the timing and location of data collection based on places the resident has frequently visited in the past. The collection unit can also analyze the resident's past behavioral patterns and concentrate data collection during a specific time period. For example, the collection unit can analyze the resident's past behavioral patterns and concentrate data collection during a specific time period. The collection unit can also select the most effective data collection method (audio, text, image, etc.) from the resident's past behavioral data. For example, the collection unit selects the most effective data collection method from the resident's past behavioral data. In this way, the optimal data collection method can be selected by analyzing the resident's past behavioral data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the resident's past behavioral data into the generation AI and cause the generation AI to select the optimal data collection method.

[0036] When collecting data, the collection unit can filter the data based on the resident's current living situation and areas of interest. For example, the collection unit prioritizes collecting data related to topics in which the resident is currently interested. For example, the collection unit prioritizes collecting data related to topics in which the resident is currently interested. The collection unit can also adjust the content of data collection depending on the resident's living situation (work, vacation, etc.). For example, the collection unit adjusts the content of data collection depending on the resident's living situation. The collection unit can also filter and collect relevant data based on the resident's current activity (sports, reading, etc.). For example, the collection unit filters and collects relevant data based on the resident's current activity. This makes it possible to collect more relevant data by filtering data based on the resident's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the resident's current living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the resident's input method. For example, if a resident prefers voice input, the collection unit prioritizes collecting voice data. For example, if a resident prefers voice input, the collection unit prioritizes collecting voice data. Furthermore, if a resident prefers text input, the collection unit can also prioritize collecting text data. For example, if a resident prefers text input, the collection unit prioritizes collecting text data. Furthermore, if a resident prefers image input, the collection unit can also prioritize collecting image data. For example, if a resident prefers image input, the collection unit prioritizes collecting image data. This allows for more efficient data collection by selecting the optimal collection means depending on the resident's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the resident's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the resident's geographical location information. For example, when a resident is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when a resident is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when a resident is traveling, the collection unit can prioritize collecting data related to the resident's travel route. For example, when a resident is traveling, the collection unit prioritizes collecting data related to the resident's travel route. Furthermore, when a resident is in a specific facility, the collection unit can prioritize collecting data related to the facility. For example, when a resident is in a specific facility, the collection unit prioritizes collecting data related to the facility. In this way, by prioritizing the collection of highly relevant data by taking into account the resident's geographical location information, more useful data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the resident's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0039] During data collection, the collection unit can analyze the social media activities of residents and collect related data. For example, the collection unit collects data related to places where residents have checked in on social media. For example, the collection unit collects data related to places where residents have checked in on social media. The collection unit can also analyze the content of residents' social media posts and collect related data. For example, the collection unit analyzes the content of residents' social media posts and collects related data. The collection unit can also collect related data by referring to the activities of residents' friends on social media. For example, the collection unit collects related data by referring to the activities of residents' friends on social media. In this way, related data can be collected by analyzing residents' social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on residents' social media activities into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the residents' past feedback when collecting data. For example, the collection unit improves the data collection method based on the residents' past feedback. For example, the collection unit improves the data collection method based on the residents' past feedback. The collection unit can also adjust the type of data to be collected by referring to the residents' past feedback. For example, the collection unit adjusts the type of data to be collected by referring to the residents' past feedback. The collection unit can also adjust the frequency of data collection by reflecting the residents' past feedback. For example, the collection unit adjusts the frequency of data collection by reflecting the residents' past feedback. In this way, by reflecting the residents' past feedback, the data collection method can be customized and more effective data collection becomes possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the residents' past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. For example, the analysis unit performs a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. The analysis unit can also apply a text analysis algorithm to social media data. For example, the analysis unit applies a text analysis algorithm to social media data. The analysis unit can also apply a geographic information analysis algorithm to location information data. For example, the analysis unit applies a geographic information analysis algorithm to location information data. In this way, by applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the resident's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the resident's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the resident's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the resident's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis by referring to the resident's past analysis results. The analysis unit can also determine the priority of the analysis by reflecting the resident's past analysis results. For example, the analysis unit determines the priority of the analysis by reflecting the resident's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the resident's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the resident's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also complement the analysis results of the most recent data by referring to past data. For example, the analysis unit complements the analysis results of the most recent data by referring to past data. The analysis unit can also adjust the level of detail of the analysis depending on the time when the data was collected. For example, the analysis unit adjusts the level of detail of the analysis depending on the time when the data was collected. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from the time when the data was collected to the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. For example, the analysis unit adjusts the level of detail of the analysis according to the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, more relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the resident's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to residents with high levels of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to residents with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to residents with low levels of expertise. For example, the analysis unit provides concise and easy-to-understand analysis results to residents with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the resident's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed according to the resident's level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the resident's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the resident's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The avatar generation unit can adjust the level of detail of the avatar based on the resident's behavioral pattern when generating the avatar. For example, if the resident's behavioral pattern is complex, the avatar generation unit generates a detailed avatar. For example, if the resident's behavioral pattern is complex, the avatar generation unit generates a detailed avatar. Furthermore, if the resident's behavioral pattern is simple, the avatar generation unit can generate a concise avatar. For example, if the resident's behavioral pattern is simple, the avatar generation unit generates a concise avatar. Furthermore, the avatar generation unit can adjust the level of detail of the avatar according to the resident's behavioral pattern. For example, the avatar generation unit adjusts the level of detail of the avatar according to the resident's behavioral pattern. In this way, by adjusting the level of detail of the avatar based on the resident's behavioral pattern, a more appropriate avatar can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the avatar generation unit can input data on the resident's behavioral pattern into the generation AI and cause the generation AI to adjust the level of detail of the avatar.

[0048] The avatar generation unit can apply different generation algorithms depending on the resident's interests when generating an avatar. The avatar generation unit, for example, generates an avatar by applying an algorithm related to the resident's field of interest. For example, the avatar generation unit generates an avatar by applying an algorithm related to the resident's field of interest. The avatar generation unit can also select an optimal generation algorithm based on the resident's field of interest. For example, the avatar generation unit selects an optimal generation algorithm based on the resident's field of interest. The avatar generation unit can also customize the avatar generation method depending on the resident's interests. For example, the avatar generation unit customizes the avatar generation method depending on the resident's interests. This allows for the generation of more appropriate avatars by applying different generation algorithms depending on the resident's interests. Some or all of the above-described processing in the avatar generation unit may be performed using, or without, AI. For example, the avatar generation unit can input data on the resident's interests into a generation AI and cause the generation AI to apply a generation algorithm.

[0049] When generating an avatar, the avatar generation unit can improve the accuracy of the generation by referring to the resident's past avatar generation results. The avatar generation unit, for example, adjusts the generation algorithm based on the resident's past avatar generation results. For example, the avatar generation unit adjusts the generation algorithm based on the resident's past avatar generation results. The avatar generation unit can also adjust the level of detail of the generation by referring to the resident's past avatar generation results. For example, the avatar generation unit adjusts the level of detail of the generation by referring to the resident's past avatar generation results. The avatar generation unit can also determine the priority of the generation by reflecting the resident's past avatar generation results. For example, the avatar generation unit determines the priority of the generation by reflecting the resident's past avatar generation results. In this way, the accuracy of the generation can be improved by referring to the resident's past avatar generation results. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the avatar generation unit can input data of the resident's past avatar generation results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0050] When generating an avatar, the avatar generation unit can set the avatar's behavior pattern based on the resident's lifestyle rhythm. For example, if a resident has a morning-type lifestyle rhythm, the avatar generation unit generates an avatar that is active in the morning. For example, if a resident has a morning-type lifestyle rhythm, the avatar generation unit generates an avatar that is active in the morning. Furthermore, if a resident has a nocturnal lifestyle rhythm, the avatar generation unit can generate an avatar that is active in the evening. For example, if a resident has a nocturnal lifestyle rhythm, the avatar generation unit generates an avatar that is active in the evening. Furthermore, the avatar generation unit can adjust the avatar's behavior pattern according to the resident's lifestyle rhythm. For example, the avatar generation unit adjusts the avatar's behavior pattern according to the resident's lifestyle rhythm. In this way, by setting the avatar's behavior pattern based on the resident's lifestyle rhythm, more appropriate avatars can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without AI. For example, the avatar generation unit can input data on the resident's lifestyle rhythm into the generation AI and cause the generation AI to set the avatar's behavior pattern.

[0051] When generating an avatar, the avatar generation unit can set the avatar's personality to reflect the resident's social media activity. For example, if a resident actively interacts on social media, the avatar generation unit generates an avatar with a sociable personality. For example, if a resident actively interacts on social media, the avatar generation unit generates an avatar with a sociable personality. Furthermore, if a resident posts modestly on social media, the avatar generation unit can generate an avatar with an introverted personality. For example, if a resident posts modestly on social media, the avatar generation unit can generate an avatar with an introverted personality. Furthermore, the avatar generation unit can customize the avatar's personality based on the resident's social media activity. For example, the avatar generation unit customizes the avatar's personality based on the resident's social media activity. This allows for the generation of a more appropriate avatar by reflecting the resident's social media activity. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without AI. For example, the avatar generation unit can input data on the resident's social media activity into the generation AI and cause the generation AI to set the avatar's personality.

[0052] When generating an avatar, the avatar generation unit can customize the avatar generation method by reflecting the resident's past feedback. The avatar generation unit, for example, adjusts the avatar generation algorithm based on the resident's past feedback. For example, the avatar generation unit adjusts the avatar generation algorithm based on the resident's past feedback. The avatar generation unit can also adjust the avatar's appearance and personality by referring to the resident's past feedback. For example, the avatar generation unit adjusts the avatar's appearance and personality by referring to the resident's past feedback. The avatar generation unit can also customize the avatar generation method by reflecting the resident's past feedback. For example, the avatar generation unit customizes the avatar generation method by reflecting the resident's past feedback. In this way, by reflecting the resident's past feedback, the avatar generation method can be customized and a more appropriate avatar can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without AI. For example, the avatar generation unit can input the resident's past feedback data into the generation AI and cause the generation AI to customize the avatar generation method.

[0053] The simulation unit can adjust the level of detail of the simulation based on the behavioral patterns of the residents during the simulation. For example, when the behavioral patterns of the residents are complex, the simulation unit performs a detailed simulation. For example, when the behavioral patterns of the residents are complex, the simulation unit performs a detailed simulation. Furthermore, when the behavioral patterns of the residents are simple, the simulation unit can perform a concise simulation. For example, when the behavioral patterns of the residents are simple, the simulation unit performs a concise simulation. Furthermore, the simulation unit can adjust the level of detail of the simulation according to the behavioral patterns of the residents. For example, the simulation unit adjusts the level of detail of the simulation according to the behavioral patterns of the residents. In this way, by adjusting the level of detail of the simulation based on the behavioral patterns of the residents, a more appropriate simulation can be performed. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the behavioral patterns of the residents to a generation AI and cause the generation AI to adjust the level of detail of the simulation.

[0054] The simulation unit can apply different simulation algorithms depending on the interests of residents during the simulation. For example, the simulation unit performs a simulation by applying an algorithm related to the area of ​​interest of residents. For example, the simulation unit performs a simulation by applying an algorithm related to the area of ​​interest of residents. The simulation unit can also select an optimal simulation algorithm based on the area of ​​interest of residents. For example, the simulation unit selects an optimal simulation algorithm based on the area of ​​interest of residents. The simulation unit can also customize the simulation method depending on the interests of residents. For example, the simulation unit customizes the simulation method depending on the interests of residents. This allows for a more appropriate simulation to be performed by applying different simulation algorithms depending on the interests of residents. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the interests of residents to the generation AI and cause the generation AI to apply a simulation algorithm.

[0055] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the resident's past simulation results. The simulation unit, for example, adjusts the simulation algorithm based on the resident's past simulation results. For example, the simulation unit adjusts the simulation algorithm based on the resident's past simulation results. The simulation unit can also adjust the level of detail of the simulation by referring to the resident's past simulation results. For example, the simulation unit adjusts the level of detail of the simulation by referring to the resident's past simulation results. The simulation unit can also determine the priority of the simulation by reflecting the resident's past simulation results. For example, the simulation unit determines the priority of the simulation by reflecting the resident's past simulation results. In this way, the accuracy of the simulation can be improved by referring to the resident's past simulation results. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data of the resident's past simulation results into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0056] During the simulation, the simulation unit can set the timing of the simulation based on the resident's lifestyle rhythm. For example, if the resident has a morning-type lifestyle rhythm, the simulation unit performs the simulation in the morning. For example, if the resident has a morning-type lifestyle rhythm, the simulation unit can perform the simulation in the morning. Also, if the resident has a night-type lifestyle rhythm, the simulation unit can perform the simulation in the evening. For example, if the resident has a night-type lifestyle rhythm, the simulation unit can perform the simulation in the evening. Also, the simulation unit can adjust the timing of the simulation according to the resident's lifestyle rhythm. For example, the simulation unit adjusts the timing of the simulation according to the resident's lifestyle rhythm. In this way, by setting the timing of the simulation based on the resident's lifestyle rhythm, a more appropriate simulation can be performed. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the resident's lifestyle rhythm to the generation AI and cause the generation AI to set the timing of the simulation.

[0057] During the simulation, the simulation unit can set a simulation scenario that reflects the residents' social media activities. For example, if the residents actively interact on social media, the simulation unit sets a sociable scenario. For example, if the residents actively interact on social media, the simulation unit sets a sociable scenario. The simulation unit can also set an introverted scenario if the residents post modestly on social media. For example, if the residents post modestly on social media, the simulation unit sets an introverted scenario. The simulation unit can also customize the simulation scenario based on the residents' social media activities. This allows for a more appropriate simulation scenario to be set by reflecting the residents' social media activities. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the residents' social media activities into the generation AI and cause the generation AI to set a simulation scenario.

[0058] During a simulation, the simulation unit can customize the simulation method by reflecting the residents' past feedback. The simulation unit, for example, adjusts the simulation algorithm based on the residents' past feedback. For example, the simulation unit adjusts the simulation algorithm based on the residents' past feedback. The simulation unit can also adjust the level of detail of the simulation by referring to the residents' past feedback. For example, the simulation unit adjusts the level of detail of the simulation by referring to the residents' past feedback. The simulation unit can also determine the priority of the simulation by reflecting the residents' past feedback. For example, the simulation unit determines the priority of the simulation by reflecting the residents' past feedback. In this way, by reflecting the residents' past feedback, the simulation method can be customized and a more appropriate simulation can be performed. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the residents' past feedback data into the generation AI and cause the generation AI to customize the simulation method.

[0059] The proposal unit can adjust the level of detail of the proposal based on the importance of the simulation result when making a proposal. For example, the proposal unit makes a detailed proposal for a simulation result with a high level of importance. For example, the proposal unit makes a detailed proposal for a simulation result with a high level of importance. The proposal unit can also make a concise proposal for a simulation result with a low level of importance. For example, the proposal unit makes a concise proposal for a simulation result with a low level of importance. The proposal unit can also determine the priority of the proposal according to the importance of the simulation result. For example, the proposal unit determines the priority of the proposal according to the importance of the simulation result. As a result, by adjusting the level of detail of the proposal based on the importance of the simulation result, more important proposals can be made in detail. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit may input data on the importance of the simulation result to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0060] The proposal unit can apply different proposal algorithms depending on the category of the simulation results when making a proposal. For example, the proposal unit applies an urban planning proposal algorithm to simulation results related to urban planning. For example, the proposal unit applies an urban planning proposal algorithm to simulation results related to urban planning. The proposal unit can also apply an environmental improvement proposal algorithm to simulation results related to environmental improvement. For example, the proposal unit applies an environmental improvement proposal algorithm to simulation results related to environmental improvement. The proposal unit can also apply a transportation plan proposal algorithm to simulation results related to transportation planning. For example, the proposal unit applies a transportation plan proposal algorithm to simulation results related to transportation planning. In this way, by applying different proposal algorithms depending on the category of the simulation results, more appropriate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data of the category of the simulation results to the generation AI and cause the generation AI to apply the proposal algorithm.

[0061] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the resident's past proposal results. The proposal unit, for example, adjusts the proposal algorithm based on the resident's past proposal results. For example, the proposal unit adjusts the proposal algorithm based on the resident's past proposal results. The proposal unit can also adjust the level of detail of the proposal by referring to the resident's past proposal results. For example, the proposal unit adjusts the level of detail of the proposal by referring to the resident's past proposal results. The proposal unit can also determine the priority of the proposal by reflecting the resident's past proposal results. For example, the proposal unit determines the priority of the proposal by reflecting the resident's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the resident's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data of the resident's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0062] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the simulation results were collected. The proposal unit, for example, prioritizes the most recent simulation results. For example, the proposal unit prioritizes the most recent simulation results. The proposal unit can also complement the most recent proposal by referring to past simulation results. For example, the proposal unit complements the most recent proposal by referring to past simulation results. The proposal unit can also adjust the level of detail of the proposal depending on the time when the simulation results were collected. For example, the proposal unit adjusts the level of detail of the proposal depending on the time when the simulation results were collected. In this way, by determining the priority of the proposal based on the time when the simulation results were collected, the most recent simulation results can be preferentially proposed. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the time when the simulation results were collected to the generation AI and cause the generation AI to determine the priority of the proposals.

[0063] The proposal unit can adjust the order of proposals based on the relevance of the simulation results when making a proposal. The proposal unit, for example, prioritizes proposing highly relevant simulation results. For example, the proposal unit prioritizes proposing highly relevant simulation results. The proposal unit can also postpone less relevant simulation results. For example, the proposal unit postpones less relevant simulation results. The proposal unit can also adjust the level of detail of the proposal according to the relevance of the simulation results. For example, the proposal unit adjusts the level of detail of the proposal according to the relevance of the simulation results. In this way, by adjusting the order of proposals based on the relevance of the simulation results, more relevant proposals can be prioritized. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the relevance of the simulation results to the generation AI and cause the generation AI to adjust the order of proposals.

[0064] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the resident's level of expertise. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a resident with high technical expertise. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a resident with high technical expertise. The suggestion unit can also provide a concise and easy-to-understand proposal to a resident with low technical expertise. For example, the suggestion unit provides a concise and easy-to-understand proposal to a resident with low technical expertise. The suggestion unit can also adjust the way the proposal is expressed according to the resident's level of expertise. For example, the suggestion unit adjusts the way the proposal is expressed according to the resident's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the resident's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the resident's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The urban planning proposal system can also collect health data of residents and propose urban plans based on their health status. For example, the collection unit collects health data such as the number of steps taken and heart rate of residents. The analysis unit analyzes the collected health data and identifies the health status of residents. The avatar generation unit generates an avatar based on the health status, and the simulation unit simulates urban plans that take the health status into consideration. The proposal unit can propose urban plans aimed at promoting health based on the simulation results. This makes it possible to provide a healthy living environment by proposing urban plans based on the health status of residents.

[0067] The urban planning proposal system can also collect energy consumption data of residents and propose urban planning that takes energy efficiency into consideration. For example, the collection unit collects energy consumption data within the residents' homes. The analysis unit analyzes the collected energy consumption data and identifies energy consumption patterns. The avatar generation unit generates an avatar based on the energy consumption patterns, and the simulation unit simulates urban planning that optimizes energy efficiency. The proposal unit can propose urban planning that improves energy efficiency based on the simulation results. In this way, a sustainable urban environment can be realized by proposing urban planning based on energy consumption data.

[0068] The urban planning proposal system can also collect residents' traffic data and propose urban plans to alleviate traffic congestion. For example, the collection unit collects data on residents' commuting routes and means of transportation. The analysis unit analyzes the collected traffic data and identifies the causes of traffic congestion. The avatar generation unit generates an avatar based on the traffic data, and the simulation unit simulates urban plans to alleviate traffic congestion. The proposal unit can propose urban plans to alleviate traffic congestion based on the simulation results. In this way, by proposing urban plans based on the traffic data, traffic congestion can be alleviated and residents' movement can be made smoother.

[0069] The urban planning proposal system can also collect environmental data from residents and propose urban plans that take environmental protection into consideration. For example, the collection unit collects environmental data such as air quality and noise levels around the residents. The analysis unit analyzes the collected environmental data and identifies the current state of the environment. The avatar generation unit generates an avatar based on the environmental data, and the simulation unit simulates urban plans that take environmental protection into consideration. The proposal unit can propose urban plans aimed at environmental protection based on the simulation results. In this way, a sustainable urban environment can be realized by proposing urban plans based on the environmental data.

[0070] The urban planning proposal system can also collect educational data from residents and propose urban plans that improve the educational environment. For example, the collection unit collects residents' learning history and educational facility usage data. The analysis unit analyzes the collected educational data and identifies the current state of the educational environment. The avatar generation unit generates an avatar based on the educational data, and the simulation unit simulates urban plans that improve the educational environment. The proposal unit can propose urban plans that improve the educational environment based on the simulation results. In this way, the educational environment for residents can be improved by proposing urban plans based on the educational data.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects resident behavior data. Resident behavior data includes movement history, purchase history, and social media postings. The collection unit collects data on places visited by residents and the content posted by them. The collection unit can also collect resident behavior data using IoT sensors. For example, the collection unit collects GPS data of places visited by residents and the content posted by them on social media. Step 2: The analysis unit analyzes the data collected by the collection unit to identify residents' behavioral patterns and preferences. The analysis is performed using data mining and machine learning algorithms. For example, the analysis unit uses data mining techniques to identify residents' behavioral patterns and machine learning algorithms to identify residents' preferences, interests, and concerns. Step 3: The avatar generation unit generates an avatar on the digital twin based on the analysis results obtained by the analysis unit. The avatar is generated based on the behavioral patterns and preferences of the resident. For example, the avatar generation unit generates an avatar based on the behavioral patterns, interests, and concerns of the resident. Step 4: The simulation unit performs a simulation using the avatars generated by the avatar generation unit. The simulation is performed using a scenario and an algorithm. For example, the simulation unit sets a scenario and performs a simulation, and then simulates optimal urban planning using an algorithm. Step 5: The proposal unit proposes an urban plan based on the simulation results obtained by the simulation unit. The proposal includes content such as infrastructure development, transportation planning, and improvements to the living environment. For example, the proposal unit proposes infrastructure development and transportation planning based on the simulation results.

[0073] (Example 2) An urban planning proposal system according to an embodiment of the present invention collects resident behavioral data, generates avatars on a digital twin, and performs simulations to propose urban plans tailored to individual preferences. The urban planning proposal system collects resident behavioral data, generates avatars on a digital twin, and performs simulations to propose urban plans tailored to individual preferences. For example, the urban planning proposal system collects resident behavioral data and social media posts. For example, data such as places visited by residents and content posted by residents is collected and analyzed by an analysis unit to identify residents' interests. Next, the urban planning proposal system uses a generation AI to analyze the collected data and identify resident behavioral patterns and preferences. The generation AI receives inputs such as resident behavioral data and social media posts, and performs analysis based on the inputs. For example, the generation AI receives a prompt such as "Please analyze the resident's behavioral patterns" and identifies the behavioral patterns. Next, in the urban planning proposal system, the generation AI generates avatars on a digital twin based on the analysis results. The generation AI generates avatars based on the resident's behavioral patterns and preferences. For example, the generation AI receives a prompt saying, "Please generate an avatar based on the behavioral patterns of residents," and generates an avatar. Then, the urban planning proposal system performs a simulation using the generated avatar. The generation AI performs a simulation using the avatar and proposes an urban plan tailored to individual preferences. For example, the generation AI receives a prompt saying, "Please perform a simulation using an avatar and propose an optimal urban plan," and performs a simulation. Then, the urban planning proposal system proposes an urban plan based on the simulation results. The generation AI proposes an urban plan based on the simulation results. For example, the generation AI receives a prompt saying, "Please propose an urban plan based on the simulation results," and proposes an urban plan. This allows the urban planning proposal system to automatically collect and analyze resident behavioral data, generate avatars, perform simulations, and propose urban plans.This allows the urban planning proposal system to automatically collect and analyze resident behavior data, generate avatars, perform simulations, and propose urban plans. For example, it can propose optimal urban plans based on residents' behavioral patterns and preferences. This allows it to propose urban plans that are tailored to residents' preferences.

[0074] The urban planning proposal system according to the embodiment includes a collection unit, an analysis unit, an avatar generation unit, a simulation unit, and a proposal unit. The collection unit collects behavioral data of residents. The behavioral data of residents includes, but is not limited to, travel history, purchase history, and social media posts. The collection unit collects, for example, data on places visited by residents and content posted by residents. The collection unit can also collect behavioral data of residents using an IoT sensor. For example, the collection unit collects GPS data of places visited by residents. The collection unit can also collect social media posts. For example, the collection unit collects content posted by residents on social media. The analysis unit analyzes the data collected by the collection unit to identify behavioral patterns and preferences of residents. The analysis is performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit uses data mining technology to identify behavioral patterns of residents. The analysis unit can also identify preferences of residents using a machine learning algorithm. For example, the analysis unit uses a machine learning algorithm to identify resident interests. The avatar generation unit generates avatars on the digital twin based on the analysis results obtained by the analysis unit. The avatars are generated based on, for example, the behavioral patterns and preferences of residents, but are not limited to such examples. For example, the avatar generation unit generates avatars based on the behavioral patterns of residents. The avatar generation unit can also generate avatars based on the preferences of residents. For example, the avatar generation unit generates avatars based on the interests and concerns of residents. The simulation unit performs a simulation using the avatars generated by the avatar generation unit. The simulation is performed using, for example, a scenario and an algorithm, but is not limited to such examples. For example, the simulation unit sets a scenario and performs the simulation. The simulation unit can also perform the simulation using an algorithm. For example, the simulation unit uses an algorithm to simulate an optimal urban plan. The proposal unit proposes an urban plan based on the simulation results obtained by the simulation unit.The proposals include, for example, infrastructure development, transportation planning, and living environment improvement, but are not limited to these examples. For example, the proposal unit proposes infrastructure development based on simulation results. The proposal unit can also propose transportation plans. For example, the proposal unit proposes transportation plans based on simulation results. As a result, the urban plan proposal system according to the embodiment can automatically collect and analyze resident behavior data, generate avatars, perform simulations, and propose urban plans. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can propose urban plans using an AI model that inputs simulation results obtained by the simulation unit and outputs urban plans.

[0075] The collection unit can collect data on places visited by residents or content posted by residents. The collection unit, for example, collects GPS data of places visited by residents. For example, the collection unit collects data using location information services of places visited by residents. The collection unit can also collect content posted by residents on social media. For example, the collection unit collects content posted by residents on social media. By collecting data such as places visited by residents and content posted by them, it is possible to more accurately understand the behavioral patterns and preferences of residents. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input GPS data of places visited by residents to the generation AI and have the generation AI analyze the location information.

[0076] The analysis unit can analyze the collected data and identify the interests and concerns of residents. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can use data mining technology to identify the behavioral patterns of residents. The analysis unit can also use a machine learning algorithm to identify the interests and concerns of residents. For example, the analysis unit can use a machine learning algorithm to identify the interests and concerns of residents. By analyzing the collected data and identifying the interests and concerns of residents, it is possible to more accurately understand the behavioral patterns and preferences of residents. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI identify the interests and concerns.

[0077] The avatar generation unit can generate avatars based on the behavioral patterns and preferences of residents. The avatar generation unit generates avatars based on, for example, the behavioral patterns of residents. For example, the avatar generation unit analyzes the behavioral patterns of residents and generates avatars based on the results. The avatar generation unit can also generate avatars based on the preferences of residents. For example, the avatar generation unit generates avatars based on the interests and concerns of residents. In this way, by generating avatars based on the behavioral patterns and preferences of residents, avatars that reflect the behavioral patterns and preferences of residents can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, or without, AI, for example. For example, the avatar generation unit inputs the analysis results into a generation AI and causes the generation AI to generate avatars.

[0078] The simulation unit can perform a simulation using the generated avatar and propose an urban plan tailored to individual preferences. The simulation unit, for example, sets a scenario and performs a simulation. For example, the simulation unit sets a scenario based on the behavior patterns and preferences of residents and performs a simulation. The simulation unit can also perform a simulation using an algorithm. For example, the simulation unit uses an algorithm to simulate an optimal urban plan. In this way, by performing a simulation using the generated avatar and proposing an urban plan tailored to individual preferences, it is possible to propose an urban plan tailored to the preferences of residents. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the generated avatar into a generation AI and have the generation AI execute a simulation.

[0079] The proposal unit can propose an urban plan based on the simulation results. The proposal unit, for example, proposes infrastructure development based on the simulation results. For example, the proposal unit proposes infrastructure development based on the simulation results. The proposal unit can also propose a transportation plan. For example, the proposal unit proposes a transportation plan based on the simulation results. By proposing an urban plan based on the simulation results, it is possible to propose an urban plan that suits the preferences of residents. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the simulation results to a generation AI and have the generation AI execute a proposed urban plan.

[0080] The collection unit can estimate the resident's emotions and adjust the timing of data collection based on the estimated resident's emotions. The collection unit, for example, collects data during times when the resident is relaxed, thereby obtaining more natural behavioral data. For example, the collection unit collects data during times when the resident is relaxed. The collection unit can also temporarily suspend data collection when the resident is feeling stressed and resume it later. For example, the collection unit can temporarily suspend data collection when the resident is feeling stressed and resume it later. The collection unit can also prioritize collecting specific behavioral data based on the resident's emotions when the resident is excited. For example, the collection unit prioritizes collecting specific behavioral data based on the resident's emotions when the resident is excited. This allows more natural behavioral data to be obtained by adjusting the timing of data collection based on the resident's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data of residents to the generation AI and have the generation AI estimate the emotions.

[0081] The collection unit can analyze the resident's past behavioral data and select the optimal data collection method. The collection unit can, for example, optimize the timing and location of data collection based on places the resident has frequently visited in the past. For example, the collection unit can optimize the timing and location of data collection based on places the resident has frequently visited in the past. The collection unit can also analyze the resident's past behavioral patterns and concentrate data collection during a specific time period. For example, the collection unit can analyze the resident's past behavioral patterns and concentrate data collection during a specific time period. The collection unit can also select the most effective data collection method (audio, text, image, etc.) from the resident's past behavioral data. For example, the collection unit selects the most effective data collection method from the resident's past behavioral data. In this way, the optimal data collection method can be selected by analyzing the resident's past behavioral data. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the resident's past behavioral data into the generation AI and cause the generation AI to select the optimal data collection method.

[0082] When collecting data, the collection unit can filter the data based on the resident's current living situation and areas of interest. For example, the collection unit prioritizes collecting data related to topics in which the resident is currently interested. For example, the collection unit prioritizes collecting data related to topics in which the resident is currently interested. The collection unit can also adjust the content of data collection depending on the resident's living situation (work, vacation, etc.). For example, the collection unit adjusts the content of data collection depending on the resident's living situation. The collection unit can also filter and collect relevant data based on the resident's current activity (sports, reading, etc.). For example, the collection unit filters and collects relevant data based on the resident's current activity. This makes it possible to collect more relevant data by filtering data based on the resident's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the resident's current living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0083] When collecting data, the collection unit can select the optimal collection means depending on the resident's input method. For example, if a resident prefers voice input, the collection unit prioritizes collecting voice data. For example, if a resident prefers voice input, the collection unit prioritizes collecting voice data. Furthermore, if a resident prefers text input, the collection unit can also prioritize collecting text data. For example, if a resident prefers text input, the collection unit prioritizes collecting text data. Furthermore, if a resident prefers image input, the collection unit can also prioritize collecting image data. For example, if a resident prefers image input, the collection unit prioritizes collecting image data. This allows for more efficient data collection by selecting the optimal collection means depending on the resident's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the resident's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0084] The collection unit can estimate the resident's emotions and determine the priority of data to be collected based on the estimated resident's emotions. For example, when the resident is relaxed, the collection unit prioritizes collecting daily behavioral data. For example, when the resident is relaxed, the collection unit prioritizes collecting daily behavioral data. Furthermore, when the resident is stressed, the collection unit can prioritize collecting behavioral data that causes stress. For example, when the resident is stressed, the collection unit prioritizes collecting behavioral data that causes stress. Furthermore, when the resident is excited, the collection unit can prioritize collecting behavioral data related to the resident's emotions. For example, when the resident is excited, the collection unit prioritizes collecting behavioral data related to the resident's emotions. In this way, by determining the priority of data to be collected based on the resident's emotions, more important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data of residents to the generation AI and have the generation AI estimate the emotions.

[0085] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the resident's geographical location information. For example, when a resident is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when a resident is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when a resident is traveling, the collection unit can prioritize collecting data related to the resident's travel route. For example, when a resident is traveling, the collection unit prioritizes collecting data related to the resident's travel route. Furthermore, when a resident is in a specific facility, the collection unit can prioritize collecting data related to the facility. For example, when a resident is in a specific facility, the collection unit prioritizes collecting data related to the facility. In this way, by prioritizing the collection of highly relevant data by taking into account the resident's geographical location information, more useful data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the resident's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0086] During data collection, the collection unit can analyze the social media activities of residents and collect related data. For example, the collection unit collects data related to places where residents have checked in on social media. For example, the collection unit collects data related to places where residents have checked in on social media. The collection unit can also analyze the content of residents' social media posts and collect related data. For example, the collection unit analyzes the content of residents' social media posts and collects related data. The collection unit can also collect related data by referring to the activities of residents' friends on social media. For example, the collection unit collects related data by referring to the activities of residents' friends on social media. In this way, related data can be collected by analyzing residents' social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on residents' social media activities into the generation AI and cause the generation AI to collect related data.

[0087] The collection unit can customize the collection method by reflecting the residents' past feedback when collecting data. For example, the collection unit improves the data collection method based on the residents' past feedback. For example, the collection unit improves the data collection method based on the residents' past feedback. The collection unit can also adjust the type of data to be collected by referring to the residents' past feedback. For example, the collection unit adjusts the type of data to be collected by referring to the residents' past feedback. The collection unit can also adjust the frequency of data collection by reflecting the residents' past feedback. For example, the collection unit adjusts the frequency of data collection by reflecting the residents' past feedback. In this way, by reflecting the residents' past feedback, the data collection method can be customized and more effective data collection becomes possible. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the residents' past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0088] The analysis unit can estimate the resident's emotions and adjust the way the analysis is presented based on the estimated resident's emotions. For example, if the resident is relaxed, the analysis unit provides a detailed analysis result. For example, if the resident is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the resident is stressed, the analysis unit can provide a concise and to-the-point analysis result. For example, if the resident is stressed, the analysis unit provides a concise and to-the-point analysis result. Furthermore, if the resident is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the resident is excited, the analysis unit provides a visually stimulating analysis result. In this way, by adjusting the way the analysis is presented based on the resident's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input residents' emotional data into the generation AI and have the generation AI estimate their emotions.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. For example, the analysis unit performs a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. The analysis unit can also apply a text analysis algorithm to social media data. For example, the analysis unit applies a text analysis algorithm to social media data. The analysis unit can also apply a geographic information analysis algorithm to location information data. For example, the analysis unit applies a geographic information analysis algorithm to location information data. In this way, by applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the resident's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the resident's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the resident's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the resident's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis by referring to the resident's past analysis results. The analysis unit can also determine the priority of the analysis by reflecting the resident's past analysis results. For example, the analysis unit determines the priority of the analysis by reflecting the resident's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the resident's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the resident's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0092] The analysis unit can estimate the resident's emotions and adjust the length of the analysis based on the estimated resident's emotions. For example, if the resident is relaxed, the analysis unit provides a detailed analysis result. For example, if the resident is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the resident is stressed, the analysis unit can provide a concise and to-the-point analysis result. For example, if the resident is stressed, the analysis unit provides a concise and to-the-point analysis result. Furthermore, if the resident is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the resident is excited, the analysis unit provides a visually stimulating analysis result. In this way, by adjusting the length of the analysis based on the resident's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or without AI. For example, the analysis unit can input residents' emotional data into the generation AI and have the generation AI estimate their emotions.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also complement the analysis results of the most recent data by referring to past data. For example, the analysis unit complements the analysis results of the most recent data by referring to past data. The analysis unit can also adjust the level of detail of the analysis depending on the time when the data was collected. For example, the analysis unit adjusts the level of detail of the analysis depending on the time when the data was collected. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from the time when the data was collected to the generation AI and have the generation AI determine the priority of analysis.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. For example, the analysis unit adjusts the level of detail of the analysis according to the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, more relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the resident's level of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to residents with high levels of expertise. For example, the analysis unit provides analysis results that use a lot of technical terminology to residents with high levels of expertise. The analysis unit can also provide concise and easy-to-understand analysis results to residents with low levels of expertise. For example, the analysis unit provides concise and easy-to-understand analysis results to residents with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed according to the resident's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed according to the resident's level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the resident's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the resident's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The avatar generation unit can estimate the resident's emotions and adjust the avatar generation method based on the estimated resident's emotions. For example, if the resident is relaxed, the avatar generation unit generates an avatar with a calm expression. For example, if the resident is relaxed, the avatar generation unit generates an avatar with a calm expression. The avatar generation unit can also generate an avatar with a calm expression if the resident is stressed. For example, if the resident is stressed, the avatar generation unit generates an avatar with a calm expression. The avatar generation unit can also generate an avatar with a lively expression if the resident is excited. For example, if the resident is excited, the avatar generation unit generates an avatar with a lively expression. In this way, by adjusting the avatar generation method based on the resident's emotions, a more appropriate avatar can be generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the avatar generation unit may input emotion data of residents into the generation AI and have the generation AI estimate the emotions.

[0097] The avatar generation unit can adjust the level of detail of the avatar based on the resident's behavioral pattern when generating the avatar. For example, if the resident's behavioral pattern is complex, the avatar generation unit generates a detailed avatar. For example, if the resident's behavioral pattern is complex, the avatar generation unit generates a detailed avatar. Furthermore, if the resident's behavioral pattern is simple, the avatar generation unit can generate a concise avatar. For example, if the resident's behavioral pattern is simple, the avatar generation unit generates a concise avatar. Furthermore, the avatar generation unit can adjust the level of detail of the avatar according to the resident's behavioral pattern. For example, the avatar generation unit adjusts the level of detail of the avatar according to the resident's behavioral pattern. In this way, by adjusting the level of detail of the avatar based on the resident's behavioral pattern, a more appropriate avatar can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the avatar generation unit can input data on the resident's behavioral pattern into the generation AI and cause the generation AI to adjust the level of detail of the avatar.

[0098] The avatar generation unit can apply different generation algorithms depending on the resident's interests when generating an avatar. The avatar generation unit, for example, generates an avatar by applying an algorithm related to the resident's field of interest. For example, the avatar generation unit generates an avatar by applying an algorithm related to the resident's field of interest. The avatar generation unit can also select an optimal generation algorithm based on the resident's field of interest. For example, the avatar generation unit selects an optimal generation algorithm based on the resident's field of interest. The avatar generation unit can also customize the avatar generation method depending on the resident's interests. For example, the avatar generation unit customizes the avatar generation method depending on the resident's interests. This allows for the generation of more appropriate avatars by applying different generation algorithms depending on the resident's interests. Some or all of the above-described processing in the avatar generation unit may be performed using, or without, AI. For example, the avatar generation unit can input data on the resident's interests into a generation AI and cause the generation AI to apply a generation algorithm.

[0099] When generating an avatar, the avatar generation unit can improve the accuracy of the generation by referring to the resident's past avatar generation results. The avatar generation unit, for example, adjusts the generation algorithm based on the resident's past avatar generation results. For example, the avatar generation unit adjusts the generation algorithm based on the resident's past avatar generation results. The avatar generation unit can also adjust the level of detail of the generation by referring to the resident's past avatar generation results. For example, the avatar generation unit adjusts the level of detail of the generation by referring to the resident's past avatar generation results. The avatar generation unit can also determine the priority of the generation by reflecting the resident's past avatar generation results. For example, the avatar generation unit determines the priority of the generation by reflecting the resident's past avatar generation results. In this way, the accuracy of the generation can be improved by referring to the resident's past avatar generation results. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the avatar generation unit can input data of the resident's past avatar generation results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0100] The avatar generation unit can estimate the resident's emotions and adjust the avatar's appearance based on the estimated resident's emotions. For example, if the resident is relaxed, the avatar generation unit generates an avatar with a calm expression. For example, if the resident is relaxed, the avatar generation unit generates an avatar with a calm expression. The avatar generation unit can also generate an avatar with a calm expression if the resident is stressed. For example, if the resident is stressed, the avatar generation unit generates an avatar with a calm expression. The avatar generation unit can also generate an avatar with a lively expression if the resident is excited. For example, if the resident is excited, the avatar generation unit generates an avatar with a lively expression. In this way, by adjusting the avatar's appearance based on the resident's emotions, a more appropriate avatar can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the avatar generation unit may input emotion data of residents into the generation AI and have the generation AI estimate the emotions.

[0101] When generating an avatar, the avatar generation unit can set the avatar's behavior pattern based on the resident's lifestyle rhythm. For example, if a resident has a morning-type lifestyle rhythm, the avatar generation unit generates an avatar that is active in the morning. For example, if a resident has a morning-type lifestyle rhythm, the avatar generation unit generates an avatar that is active in the morning. Furthermore, if a resident has a nocturnal lifestyle rhythm, the avatar generation unit can generate an avatar that is active in the evening. For example, if a resident has a nocturnal lifestyle rhythm, the avatar generation unit generates an avatar that is active in the evening. Furthermore, the avatar generation unit can adjust the avatar's behavior pattern according to the resident's lifestyle rhythm. For example, the avatar generation unit adjusts the avatar's behavior pattern according to the resident's lifestyle rhythm. In this way, by setting the avatar's behavior pattern based on the resident's lifestyle rhythm, more appropriate avatars can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without AI. For example, the avatar generation unit can input data on the resident's lifestyle rhythm into the generation AI and cause the generation AI to set the avatar's behavior pattern.

[0102] When generating an avatar, the avatar generation unit can set the avatar's personality to reflect the resident's social media activity. For example, if a resident actively interacts on social media, the avatar generation unit generates an avatar with a sociable personality. For example, if a resident actively interacts on social media, the avatar generation unit generates an avatar with a sociable personality. Furthermore, if a resident posts modestly on social media, the avatar generation unit can generate an avatar with an introverted personality. For example, if a resident posts modestly on social media, the avatar generation unit can generate an avatar with an introverted personality. Furthermore, the avatar generation unit can customize the avatar's personality based on the resident's social media activity. For example, the avatar generation unit customizes the avatar's personality based on the resident's social media activity. This allows for the generation of a more appropriate avatar by reflecting the resident's social media activity. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without AI. For example, the avatar generation unit can input data on the resident's social media activity into the generation AI and cause the generation AI to set the avatar's personality.

[0103] When generating an avatar, the avatar generation unit can customize the avatar generation method by reflecting the resident's past feedback. The avatar generation unit, for example, adjusts the avatar generation algorithm based on the resident's past feedback. For example, the avatar generation unit adjusts the avatar generation algorithm based on the resident's past feedback. The avatar generation unit can also adjust the avatar's appearance and personality by referring to the resident's past feedback. For example, the avatar generation unit adjusts the avatar's appearance and personality by referring to the resident's past feedback. The avatar generation unit can also customize the avatar generation method by reflecting the resident's past feedback. For example, the avatar generation unit customizes the avatar generation method by reflecting the resident's past feedback. In this way, by reflecting the resident's past feedback, the avatar generation method can be customized and a more appropriate avatar can be generated. Some or all of the above-described processing in the avatar generation unit may be performed using, for example, AI, or may be performed without AI. For example, the avatar generation unit can input the resident's past feedback data into the generation AI and cause the generation AI to customize the avatar generation method.

[0104] The simulation unit can estimate the emotions of the residents and adjust the simulation settings based on the estimated emotions of the residents. For example, if the residents are relaxed, the simulation unit sets a calm simulation scenario. For example, if the residents are relaxed, the simulation unit sets a calm simulation scenario. Furthermore, if the residents are stressed, the simulation unit can set a simulation scenario aimed at stress reduction. For example, if the residents are stressed, the simulation unit sets a simulation scenario aimed at stress reduction. Furthermore, if the residents are excited, the simulation unit can set an active simulation scenario. For example, if the residents are excited, the simulation unit sets an active simulation scenario. This allows for adjusting the simulation settings based on the emotions of the residents, resulting in a more appropriate simulation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input residents' emotional data into the generation AI and have the generation AI estimate the emotions.

[0105] The simulation unit can adjust the level of detail of the simulation based on the behavioral patterns of the residents during the simulation. For example, when the behavioral patterns of the residents are complex, the simulation unit performs a detailed simulation. For example, when the behavioral patterns of the residents are complex, the simulation unit performs a detailed simulation. Furthermore, when the behavioral patterns of the residents are simple, the simulation unit can perform a concise simulation. For example, when the behavioral patterns of the residents are simple, the simulation unit performs a concise simulation. Furthermore, the simulation unit can adjust the level of detail of the simulation according to the behavioral patterns of the residents. For example, the simulation unit adjusts the level of detail of the simulation according to the behavioral patterns of the residents. In this way, by adjusting the level of detail of the simulation based on the behavioral patterns of the residents, a more appropriate simulation can be performed. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the behavioral patterns of the residents to a generation AI and cause the generation AI to adjust the level of detail of the simulation.

[0106] The simulation unit can apply different simulation algorithms depending on the interests of residents during the simulation. For example, the simulation unit performs a simulation by applying an algorithm related to the area of ​​interest of residents. For example, the simulation unit performs a simulation by applying an algorithm related to the area of ​​interest of residents. The simulation unit can also select an optimal simulation algorithm based on the area of ​​interest of residents. For example, the simulation unit selects an optimal simulation algorithm based on the area of ​​interest of residents. The simulation unit can also customize the simulation method depending on the interests of residents. For example, the simulation unit customizes the simulation method depending on the interests of residents. This allows for a more appropriate simulation to be performed by applying different simulation algorithms depending on the interests of residents. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the interests of residents to the generation AI and cause the generation AI to apply a simulation algorithm.

[0107] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the resident's past simulation results. The simulation unit, for example, adjusts the simulation algorithm based on the resident's past simulation results. For example, the simulation unit adjusts the simulation algorithm based on the resident's past simulation results. The simulation unit can also adjust the level of detail of the simulation by referring to the resident's past simulation results. For example, the simulation unit adjusts the level of detail of the simulation by referring to the resident's past simulation results. The simulation unit can also determine the priority of the simulation by reflecting the resident's past simulation results. For example, the simulation unit determines the priority of the simulation by reflecting the resident's past simulation results. In this way, the accuracy of the simulation can be improved by referring to the resident's past simulation results. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data of the resident's past simulation results into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0108] The simulation unit can estimate the emotions of the residents and adjust the display method of the simulation results based on the estimated emotions of the residents. For example, when the residents are relaxed, the simulation unit displays detailed simulation results. For example, when the residents are relaxed, the simulation unit displays detailed simulation results. Furthermore, when the residents are stressed, the simulation unit can display concise and to-the-point simulation results. For example, when the residents are stressed, the simulation unit displays concise and to-the-point simulation results. Furthermore, when the residents are excited, the simulation unit can display visually stimulating simulation results. For example, when the residents are excited, the simulation unit displays visually stimulating simulation results. In this way, by adjusting the display method of the simulation results based on the emotions of the residents, more appropriate simulation results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the simulation unit can be performed, for example, using AI, or without AI. For example, the simulation unit can input residents' emotional data into the generation AI and have the generation AI estimate the emotions.

[0109] During the simulation, the simulation unit can set the timing of the simulation based on the resident's lifestyle rhythm. For example, if the resident has a morning-type lifestyle rhythm, the simulation unit performs the simulation in the morning. For example, if the resident has a morning-type lifestyle rhythm, the simulation unit can perform the simulation in the morning. Also, if the resident has a night-type lifestyle rhythm, the simulation unit can perform the simulation in the evening. For example, if the resident has a night-type lifestyle rhythm, the simulation unit can perform the simulation in the evening. Also, the simulation unit can adjust the timing of the simulation according to the resident's lifestyle rhythm. For example, the simulation unit adjusts the timing of the simulation according to the resident's lifestyle rhythm. In this way, by setting the timing of the simulation based on the resident's lifestyle rhythm, a more appropriate simulation can be performed. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the resident's lifestyle rhythm to the generation AI and cause the generation AI to set the timing of the simulation.

[0110] During the simulation, the simulation unit can set a simulation scenario that reflects the residents' social media activities. For example, if the residents actively interact on social media, the simulation unit sets a sociable scenario. For example, if the residents actively interact on social media, the simulation unit sets a sociable scenario. The simulation unit can also set an introverted scenario if the residents post modestly on social media. For example, if the residents post modestly on social media, the simulation unit sets an introverted scenario. The simulation unit can also customize the simulation scenario based on the residents' social media activities. This allows for a more appropriate simulation scenario to be set by reflecting the residents' social media activities. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the residents' social media activities into the generation AI and cause the generation AI to set a simulation scenario.

[0111] During a simulation, the simulation unit can customize the simulation method by reflecting the residents' past feedback. The simulation unit, for example, adjusts the simulation algorithm based on the residents' past feedback. For example, the simulation unit adjusts the simulation algorithm based on the residents' past feedback. The simulation unit can also adjust the level of detail of the simulation by referring to the residents' past feedback. For example, the simulation unit adjusts the level of detail of the simulation by referring to the residents' past feedback. The simulation unit can also determine the priority of the simulation by reflecting the residents' past feedback. For example, the simulation unit determines the priority of the simulation by reflecting the residents' past feedback. In this way, by reflecting the residents' past feedback, the simulation method can be customized and a more appropriate simulation can be performed. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input the residents' past feedback data into the generation AI and cause the generation AI to customize the simulation method.

[0112] The suggestion unit can estimate the resident's emotions and adjust the way the suggestions are expressed based on the estimated resident's emotions. For example, when the resident is relaxed, the suggestion unit provides detailed suggestions. For example, when the resident is relaxed, the suggestion unit provides detailed suggestions. Furthermore, when the resident is stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, when the resident is stressed, the suggestion unit provides concise and to-the-point suggestions. Furthermore, when the resident is excited, the suggestion unit can provide visually stimulating suggestions. For example, when the resident is excited, the suggestion unit provides visually stimulating suggestions. In this way, by adjusting the way the suggestions are expressed based on the resident's emotions, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input residents' emotional data into the generation AI and have the generation AI perform emotion estimation.

[0113] The proposal unit can adjust the level of detail of the proposal based on the importance of the simulation result when making a proposal. For example, the proposal unit makes a detailed proposal for a simulation result with a high level of importance. For example, the proposal unit makes a detailed proposal for a simulation result with a high level of importance. The proposal unit can also make a concise proposal for a simulation result with a low level of importance. For example, the proposal unit makes a concise proposal for a simulation result with a low level of importance. The proposal unit can also determine the priority of the proposal according to the importance of the simulation result. For example, the proposal unit determines the priority of the proposal according to the importance of the simulation result. As a result, by adjusting the level of detail of the proposal based on the importance of the simulation result, more important proposals can be made in detail. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit may input data on the importance of the simulation result to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0114] The proposal unit can apply different proposal algorithms depending on the category of the simulation results when making a proposal. For example, the proposal unit applies an urban planning proposal algorithm to simulation results related to urban planning. For example, the proposal unit applies an urban planning proposal algorithm to simulation results related to urban planning. The proposal unit can also apply an environmental improvement proposal algorithm to simulation results related to environmental improvement. For example, the proposal unit applies an environmental improvement proposal algorithm to simulation results related to environmental improvement. The proposal unit can also apply a transportation plan proposal algorithm to simulation results related to transportation planning. For example, the proposal unit applies a transportation plan proposal algorithm to simulation results related to transportation planning. In this way, by applying different proposal algorithms depending on the category of the simulation results, more appropriate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data of the category of the simulation results to the generation AI and cause the generation AI to apply the proposal algorithm.

[0115] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the resident's past proposal results. The proposal unit, for example, adjusts the proposal algorithm based on the resident's past proposal results. For example, the proposal unit adjusts the proposal algorithm based on the resident's past proposal results. The proposal unit can also adjust the level of detail of the proposal by referring to the resident's past proposal results. For example, the proposal unit adjusts the level of detail of the proposal by referring to the resident's past proposal results. The proposal unit can also determine the priority of the proposal by reflecting the resident's past proposal results. For example, the proposal unit determines the priority of the proposal by reflecting the resident's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the resident's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input data of the resident's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0116] The suggestion unit can estimate the resident's emotions and adjust the length of the suggestions based on the estimated resident's emotions. For example, if the resident is relaxed, the suggestion unit provides detailed suggestions. For example, if the resident is relaxed, the suggestion unit provides detailed suggestions. Furthermore, if the resident is stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, if the resident is stressed, the suggestion unit can provide concise and to-the-point suggestions. Furthermore, if the resident is excited, the suggestion unit can provide visually stimulating suggestions. For example, if the resident is excited, the suggestion unit provides visually stimulating suggestions. In this way, by adjusting the length of the suggestions based on the resident's emotions, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI, or without AI. For example, the proposal unit can input residents' emotional data into the generation AI and have the generation AI perform emotion estimation.

[0117] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the simulation results were collected. The proposal unit, for example, prioritizes the most recent simulation results. For example, the proposal unit prioritizes the most recent simulation results. The proposal unit can also complement the most recent proposal by referring to past simulation results. For example, the proposal unit complements the most recent proposal by referring to past simulation results. The proposal unit can also adjust the level of detail of the proposal depending on the time when the simulation results were collected. For example, the proposal unit adjusts the level of detail of the proposal depending on the time when the simulation results were collected. In this way, by determining the priority of the proposal based on the time when the simulation results were collected, the most recent simulation results can be preferentially proposed. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the time when the simulation results were collected to the generation AI and cause the generation AI to determine the priority of the proposals.

[0118] The proposal unit can adjust the order of proposals based on the relevance of the simulation results when making a proposal. The proposal unit, for example, prioritizes proposing highly relevant simulation results. For example, the proposal unit prioritizes proposing highly relevant simulation results. The proposal unit can also postpone less relevant simulation results. For example, the proposal unit postpones less relevant simulation results. The proposal unit can also adjust the level of detail of the proposal according to the relevance of the simulation results. For example, the proposal unit adjusts the level of detail of the proposal according to the relevance of the simulation results. In this way, by adjusting the order of proposals based on the relevance of the simulation results, more relevant proposals can be prioritized. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the relevance of the simulation results to the generation AI and cause the generation AI to adjust the order of proposals.

[0119] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the resident's level of expertise. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a resident with high technical expertise. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a resident with high technical expertise. The suggestion unit can also provide a concise and easy-to-understand proposal to a resident with low technical expertise. For example, the suggestion unit provides a concise and easy-to-understand proposal to a resident with low technical expertise. The suggestion unit can also adjust the way the proposal is expressed according to the resident's level of expertise. For example, the suggestion unit adjusts the way the proposal is expressed according to the resident's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the resident's level of expertise, it is possible to provide a more understandable proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the resident's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, avatar generation unit, simulation unit, and proposal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects behavioral data of residents using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the behavioral patterns and preferences of residents. The avatar generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates avatars on the digital twin based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs a simulation using the generated avatar. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes urban planning based on the simulation results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, avatar generation unit, simulation unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects behavioral data of residents using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the behavioral patterns and preferences of residents. The avatar generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates avatars on the digital twin based on the analysis results. The simulation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and performs a simulation using the generated avatar. The proposal unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and proposes urban planning based on the simulation results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, avatar generation unit, simulation unit, and proposal unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects behavioral data of residents using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the behavioral patterns and preferences of residents. The avatar generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates avatars on the digital twin based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs simulations using the generated avatars. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes urban plans based on the simulation results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, avatar generation unit, simulation unit, and proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavioral data of residents using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify the behavioral patterns and preferences of residents. The avatar generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates avatars on the digital twin based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs simulations using the generated avatars. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes urban planning based on the simulation results.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The urban planning proposal system can also collect health data of residents and propose urban plans based on their health status. For example, the collection unit collects health data such as the number of steps taken and heart rate of residents. The analysis unit analyzes the collected health data and identifies the health status of residents. The avatar generation unit generates an avatar based on the health status, and the simulation unit simulates urban plans that take the health status into consideration. The proposal unit can propose urban plans aimed at promoting health based on the simulation results. This makes it possible to provide a healthy living environment by proposing urban plans based on the health status of residents.

[0122] The urban planning proposal system can also collect energy consumption data of residents and propose urban planning that takes energy efficiency into consideration. For example, the collection unit collects energy consumption data within the residents' homes. The analysis unit analyzes the collected energy consumption data and identifies energy consumption patterns. The avatar generation unit generates an avatar based on the energy consumption patterns, and the simulation unit simulates urban planning that optimizes energy efficiency. The proposal unit can propose urban planning that improves energy efficiency based on the simulation results. In this way, a sustainable urban environment can be realized by proposing urban planning based on energy consumption data.

[0123] The urban planning proposal system can also collect residents' traffic data and propose urban plans to alleviate traffic congestion. For example, the collection unit collects data on residents' commuting routes and means of transportation. The analysis unit analyzes the collected traffic data and identifies the causes of traffic congestion. The avatar generation unit generates an avatar based on the traffic data, and the simulation unit simulates urban plans to alleviate traffic congestion. The proposal unit can propose urban plans to alleviate traffic congestion based on the simulation results. In this way, by proposing urban plans based on the traffic data, traffic congestion can be alleviated and residents' movement can be made smoother.

[0124] The urban planning proposal system can also collect environmental data from residents and propose urban plans that take environmental protection into consideration. For example, the collection unit collects environmental data such as air quality and noise levels around the residents. The analysis unit analyzes the collected environmental data and identifies the current state of the environment. The avatar generation unit generates an avatar based on the environmental data, and the simulation unit simulates urban plans that take environmental protection into consideration. The proposal unit can propose urban plans aimed at environmental protection based on the simulation results. In this way, a sustainable urban environment can be realized by proposing urban plans based on the environmental data.

[0125] The urban planning proposal system can also collect educational data from residents and propose urban plans that improve the educational environment. For example, the collection unit collects residents' learning history and educational facility usage data. The analysis unit analyzes the collected educational data and identifies the current state of the educational environment. The avatar generation unit generates an avatar based on the educational data, and the simulation unit simulates urban plans that improve the educational environment. The proposal unit can propose urban plans that improve the educational environment based on the simulation results. In this way, the educational environment for residents can be improved by proposing urban plans based on the educational data.

[0126] The urban planning proposal system can estimate the emotions of residents and adjust the content of urban planning proposals based on the estimated emotions. For example, the collection unit collects emotion data of residents, and the analysis unit analyzes the collected emotion data to identify the emotions of residents. The avatar generation unit generates an avatar based on the emotion data, and the simulation unit simulates urban planning that takes into account the emotions of residents. The proposal unit can propose urban planning that takes into account the emotions of residents based on the simulation results. In this way, resident satisfaction can be improved by proposing urban planning based on the emotions of residents.

[0127] The urban planning proposal system can estimate the emotions of residents and adjust the frequency of data collection based on the estimated emotions. For example, the collection unit collects emotion data of residents, and the analysis unit analyzes the collected emotion data to identify the emotions of the residents. The collection unit can increase the frequency of data collection when the residents are relaxed and decrease the frequency of data collection when the residents are stressed. In this way, adjusting the frequency of data collection based on the emotions of residents reduces the burden on residents and allows for more natural data collection.

[0128] The urban planning proposal system can estimate the emotions of residents and adjust the simulation scenario based on the estimated emotions. For example, the simulation unit collects emotion data of residents, and the analysis unit analyzes the collected emotion data to identify the emotions of residents. The simulation unit can set a calm scenario if the residents are relaxed, and a scenario aimed at reducing stress if the residents are feeling stressed. This allows for a more appropriate simulation by adjusting the simulation scenario based on the emotions of residents.

[0129] The urban planning proposal system can estimate the emotions of residents and adjust the way in which proposals are presented based on the estimated emotions. For example, the proposal unit collects emotion data of residents, and the analysis unit analyzes the collected emotion data to identify the emotions of the residents. The proposal unit can provide detailed proposals when the residents are relaxed, and provide concise and to-the-point proposals when the residents are stressed. This allows the system to provide more appropriate proposals by adjusting the way in which proposals are presented based on the emotions of the residents.

[0130] The urban planning proposal system can estimate the emotions of residents and adjust the appearance of an avatar based on the estimated emotions. For example, the avatar generation unit collects emotion data of residents, and the analysis unit analyzes the collected emotion data to identify the emotions of the residents. The avatar generation unit can generate an avatar with a calm expression when a resident is relaxed, and an avatar with a calm expression when a resident is stressed. This allows for the generation of a more appropriate avatar by adjusting the appearance of the avatar based on the emotions of the resident.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects resident behavior data. Resident behavior data includes movement history, purchase history, and social media postings. The collection unit collects data on places visited by residents and the content posted by them. The collection unit can also collect resident behavior data using IoT sensors. For example, the collection unit collects GPS data of places visited by residents and the content posted by them on social media. Step 2: The analysis unit analyzes the data collected by the collection unit to identify residents' behavioral patterns and preferences. The analysis is performed using data mining and machine learning algorithms. For example, the analysis unit uses data mining techniques to identify residents' behavioral patterns and machine learning algorithms to identify residents' preferences, interests, and concerns. Step 3: The avatar generation unit generates an avatar on the digital twin based on the analysis results obtained by the analysis unit. The avatar is generated based on the behavioral patterns and preferences of the resident. For example, the avatar generation unit generates an avatar based on the behavioral patterns, interests, and concerns of the resident. Step 4: The simulation unit performs a simulation using the avatars generated by the avatar generation unit. The simulation is performed using a scenario and an algorithm. For example, the simulation unit sets a scenario and performs a simulation, and then simulates optimal urban planning using an algorithm. Step 5: The proposal unit proposes an urban plan based on the simulation results obtained by the simulation unit. The proposal includes content such as infrastructure development, transportation planning, and improvements to the living environment. For example, the proposal unit proposes infrastructure development and transportation planning based on the simulation results.

[0133] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects resident behavior data; an analysis unit that analyzes the data collected by the collection unit and identifies behavioral patterns and preferences of residents; an avatar generation unit that generates an avatar on the digital twin based on the analysis results obtained by the analysis unit; a simulation unit that performs a simulation using the avatar generated by the avatar generation unit; a proposal unit that proposes an urban plan based on the simulation results obtained by the simulation unit. A system characterized by:

2. The collecting unit Collect data on where residents visit or what they post 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify residents' interests and concerns 2. The system of claim 1.

4. The avatar generation unit Generate avatars based on residents' behavioral patterns and preferences 2. The system of claim 1.

5. The simulation unit Simulations are performed using the generated avatars, and urban planning proposals tailored to individual preferences are proposed.

2. The system of claim 1.

6. The proposal unit Proposing urban plans based on simulation results 2. The system of claim 1.

7. The collecting unit Estimate residents' emotions and adjust the timing of data collection based on the estimated emotions of residents.

2. The system of claim 1.

8. The collecting unit Analyze residents' past behavioral data and select the appropriate data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filter it based on residents' current living situations and areas of interest.

2. The system of claim 1.

10. The collecting unit When collecting data, select the most appropriate collection method depending on the residents' input method.

2. The system of claim 1.

11. The collecting unit Estimate residents' sentiment and prioritize data collection based on the estimated sentiment 2. The system of claim 1.

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