system

Abstract

An object of a system according to an embodiment is to efficiently formulate, evaluate, and review a location optimization plan for realizing sustainable development of a local government.SOLUTION: A system includes a GIS data collection part, a population dynamics analysis part, a location optimization plan generation part, and an evaluation review part. The GIS data collection unit collects GIS data. The demographic analysis unit analyzes the GIS data collected by the GIS data collection unit. A location optimization plan generation part generates a custom-made location optimization plan on the basis of the population dynamics and human flow information analyzed by the population dynamics analysis part. The evaluation review unit performs continuous evaluation of the location optimization plan and review of the plan as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, local governments face challenges when formulating location optimization plans to achieve sustainable development due to a lack of specialized knowledge and resource constraints.

[0005] The system according to the embodiment aims to enable local governments to efficiently formulate, evaluate, and review location optimization plans to achieve sustainable development. [Means for solving the problem]

[0006] The system according to the embodiment includes a GIS data collection unit, a demographic analysis unit, a location optimization plan generation unit, and an evaluation and review unit. The GIS data collection unit collects GIS data. The demographic analysis unit analyzes the GIS data collected by the GIS data collection unit. The location optimization plan generation unit generates a custom location optimization plan based on the demographic and people flow information analyzed by the demographic analysis unit. The evaluation and review unit continuously evaluates the location optimization plan and reviews the plan as necessary. [Effects of the Invention]

[0007] The system according to the embodiment enables local governments to efficiently formulate, evaluate, and review location optimization plans to achieve sustainable development. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The location optimization plan generation system according to an embodiment of the present invention is a system for preventing the decline of local communities due to the population decline and aging that local governments face and for realizing sustainable development. This system generates a custom-made location optimization plan that takes into account the characteristics of the local government based on GIS data, demographics, and people flow information, and continuously evaluates and revises the plan as necessary. As a result, the location optimization plan generation system can prevent the decline of local communities due to the population decline and aging that local governments face and contribute to the creation of sustainable local communities.

[0029] The location optimization plan generation system according to the embodiment includes a GIS data collection unit, a demographic analysis unit, a location optimization plan generation unit, and an evaluation and review unit. The GIS data collection unit collects GIS data, such as topographical data, land use data, and transportation data. The GIS data collection unit also collects and analyzes local government geographic information system (GIS) data, such as information on land use, transportation networks, and the natural environment. The demographic analysis unit analyzes the GIS data collected by the GIS data collection unit, such as age distribution, gender distribution, and travel patterns. The demographic analysis unit also collects and analyzes local government demographic data and people flow information, such as understanding the local population structure and travel patterns and predicting future population changes. The location optimization plan generation unit generates a customized location optimization plan based on the demographic and people flow information analyzed by the demographic analysis unit. For example, the system proposes optimal placement of residential areas, commercial areas, and public facilities based on the characteristics of the area. The location optimization plan generation unit generates a custom-made location optimization plan that takes into account the characteristics of the local government based on the collected and analyzed GIS data, demographic trends, and people flow information. The evaluation and review unit continuously evaluates the location optimization plan and reviews the plan as needed. For example, it regularly collects and analyzes local data to evaluate the progress and effectiveness of the plan. The evaluation and review unit also supports the continuous evaluation required by law and the review of the plan as needed. As a result, the location optimization plan generation system according to the embodiment can prevent the decline of local communities due to the population decline and aging that local governments face, and contribute to building sustainable local communities.

[0030] The GIS data collection unit uses drones to collect geographic information in real time, allowing the latest GIS data to be maintained at all times. For example, the generation AI controls the drones to collect local government geographic information in real time. For example, the drones are flown periodically to grasp the latest land use status and changes in the transportation network. The generation AI also analyzes high-resolution image data acquired from the drones and updates the GIS database. For example, information on newly constructed buildings and roads is instantly reflected. The GIS data collection unit also automates the collection of geographic information by drones, building a system in which the generation AI periodically updates the data. For example, seasonal changes and post-disaster conditions can be quickly grasped. This allows the latest geographic information to be maintained at all times, enabling more accurate location optimization plans to be generated.

[0031] The GIS data collection unit can perform 3D modeling on GIS data and propose three-dimensional layout plans. For example, the GIS data collection unit performs 3D modeling based on GIS data collected by the generation AI to create three-dimensional layout plans. For example, it proposes urban plans that take into account the height and shape of buildings. The GIS data collection unit also uses 3D modeling to simulate the local landscape and sunlight conditions and propose optimal layouts. For example, it could place a park in a location that is less likely to be shaded. The generation AI also uses 3D modeling in the GIS data collection unit to evaluate disaster risks. For example, it could simulate the effects of floods and earthquakes and propose safe layout plans. This allows for the proposal of three-dimensional layout plans, making more realistic and practical urban planning possible.

[0032] The GIS data collection unit can use satellite data to collect and analyze wide-area geographic information. For example, the generation AI uses satellite data to collect wide-area geographic information. For example, it can grasp the land use situation and changes in the natural environment throughout the city. The GIS data collection unit also analyzes satellite data to identify regional characteristics and issues. For example, it can identify areas where green space is decreasing or where traffic congestion occurs. The GIS data collection unit also periodically collects satellite data, and the generation AI updates the GIS database. For example, it can quickly reflect seasonal changes and post-disaster conditions. This allows for the collection and analysis of wide-area geographic information to generate more comprehensive location optimization plans.

[0033] The GIS data collection unit combines historical data with geographic information to propose layout plans that take into account past changes in land use. In the GIS data collection unit, for example, the generation AI collects historical geographic data and combines it with current GIS data. For example, it proposes a layout plan that takes into account past changes in land use. The GIS data collection unit also analyzes historical data to understand the development and changes in the region. For example, it proposes a layout plan that takes into account past changes in commercial and residential areas. The GIS data collection unit also uses historical data to generate a layout plan that reflects the region's culture and history. For example, it proposes a layout plan that protects historical buildings and ruins. In this way, by proposing a layout plan that takes into account past changes in land use, urban planning that respects the region's history and culture becomes possible.

[0034] The demographic analysis unit can analyze detailed pedestrian flow patterns using smartphone location information. For example, the demographic analysis unit uses a generation AI to collect smartphone location information and analyze detailed pedestrian flow patterns. For example, it can understand commuter and school routes and tourist trends. The demographic analysis unit also visualizes local pedestrian flow patterns based on smartphone location information. For example, it can analyze the flow of people during specific time periods and propose layout plans to avoid congestion. The demographic analysis unit also analyzes location data to understand changes in local pedestrian flow patterns. For example, it can propose layout plans that take into account changes in pedestrian flow during events. This allows for more accurate location optimization plans by analyzing detailed pedestrian flow patterns.

[0035] The demographic analysis unit analyzes social media data to understand trends in local demographics and human flow. For example, the demographic analysis unit uses generative AI to collect social media data and analyze local demographics and human flow trends. For example, it analyzes posts about specific events or locations to identify popular areas. The demographic analysis unit also understands changes in local demographics based on social media data. For example, it analyzes the outflow of young people and the increase in elderly people and proposes appropriate deployment plans. The demographic analysis unit also analyzes social media trend data to predict local human flow patterns. For example, it proposes deployment plans that take into account seasonal tourist trends. In this way, by analyzing social media data, it is possible to understand local demographics and human flow trends and make more appropriate deployment plans.

[0036] The demographic analysis unit can analyze traffic data and propose layout plans that take traffic congestion and public transportation usage into account. For example, the generation AI in the demographic analysis unit collects traffic data and identifies locations of traffic congestion. For example, it proposes road layouts and public transportation improvement measures to avoid congestion. The demographic analysis unit also analyzes public transportation usage and proposes efficient layout plans. For example, it optimizes the placement of bus stops and stations to improve convenience for residents. The demographic analysis unit also analyzes local traffic patterns based on traffic data and generates optimal layout plans. For example, it proposes layout plans that take commuter and school routes into account. By analyzing traffic data, it is possible to make layout plans that take traffic congestion and public transportation usage into account.

[0037] The demographic analysis unit can analyze tourism data and propose layout plans that take tourist trends into account. For example, the generation AI collects tourism data and analyzes tourist trends. For example, it identifies popular tourist spots and the flow of tourists by season. The demographic analysis unit also proposes layout plans that take tourist trends into account based on the tourism data. For example, it places tourist facilities and accommodations in places that are easy for tourists to visit. The demographic analysis unit also analyzes tourism data and generates layout plans that meet the needs of tourists. For example, it appropriately places services and facilities that tourists desire. In this way, by analyzing tourism data, it becomes possible to create layout plans that take tourist trends into account.

[0038] The location optimization plan generation unit can perform simulations to generate multiple placement scenarios and select the optimal scenario. In the location optimization plan generation unit, for example, a generation AI simulates multiple placement scenarios and selects the optimal scenario. For example, it proposes a placement plan that takes into account resident convenience and environmental impact. The location optimization plan generation unit also compares and evaluates multiple placement scenarios based on the simulation results. For example, it selects the optimal placement plan that takes into account cost and feasibility. In addition, the location optimization plan generation unit performs simulations using the generation AI to generate placement scenarios that predict future changes. For example, it proposes a placement plan that takes into account population increases and decreases and climate change. In this way, by simulating multiple placement scenarios and selecting the optimal scenario, more effective location optimization plans are possible.

[0039] The location optimization plan generation unit can analyze environmental data and propose a layout plan that minimizes the environmental impact. In the location optimization plan generation unit, for example, the generation AI collects environmental data and proposes a layout plan that minimizes the environmental impact. For example, it generates a layout plan that takes into account the protection of green spaces and improving energy efficiency. The location optimization plan generation unit also analyzes environmental data and proposes a layout plan that takes into account the environmental characteristics of the region. For example, it generates a layout plan to reduce the risk of natural disasters. In addition, the location optimization plan generation unit uses the generation AI to propose a sustainable layout plan based on the environmental data. For example, it generates a layout plan that takes into account the use of renewable energy and waste management. In this way, by analyzing environmental data, it becomes possible to create a layout plan that minimizes the environmental impact.

[0040] The location optimization plan generation unit can compare data from different municipalities and propose placement plans that incorporate best practices. For example, the generation AI in the location optimization plan generation unit collects data from different municipalities and proposes placement plans that incorporate best practices. For example, it generates an optimal placement plan based on success stories. The location optimization plan generation unit also compares data from different municipalities and identifies common issues and success factors. For example, it proposes a placement plan based on data from municipalities with similar regional characteristics. The location optimization plan generation unit also analyzes data from different municipalities and generates an optimal placement plan. For example, it makes proposals based on placement plans that have been successful in other municipalities. This makes it possible to compare data from different municipalities and incorporate best practices, enabling more effective placement plans.

[0041] The location optimization plan generation unit can propose a layout plan that takes into account the culture and history of the region. For example, the location optimization plan generation unit uses a generation AI to collect cultural and historical data about the region and reflect it in the layout plan. For example, it proposes a layout plan that protects historical buildings and cultural assets. The location optimization plan generation unit also generates a layout plan that takes into account the culture and history of the region and respects the identity of the residents. For example, it proposes a layout plan that takes into account traditional events and festivals in the region. The location optimization plan generation unit also uses a generation AI to analyze the culture and history of the region and propose a sustainable layout plan. For example, it generates a layout plan that supports traditional crafts and industries in the region. In this way, by proposing a layout plan that takes into account the culture and history of the region, urban planning that respects the identity of the region becomes possible.

[0042] The evaluation and review unit can utilize sensor data to monitor and evaluate local conditions in real time. For example, the generation AI in the evaluation and review unit collects sensor data and monitors local conditions in real time. For example, evaluations are made based on traffic volume and environmental data. The evaluation and review unit also analyzes the sensor data and builds a system to evaluate local conditions. For example, it monitors air quality and noise levels and proposes improvement measures. The generation AI in the evaluation and review unit also evaluates local conditions in real time based on the sensor data. For example, it can quickly grasp the situation during a disaster and propose countermeasures. This makes it possible to utilize sensor data to monitor local conditions in real time and make rapid evaluations.

[0043] The evaluation review unit can collect resident feedback and reflect it in the evaluation. For example, the evaluation review unit builds a system in which the generation AI collects resident feedback and reflects it in the evaluation. For example, it uses online surveys and comment functions. The evaluation review unit also analyzes resident feedback and reflects it in the evaluation of the plan. For example, it identifies areas for improvement in the layout plan based on resident opinions. The evaluation review unit also evaluates the plan based on resident feedback by the generation AI. For example, it analyzes resident satisfaction and dissatisfaction and proposes revisions to the plan. In this way, by collecting resident feedback and reflecting it in the evaluation, it becomes possible to revise the plan to reflect resident opinions.

[0044] The evaluation and review unit can combine different evaluation criteria to perform a comprehensive evaluation. For example, the generation AI collects different evaluation criteria and builds a system to perform a comprehensive evaluation. For example, the evaluation combines environmental data and resident feedback. The evaluation and review unit also analyzes different evaluation criteria to perform a comprehensive evaluation. For example, it performs an evaluation that takes into account economic and social impacts. The generation AI also performs a comprehensive evaluation based on different evaluation criteria. For example, it comprehensively evaluates sustainability and resident satisfaction. This makes it possible to perform a comprehensive evaluation by combining different evaluation criteria.

[0045] The evaluation review unit can visualize the evaluation results to make them intuitively understandable. The evaluation review unit, for example, builds a system in which the generation AI visualizes the evaluation results to make them intuitively understandable. For example, it displays the evaluation results using graphs and charts. The evaluation review unit also visualizes the evaluation results to make them intuitively understandable for residents and stakeholders. For example, it displays the evaluation results on a map to make them visually understandable. The evaluation review unit also builds a system in which the generation AI visualizes the evaluation results and updates them in real time. For example, it displays the evaluation results on a dashboard to always provide the latest information. In this way, visualizing the evaluation results makes them intuitively understandable.

[0046] The evaluation and review department can provide cloud-based services to enable use by local governments with limited resources. For example, the generation AI can provide cloud-based services to enable use by local governments with limited resources. For example, the generation AI can provide a deployment planning tool that can be accessed via the internet. The evaluation and review department can also use cloud-based services to enable local governments to access the necessary data and tools. For example, the generation AI can manage GIS data and demographic data on the cloud. The evaluation and review department can also provide cloud-based services to enable local governments to efficiently collect and analyze data. For example, the generation AI can share data on the cloud and jointly create deployment plans. In this way, cloud-based services can be provided to enable use by local governments with limited resources.

[0047] The Evaluation and Review Department can provide training programs for local government employees to complement their specialized knowledge. For example, the Generative AI can provide training programs for local government employees to complement their specialized knowledge. For example, it can provide knowledge about deployment plans through online courses and workshops. The Evaluation and Review Department can also enable local government employees to effectively utilize the Generative AI's tools and data through training programs. For example, they can learn how to analyze GIS data and develop deployment plans. The Generative AI can also provide training programs for local government employees to help them acquire practical skills. For example, they can improve their skills through exercises and case studies using actual data. In this way, the Generative AI can provide training programs for local government employees to complement their specialized knowledge.

[0048] The Evaluation and Review Department can promote collaboration with other local governments and share resources. For example, the Generative AI in the Evaluation and Review Department will build a system to promote collaboration with other local governments and share resources. For example, they will share data and tools and jointly create deployment plans. The Evaluation and Review Department will also efficiently utilize resources through collaboration with other local governments. For example, they will share success stories and best practices and reflect them in deployment plans. The Generative AI in the Evaluation and Review Department will also support collaboration with other local governments and promote resource sharing. For example, they will jointly collect and analyze data and propose optimal deployment plans. This will promote collaboration with other local governments and share resources, enabling efficient plan review.

[0049] The evaluation and review unit can provide a customized plan according to the local government's budget. In the evaluation and review unit, for example, the generation AI collects budget data from the local government and provides a customized plan according to the budget. For example, it proposes an optimal placement plan within a limited budget. The evaluation and review unit also generates an efficient placement plan based on the local government's budget. For example, it proposes a placement plan that takes cost reduction into consideration. In addition, the evaluation and review unit provides a customized plan according to the local government's budget using the generation AI, and proposes a placement plan that will achieve the maximum effect within the budget. For example, it generates a placement plan that maximizes cost-effectiveness. In this way, by providing a customized plan according to the local government's budget, it becomes possible to review the plan optimally within the budget.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The location optimization plan generation system can further include an energy consumption analysis unit. The energy consumption analysis unit collects and analyzes local energy consumption data. For example, it understands the energy consumption patterns of residential and commercial facilities and proposes highly energy-efficient layout plans. The energy consumption analysis unit also evaluates the availability of renewable energy and generates an optimal energy supply plan. For example, it proposes locations for installing solar power generation and wind power generation. The energy consumption analysis unit also proposes measures to minimize local energy consumption. For example, it recommends energy-efficient building designs and the introduction of low-energy-consumption transportation methods. This makes it possible to create sustainable layout plans that take energy consumption into consideration.

[0052] The location optimization plan generation system can further include a health data analysis unit. The health data analysis unit collects and analyzes health data of local residents. For example, it grasps the health status of residents and their use of medical services, and proposes location plans that promote health. The health data analysis unit also evaluates local health risks and proposes preventive measures. For example, it may locate exercise facilities or health checkup centers. The health data analysis unit may also propose health promotion programs based on residents' health data. For example, it may recommend walking courses or the holding of health events. This makes it possible to plan location plans that take residents' health into consideration.

[0053] The location optimization plan generation system can further include an education data analysis unit. The education data analysis unit collects and analyzes local education data. For example, it grasps the location of schools and the usage status of educational facilities and proposes a location plan that optimizes the educational environment. The education data analysis unit also evaluates the educational needs of the region and proposes appropriate allocation of educational resources. For example, it recommends the establishment of new schools or the expansion of existing educational facilities. The education data analysis unit also proposes improvements to educational programs based on the local education data. For example, it could introduce an educational program specialized in a specific subject or skill. This makes it possible to create location plans that take the educational environment into consideration.

[0054] The location optimization plan generation system can further include an economic data analysis unit. The economic data analysis unit collects and analyzes regional economic data. For example, it grasps sales data and employment status of commercial facilities and proposes a location plan that promotes economic activity. The economic data analysis unit also evaluates the economic growth of the region and proposes measures to support economic development. For example, it recommends the establishment of a new business area or the expansion of existing commercial facilities. The economic data analysis unit also proposes an economic revitalization program based on regional economic data. For example, it recommends supporting local businesses and promoting the tourism industry. This makes it possible to create a location plan that takes economic activity into consideration.

[0055] The location optimization plan generation system can further include an environmental data analysis unit. The environmental data analysis unit collects and analyzes local environmental data. For example, it grasps air quality, water quality, and noise levels, and proposes a location plan that takes environmental protection into consideration. The environmental data analysis unit also evaluates local environmental risks and proposes environmental protection measures. For example, it recommends the preservation of green spaces and the establishment of new parks. The environmental data analysis unit also proposes sustainable environmental programs based on the local environmental data. For example, it recommends the use of renewable energy and improvements in waste management. This makes it possible to create a location plan that takes environmental protection into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The GIS Data Collection Department collects GIS data, such as topographical data, land use data, and transportation data. It also collects and analyzes local government geographic information system (GIS) data. Specifically, it collects information on land use, transportation networks, the natural environment, and other areas. Step 2: The Demographic Analysis Department analyzes the GIS data collected by the GIS Data Collection Department. For example, it analyzes age distribution, gender distribution, and movement patterns. It also collects and analyzes demographic data and people flow information from local governments. Specifically, it grasps the population structure and movement patterns of the region and predicts future population changes. Step 3: The location optimization plan generation unit generates a custom location optimization plan based on the demographic and people flow information analyzed by the demographic analysis unit. For example, it proposes the optimal layout of residential areas, commercial areas, and public facilities based on the characteristics of the area. In addition, it generates a custom location optimization plan based on the characteristics of the local government based on the collected and analyzed GIS data, demographic and people flow information. Step 4: The Evaluation and Review Department will continuously evaluate the site optimization plan and revise it as necessary. For example, it will periodically collect and analyze local data to evaluate the plan's progress and effectiveness. It will also support the ongoing evaluation and revision of the plan as required by law.

[0058] (Example 2) The location optimization plan generation system according to an embodiment of the present invention is a system for preventing the decline of local communities due to the population decline and aging that local governments face and for realizing sustainable development. This system generates a custom-made location optimization plan that takes into account the characteristics of the local government based on GIS data, demographics, and people flow information, and continuously evaluates and revises the plan as necessary. As a result, the location optimization plan generation system can prevent the decline of local communities due to the population decline and aging that local governments face and contribute to the creation of sustainable local communities.

[0059] The location optimization plan generation system according to the embodiment includes a GIS data collection unit, a demographic analysis unit, a location optimization plan generation unit, and an evaluation and review unit. The GIS data collection unit collects GIS data, such as topographical data, land use data, and transportation data. The GIS data collection unit also collects and analyzes local government geographic information system (GIS) data, such as information on land use, transportation networks, and the natural environment. The demographic analysis unit analyzes the GIS data collected by the GIS data collection unit, such as age distribution, gender distribution, and travel patterns. The demographic analysis unit also collects and analyzes local government demographic data and people flow information, such as understanding the local population structure and travel patterns and predicting future population changes. The location optimization plan generation unit generates a customized location optimization plan based on the demographic and people flow information analyzed by the demographic analysis unit. For example, the system proposes optimal placement of residential areas, commercial areas, and public facilities based on the characteristics of the area. The location optimization plan generation unit generates a custom-made location optimization plan that takes into account the characteristics of the local government based on the collected and analyzed GIS data, demographic trends, and people flow information. The evaluation and review unit continuously evaluates the location optimization plan and reviews the plan as needed. For example, it regularly collects and analyzes local data to evaluate the progress and effectiveness of the plan. The evaluation and review unit also supports the continuous evaluation required by law and the review of the plan as needed. As a result, the location optimization plan generation system according to the embodiment can prevent the decline of local communities due to the population decline and aging that local governments face, and contribute to building sustainable local communities.

[0060] The GIS data collection unit uses drones to collect geographic information in real time, allowing the latest GIS data to be maintained at all times. For example, the generation AI controls the drones to collect local government geographic information in real time. For example, the drones are flown periodically to grasp the latest land use status and changes in the transportation network. The generation AI also analyzes high-resolution image data acquired from the drones and updates the GIS database. For example, information on newly constructed buildings and roads is instantly reflected. The GIS data collection unit also automates the collection of geographic information by drones, building a system in which the generation AI periodically updates the data. For example, seasonal changes and post-disaster conditions can be quickly grasped. This allows the latest geographic information to be maintained at all times, enabling more accurate location optimization plans to be generated.

[0061] The GIS data collection unit can perform 3D modeling on GIS data and propose three-dimensional layout plans. For example, the GIS data collection unit performs 3D modeling based on GIS data collected by the generation AI to create three-dimensional layout plans. For example, it proposes urban plans that take into account the height and shape of buildings. The GIS data collection unit also uses 3D modeling to simulate the local landscape and sunlight conditions and propose optimal layouts. For example, it could place a park in a location that is less likely to be shaded. The generation AI also uses 3D modeling in the GIS data collection unit to evaluate disaster risks. For example, it could simulate the effects of floods and earthquakes and propose safe layout plans. This allows for the proposal of three-dimensional layout plans, making more realistic and practical urban planning possible.

[0062] The GIS data collection unit uses the emotion estimation function to analyze geographic information that reflects residents' emotions and can propose layouts that take into account the landscapes and environments that residents prefer. For example, the GIS data collection unit uses the emotion estimation function to collect residents' emotion data and reflect it in GIS data. For example, it proposes layout plans that take into account the landscapes and environments that residents prefer. The GIS data collection unit also analyzes residents' emotion data and identifies areas where there are many positive emotions. For example, it places parks and plazas where residents can relax. The GIS data collection unit also uses the emotion estimation function to generate layout plans based on residents' emotions. For example, it proposes transportation routes that will reduce stress for residents. In this way, by proposing layout plans that reflect residents' emotions, resident satisfaction is improved.

[0063] The GIS data collection unit can use satellite data to collect and analyze wide-area geographic information. For example, the generation AI uses satellite data to collect wide-area geographic information. For example, it can grasp the land use situation and changes in the natural environment throughout the city. The GIS data collection unit also analyzes satellite data to identify regional characteristics and issues. For example, it can identify areas where green space is decreasing or where traffic congestion occurs. The GIS data collection unit also periodically collects satellite data, and the generation AI updates the GIS database. For example, it can quickly reflect seasonal changes and post-disaster conditions. This allows for the collection and analysis of wide-area geographic information to generate more comprehensive location optimization plans.

[0064] The GIS data collection unit combines historical data with geographic information to propose layout plans that take into account past changes in land use. In the GIS data collection unit, for example, the generation AI collects historical geographic data and combines it with current GIS data. For example, it proposes a layout plan that takes into account past changes in land use. The GIS data collection unit also analyzes historical data to understand the development and changes in the region. For example, it proposes a layout plan that takes into account past changes in commercial and residential areas. The GIS data collection unit also uses historical data to generate a layout plan that reflects the region's culture and history. For example, it proposes a layout plan that protects historical buildings and ruins. In this way, by proposing a layout plan that takes into account past changes in land use, urban planning that respects the region's history and culture becomes possible.

[0065] The GIS data collection unit can use the emotion estimation function to monitor residents' emotions in real time and update geographic information based on their emotions. For example, the GIS data collection unit uses the emotion estimation function to build a system that monitors residents' emotions in real time. For example, it updates GIS data based on residents' emotion data. The GIS data collection unit also analyzes residents' emotional changes in real time and proposes placement plans based on their emotions. For example, it creates an environment where residents feel less stressed. The GIS data collection unit also updates geographic information based on residents' emotions based on the emotion estimation data. For example, it prioritizes development of areas where residents' emotions are positive. This allows placement plans to be more in line with residents' needs by reflecting residents' emotions in real time.

[0066] The demographic analysis unit can analyze detailed pedestrian flow patterns using smartphone location information. For example, the demographic analysis unit uses a generation AI to collect smartphone location information and analyze detailed pedestrian flow patterns. For example, it can understand commuter and school routes and tourist trends. The demographic analysis unit also visualizes local pedestrian flow patterns based on smartphone location information. For example, it can analyze the flow of people during specific time periods and propose layout plans to avoid congestion. The demographic analysis unit also analyzes location data to understand changes in local pedestrian flow patterns. For example, it can propose layout plans that take into account changes in pedestrian flow during events. This allows for more accurate location optimization plans by analyzing detailed pedestrian flow patterns.

[0067] The demographic analysis unit analyzes social media data to understand trends in local demographics and human flow. For example, the demographic analysis unit uses generative AI to collect social media data and analyze local demographics and human flow trends. For example, it analyzes posts about specific events or locations to identify popular areas. The demographic analysis unit also understands changes in local demographics based on social media data. For example, it analyzes the outflow of young people and the increase in elderly people and proposes appropriate deployment plans. The demographic analysis unit also analyzes social media trend data to predict local human flow patterns. For example, it proposes deployment plans that take into account seasonal tourist trends. In this way, by analyzing social media data, it is possible to understand local demographics and human flow trends and make more appropriate deployment plans.

[0068] The demographic analysis unit uses the emotion estimation function to perform demographic analysis that reflects the emotions of residents, and can propose layouts that improve resident satisfaction. The demographic analysis unit, for example, uses the emotion estimation function to collect resident emotion data and reflect it in the demographic analysis. For example, areas where residents have positive emotions are prioritized for development. The demographic analysis unit also analyzes demographic changes based on the resident emotion data and proposes layout plans that improve resident satisfaction. For example, parks and plazas where residents can relax are located. The demographic analysis unit also uses the emotion estimation function to perform demographic analysis based on resident emotions. For example, improvement measures are proposed for areas where residents have negative emotions. In this way, by performing demographic analysis that reflects resident emotions, it becomes possible to plan layouts that improve resident satisfaction.

[0069] The demographic analysis unit can analyze traffic data and propose layout plans that take traffic congestion and public transportation usage into account. For example, the generation AI in the demographic analysis unit collects traffic data and identifies locations of traffic congestion. For example, it proposes road layouts and public transportation improvement measures to avoid congestion. The demographic analysis unit also analyzes public transportation usage and proposes efficient layout plans. For example, it optimizes the placement of bus stops and stations to improve convenience for residents. The demographic analysis unit also analyzes local traffic patterns based on traffic data and generates optimal layout plans. For example, it proposes layout plans that take commuter and school routes into account. By analyzing traffic data, it is possible to make layout plans that take traffic congestion and public transportation usage into account.

[0070] The demographic analysis unit can analyze tourism data and propose layout plans that take tourist trends into account. For example, the generation AI collects tourism data and analyzes tourist trends. For example, it identifies popular tourist spots and the flow of tourists by season. The demographic analysis unit also proposes layout plans that take tourist trends into account based on the tourism data. For example, it places tourist facilities and accommodations in places that are easy for tourists to visit. The demographic analysis unit also analyzes tourism data and generates layout plans that meet the needs of tourists. For example, it appropriately places services and facilities that tourists desire. In this way, by analyzing tourism data, it becomes possible to create layout plans that take tourist trends into account.

[0071] The demographic analysis unit can use the emotion estimation function to monitor residents' emotions in real time and update demographics based on the emotions. The demographic analysis unit, for example, uses the emotion estimation function to build a system that monitors residents' emotions in real time. For example, it updates demographics based on residents' emotion data. The demographic analysis unit also analyzes changes in residents' emotions in real time and updates demographics based on the emotions. For example, it prioritizes development of areas where residents' emotions are positive. The demographic analysis unit also updates demographics according to residents' emotions based on the emotion estimation data. For example, it proposes improvement measures for areas where residents' emotions are negative. This allows demographic analysis to be more in line with residents' needs by reflecting residents' emotions in real time.

[0072] The location optimization plan generation unit can perform simulations to generate multiple placement scenarios and select the optimal scenario. In the location optimization plan generation unit, for example, a generation AI simulates multiple placement scenarios and selects the optimal scenario. For example, it proposes a placement plan that takes into account resident convenience and environmental impact. The location optimization plan generation unit also compares and evaluates multiple placement scenarios based on the simulation results. For example, it selects the optimal placement plan that takes into account cost and feasibility. In addition, the location optimization plan generation unit performs simulations using the generation AI to generate placement scenarios that predict future changes. For example, it proposes a placement plan that takes into account population increases and decreases and climate change. In this way, by simulating multiple placement scenarios and selecting the optimal scenario, more effective location optimization plans are possible.

[0073] The location optimization plan generation unit can analyze environmental data and propose a layout plan that minimizes the environmental impact. In the location optimization plan generation unit, for example, the generation AI collects environmental data and proposes a layout plan that minimizes the environmental impact. For example, it generates a layout plan that takes into account the protection of green spaces and improving energy efficiency. The location optimization plan generation unit also analyzes environmental data and proposes a layout plan that takes into account the environmental characteristics of the region. For example, it generates a layout plan to reduce the risk of natural disasters. In addition, the location optimization plan generation unit uses the generation AI to propose a sustainable layout plan based on the environmental data. For example, it generates a layout plan that takes into account the use of renewable energy and waste management. In this way, by analyzing environmental data, it becomes possible to create a layout plan that minimizes the environmental impact.

[0074] The location optimization plan generation unit uses the emotion estimation function to generate a layout plan that reflects the emotions of residents, thereby improving resident satisfaction. The location optimization plan generation unit, for example, uses the emotion estimation function to collect emotion data of residents and reflect it in the layout plan. For example, it may place parks and plazas where residents can relax. The location optimization plan generation unit also proposes a layout plan that improves resident satisfaction based on the emotion data of residents. For example, it generates a layout plan that takes into account the scenery and environment that residents prefer. The location optimization plan generation unit also uses the emotion estimation function to generate a layout plan based on resident emotions. For example, it may prioritize development of areas where residents have positive emotions. In this way, resident satisfaction is improved by generating a layout plan that reflects resident emotions.

[0075] The location optimization plan generation unit can compare data from different municipalities and propose placement plans that incorporate best practices. For example, the generation AI in the location optimization plan generation unit collects data from different municipalities and proposes placement plans that incorporate best practices. For example, it generates an optimal placement plan based on success stories. The location optimization plan generation unit also compares data from different municipalities and identifies common issues and success factors. For example, it proposes a placement plan based on data from municipalities with similar regional characteristics. The location optimization plan generation unit also analyzes data from different municipalities and generates an optimal placement plan. For example, it makes proposals based on placement plans that have been successful in other municipalities. This makes it possible to compare data from different municipalities and incorporate best practices, enabling more effective placement plans.

[0076] The location optimization plan generation unit can propose a layout plan that takes into account the culture and history of the region. For example, the location optimization plan generation unit uses a generation AI to collect cultural and historical data about the region and reflect it in the layout plan. For example, it proposes a layout plan that protects historical buildings and cultural assets. The location optimization plan generation unit also generates a layout plan that takes into account the culture and history of the region and respects the identity of the residents. For example, it proposes a layout plan that takes into account traditional events and festivals in the region. The location optimization plan generation unit also uses a generation AI to analyze the culture and history of the region and propose a sustainable layout plan. For example, it generates a layout plan that supports traditional crafts and industries in the region. In this way, by proposing a layout plan that takes into account the culture and history of the region, urban planning that respects the identity of the region becomes possible.

[0077] The location optimization plan generation unit can use the emotion estimation function to monitor residents' emotions in real time and update the placement plan based on the emotions. The location optimization plan generation unit, for example, uses the emotion estimation function to build a system that monitors residents' emotions in real time. For example, it updates the placement plan based on residents' emotion data. The location optimization plan generation unit also analyzes changes in residents' emotions in real time and updates the placement plan based on the emotions. For example, it prioritizes development of areas where residents' emotions are positive. The location optimization plan generation unit also updates the placement plan according to residents' emotions based on the emotion estimation data. For example, it proposes improvement measures for areas where residents' emotions are negative. This makes it possible to update the placement plan to better meet the needs of residents by reflecting residents' emotions in real time.

[0078] The evaluation and review unit can utilize sensor data to monitor and evaluate local conditions in real time. For example, the generation AI in the evaluation and review unit collects sensor data and monitors local conditions in real time. For example, evaluations are made based on traffic volume and environmental data. The evaluation and review unit also analyzes the sensor data and builds a system to evaluate local conditions. For example, it monitors air quality and noise levels and proposes improvement measures. The generation AI in the evaluation and review unit also evaluates local conditions in real time based on the sensor data. For example, it can quickly grasp the situation during a disaster and propose countermeasures. This makes it possible to utilize sensor data to monitor local conditions in real time and make rapid evaluations.

[0079] The evaluation review unit can collect resident feedback and reflect it in the evaluation. For example, the evaluation review unit builds a system in which the generation AI collects resident feedback and reflects it in the evaluation. For example, it uses online surveys and comment functions. The evaluation review unit also analyzes resident feedback and reflects it in the evaluation of the plan. For example, it identifies areas for improvement in the layout plan based on resident opinions. The evaluation review unit also evaluates the plan based on resident feedback by the generation AI. For example, it analyzes resident satisfaction and dissatisfaction and proposes revisions to the plan. In this way, by collecting resident feedback and reflecting it in the evaluation, it becomes possible to revise the plan to reflect resident opinions.

[0080] The evaluation review unit uses the emotion estimation function to perform an evaluation that reflects the emotions of residents, thereby improving resident satisfaction. The evaluation review unit, for example, uses the emotion estimation function to collect resident emotion data and reflect it in the evaluation. For example, it prioritizes evaluation of areas where residents' emotions are positive. The evaluation review unit also evaluates the plan based on the resident emotion data to improve resident satisfaction. For example, it arranges parks and plazas where residents can relax. The evaluation review unit also uses the emotion estimation function to perform an evaluation based on resident emotions. For example, it proposes improvement measures for areas where residents' emotions are negative. In this way, by performing an evaluation that reflects resident emotions, it becomes possible to review the plan to improve resident satisfaction.

[0081] The evaluation and review unit can combine different evaluation criteria to perform a comprehensive evaluation. For example, the generation AI collects different evaluation criteria and builds a system to perform a comprehensive evaluation. For example, the evaluation combines environmental data and resident feedback. The evaluation and review unit also analyzes different evaluation criteria to perform a comprehensive evaluation. For example, it performs an evaluation that takes into account economic and social impacts. The generation AI also performs a comprehensive evaluation based on different evaluation criteria. For example, it comprehensively evaluates sustainability and resident satisfaction. This makes it possible to perform a comprehensive evaluation by combining different evaluation criteria.

[0082] The evaluation review unit can visualize the evaluation results to make them intuitively understandable. The evaluation review unit, for example, builds a system in which the generation AI visualizes the evaluation results to make them intuitively understandable. For example, it displays the evaluation results using graphs and charts. The evaluation review unit also visualizes the evaluation results to make them intuitively understandable for residents and stakeholders. For example, it displays the evaluation results on a map to make them visually understandable. The evaluation review unit also builds a system in which the generation AI visualizes the evaluation results and updates them in real time. For example, it displays the evaluation results on a dashboard to always provide the latest information. In this way, visualizing the evaluation results makes them intuitively understandable.

[0083] The evaluation review unit can use the emotion estimation function to monitor the emotions of residents in real time and update the evaluation based on the emotions. The evaluation review unit, for example, uses the emotion estimation function to build a system that monitors the emotions of residents in real time. For example, it updates the evaluation based on the emotion data of residents. The evaluation review unit also analyzes changes in the emotions of residents in real time and updates the evaluation based on the emotions. For example, it prioritizes evaluation of areas where residents' emotions are positive. The evaluation review unit also updates the evaluation according to the emotions of residents based on the emotion estimation data. For example, it proposes improvement measures for areas where residents' emotions are negative. This makes it possible to update the evaluation in accordance with the needs of residents by reflecting the emotions of residents in real time.

[0084] The evaluation and review department can provide cloud-based services to enable use by local governments with limited resources. For example, the generation AI can provide cloud-based services to enable use by local governments with limited resources. For example, the generation AI can provide a deployment planning tool that can be accessed via the internet. The evaluation and review department can also use cloud-based services to enable local governments to access the necessary data and tools. For example, the generation AI can manage GIS data and demographic data on the cloud. The evaluation and review department can also provide cloud-based services to enable local governments to efficiently collect and analyze data. For example, the generation AI can share data on the cloud and jointly create deployment plans. In this way, cloud-based services can be provided to enable use by local governments with limited resources.

[0085] The Evaluation and Review Department can provide training programs for local government employees to complement their specialized knowledge. For example, the Generative AI can provide training programs for local government employees to complement their specialized knowledge. For example, it can provide knowledge about deployment plans through online courses and workshops. The Evaluation and Review Department can also enable local government employees to effectively utilize the Generative AI's tools and data through training programs. For example, they can learn how to analyze GIS data and develop deployment plans. The Generative AI can also provide training programs for local government employees to help them acquire practical skills. For example, they can improve their skills through exercises and case studies using actual data. In this way, the Generative AI can provide training programs for local government employees to complement their specialized knowledge.

[0086] The evaluation review unit can use the emotion estimation function to provide support that reflects the emotions of local government employees, thereby improving employee satisfaction. The evaluation review unit, for example, uses the emotion estimation function to collect emotion data of local government employees and reflect it in support. For example, it provides support to reduce employee stress. The evaluation review unit also proposes support to improve employee satisfaction based on the emotion data of local government employees. For example, it creates an environment where employees can relax. The evaluation review unit also uses the emotion estimation function to provide support based on the emotions of local government employees. For example, it proposes measures if the employee's emotions are negative. In this way, employee satisfaction is improved by providing support that reflects the emotions of local government employees.

[0087] The Evaluation and Review Department can promote collaboration with other local governments and share resources. For example, the Generative AI in the Evaluation and Review Department will build a system to promote collaboration with other local governments and share resources. For example, they will share data and tools and jointly create deployment plans. The Evaluation and Review Department will also efficiently utilize resources through collaboration with other local governments. For example, they will share success stories and best practices and reflect them in deployment plans. The Generative AI in the Evaluation and Review Department will also support collaboration with other local governments and promote resource sharing. For example, they will jointly collect and analyze data and propose optimal deployment plans. This will promote collaboration with other local governments and share resources, enabling efficient plan review.

[0088] The evaluation and review unit can provide a customized plan according to the local government's budget. In the evaluation and review unit, for example, the generation AI collects budget data from the local government and provides a customized plan according to the budget. For example, it proposes an optimal placement plan within a limited budget. The evaluation and review unit also generates an efficient placement plan based on the local government's budget. For example, it proposes a placement plan that takes cost reduction into consideration. In addition, the evaluation and review unit provides a customized plan according to the local government's budget using the generation AI, and proposes a placement plan that will achieve the maximum effect within the budget. For example, it generates a placement plan that maximizes cost-effectiveness. In this way, by providing a customized plan according to the local government's budget, it becomes possible to review the plan optimally within the budget.

[0089] The evaluation review unit can use the emotion estimation function to monitor the emotions of local government employees in real time and update support based on their emotions. The evaluation review unit, for example, uses the emotion estimation function to build a system that monitors the emotions of local government employees in real time. For example, it updates support based on employee emotion data. The evaluation review unit also analyzes changes in the emotions of local government employees in real time and updates support based on their emotions. For example, it strengthens support when an employee's emotions are positive. The evaluation review unit also updates support in accordance with the emotions of the local government employees based on the emotion estimation data. For example, it proposes measures when an employee's emotions are negative. This makes it possible to update support that is more in line with the needs of employees by reflecting the emotions of local government employees in real time.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The location optimization plan generation system can further include an energy consumption analysis unit. The energy consumption analysis unit collects and analyzes local energy consumption data. For example, it understands the energy consumption patterns of residential and commercial facilities and proposes highly energy-efficient layout plans. The energy consumption analysis unit also evaluates the availability of renewable energy and generates an optimal energy supply plan. For example, it proposes locations for installing solar power generation and wind power generation. The energy consumption analysis unit also proposes measures to minimize local energy consumption. For example, it recommends energy-efficient building designs and the introduction of low-energy-consumption transportation methods. This makes it possible to create sustainable layout plans that take energy consumption into consideration.

[0092] The location optimization plan generation system can further include a health data analysis unit. The health data analysis unit collects and analyzes health data of local residents. For example, it grasps the health status of residents and their use of medical services, and proposes location plans that promote health. The health data analysis unit also evaluates local health risks and proposes preventive measures. For example, it may locate exercise facilities or health checkup centers. The health data analysis unit may also propose health promotion programs based on residents' health data. For example, it may recommend walking courses or the holding of health events. This makes it possible to plan location plans that take residents' health into consideration.

[0093] The location optimization plan generation system can further include an education data analysis unit. The education data analysis unit collects and analyzes local education data. For example, it grasps the location of schools and the usage status of educational facilities and proposes a location plan that optimizes the educational environment. The education data analysis unit also evaluates the educational needs of the region and proposes appropriate allocation of educational resources. For example, it recommends the establishment of new schools or the expansion of existing educational facilities. The education data analysis unit also proposes improvements to educational programs based on the local education data. For example, it could introduce an educational program specialized in a specific subject or skill. This makes it possible to create location plans that take the educational environment into consideration.

[0094] The location optimization plan generation system can further include an economic data analysis unit. The economic data analysis unit collects and analyzes regional economic data. For example, it grasps sales data and employment status of commercial facilities and proposes a location plan that promotes economic activity. The economic data analysis unit also evaluates the economic growth of the region and proposes measures to support economic development. For example, it recommends the establishment of a new business area or the expansion of existing commercial facilities. The economic data analysis unit also proposes an economic revitalization program based on regional economic data. For example, it recommends supporting local businesses and promoting the tourism industry. This makes it possible to create a location plan that takes economic activity into consideration.

[0095] The location optimization plan generation system can further include an environmental data analysis unit. The environmental data analysis unit collects and analyzes local environmental data. For example, it grasps air quality, water quality, and noise levels, and proposes a location plan that takes environmental protection into consideration. The environmental data analysis unit also evaluates local environmental risks and proposes environmental protection measures. For example, it recommends the preservation of green spaces and the establishment of new parks. The environmental data analysis unit also proposes sustainable environmental programs based on the local environmental data. For example, it recommends the use of renewable energy and improvements in waste management. This makes it possible to create a location plan that takes environmental protection into consideration.

[0096] The location optimization plan generation system can further use an emotion estimation function to generate a layout plan that reflects the emotions of residents. For example, it can collect resident emotion data and place parks and plazas where residents can relax. It can also propose a layout plan that improves resident satisfaction based on resident emotion data. For example, it can generate a layout plan that takes into account the landscapes and environments that residents prefer. It can also use the emotion estimation function to generate a layout plan based on resident emotions. For example, it can prioritize development of areas where residents have positive emotions. In this way, resident satisfaction can be improved by generating a layout plan that reflects resident emotions.

[0097] The location optimization plan generation system can also use an emotion estimation function to monitor residents' emotions in real time and update the placement plan based on their emotions. For example, the placement plan can be updated based on residents' emotion data. It can also analyze changes in residents' emotions in real time and update the placement plan based on their emotions. For example, it can prioritize development in areas where residents' emotions are positive. It can also update the placement plan based on residents' emotions based on the emotion estimation data. For example, it can propose improvement measures for areas where residents' emotions are negative. This makes it possible to update the placement plan to better meet residents' needs by reflecting residents' emotions in real time.

[0098] The location optimization plan generation system can also use an emotion estimation function to perform evaluations that reflect the emotions of residents, thereby improving resident satisfaction. For example, it can collect resident emotion data and reflect it in the evaluation. For example, it can prioritize evaluations of areas where residents have positive emotions. It can also evaluate plans based on resident emotion data to improve resident satisfaction. For example, it can place parks and plazas where residents can relax. It can also use the emotion estimation function to perform evaluations based on resident emotions. For example, it can propose improvement measures for areas where residents have negative emotions. This makes it possible to revise plans to improve resident satisfaction by performing evaluations that reflect resident emotions.

[0099] The location optimization plan generation system can also use an emotion estimation function to provide support that reflects the emotions of local government employees, thereby improving employee satisfaction. For example, it can collect employee emotion data and reflect it in support. For example, it can provide support to reduce employee stress. It can also suggest support to improve employee satisfaction based on employee emotion data. For example, it can create an environment where employees can relax. It can also use the emotion estimation function to provide support based on the emotions of local government employees. For example, it can suggest measures if an employee's emotions are negative. In this way, support that reflects the emotions of local government employees can improve employee satisfaction.

[0100] The location optimization plan generation system can also use an emotion estimation function to monitor the emotions of local government employees in real time and update support based on their emotions. For example, support can be updated based on employee emotion data. The system can also analyze changes in the emotions of local government employees in real time and update support based on their emotions. For example, support can be strengthened if an employee's emotion is positive. The system can also update support based on the emotion estimation data according to the employee's emotion. For example, measures can be proposed if an employee's emotion is negative. This makes it possible to reflect the emotions of local government employees in real time and update support to better meet the needs of employees.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The GIS Data Collection Department collects GIS data, such as topographical data, land use data, and transportation data. It also collects and analyzes local government geographic information system (GIS) data. Specifically, it collects information on land use, transportation networks, the natural environment, and other areas. Step 2: The Demographic Analysis Department analyzes the GIS data collected by the GIS Data Collection Department. For example, it analyzes age distribution, gender distribution, and movement patterns. It also collects and analyzes demographic data and people flow information from local governments. Specifically, it grasps the population structure and movement patterns of the region and predicts future population changes. Step 3: The location optimization plan generation unit generates a custom location optimization plan based on the demographic and people flow information analyzed by the demographic analysis unit. For example, it proposes the optimal layout of residential areas, commercial areas, and public facilities based on the characteristics of the area. In addition, it generates a custom location optimization plan based on the characteristics of the local government based on the collected and analyzed GIS data, demographic and people flow information. Step 4: The Evaluation and Review Department will continuously evaluate the site optimization plan and revise it as necessary. For example, it will periodically collect and analyze local data to evaluate the plan's progress and effectiveness. It will also support the ongoing evaluation and revision of the plan as required by law.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

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

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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, to avoid confusion and 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.

[0169] 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. [Explanation of symbols]

[0170] 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 GIS data collection unit that collects GIS data; a demographic analysis unit that analyzes the GIS data collected by the GIS data collection unit; a location optimization plan generation unit that generates a custom-made location optimization plan based on the demographic and human flow information analyzed by the demographic analysis unit; and an evaluation and review unit that continuously evaluates the location optimization plan and reviews the plan as necessary. A system characterized by:

2. The GIS data collection unit Using drones to collect geographical information in real time, the GIS data is always kept up to date.

2. The system of claim 1.

3. The GIS data collection unit Perform 3D modeling on the GIS data and propose a three-dimensional layout plan 2. The system of claim 1.

4. The GIS data collection unit Conduct geographical information analysis that reflects residents' feelings and propose layouts that take into account the landscape and environment preferred by said residents.

2. The system of claim 1.

5. The GIS data collection unit Utilizing satellite data to collect and analyze wide-area geographic information 2. The system of claim 1.

6. The GIS data collection unit Combining geographical information with historical data to propose layout plans that take into account past changes in land use 2. The system of claim 1.

Images