system

Abstract

The system according to the embodiment aims to understand the characteristics and current problems of local governments, and based on that, generate and evaluate optimal urban structure plans. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects data on the characteristics and current problems of a local government. The analysis unit analyzes the data collected by the collection unit and identifies current problems and areas for improvement. The generation unit generates a proposed urban structure plan based on the problems and areas for improvement identified by the analysis unit. The evaluation unit evaluates the proposed plan generated by the generation unit based on evaluation criteria.

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 has the problem that it is difficult to fully understand the characteristics and current problems of local governments and, based on that, generate and evaluate optimal urban structure plans.

[0005] The system according to the embodiment aims to understand the characteristics and current problems of local governments, and based on that, generate and evaluate optimal urban structure plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects data on the characteristics and current problems of a local government. The analysis unit analyzes the data collected by the collection unit and identifies current problems and areas for improvement. The generation unit generates a proposed plan for urban structure based on the problems and areas for improvement identified by the analysis unit. The evaluation unit evaluates the proposed plan generated by the generation unit based on evaluation criteria. [Effects of the Invention]

[0007] The system according to the embodiment is capable of grasping the characteristics and current problems of a local government, and based on that, generating and evaluating optimal urban structure plans. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 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) The urban structure planning system according to an embodiment of the present invention aims to solve various problems faced by local governments, such as population decline, aging, and the deterioration of urban structures. It also aims to create cities where diverse people can live comfortably with various lifestyles, based on the perspectives of the Sustainable Development Goals (SDGs) and Diversity, Equity, and Inclusion (DE&I). This urban structure planning system proposes the optimal urban structure for the residents of the city, presents a proposed urban structure that is safe, secure, and barrier-free, and is tailored to the characteristics of the local government, and provides a service that allows for objective evaluation. First, the urban structure planning system performs a detailed analysis of the characteristics and current issues of the local government. For example, it collects data on demographics, age composition, the state of aging infrastructure, and the state of the transportation network. Next, the urban structure planning system uses AI to analyze the collected data and identify current issues and areas for improvement. For example, it may be determined that areas with significant population decline require measures to encourage young people to settle there. Next, the urban structure planning system generates an optimal urban structure proposal based on the analysis results. Based on the collected data and analysis results, the AI ​​proposes an urban structure that suits the characteristics of the local government. For example, from the perspective of barrier-free access, it can propose locations for pedestrian-only roads and elevators. Furthermore, from the perspective of SDGs, it can also propose measures such as the introduction of renewable energy and the expansion of green spaces. Furthermore, the urban structure planning system sets evaluation criteria for objectively evaluating the generated plan proposals. For example, resident satisfaction surveys and the effectiveness of reducing environmental impacts can be set as evaluation criteria, and the AI ​​evaluates the plan proposals based on these criteria. The evaluation results are fed back to the local government and used to revise and improve the plan proposals. In this way, the urban structure planning system generates and objectively evaluates optimal urban structure plan proposals that match the characteristics of the local government, thereby realizing a city where a wide variety of people can live comfortably. This allows the urban structure planning system to generate and objectively evaluate optimal urban structure plan proposals that match the characteristics of the local government.

[0029] An urban structure planning system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects data on the characteristics and current problems of a local government. For example, the collection unit can collect data on demographics, age distribution, the state of aging infrastructure, the state of development of a transportation network, and the like. For example, the collection unit can survey the demographics of a local government and collect data on age-specific population distribution, birth rates, death rates, and the like. The collection unit can also survey the state of aging infrastructure and collect data on the deterioration of roads and the earthquake resistance of buildings. Furthermore, the collection unit can survey the state of development of a transportation network and collect data on the state of development of the road network and the operation status of public transportation, and the like. The analysis unit analyzes the data collected by the collection unit and identifies current problems and areas for improvement. For example, the analysis unit can analyze the collected data using statistical analysis or a machine learning algorithm to identify current problems and areas for improvement. For example, the analysis unit can analyze the causes of traffic congestion and identify areas for improvement to facilitate traffic flow. The analysis unit can also analyze causes of environmental pollution and identify environmental improvement measures. Furthermore, the analysis unit can analyze the aging status of infrastructure and identify improvements for infrastructure renewal. The generation unit generates an optimal urban structure plan based on the problems and improvements identified by the analysis unit. For example, the generation unit can generate urban structure plan proposals, such as urban zoning plans, transportation plans, and green space plans, based on the identified problems and improvements. For example, the generation unit can propose locations for pedestrian-only roads and elevators from a barrier-free perspective. The generation unit can also propose measures such as introducing renewable energy and expanding green spaces from a perspective of the SDGs. Furthermore, the generation unit can apply different generation algorithms to generate urban structure plan proposals that suit the characteristics of the local government. The evaluation unit evaluates the plan proposals generated by the generation unit based on evaluation criteria. For example, the evaluation unit can evaluate the plan proposals based on evaluation criteria such as resident satisfaction surveys and the effect of reducing environmental impact. For example, the evaluation unit can conduct resident satisfaction surveys and evaluate the impact of the plan proposals on residents. The evaluation unit can also evaluate the effect of reducing environmental load and evaluate the environmental impact of the plan.Furthermore, the evaluation unit can also optimize the current evaluation criteria by referring to past evaluation data. This allows the urban structure planning system according to the embodiment to generate and objectively evaluate an optimal urban structure plan that matches the characteristics of the local government.

[0030] The collection unit can collect data on demographics, age structure, infrastructure deterioration, and transportation network development status. The collection unit, for example, investigates demographics and collects data such as age-specific population distribution, birth rate, and death rate. For example, the collection unit can investigate the demographics of a local government and collect data such as age-specific population distribution, birth rate, and death rate. The collection unit can also investigate age structure and collect data such as age-specific population distribution and average age. For example, the collection unit can investigate the age structure of a local government and collect data such as age-specific population distribution and average age. The collection unit can also investigate the aging status of infrastructure and collect data such as road deterioration status and building earthquake resistance. For example, the collection unit can investigate the aging status of a local government's infrastructure and collect data such as road deterioration status and building earthquake resistance. The collection unit can also investigate the transportation network development status and collect data such as road network development status and public transportation operation status. For example, the collection unit can investigate the development status of a local government's transportation network and collect data such as the development status of the road network and the operation status of public transportation. This allows the collection unit to grasp the characteristics and current problems of the local government in detail. Some or all of the above-mentioned 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 local government's demographic data into AI, which then analyzes and collects the data.

[0031] The analysis unit can analyze the collected data and identify current problems and areas for improvement. The analysis unit can analyze the collected data using, for example, statistical analysis or a machine learning algorithm to identify current problems and areas for improvement. For example, the analysis unit can analyze the causes of traffic congestion and identify areas for improvement to facilitate traffic. The analysis unit can also analyze the causes of environmental pollution and identify environmental improvement measures. For example, the analysis unit can analyze the causes of environmental pollution and identify areas for improvement. Furthermore, the analysis unit can analyze the aging status of infrastructure and identify areas for improvement for infrastructure renewal. For example, the analysis unit can analyze the aging status of infrastructure and identify areas for improvement for infrastructure renewal. This allows the analysis unit to clarify current problems and areas for improvement. 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 the collected data into AI, which analyzes the data and identifies current problems and areas for improvement.

[0032] The generation unit can generate a proposed urban structure plan based on the identified problems and improvements. The generation unit can generate, for example, a proposed urban structure plan, such as a city zoning plan, a transportation plan, or a green space plan, based on the identified problems and improvements. For example, the generation unit can generate a proposed urban structure plan, such as a city zoning plan, a transportation plan, or a green space plan, based on the identified problems and improvements. The generation unit can also propose locations for pedestrian-only roads and elevators from a barrier-free perspective. For example, the generation unit can propose locations for pedestrian-only roads and elevators from a barrier-free perspective. Furthermore, the generation unit can propose measures, such as the introduction of renewable energy and the expansion of green spaces, from a perspective of the SDGs. For example, the generation unit can propose measures, such as the introduction of renewable energy and the expansion of green spaces, from a perspective of the SDGs. This allows the generation unit to generate a proposed urban structure plan that suits the characteristics of the local government. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input identified problems and areas for improvement into the AI, which can then generate a proposed plan for the urban structure.

[0033] The evaluation unit can evaluate the proposed plan based on evaluation criteria of a resident satisfaction survey or an effect of reducing environmental load. The evaluation unit, for example, conducts a resident satisfaction survey and evaluates the impact of the proposed plan on residents. For example, the evaluation unit can conduct a resident satisfaction survey and evaluate the impact of the proposed plan on residents. The evaluation unit can also evaluate the effect of reducing environmental load and evaluate the environmental impact of the proposed plan. For example, the evaluation unit can evaluate the effect of reducing environmental load and evaluate the environmental impact of the proposed plan. Furthermore, the evaluation unit can optimize the current evaluation criteria by referring to past evaluation data. For example, the evaluation unit can optimize the current evaluation criteria by referring to past evaluation data. This enables the evaluation unit to objectively evaluate the proposed plan. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input resident satisfaction survey data into AI, and the AI ​​can analyze the data to evaluate the proposed plan.

[0034] The generation unit can propose locations for pedestrian roads or elevators from a barrier-free perspective. The generation unit, for example, proposes locations for pedestrian roads or elevators from a barrier-free perspective. For example, the generation unit can propose locations for pedestrian roads or elevators from a barrier-free perspective. The generation unit, for example, proposes locations for pedestrian roads based on pedestrian road design standards. The generation unit can also propose locations for elevators based on elevator installation standards. For example, the generation unit can propose locations for elevators based on elevator installation standards. This allows the generation unit to realize a barrier-free urban structure. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input locations for pedestrian roads or elevators from a barrier-free perspective into AI, and the AI ​​can suggest installation locations.

[0035] The generation unit can propose measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs. For example, the generation unit can propose measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs. For example, the generation unit can propose measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs. For example, the generation unit can propose the introduction of renewable energy based on a renewable energy introduction target. The generation unit can also propose the expansion of green spaces based on a green space expansion target. For example, the generation unit can propose the expansion of green spaces based on a green space expansion target. This allows the generation unit to realize a sustainable urban structure. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs into AI, and the AI ​​can propose the measures.

[0036] The collection unit can analyze past data collection history and select a collection method. For example, the collection unit can identify the most efficient collection method from the past data collection history and apply it to future data collection. For example, the collection unit can identify the most efficient collection method from the past data collection history and apply it to future data collection. The collection unit can also analyze the past data collection history and optimize the collection frequency and timing. For example, the collection unit can analyze the past data collection history and optimize the collection frequency and timing. Furthermore, the collection unit can prioritize collection from a specific data source based on the past data collection history. For example, the collection unit can prioritize collection from a specific data source based on the past data collection history. This allows the collection unit to select the optimal collection method based on the past data collection history. Some or all of the above-mentioned 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 past data collection history into AI, which can select the optimal collection method.

[0037] The collection unit can perform filtering based on the characteristics of the local government and current problems when collecting data. The collection unit, for example, collects only specific data based on the characteristics of the local government. For example, the collection unit can collect only specific data based on the characteristics of the local government. The collection unit can also prioritize collecting related data based on current problems. For example, the collection unit can prioritize collecting related data based on current problems. Furthermore, the collection unit can filter unnecessary data taking into account the characteristics of the local government and the current problems. For example, the collection unit can filter unnecessary data taking into account the characteristics of the local government and the current problems. This allows the collection unit to efficiently collect necessary data based on the characteristics of the local government and the current problems. Some or all of the above-mentioned 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 characteristics of the local government and current problems into AI, and the AI ​​can perform filtering when collecting data.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the local government. The collection unit, for example, prioritizes collecting relevant data based on the geographical location information of the local government. For example, the collection unit can prioritize collecting relevant data based on the geographical location information of the local government. The collection unit can also prioritize data from a specific region by taking into account the geographical location information. For example, the collection unit can prioritize data from a specific region by taking into account the geographical location information. Furthermore, the collection unit can limit the range of data to be collected based on the geographical location information. For example, the collection unit can limit the range of data to be collected based on the geographical location information. This allows the collection unit to efficiently collect highly relevant data by taking into account the geographical location information of the local government. 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 geographical location information of the local government into AI, which can then prioritize collecting highly relevant data.

[0039] The collection unit may analyze social media activities and collect related data during data collection. The collection unit may, for example, analyze social media posts and collect related data. For example, the collection unit may analyze social media posts and collect related data. The collection unit may also select data to collect based on social media trends. For example, the collection unit may select data to collect based on social media trends. Furthermore, the collection unit may monitor social media activities in real time and collect related data. For example, the collection unit may monitor social media activities in real time and collect related data. This allows the collection unit to analyze social media activities and efficiently collect related data. 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 social media post data into AI, which may collect related data.

[0040] 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 can perform a detailed analysis on important data and a simplified analysis on less important data. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data. For example, the analysis unit can apply multiple analysis methods to important data. This allows the analysis unit to optimize the level of detail of the analysis depending on the importance of the data. 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 the importance of the data to AI, and the AI ​​can adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can select an optimal analysis algorithm depending on the category of data. The analysis unit can also apply different analysis methods to different categories of data. For example, the analysis unit can apply different analysis methods to different categories of data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the category of data. For example, the analysis unit can adjust the level of detail of the analysis based on the category of data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data. 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 the category of data into AI, which then applies the optimal analysis algorithm.

[0042] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the latest data and postpone analyzing older data. For example, the analysis unit can prioritize analyzing the latest data and postpone analyzing older data. The analysis unit can also adjust the level of detail of the analysis based on the time when the data was collected. For example, the analysis unit can adjust the level of detail of the analysis based on the time when the data was collected. Furthermore, the analysis unit can determine the order of analysis taking into account the time when the data was collected. For example, the analysis unit can determine the order of analysis taking into account the time when the data was collected. This allows the analysis unit to optimize the priority of the analysis based on the time when the data was collected. 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 the time when the data was collected into AI, and the AI ​​can determine the priority of the analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data and postpone analysis of less relevant data. For example, the analysis unit can prioritize analysis of highly relevant data and postpone analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the relevance of the data. Furthermore, the analysis unit can determine the order of analysis taking into account the relevance of the data. For example, the analysis unit can determine the order of analysis taking into account the relevance of the data. This allows the analysis unit to optimize the order of analysis based on the relevance of the data. 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 the relevance of the data into AI, and the AI ​​can adjust the order of analysis.

[0044] When generating a plan, the generation unit can adjust the level of detail of the generated plan based on the importance of the identified problems and improvements. For example, the generation unit can generate a detailed plan for an important problem and a simplified plan for a less important problem. For example, the generation unit can generate a detailed plan for an important problem and a simplified plan for a less important problem. The generation unit can also determine the priority of the plan based on the importance of the problems and improvements. For example, the generation unit can determine the priority of the plan based on the importance of the problems and improvements. Furthermore, the generation unit can propose multiple solutions for an important problem. For example, the generation unit can propose multiple solutions for an important problem. This allows the generation unit to optimize the level of detail of the plan based on the importance of the identified problems and improvements. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the importance of the problems and improvements to AI, which can adjust the level of detail of the generated plan.

[0045] When generating a draft plan, the generation unit can apply different generation algorithms depending on the characteristics of the local government. The generation unit, for example, selects an optimal generation algorithm based on the characteristics of the local government. For example, the generation unit can select an optimal generation algorithm based on the characteristics of the local government. The generation unit can also apply different generation methods to different local governments. For example, the generation unit can apply different generation methods to different local governments. Furthermore, the generation unit can adjust the level of detail of the generation algorithm taking into account the characteristics of the local government. For example, the generation unit can adjust the level of detail of the generation algorithm taking into account the characteristics of the local government. This allows the generation unit to apply the optimal generation algorithm depending on the characteristics of the local government. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the characteristics of the local government into AI, and the AI ​​can apply the optimal generation algorithm.

[0046] When generating a proposed plan, the generation unit can determine the priority of generation based on the submission dates of identified problems and improvements. For example, the generation unit can generate a proposed plan that prioritizes solving problems whose submission dates are approaching. For example, the generation unit can generate a proposed plan that prioritizes solving problems whose submission dates are approaching. The generation unit can also adjust the level of detail of the proposed plan based on the submission dates. For example, the generation unit can adjust the level of detail of the proposed plan based on the submission dates. Furthermore, the generation unit can determine the order of the proposed plans taking into account the submission dates. For example, the generation unit can determine the order of the proposed plans taking into account the submission dates. This allows the generation unit to optimize the priority of the proposed plans according to the submission dates of identified problems and improvements. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the submission dates of problems and improvements into AI, and the AI ​​can determine the priority of generation.

[0047] When generating a proposed plan, the generation unit can adjust the order of generation based on the relevance of identified problems and improvements. For example, the generation unit can generate a proposed plan that prioritizes solving highly relevant problems. For example, the generation unit can generate a proposed plan that prioritizes solving highly relevant problems. The generation unit can also adjust the level of detail of the proposed plan based on the relevance of the problems and improvements. For example, the generation unit can adjust the level of detail of the proposed plan based on the relevance of the problems and improvements. Furthermore, the generation unit can determine the order of the proposed plans taking into account the relevance of the problems and improvements. For example, the generation unit can determine the order of the proposed plans taking into account the relevance of the problems and improvements. In this way, the generation unit can optimize the order of the proposed plans according to the relevance of the identified problems and improvements. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the relevance of the problems and improvements to AI, and the AI ​​can adjust the order of generation.

[0048] The evaluation unit can optimize the current evaluation criteria by referring to past evaluation data during evaluation. The evaluation unit, for example, optimizes the current evaluation criteria based on past evaluation data. For example, the evaluation unit can optimize the current evaluation criteria based on past evaluation data. The evaluation unit can also analyze the past evaluation data and identify areas for improvement in the evaluation criteria. For example, the evaluation unit can analyze the past evaluation data and identify areas for improvement in the evaluation criteria. Furthermore, the evaluation unit can adjust the level of detail of the evaluation criteria by referring to the past evaluation data. For example, the evaluation unit can adjust the level of detail of the evaluation criteria by referring to the past evaluation data. This allows the evaluation unit to optimize the current evaluation criteria based on the past evaluation data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation data into AI, which then optimizes the current evaluation criteria.

[0049] The evaluation unit can apply different evaluation methods depending on the characteristics of the local government during evaluation. The evaluation unit, for example, selects the optimal evaluation method based on the characteristics of the local government. For example, the evaluation unit can select the optimal evaluation method based on the characteristics of the local government. The evaluation unit can also apply different evaluation methods to different local governments. For example, the evaluation unit can apply different evaluation methods to different local governments. Furthermore, the evaluation unit can adjust the level of detail of the evaluation method taking into account the characteristics of the local government. For example, the evaluation unit can adjust the level of detail of the evaluation method taking into account the characteristics of the local government. This allows the evaluation unit to apply the optimal evaluation method depending on the characteristics of the local government. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the characteristics of the local government into AI, which can then apply the optimal evaluation method.

[0050] During evaluation, the evaluation unit can analyze changes in evaluation based on the submission time of the proposed plan. The evaluation unit, for example, analyzes changes in evaluation and adjusts the evaluation criteria based on the submission time of the proposed plan. For example, the evaluation unit can analyze changes in evaluation and adjust the evaluation criteria based on the submission time of the proposed plan. The evaluation unit can also prioritize evaluation of proposed plans that are close to being submitted and analyze changes in evaluation. For example, the evaluation unit can prioritize evaluation of proposed plans that are close to being submitted and analyze changes in evaluation. Furthermore, the evaluation unit can adjust the level of detail of the evaluation based on the submission time. For example, the evaluation unit can adjust the level of detail of the evaluation based on the submission time. This allows the evaluation unit to optimize changes in evaluation based on the submission time of the proposed plan. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submission time of the proposed plan into AI, and the AI ​​can analyze changes in evaluation.

[0051] During the evaluation, the evaluation unit can analyze the evaluation by referring to market data related to the draft plan. For example, the evaluation unit can improve the accuracy of the evaluation by referring to market data related to the draft plan. For example, the evaluation unit can improve the accuracy of the evaluation by referring to market data related to the draft plan. The evaluation unit can also optimize the evaluation criteria based on the relevant market data. For example, the evaluation unit can optimize the evaluation criteria based on the relevant market data. Furthermore, the evaluation unit can adjust the level of detail of the evaluation by referring to the relevant market data. For example, the evaluation unit can adjust the level of detail of the evaluation by referring to the relevant market data. This allows the evaluation unit to optimize the evaluation based on the market data related to the draft plan. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input market data related to the draft plan into AI, which can analyze the evaluation.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The urban structure planning system may further include a prediction unit. The prediction unit can predict future demographic trends and infrastructure deterioration based on the data obtained from the collection unit and analysis unit. For example, the prediction unit can predict future population increases and decreases using past demographic data. The prediction unit can also identify areas that require future repairs or updates based on infrastructure deterioration data. Furthermore, the prediction unit can predict areas where future traffic congestion will occur based on the development status of the transportation network and take measures in advance. This allows the urban structure planning system to generate planning proposals to prevent future problems.

[0054] The urban structure planning system may further include a feedback unit. The feedback unit may collect opinions and requests from residents and feed them back to the analysis unit and the generation unit. For example, the feedback unit may conduct a resident survey to collect resident satisfaction and areas for improvement. The feedback unit may also collect resident opinions in real time and provide them to the analysis unit. Furthermore, the feedback unit may assist the generation unit in revising the plan proposal based on resident requests. This allows the urban structure planning system to generate a plan proposal that meets the needs of residents.

[0055] The urban structure planning system may further include a simulation unit. The simulation unit may construct a virtual urban environment based on the generated plan proposal and simulate the effects of the plan proposal. For example, the simulation unit may simulate the effect of mitigating traffic congestion and evaluate the effectiveness of the plan proposal. The simulation unit may also simulate the effect of reducing environmental load and evaluate the environmental impact of the plan proposal. Furthermore, the simulation unit may simulate the life satisfaction of residents and evaluate the social impact of the plan proposal. This allows the urban structure planning system to evaluate the feasibility of the plan proposal in advance.

[0056] The urban structure planning system can further include a collaboration unit. The collaboration unit can share information with other local governments and related organizations and work together to improve urban structure. For example, the collaboration unit can collect success stories from other local governments and incorporate them into its own planning proposals. The collaboration unit can also strengthen collaboration with related organizations and promote joint projects for infrastructure development and environmental improvement. Furthermore, the collaboration unit can collect international urban planning trends and incorporate the latest knowledge into planning proposals. This allows the urban structure planning system to generate optimal planning proposals from a broader perspective.

[0057] The urban structure planning system may further include an education department. The education department may educate residents about the importance of urban structure planning and the contents of the proposed plan to deepen their understanding. For example, the education department may hold workshops for residents to explain the contents of the proposed plan. The education department may also raise awareness of the importance of urban structure planning through lectures at schools and local communities. Furthermore, the education department may utilize an online platform to make information about the proposed plan easily accessible to residents. In this way, the urban structure planning system may gain the understanding and cooperation of residents and promote the realization of the proposed plan.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects data on the characteristics and current problems of the local government. For example, the collection unit can collect data on demographics, age structure, the state of aging infrastructure, and the state of development of the transportation network. For example, the collection unit can survey the demographics of the local government and collect data on the population distribution by age, birth rate, death rate, etc. The collection unit can also survey the state of aging infrastructure and collect data on the deterioration of roads and the earthquake resistance of buildings. Furthermore, the collection unit can survey the state of development of the transportation network and collect data on the state of development of the road network and the operation status of public transportation. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies current problems and areas for improvement. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms to identify current problems and areas for improvement. For example, the analysis unit can analyze the causes of traffic congestion and identify areas for improvement to facilitate traffic. The analysis unit can also analyze the causes of environmental pollution and identify environmental improvement measures. Furthermore, the analysis unit can analyze the aging state of infrastructure and identify areas for improvement to renew the infrastructure. Step 3: The generation unit generates an optimal urban structure plan based on the problems and areas for improvement identified by the analysis unit. For example, the generation unit can generate urban structure plan proposals such as urban zoning plans, transportation plans, and green space plans based on the identified problems and areas for improvement. For example, the generation unit can propose locations for pedestrian-only roads and elevators from the perspective of barrier-free access. The generation unit can also propose measures such as introducing renewable energy and expanding green spaces from the perspective of SDGs. Furthermore, the generation unit can apply different generation algorithms to generate urban structure plan proposals that suit the characteristics of the local government. Step 4: The evaluation unit evaluates the plan proposal generated by the generation unit based on evaluation criteria. For example, the evaluation unit can evaluate the plan proposal based on evaluation criteria such as a resident satisfaction survey or the effect of reducing environmental load. The evaluation unit, for example, conducts a resident satisfaction survey and evaluates the impact of the plan proposal on residents. The evaluation unit can also evaluate the effect of reducing environmental load and evaluate the impact of the plan proposal on the environment. Furthermore, the evaluation unit can optimize the current evaluation criteria by referring to past evaluation data.

[0060] (Example 2) The urban structure planning system according to an embodiment of the present invention aims to solve various problems faced by local governments, such as population decline, aging, and the deterioration of urban structures. It also aims to create cities where diverse people can live comfortably with various lifestyles, based on the perspectives of the Sustainable Development Goals (SDGs) and Diversity, Equity, and Inclusion (DE&I). This urban structure planning system proposes the optimal urban structure for the residents of the city, presents a proposed urban structure that is safe, secure, and barrier-free, and is tailored to the characteristics of the local government, and provides a service that allows for objective evaluation. First, the urban structure planning system performs a detailed analysis of the characteristics and current issues of the local government. For example, it collects data on demographics, age composition, the state of aging infrastructure, and the state of the transportation network. Next, the urban structure planning system uses AI to analyze the collected data and identify current issues and areas for improvement. For example, it may be determined that areas with significant population decline require measures to encourage young people to settle there. Next, the urban structure planning system generates an optimal urban structure proposal based on the analysis results. Based on the collected data and analysis results, the AI ​​proposes an urban structure that suits the characteristics of the local government. For example, from the perspective of barrier-free access, it can propose locations for pedestrian-only roads and elevators. Furthermore, from the perspective of SDGs, it can also propose measures such as the introduction of renewable energy and the expansion of green spaces. Furthermore, the urban structure planning system sets evaluation criteria for objectively evaluating the generated plan proposals. For example, resident satisfaction surveys and the effectiveness of reducing environmental impacts can be set as evaluation criteria, and the AI ​​evaluates the plan proposals based on these criteria. The evaluation results are fed back to the local government and used to revise and improve the plan proposals. In this way, the urban structure planning system generates and objectively evaluates optimal urban structure plan proposals that match the characteristics of the local government, thereby realizing a city where a wide variety of people can live comfortably. This allows the urban structure planning system to generate and objectively evaluate optimal urban structure plan proposals that match the characteristics of the local government.

[0061] An urban structure planning system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects data on the characteristics and current problems of a local government. For example, the collection unit can collect data on demographics, age distribution, the state of aging infrastructure, the state of development of a transportation network, and the like. For example, the collection unit can survey the demographics of a local government and collect data on age-specific population distribution, birth rates, death rates, and the like. The collection unit can also survey the state of aging infrastructure and collect data on the deterioration of roads and the earthquake resistance of buildings. Furthermore, the collection unit can survey the state of development of a transportation network and collect data on the state of development of the road network and the operation status of public transportation, and the like. The analysis unit analyzes the data collected by the collection unit and identifies current problems and areas for improvement. For example, the analysis unit can analyze the collected data using statistical analysis or a machine learning algorithm to identify current problems and areas for improvement. For example, the analysis unit can analyze the causes of traffic congestion and identify areas for improvement to facilitate traffic flow. The analysis unit can also analyze causes of environmental pollution and identify environmental improvement measures. Furthermore, the analysis unit can analyze the aging status of infrastructure and identify improvements for infrastructure renewal. The generation unit generates an optimal urban structure plan based on the problems and improvements identified by the analysis unit. For example, the generation unit can generate urban structure plan proposals, such as urban zoning plans, transportation plans, and green space plans, based on the identified problems and improvements. For example, the generation unit can propose locations for pedestrian-only roads and elevators from a barrier-free perspective. The generation unit can also propose measures such as introducing renewable energy and expanding green spaces from a perspective of the SDGs. Furthermore, the generation unit can apply different generation algorithms to generate urban structure plan proposals that suit the characteristics of the local government. The evaluation unit evaluates the plan proposals generated by the generation unit based on evaluation criteria. For example, the evaluation unit can evaluate the plan proposals based on evaluation criteria such as resident satisfaction surveys and the effect of reducing environmental impact. For example, the evaluation unit can conduct resident satisfaction surveys and evaluate the impact of the plan proposals on residents. The evaluation unit can also evaluate the effect of reducing environmental load and evaluate the environmental impact of the plan.Furthermore, the evaluation unit can also optimize the current evaluation criteria by referring to past evaluation data. This allows the urban structure planning system according to the embodiment to generate and objectively evaluate an optimal urban structure plan that matches the characteristics of the local government.

[0062] The collection unit can collect data on demographics, age structure, infrastructure deterioration, and transportation network development status. The collection unit, for example, investigates demographics and collects data such as age-specific population distribution, birth rate, and death rate. For example, the collection unit can investigate the demographics of a local government and collect data such as age-specific population distribution, birth rate, and death rate. The collection unit can also investigate age structure and collect data such as age-specific population distribution and average age. For example, the collection unit can investigate the age structure of a local government and collect data such as age-specific population distribution and average age. The collection unit can also investigate the aging status of infrastructure and collect data such as road deterioration status and building earthquake resistance. For example, the collection unit can investigate the aging status of a local government's infrastructure and collect data such as road deterioration status and building earthquake resistance. The collection unit can also investigate the transportation network development status and collect data such as road network development status and public transportation operation status. For example, the collection unit can investigate the development status of a local government's transportation network and collect data such as the development status of the road network and the operation status of public transportation. This allows the collection unit to grasp the characteristics and current problems of the local government in detail. Some or all of the above-mentioned 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 local government's demographic data into AI, which then analyzes and collects the data.

[0063] The analysis unit can analyze the collected data and identify current problems and areas for improvement. The analysis unit can analyze the collected data using, for example, statistical analysis or a machine learning algorithm to identify current problems and areas for improvement. For example, the analysis unit can analyze the causes of traffic congestion and identify areas for improvement to facilitate traffic. The analysis unit can also analyze the causes of environmental pollution and identify environmental improvement measures. For example, the analysis unit can analyze the causes of environmental pollution and identify areas for improvement. Furthermore, the analysis unit can analyze the aging status of infrastructure and identify areas for improvement for infrastructure renewal. For example, the analysis unit can analyze the aging status of infrastructure and identify areas for improvement for infrastructure renewal. This allows the analysis unit to clarify current problems and areas for improvement. 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 the collected data into AI, which analyzes the data and identifies current problems and areas for improvement.

[0064] The generation unit can generate a proposed urban structure plan based on the identified problems and improvements. The generation unit can generate, for example, a proposed urban structure plan, such as a city zoning plan, a transportation plan, or a green space plan, based on the identified problems and improvements. For example, the generation unit can generate a proposed urban structure plan, such as a city zoning plan, a transportation plan, or a green space plan, based on the identified problems and improvements. The generation unit can also propose locations for pedestrian-only roads and elevators from a barrier-free perspective. For example, the generation unit can propose locations for pedestrian-only roads and elevators from a barrier-free perspective. Furthermore, the generation unit can propose measures, such as the introduction of renewable energy and the expansion of green spaces, from a perspective of the SDGs. For example, the generation unit can propose measures, such as the introduction of renewable energy and the expansion of green spaces, from a perspective of the SDGs. This allows the generation unit to generate a proposed urban structure plan that suits the characteristics of the local government. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input identified problems and areas for improvement into the AI, which can then generate a proposed plan for the urban structure.

[0065] The evaluation unit can evaluate the proposed plan based on evaluation criteria of a resident satisfaction survey or an effect of reducing environmental load. The evaluation unit, for example, conducts a resident satisfaction survey and evaluates the impact of the proposed plan on residents. For example, the evaluation unit can conduct a resident satisfaction survey and evaluate the impact of the proposed plan on residents. The evaluation unit can also evaluate the effect of reducing environmental load and evaluate the environmental impact of the proposed plan. For example, the evaluation unit can evaluate the effect of reducing environmental load and evaluate the environmental impact of the proposed plan. Furthermore, the evaluation unit can optimize the current evaluation criteria by referring to past evaluation data. For example, the evaluation unit can optimize the current evaluation criteria by referring to past evaluation data. This enables the evaluation unit to objectively evaluate the proposed plan. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input resident satisfaction survey data into AI, and the AI ​​can analyze the data to evaluate the proposed plan.

[0066] The generation unit can propose locations for pedestrian roads or elevators from a barrier-free perspective. The generation unit, for example, proposes locations for pedestrian roads or elevators from a barrier-free perspective. For example, the generation unit can propose locations for pedestrian roads or elevators from a barrier-free perspective. The generation unit, for example, proposes locations for pedestrian roads based on pedestrian road design standards. The generation unit can also propose locations for elevators based on elevator installation standards. For example, the generation unit can propose locations for elevators based on elevator installation standards. This allows the generation unit to realize a barrier-free urban structure. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input locations for pedestrian roads or elevators from a barrier-free perspective into AI, and the AI ​​can suggest installation locations.

[0067] The generation unit can propose measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs. For example, the generation unit can propose measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs. For example, the generation unit can propose measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs. For example, the generation unit can propose the introduction of renewable energy based on a renewable energy introduction target. The generation unit can also propose the expansion of green spaces based on a green space expansion target. For example, the generation unit can propose the expansion of green spaces based on a green space expansion target. This allows the generation unit to realize a sustainable urban structure. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input measures for introducing renewable energy or expanding green spaces from the perspective of the SDGs into AI, and the AI ​​can propose the measures.

[0068] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can collect detailed data to acquire more information. For example, when the user is relaxed, the collection unit can collect detailed data to acquire more information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important data and processing it quickly. For example, when the user is in a hurry, the collection unit can prioritize collecting only important data and processing it quickly. This allows the collection unit to optimize the timing of data collection according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit can input the user's emotion data into AI, which can adjust the timing of data collection.

[0069] The collection unit can analyze past data collection history and select a collection method. For example, the collection unit can identify the most efficient collection method from the past data collection history and apply it to future data collection. For example, the collection unit can identify the most efficient collection method from the past data collection history and apply it to future data collection. The collection unit can also analyze the past data collection history and optimize the collection frequency and timing. For example, the collection unit can analyze the past data collection history and optimize the collection frequency and timing. Furthermore, the collection unit can prioritize collection from a specific data source based on the past data collection history. For example, the collection unit can prioritize collection from a specific data source based on the past data collection history. This allows the collection unit to select the optimal collection method based on the past data collection history. Some or all of the above-mentioned 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 past data collection history into AI, which can select the optimal collection method.

[0070] The collection unit can perform filtering based on the characteristics of the local government and current problems when collecting data. The collection unit, for example, collects only specific data based on the characteristics of the local government. For example, the collection unit can collect only specific data based on the characteristics of the local government. The collection unit can also prioritize collecting related data based on current problems. For example, the collection unit can prioritize collecting related data based on current problems. Furthermore, the collection unit can filter unnecessary data taking into account the characteristics of the local government and the current problems. For example, the collection unit can filter unnecessary data taking into account the characteristics of the local government and the current problems. This allows the collection unit to efficiently collect necessary data based on the characteristics of the local government and the current problems. Some or all of the above-mentioned 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 characteristics of the local government and current problems into AI, and the AI ​​can perform filtering when collecting data.

[0071] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize data that can be collected quickly. For example, when the user is in a hurry, the collection unit can prioritize data that can be collected quickly. This allows the collection unit to optimize the priority of data to be collected according to the user's emotions. Some or all of the above-mentioned 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 user's emotional data into AI, and the AI ​​can determine the priority of data to be collected.

[0072] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the local government. The collection unit, for example, prioritizes collecting relevant data based on the geographical location information of the local government. For example, the collection unit can prioritize collecting relevant data based on the geographical location information of the local government. The collection unit can also prioritize data from a specific region by taking into account the geographical location information. For example, the collection unit can prioritize data from a specific region by taking into account the geographical location information. Furthermore, the collection unit can limit the range of data to be collected based on the geographical location information. For example, the collection unit can limit the range of data to be collected based on the geographical location information. This allows the collection unit to efficiently collect highly relevant data by taking into account the geographical location information of the local government. 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 geographical location information of the local government into AI, which can then prioritize collecting highly relevant data.

[0073] The collection unit may analyze social media activities and collect related data during data collection. The collection unit may, for example, analyze social media posts and collect related data. For example, the collection unit may analyze social media posts and collect related data. The collection unit may also select data to collect based on social media trends. For example, the collection unit may select data to collect based on social media trends. Furthermore, the collection unit may monitor social media activities in real time and collect related data. For example, the collection unit may monitor social media activities in real time and collect related data. This allows the collection unit to analyze social media activities and efficiently collect related data. 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 social media post data into AI, which may collect related data.

[0074] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can provide a simple and highly visible analysis result. For example, when the user is nervous, the analysis unit can provide a simple and highly visible analysis result. Furthermore, when the user is relaxed, the analysis unit can provide a detailed analysis result. For example, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. For example, when the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. This allows the analysis unit to optimize the method of expressing the analysis according to the user's emotions. 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 the user's emotion data into AI, which can adjust the method of expressing the analysis.

[0075] 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 can perform a detailed analysis on important data and a simplified analysis on less important data. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data. For example, the analysis unit can apply multiple analysis methods to important data. This allows the analysis unit to optimize the level of detail of the analysis depending on the importance of the data. 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 the importance of the data to AI, and the AI ​​can adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can select an optimal analysis algorithm depending on the category of data. The analysis unit can also apply different analysis methods to different categories of data. For example, the analysis unit can apply different analysis methods to different categories of data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the category of data. For example, the analysis unit can adjust the level of detail of the analysis based on the category of data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data. 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 the category of data into AI, which then applies the optimal analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the analysis unit to optimize the length of the analysis according to the user's emotions. 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 the user's emotion data into AI, which can adjust the length of the analysis.

[0078] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the latest data and postpone analyzing older data. For example, the analysis unit can prioritize analyzing the latest data and postpone analyzing older data. The analysis unit can also adjust the level of detail of the analysis based on the time when the data was collected. For example, the analysis unit can adjust the level of detail of the analysis based on the time when the data was collected. Furthermore, the analysis unit can determine the order of analysis taking into account the time when the data was collected. For example, the analysis unit can determine the order of analysis taking into account the time when the data was collected. This allows the analysis unit to optimize the priority of the analysis based on the time when the data was collected. 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 the time when the data was collected into AI, and the AI ​​can determine the priority of the analysis.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data and postpone analysis of less relevant data. For example, the analysis unit can prioritize analysis of highly relevant data and postpone analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis based on the relevance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the relevance of the data. Furthermore, the analysis unit can determine the order of analysis taking into account the relevance of the data. For example, the analysis unit can determine the order of analysis taking into account the relevance of the data. This allows the analysis unit to optimize the order of analysis based on the relevance of the data. 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 the relevance of the data into AI, and the AI ​​can adjust the order of analysis.

[0080] The generation unit can estimate the user's emotions and adjust the method for generating the plan proposal based on the estimated user's emotions. For example, when the user is relaxed, the generation unit can generate a plan proposal that proceeds at a leisurely pace. For example, when the user is relaxed, the generation unit can generate a plan proposal that proceeds at a leisurely pace. Furthermore, when the user is in a hurry, the generation unit can generate a plan proposal that emphasizes the shortest route. For example, when the user is in a hurry, the generation unit can generate a plan proposal that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate a plan proposal that adds visually stimulating effects. For example, when the user is excited, the generation unit can generate a plan proposal that adds visually stimulating effects. This allows the generation unit to optimize the method for generating the plan proposal according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user emotion data into AI, and the AI ​​can adjust the method for generating the plan proposal.

[0081] When generating a plan, the generation unit can adjust the level of detail of the generated plan based on the importance of the identified problems and improvements. For example, the generation unit can generate a detailed plan for an important problem and a simplified plan for a less important problem. For example, the generation unit can generate a detailed plan for an important problem and a simplified plan for a less important problem. The generation unit can also determine the priority of the plan based on the importance of the problems and improvements. For example, the generation unit can determine the priority of the plan based on the importance of the problems and improvements. Furthermore, the generation unit can propose multiple solutions for an important problem. For example, the generation unit can propose multiple solutions for an important problem. This allows the generation unit to optimize the level of detail of the plan based on the importance of the identified problems and improvements. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the importance of the problems and improvements to AI, which can adjust the level of detail of the generated plan.

[0082] When generating a draft plan, the generation unit can apply different generation algorithms depending on the characteristics of the local government. The generation unit, for example, selects an optimal generation algorithm based on the characteristics of the local government. For example, the generation unit can select an optimal generation algorithm based on the characteristics of the local government. The generation unit can also apply different generation methods to different local governments. For example, the generation unit can apply different generation methods to different local governments. Furthermore, the generation unit can adjust the level of detail of the generation algorithm taking into account the characteristics of the local government. For example, the generation unit can adjust the level of detail of the generation algorithm taking into account the characteristics of the local government. This allows the generation unit to apply the optimal generation algorithm depending on the characteristics of the local government. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the characteristics of the local government into AI, and the AI ​​can apply the optimal generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the plan based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point plan. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point plan. Furthermore, if the user is relaxed, the generation unit can generate a longer plan with detailed explanations. For example, if the user is relaxed, the generation unit can generate a longer plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a plan with visually stimulating effects. For example, if the user is excited, the generation unit can generate a plan with visually stimulating effects. This allows the generation unit to optimize the length of the plan according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's emotion data into AI, which can adjust the length of the plan.

[0084] When generating a proposed plan, the generation unit can determine the priority of generation based on the submission dates of identified problems and improvements. For example, the generation unit can generate a proposed plan that prioritizes solving problems whose submission dates are approaching. For example, the generation unit can generate a proposed plan that prioritizes solving problems whose submission dates are approaching. The generation unit can also adjust the level of detail of the proposed plan based on the submission dates. For example, the generation unit can adjust the level of detail of the proposed plan based on the submission dates. Furthermore, the generation unit can determine the order of the proposed plans taking into account the submission dates. For example, the generation unit can determine the order of the proposed plans taking into account the submission dates. This allows the generation unit to optimize the priority of the proposed plans according to the submission dates of identified problems and improvements. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the submission dates of problems and improvements into AI, and the AI ​​can determine the priority of generation.

[0085] When generating a proposed plan, the generation unit can adjust the order of generation based on the relevance of identified problems and improvements. For example, the generation unit can generate a proposed plan that prioritizes solving highly relevant problems. For example, the generation unit can generate a proposed plan that prioritizes solving highly relevant problems. The generation unit can also adjust the level of detail of the proposed plan based on the relevance of the problems and improvements. For example, the generation unit can adjust the level of detail of the proposed plan based on the relevance of the problems and improvements. Furthermore, the generation unit can determine the order of the proposed plans taking into account the relevance of the problems and improvements. For example, the generation unit can determine the order of the proposed plans taking into account the relevance of the problems and improvements. In this way, the generation unit can optimize the order of the proposed plans according to the relevance of the identified problems and improvements. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the relevance of the problems and improvements to AI, and the AI ​​can adjust the order of generation.

[0086] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible display method. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the evaluation unit can provide a display method including detailed information. For example, if the user is relaxed, the evaluation unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows the evaluation unit to optimize the display method of the evaluation according to the user's emotions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's emotion data into AI, which can adjust the display method of the evaluation.

[0087] The evaluation unit can optimize the current evaluation criteria by referring to past evaluation data during evaluation. The evaluation unit, for example, optimizes the current evaluation criteria based on past evaluation data. For example, the evaluation unit can optimize the current evaluation criteria based on past evaluation data. The evaluation unit can also analyze the past evaluation data and identify areas for improvement in the evaluation criteria. For example, the evaluation unit can analyze the past evaluation data and identify areas for improvement in the evaluation criteria. Furthermore, the evaluation unit can adjust the level of detail of the evaluation criteria by referring to the past evaluation data. For example, the evaluation unit can adjust the level of detail of the evaluation criteria by referring to the past evaluation data. This allows the evaluation unit to optimize the current evaluation criteria based on the past evaluation data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation data into AI, which then optimizes the current evaluation criteria.

[0088] The evaluation unit can apply different evaluation methods depending on the characteristics of the local government during evaluation. The evaluation unit, for example, selects the optimal evaluation method based on the characteristics of the local government. For example, the evaluation unit can select the optimal evaluation method based on the characteristics of the local government. The evaluation unit can also apply different evaluation methods to different local governments. For example, the evaluation unit can apply different evaluation methods to different local governments. Furthermore, the evaluation unit can adjust the level of detail of the evaluation method taking into account the characteristics of the local government. For example, the evaluation unit can adjust the level of detail of the evaluation method taking into account the characteristics of the local government. This allows the evaluation unit to apply the optimal evaluation method depending on the characteristics of the local government. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the characteristics of the local government into AI, which can then apply the optimal evaluation method.

[0089] The evaluation unit can estimate the user's emotions and adjust the importance of the evaluations based on the estimated user's emotions. For example, when the user is nervous, the evaluation unit can prioritize displaying only important evaluation items. For example, when the user is nervous, the evaluation unit can prioritize displaying only important evaluation items. Furthermore, when the user is relaxed, the evaluation unit can display detailed evaluation items. For example, when the user is relaxed, the evaluation unit can display detailed evaluation items. Furthermore, when the user is in a hurry, the evaluation unit can display evaluation items that emphasize the main points. For example, when the user is in a hurry, the evaluation unit can display evaluation items that emphasize the main points. This allows the evaluation unit to optimize the importance of the evaluations according to the user's emotions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's emotion data into AI, and the AI ​​can adjust the importance of the evaluations.

[0090] During evaluation, the evaluation unit can analyze changes in evaluation based on the submission time of the proposed plan. The evaluation unit, for example, analyzes changes in evaluation and adjusts the evaluation criteria based on the submission time of the proposed plan. For example, the evaluation unit can analyze changes in evaluation and adjust the evaluation criteria based on the submission time of the proposed plan. The evaluation unit can also prioritize evaluation of proposed plans that are close to being submitted and analyze changes in evaluation. For example, the evaluation unit can prioritize evaluation of proposed plans that are close to being submitted and analyze changes in evaluation. Furthermore, the evaluation unit can adjust the level of detail of the evaluation based on the submission time. For example, the evaluation unit can adjust the level of detail of the evaluation based on the submission time. This allows the evaluation unit to optimize changes in evaluation based on the submission time of the proposed plan. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submission time of the proposed plan into AI, and the AI ​​can analyze changes in evaluation.

[0091] During the evaluation, the evaluation unit can analyze the evaluation by referring to market data related to the draft plan. For example, the evaluation unit can improve the accuracy of the evaluation by referring to market data related to the draft plan. For example, the evaluation unit can improve the accuracy of the evaluation by referring to market data related to the draft plan. The evaluation unit can also optimize the evaluation criteria based on the relevant market data. For example, the evaluation unit can optimize the evaluation criteria based on the relevant market data. Furthermore, the evaluation unit can adjust the level of detail of the evaluation by referring to the relevant market data. For example, the evaluation unit can adjust the level of detail of the evaluation by referring to the relevant market data. This allows the evaluation unit to optimize the evaluation based on the market data related to the draft plan. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input market data related to the draft plan into AI, which can analyze the evaluation. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and evaluation unit, described above, 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 data on the characteristics and current problems of the local government using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies current problems and areas for improvement. The generation unit generates a plan for an optimal urban structure based on the analysis results by the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the plan generated by the specific processing unit 290 of the data processing device 12 based on evaluation criteria. The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and evaluation 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 data on the characteristics and current problems of the local government using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies current problems and areas for improvement. The generation unit generates a plan for an optimal urban structure based on the analysis results by the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the plan generated by the specific processing unit 290 of the data processing device 12 based on evaluation criteria. The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user's emotions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and evaluation unit described above 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 data on the characteristics and current problems of the local government using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies current problems and areas for improvement. The generation unit generates a plan for an optimal urban structure based on the analysis results by the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the plan generated by the specific processing unit 290 of the data processing device 12 based on evaluation criteria. The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the characteristics and current problems of the local government using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and identifies current problems and areas for improvement. The generation unit generates a plan for an optimal urban structure based on the analysis results by the specific processing unit 290 of the data processing device 12. The evaluation unit evaluates the plan generated by the specific processing unit 290 of the data processing device 12 based on evaluation criteria. The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The urban structure planning system may further include a prediction unit. The prediction unit can predict future demographic trends and infrastructure deterioration based on the data obtained from the collection unit and analysis unit. For example, the prediction unit can predict future population increases and decreases using past demographic data. The prediction unit can also identify areas that require future repairs or updates based on infrastructure deterioration data. Furthermore, the prediction unit can predict areas where future traffic congestion will occur based on the development status of the transportation network and take measures in advance. This allows the urban structure planning system to generate planning proposals to prevent future problems.

[0094] The urban structure planning system may further include a feedback unit. The feedback unit may collect opinions and requests from residents and feed them back to the analysis unit and the generation unit. For example, the feedback unit may conduct a resident survey to collect resident satisfaction and areas for improvement. The feedback unit may also collect resident opinions in real time and provide them to the analysis unit. Furthermore, the feedback unit may assist the generation unit in revising the plan proposal based on resident requests. This allows the urban structure planning system to generate a plan proposal that meets the needs of residents.

[0095] The urban structure planning system may further include a simulation unit. The simulation unit may construct a virtual urban environment based on the generated plan proposal and simulate the effects of the plan proposal. For example, the simulation unit may simulate the effect of mitigating traffic congestion and evaluate the effectiveness of the plan proposal. The simulation unit may also simulate the effect of reducing environmental load and evaluate the environmental impact of the plan proposal. Furthermore, the simulation unit may simulate the life satisfaction of residents and evaluate the social impact of the plan proposal. This allows the urban structure planning system to evaluate the feasibility of the plan proposal in advance.

[0096] The urban structure planning system can further include a collaboration unit. The collaboration unit can share information with other local governments and related organizations and work together to improve urban structure. For example, the collaboration unit can collect success stories from other local governments and incorporate them into its own planning proposals. The collaboration unit can also strengthen collaboration with related organizations and promote joint projects for infrastructure development and environmental improvement. Furthermore, the collaboration unit can collect international urban planning trends and incorporate the latest knowledge into planning proposals. This allows the urban structure planning system to generate optimal planning proposals from a broader perspective.

[0097] The urban structure planning system may further include an education department. The education department may educate residents about the importance of urban structure planning and the contents of the proposed plan to deepen their understanding. For example, the education department may hold workshops for residents to explain the contents of the proposed plan. The education department may also raise awareness of the importance of urban structure planning through lectures at schools and local communities. Furthermore, the education department may utilize an online platform to make information about the proposed plan easily accessible to residents. In this way, the urban structure planning system may gain the understanding and cooperation of residents and promote the realization of the proposed plan.

[0098] The urban structure planning system can estimate the user's emotions in the analysis unit and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows the analysis unit to optimize the presentation method of the analysis results according to the user's emotions.

[0099] The urban structure planning system can estimate the user's emotions in the generation unit and adjust the method for generating a plan proposal based on the estimated user's emotions. For example, if the user is relaxed, a detailed plan proposal can be generated. If the user is in a hurry, a concise plan proposal that focuses on the main points can be generated. Furthermore, if the user is excited, a plan proposal with visually appealing effects can be generated. This allows the generation unit to optimize the method for generating a plan proposal according to the user's emotions.

[0100] The urban structure planning system can estimate the user's emotions in the evaluation unit and adjust the display method of the evaluation results based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible display method. If the user is relaxed, the evaluation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows the evaluation unit to optimize the display method of the evaluation results according to the user's emotions.

[0101] The urban structure planning system can estimate the user's emotions in the collection unit and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the user's burden. Also, if the user is relaxed, detailed data can be collected to obtain more information. Furthermore, if the user is in a hurry, only important data can be collected with priority and processed quickly. This allows the collection unit to optimize the timing of data collection according to the user's emotions.

[0102] The urban structure planning system can estimate the user's emotions in the evaluation unit and adjust the importance of the evaluation based on the estimated user's emotions. For example, if the user is nervous, it can prioritize and display only important evaluation items. Also, if the user is relaxed, it can display detailed evaluation items. Furthermore, if the user is in a hurry, it can display evaluation items that focus on the main points. This allows the evaluation unit to optimize the importance of the evaluation according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects data on the characteristics and current problems of the local government. For example, the collection unit can collect data on demographics, age structure, the state of aging infrastructure, and the state of development of the transportation network. For example, the collection unit can survey the demographics of the local government and collect data on the population distribution by age, birth rate, death rate, etc. The collection unit can also survey the state of aging infrastructure and collect data on the deterioration of roads and the earthquake resistance of buildings. Furthermore, the collection unit can survey the state of development of the transportation network and collect data on the state of development of the road network and the operation status of public transportation. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies current problems and areas for improvement. For example, the analysis unit can analyze the collected data using statistical analysis or machine learning algorithms to identify current problems and areas for improvement. For example, the analysis unit can analyze the causes of traffic congestion and identify areas for improvement to facilitate traffic. The analysis unit can also analyze the causes of environmental pollution and identify environmental improvement measures. Furthermore, the analysis unit can analyze the aging state of infrastructure and identify areas for improvement to renew the infrastructure. Step 3: The generation unit generates an optimal urban structure plan based on the problems and areas for improvement identified by the analysis unit. For example, the generation unit can generate urban structure plan proposals such as urban zoning plans, transportation plans, and green space plans based on the identified problems and areas for improvement. For example, the generation unit can propose locations for pedestrian-only roads and elevators from the perspective of barrier-free access. The generation unit can also propose measures such as introducing renewable energy and expanding green spaces from the perspective of SDGs. Furthermore, the generation unit can apply different generation algorithms to generate urban structure plan proposals that suit the characteristics of the local government. Step 4: The evaluation unit evaluates the plan proposal generated by the generation unit based on evaluation criteria. For example, the evaluation unit can evaluate the plan proposal based on evaluation criteria such as a resident satisfaction survey or the effect of reducing environmental load. The evaluation unit, for example, conducts a resident satisfaction survey and evaluates the impact of the plan proposal on residents. The evaluation unit can also evaluate the effect of reducing environmental load and evaluate the impact of the plan proposal on the environment. Furthermore, the evaluation unit can optimize the current evaluation criteria by referring to past evaluation data.

[0105] 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.

[0106] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 department that collects data on the characteristics and current problems of local governments, and an analysis unit that analyzes the data collected by the collection unit and identifies current problems and areas for improvement; a generation unit that generates a plan for urban structure based on the problems and improvements identified by the analysis unit; an evaluation unit that evaluates the plan generated by the generation unit based on evaluation criteria; Equipped with A system characterized by:

2. The collecting unit Collect data on demographics, age distribution, aging infrastructure, and transportation network development. The system of claim 1 .

3. The analysis unit Analyze the collected data and identify current issues and areas for improvement The system of claim 1 .

4. The generation unit Generate a proposed urban structure plan based on identified problems and improvements The system of claim 1 .

5. The evaluation unit Evaluate the plan based on resident satisfaction surveys or criteria for reducing environmental impacts The system of claim 1 .

6. The generation unit Propose locations for pedestrian walkways or elevators from a barrier-free perspective. The system of claim 1 .

7. The generation unit Propose measures to introduce renewable energy or expand green spaces from the perspective of SDGs The system of claim 1 .

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

Images