system

Abstract

The system according to this embodiment aims to analyze underutilized local resources and propose new industries and business models. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an evaluation unit. The collection unit collects regional data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates ideas based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the feasibility of the ideas generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the ineffective analysis of local unused resources and the proposal of new industries or business models have not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze local unused resources and propose new industries or business models.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and an evaluation unit. The data collection unit collects regional data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates ideas based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the feasibility of the ideas generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze underutilized local resources and propose new industries and business models. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

[0016] [First Embodiment] [[ID=!0]]FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

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

[0018] [[ID=!7]] The data processing device 12 includes a computer!2, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] Note: There seems to be a formatting issue in the original text where "コンピュータ22" is likely meant to be "computer 22" in the translation for better readability. I've made the correction in the translation above. Also, "!0" and "!2" and "!7" are likely formatting errors in the original text and are left as they are in the translation to match the original structure. If these are actual part of the content, more context would be needed for accurate translation.The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Smart Village Creator according to an embodiment of the present invention is a system that uses generative AI to analyze underutilized local resources and propose new industries and business models. This system comprehensively analyzes data such as local natural resources, cultural assets, human resources, and infrastructure to explore the potential of industries and businesses best suited to the region. The generative AI learns from past success stories and the latest market trends to generate innovative ideas tailored to the characteristics of the region. It also simulates the feasibility and economic effects of the proposed ideas and formulates concrete implementation plans. Furthermore, it has a function to support consensus building by incorporating the opinions of various stakeholders such as local residents, businesses, and government agencies. The Smart Village Creator is not merely a proposal tool, but functions as a platform for the sustainable development of the region. For example, the Smart Village Creator provides local governments, local companies, and entrepreneurs with new perspectives and possibilities, accelerating the realization of regional revitalization. For example, the Smart Village Creator collects data such as local natural resources, cultural assets, human resources, and infrastructure. For example, the generative AI comprehensively analyzes the collected data to discover the potential value of the region. The generative AI learns from past success stories and the latest market trends to generate innovative ideas tailored to the characteristics of the region. For example, it simulates the feasibility and economic impact of generated ideas and develops concrete implementation plans. Furthermore, it incorporates the opinions of diverse stakeholders, such as local residents, businesses, and government agencies, and supports consensus building. This supports the revitalization of local economies and job creation, and realizes sustainable development in the region. As a result, smart village creators can propose new industries and business models that utilize untapped local resources.

[0029] The smart village creator according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and an evaluation unit. The data collection unit collects local data. Local data includes, but is not limited to, demographic data, economic data, and environmental data. The data collection unit collects, for example, data on local natural resources, cultural assets, human resources, and infrastructure. The data collection unit can acquire data from, for example, sensors or databases. The data collection unit can also collect data through surveys and interviews with local residents and businesses. For example, the data collection unit collects data on local natural resources using sensors. The data collection unit can acquire data on local cultural assets from databases. The data collection unit can also collect data through surveys and interviews with local residents and businesses. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the collected data using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze the data using generative AI. For example, the analysis unit can analyze regional demographics using statistical analysis. The analysis unit can analyze regional economic data using machine learning algorithms. The analysis unit can also analyze regional environmental data using generative AI. The generation unit generates ideas based on the analysis results obtained by the analysis unit. These generated ideas include, but are not limited to, business ideas and technical ideas. For example, the generation unit can generate innovative ideas tailored to the characteristics of the region using generative AI. The generation unit can also learn from past success stories and the latest market trends to generate ideas tailored to the characteristics of the region. Furthermore, the generation unit can use generative AI to generate ideas that uncover the potential value of the region. For example, the generation unit can use generative AI to generate business ideas that utilize regional tourism resources. The generation unit can use generative AI to generate technical ideas that utilize regional agricultural products. The generation unit can also use generative AI to generate ideas that utilize regional cultural assets.The evaluation unit evaluates the feasibility of the ideas generated by the generation unit. The evaluation of feasibility includes, but is not limited to, technical feasibility and economic feasibility. For example, the evaluation unit may evaluate the technical feasibility of the generated ideas. The evaluation unit may also evaluate the economic feasibility of the generated ideas. Furthermore, the evaluation unit may also evaluate the social feasibility of the generated ideas. For example, the evaluation unit may evaluate the technical feasibility of the generated ideas using simulation. The evaluation unit may evaluate the economic feasibility of the generated ideas using cost-benefit analysis. The evaluation unit may also evaluate the social feasibility of the generated ideas using questionnaire surveys. This allows the smart village creator according to the embodiment to propose new industries and business models that utilize underutilized local resources. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit may use an AI model to evaluate the feasibility of the ideas generated by the generation unit.

[0030] The data collection unit collects local data. This data includes, but is not limited to, demographic, economic, and environmental data. For example, the unit collects data on local natural resources, cultural assets, human resources, and infrastructure. Specifically, it collects data on local natural resources using sensors. For instance, it can install underground sensors and weather sensors to monitor soil quality, water quality, and weather data in real time. This allows for a detailed understanding of changes in the local natural environment. Furthermore, data on cultural assets can be obtained from databases. For example, information on local historical buildings and traditional events can be collected from digital archives to serve as foundational data for evaluating the cultural value of the region. The data collection unit can also collect data through surveys and interviews with local residents and businesses. For example, it can conduct surveys of local residents regarding their living environment and local issues to gather their needs and opinions. It can also conduct interviews with local businesses regarding their economic activities and business environment to understand the current state and challenges of the local economy. This allows the data collection unit to collect comprehensive local data from diverse data sources and gain a multifaceted understanding of the region's current situation. The collected data is centrally managed in a central database, making it accessible to the analysis and generation departments. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis department analyzes collected data using statistical analysis. For example, to analyze regional demographics, statistical data such as age distribution, birth rates, and death rates can be used to predict future population trends. Machine learning algorithms can also be used to analyze data. For example, sales data and employment data can be used to predict regional economic growth patterns and changes in industrial structure. Furthermore, generative AI can be used to analyze data. For example, weather data and environmental sensor data can be used to predict the impacts of climate change and environmental risks when analyzing regional environmental data. Generative AI can process large amounts of data quickly and extract complex patterns and trends, enabling a detailed understanding of regional characteristics and challenges. This allows the analysis department to analyze collected data from multiple perspectives and accurately grasp the current situation and future risks of the region. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical economic data, it is possible to identify growth patterns and risk factors for specific industries and formulate future countermeasures. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and strategy formulation, thereby improving the reliability and security of the entire system.

[0032] The generation unit generates ideas based on the analysis results obtained by the analysis unit. These generated ideas include, but are not limited to, business ideas and technical ideas. Specifically, the generation unit uses generational AI to generate innovative ideas tailored to regional characteristics. For example, to generate business ideas utilizing regional tourism resources, it can learn from past success stories and the latest market trends to propose tourism plans and event planning tailored to regional characteristics. Furthermore, to generate technical ideas utilizing regional agricultural products, it can learn from the latest information on agricultural and processing technologies to propose new products and services utilizing regional specialties. In addition, the generation unit can generate ideas utilizing regional cultural assets. For example, it can propose tourism and educational programs utilizing regional traditional crafts and cultural events to enhance the cultural value of the region. Based on a large amount of data, the generational AI can learn from past success stories and the latest trends to generate optimal ideas tailored to regional characteristics. This allows the generation unit to maximize the use of underutilized regional resources and propose new industries and business models. Furthermore, the generation unit can collaborate with the evaluation unit to concretize the generated ideas in order to assess their feasibility. This allows the generation unit to quickly generate innovative ideas tailored to local characteristics, thereby contributing to regional development.

[0033] The evaluation unit assesses the feasibility of the ideas generated by the generation unit. This feasibility assessment includes, but is not limited to, technical and economic feasibility. Specifically, the evaluation unit can assess the technical feasibility of the generated ideas. For example, it can evaluate the technical challenges and necessary equipment for introducing new agricultural technologies and determine feasibility. It can also assess the economic feasibility of the generated ideas. For example, it can evaluate the profitability and cost structure of a new business model and determine whether it is economically sustainable. Furthermore, the evaluation unit can assess the social feasibility of the generated ideas. For example, it can use surveys to evaluate whether a new tourism program will be accepted by local residents and tourists and determine its social acceptance. The evaluation unit can use methods such as simulations, cost-benefit analyses, and surveys to perform these evaluations. For example, it can evaluate technical feasibility using simulations and economic feasibility using cost-benefit analyses. It can also evaluate social feasibility using surveys and reflect the opinions of local residents and stakeholders. Furthermore, the evaluation unit can also use AI models for evaluation. For example, to evaluate the feasibility of ideas generated by the generation unit, an AI model can be used to assess technical challenges and economic risks, enabling a rapid and accurate evaluation. This allows the evaluation unit to comprehensively assess the feasibility of the generated ideas and select the optimal ideas to contribute to regional development.

[0034] The opinion gathering department can collect opinions from stakeholders. For example, it can collect opinions from stakeholders such as local residents, businesses, and government agencies. The opinion gathering department can collect opinions through methods such as surveys and interviews. It can also collect opinions using online platforms. For example, the opinion gathering department can collect opinions from local residents through surveys. It can collect opinions from businesses through interviews. It can also collect opinions from government agencies through online platforms. This allows for proposals that reflect diverse opinions from the community by collecting the opinions of stakeholders. Some or all of the above-described processes in the opinion gathering department may be performed using AI, or not. For example, the opinion gathering department can input the results of surveys and interviews into an AI model and have the AI ​​perform the opinion collection.

[0035] The consensus-building unit can analyze opinions and support consensus building. For example, the consensus-building unit can analyze collected opinions and provide information for consensus building. The consensus-building unit can support consensus building through, for example, voting or discussion. The consensus-building unit can also support consensus building using an online platform. For example, the consensus-building unit can analyze collected opinions using statistical analysis. The consensus-building unit can support consensus building through voting. The consensus-building unit can also support consensus building through discussion. The consensus-building unit can also support consensus building using an online platform. This allows for smoother regional consensus building by analyzing opinions and supporting consensus building. Some or all of the above-described processes in the consensus-building unit may be performed using, for example, AI, or not using AI. For example, the consensus-building unit can input collected opinions into an AI model and have the AI ​​perform the analysis of the opinions.

[0036] The planning department can translate proposed ideas into concrete implementation plans. For example, the planning department can formulate schedules and resource allocations based on proposed ideas. The planning department can formulate implementation plans using project management tools, for example. The planning department can also formulate implementation plans using online platforms. For example, the planning department can formulate schedules based on proposed ideas. The planning department can formulate resource allocations based on proposed ideas. The planning department can also formulate implementation plans using project management tools, for example. The planning department can also formulate implementation plans using online platforms. This allows for the formulation of feasible plans by translating proposed ideas into concrete implementation plans. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input proposed ideas into an AI model and have the AI ​​formulate the implementation plan.

[0037] The learning unit can continuously learn and improve the accuracy of its suggestions. For example, the learning unit can learn using historical data or newly collected data. The learning unit can also learn using machine learning algorithms. Furthermore, the learning unit can learn using generative AI. For example, the learning unit can learn using historical data. The learning unit can learn using newly collected data. The learning unit can also learn using machine learning algorithms. The learning unit can also learn using generative AI. This continuous learning improves the accuracy of suggestions, enabling more appropriate suggestions. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input historical data into an AI model and have the AI ​​perform the learning.

[0038] The data collection unit can analyze past data collection history and select an efficient collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. Furthermore, the data collection unit can identify patterns in collection methods based on past data collection history and select the optimal method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. The data collection unit can also identify patterns in collection methods based on past data collection history and select the optimal method. This enables efficient data collection by analyzing past data collection history and selecting the optimal method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into an AI model and have the AI ​​select the collection method.

[0039] The data collection unit can filter data based on local seasons and events during data collection. For example, the data collection unit can change the type of data collected depending on the local season. The data collection unit can prioritize the collection of relevant data based on local event information. The data collection unit can also adjust the timing of data collection based on seasons and events. For example, the data collection unit can change the type of data collected depending on the local season. The data collection unit can prioritize the collection of relevant data based on local event information. The data collection unit can also adjust the timing of data collection based on seasons and events. This allows for the collection of highly relevant data by filtering data based on local seasons and events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input local season and event information into an AI model and have the AI ​​perform the data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the region. The data collection unit can set the scope of data collection considering the geographical location information. The data collection unit can also determine the type of data to collect based on the geographical location information. For example, the data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the region. The data collection unit can set the scope of data collection considering the geographical location information. The data collection unit can also determine the type of data to collect based on the geographical location information. This allows for the efficient collection of highly relevant data by considering the geographical location information of the region. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the region into an AI model and have the AI ​​perform data collection.

[0041] The data collection unit can analyze local social media activity and collect relevant data during data collection. For example, the data collection unit can analyze local social media activity and collect relevant data. The data collection unit can determine the type of data to collect based on social media trends. Furthermore, the data collection unit can adjust the frequency of data collection according to the level of social media activity. This allows for efficient collection of relevant data by analyzing local social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input local social media activity data into an AI model and have the AI ​​perform the data collection.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a concise analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a concise analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an environmental analysis algorithm to natural resource data. For example, the analysis unit can apply a historical value analysis algorithm to cultural asset data. For example, the analysis unit can apply a skill matching algorithm to human resource data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis according to the data collection period. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis according to the data collection period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0046] The generation unit can adjust the level of detail generated based on the importance of the data when generating ideas. For example, the generation unit can generate detailed ideas based on high-importance data. The generation unit can generate concise ideas based on low-importance data. The generation unit can also determine the priority of the ideas to be generated according to the importance of the data. For example, the generation unit can generate detailed ideas based on high-importance data. The generation unit can generate concise ideas based on low-importance data. The generation unit can also determine the priority of the ideas to be generated according to the importance of the data. This makes efficient idea generation possible by adjusting the level of detail generated based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the generation.

[0047] The generation unit can apply different generation algorithms depending on the data category when generating ideas. For example, the generation unit can generate ideas related to environmental protection based on natural resource data. The generation unit can generate ideas related to the tourism industry based on cultural asset data. The generation unit can also generate ideas related to education and training based on human resource data. For example, the generation unit can generate ideas related to environmental protection based on natural resource data. The generation unit can generate ideas related to the tourism industry based on cultural asset data. The generation unit can also generate ideas related to education and training based on human resource data. By applying different generation algorithms depending on the data category, more appropriate ideas can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI execute the application of the generation algorithm.

[0048] The generation unit can determine the generation priority based on the data collection timing when generating ideas. For example, the generation unit can prioritize generating ideas based on the latest data. The generation unit can generate ideas based on the latest data while referring to past data. The generation unit can also determine the priority of ideas to generate depending on the data collection timing. For example, the generation unit can prioritize generating ideas based on the latest data. The generation unit can generate ideas based on the latest data while referring to past data. The generation unit can also determine the priority of ideas to generate depending on the data collection timing. This allows for the priority generation of ideas based on the latest data by determining the generation priority based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the data collection timing into the generation AI and have the generation AI determine the generation priority.

[0049] The generation unit can adjust the order of idea generation based on the relevance of the data. For example, the generation unit can prioritize generating ideas based on highly relevant data. The generation unit can postpone generating ideas based on less relevant data. The generation unit can also adjust the order of ideas to be generated according to the relevance of the data. For example, the generation unit can prioritize generating ideas based on highly relevant data. The generation unit can postpone generating ideas based on less relevant data. The generation unit can also adjust the order of ideas to be generated according to the relevance of the data. This allows for efficient idea generation by adjusting the order of generation based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of generation.

[0050] The simulation unit can select an efficient simulation method by referring to past simulation results during the simulation. For example, the simulation unit can select the optimal simulation method based on past simulation results. The simulation unit can analyze past simulation results, find areas for improvement in the simulation method, and optimize it. Furthermore, the simulation unit can find patterns in simulation methods based on past simulation results and select the optimal method. For example, the simulation unit can select the optimal simulation method based on past simulation results. The simulation unit can analyze past simulation results, find areas for improvement in the simulation method, and optimize it. The simulation unit can also find patterns in simulation methods based on past simulation results and select the optimal method. This makes it possible to select the optimal simulation method by referring to past simulation results, enabling efficient simulation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input past simulation results into an AI model and have the AI ​​perform the selection of the simulation method.

[0051] The simulation unit can customize the simulation methods based on regional characteristics during the simulation. For example, the simulation unit can customize the simulation methods according to regional characteristics. The simulation unit can set simulation parameters considering regional characteristics. The simulation unit can also adjust the simulation results based on regional characteristics. For example, the simulation unit can customize the simulation methods according to regional characteristics. The simulation unit can set simulation parameters considering regional characteristics. The simulation unit can also adjust the simulation results based on regional characteristics. By customizing the simulation methods based on regional characteristics, more appropriate simulation results can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input regional characteristics into an AI model and have the AI ​​perform the customization of the simulation methods.

[0052] The simulation unit can select the optimal simulation method during simulation, taking into account the geographical location information of the region. For example, the simulation unit can select the optimal simulation method based on the geographical location information of the region. The simulation unit can set the simulation range, taking into account the geographical location information. The simulation unit can also set the simulation parameters based on the geographical location information. For example, the simulation unit can select the optimal simulation method based on the geographical location information of the region. The simulation unit can set the simulation range, taking into account the geographical location information. The simulation unit can also set the simulation parameters, taking into account the geographical location information of the region. This enables efficient simulation by selecting the simulation method while considering the geographical location information of the region. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the geographical location information of the region into an AI model and have the AI ​​perform the selection of the simulation method.

[0053] The simulation unit can analyze local social media activity during simulation and propose simulation methods. For example, the simulation unit can analyze local social media activity and propose simulation methods. The simulation unit can determine simulation methods based on social media trends. The simulation unit can also adjust simulation methods according to the volume of social media activity. For example, the simulation unit can analyze local social media activity and propose simulation methods. The simulation unit can determine simulation methods based on social media trends. The simulation unit can also adjust simulation methods according to the volume of social media activity. This allows for the proposal of more appropriate simulation methods by analyzing local social media activity. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input local social media activity data into an AI model and have the AI ​​execute the proposal of simulation methods.

[0054] The opinion collection unit can analyze past opinion collection history and select the optimal collection method. For example, the opinion collection unit can identify and apply the most efficient collection method from past opinion collection history. The opinion collection unit can analyze past opinion collection history, find areas for improvement in collection methods, and optimize them. Furthermore, the opinion collection unit can identify patterns in collection methods based on past opinion collection history and select the optimal method. For example, the opinion collection unit can identify and apply the most efficient collection method from past opinion collection history. The opinion collection unit can analyze past opinion collection history, find areas for improvement in collection methods, and optimize them. The opinion collection unit can also identify patterns in collection methods based on past opinion collection history and select the optimal method. This makes it possible to select the optimal collection method and collect opinions efficiently by analyzing past opinion collection history. Some or all of the above processes in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input past opinion collection history into an AI model and have the AI ​​select the collection method.

[0055] The opinion collection unit can prioritize collecting highly relevant opinions by considering the geographical location information of the region. For example, the opinion collection unit can prioritize collecting highly relevant opinions based on the geographical location information of the region. The opinion collection unit can set the scope of opinion collection considering the geographical location information. The opinion collection unit can also determine the types of opinions to collect based on the geographical location information. For example, the opinion collection unit can prioritize collecting highly relevant opinions based on the geographical location information of the region. The opinion collection unit can set the scope of opinion collection considering the geographical location information. The opinion collection unit can also determine the types of opinions to collect based on the geographical location information. This allows for the efficient collection of highly relevant opinions by considering the geographical location information of the region. Some or all of the above processing in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input the geographical location information of the region into an AI model and have the AI ​​perform the opinion collection.

[0056] The consensus-building unit can select the optimal method by referring to past consensus-building history during consensus-building. For example, the consensus-building unit can select the optimal method based on past consensus-building history. The consensus-building unit can analyze past consensus-building history, find areas for improvement in the method, and optimize it. Furthermore, the consensus-building unit can also find patterns in the method based on past consensus-building history and select the optimal method. For example, the consensus-building unit can select the optimal method based on past consensus-building history. The consensus-building unit can analyze past consensus-building history, find areas for improvement in the method, and optimize it. The consensus-building unit can also find patterns in the method based on past consensus-building history and select the optimal method. This makes it possible to select the optimal method by referring to past consensus-building history and achieve efficient consensus-building. Some or all of the above-described processes in the consensus-building unit may be performed using AI, for example, or without using AI. For example, the consensus-building unit can input past consensus-building history into an AI model and have the AI ​​perform the method selection.

[0057] The consensus-building unit can select the optimal method when forming a consensus, taking into account the geographical location information of the region. For example, the consensus-building unit can select the optimal method based on the geographical location information of the region. The consensus-building unit can set the scope of consensus formation, taking into account the geographical location information. The consensus-building unit can also set the parameters of consensus formation, taking into account the geographical location information. This enables efficient consensus formation by selecting a consensus-building method that takes into account the geographical location information of the region. Some or all of the above-described processes in the consensus-building unit may be performed using AI, for example, or without using AI. For example, the consensus-building unit can input the geographical location information of the region into an AI model and have the AI ​​select a consensus-building method.

[0058] The planning department can select the optimal method by referring to past planning history when formulating a plan. For example, the planning department can select the optimal method based on past planning history. The planning department can analyze past planning history to find areas for improvement in the methods and optimize them. Furthermore, the planning department can also find patterns in the methods based on past planning history and select the optimal method. For example, the planning department can select the optimal method based on past planning history. The planning department can analyze past planning history to find areas for improvement in the methods and optimize them. The planning department can also find patterns in the methods based on past planning history and select the optimal method. This makes it possible to select the optimal method by referring to past planning history and to formulate a plan efficiently. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input past planning history into an AI model and have the AI ​​perform the method selection.

[0059] The planning department can select the optimal method when formulating a plan, taking into account the geographical location information of the region. For example, the planning department can select the optimal method based on the geographical location information of the region. The planning department can set the scope of the plan, taking into account the geographical location information. The planning department can also set the parameters of the plan based on the geographical location information. For example, the planning department can select the optimal method based on the geographical location information of the region. The planning department can set the scope of the plan, taking into account the geographical location information. The planning department can also set the parameters of the plan, taking into account the geographical location information of the region. This makes efficient plan formulation possible by selecting a plan formulation method while taking into account the geographical location information of the region. Some or all of the above processes in the planning department may be performed using AI, for example, or without using AI. For example, the planning department can input the geographical location information of the region into an AI model and have the AI ​​select a plan formulation method.

[0060] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data to find areas for improvement in the algorithm and optimize it. The learning unit can also find algorithmic patterns based on past learning data and select the optimal method. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data to find areas for improvement in the algorithm and optimize it. The learning unit can also find algorithmic patterns based on past learning data and select the optimal method. This allows for efficient learning by selecting the optimal learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into an AI model and have the AI ​​perform the optimization of the learning algorithm.

[0061] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can weight the training data based on the latest data. The learning unit can also weight the training data based on the latest data while referring to past data. Furthermore, the learning unit can adjust the weighting of the training data according to the data collection timing. For example, the learning unit can weight the training data based on the latest data. The learning unit can also weight the training data based on the latest data while referring to past data. The learning unit can also adjust the weighting of the training data according to the data collection timing. This enables efficient training by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the data collection timing into an AI model and have the AI ​​perform the training data weighting.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can filter local data based on local seasons and events. For example, it can change the type of data collected depending on the local season. Based on local event information, the data collection unit can prioritize the collection of relevant data. Furthermore, the data collection unit can adjust the timing of data collection based on seasons and events. This allows for the collection of highly relevant data by filtering data based on local seasons and events.

[0064] The data collection unit can analyze past data collection history and select the most efficient collection method. For example, it can identify and apply the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. Furthermore, the data collection unit can identify patterns in collection methods based on past data collection history and select the optimal method. As a result, by analyzing past data collection history, the optimal collection method can be selected, enabling efficient data collection.

[0065] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, it can prioritize the collection of highly relevant data based on the geographical location information of the region. The data collection unit can set the scope of data collection considering the geographical location information. Furthermore, the data collection unit can also determine the type of data to collect based on the geographical location information. As a result, by collecting data while considering the geographical location information of the region, highly relevant data can be collected efficiently.

[0066] The data collection unit can analyze local social media activity and collect relevant data during the data collection process. For example, it can analyze local social media activity and collect relevant data. The data collection unit can determine the type of data to collect based on social media trends. Furthermore, the data collection unit can adjust the frequency of data collection according to the level of social media activity. This allows for the efficient collection of relevant data by analyzing local social media activity.

[0067] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, it can apply an environmental analysis algorithm to natural resource data, a historical value analysis algorithm to cultural asset data, and a skills matching algorithm to human resource data. By applying different analysis algorithms depending on the data category, it can provide more appropriate analysis results.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects local data. Local data includes demographic data, economic data, environmental data, natural resources, cultural assets, human resources, and infrastructure. The data collection unit can acquire data from sensors and databases, as well as through surveys and interviews with local residents and businesses. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, the analysis unit can use statistical analysis to analyze regional demographics, machine learning algorithms to analyze regional economic data, and generative AI to analyze regional environmental data. Step 3: The generation unit generates ideas based on the analysis results obtained by the analysis unit. The generated ideas include business ideas and technical ideas. The generation unit can generate innovative ideas tailored to the characteristics of the region using generation AI, and can generate ideas tailored to the characteristics of the region by learning from past success stories and the latest market trends. Step 4: The evaluation unit assesses the feasibility of the ideas generated by the generation unit. Feasibility assessment includes technical feasibility, economic feasibility, and social feasibility. The evaluation unit can assess technical feasibility using simulations, economic feasibility using cost-benefit analysis, and social feasibility using surveys.

[0070] (Example of form 2) The Smart Village Creator according to an embodiment of the present invention is a system that uses generative AI to analyze underutilized local resources and propose new industries and business models. This system comprehensively analyzes data such as local natural resources, cultural assets, human resources, and infrastructure to explore the potential of industries and businesses best suited to the region. The generative AI learns from past success stories and the latest market trends to generate innovative ideas tailored to the characteristics of the region. It also simulates the feasibility and economic effects of the proposed ideas and formulates concrete implementation plans. Furthermore, it has a function to support consensus building by incorporating the opinions of various stakeholders such as local residents, businesses, and government agencies. The Smart Village Creator is not merely a proposal tool, but functions as a platform for the sustainable development of the region. For example, the Smart Village Creator provides local governments, local companies, and entrepreneurs with new perspectives and possibilities, accelerating the realization of regional revitalization. For example, the Smart Village Creator collects data such as local natural resources, cultural assets, human resources, and infrastructure. For example, the generative AI comprehensively analyzes the collected data to discover the potential value of the region. The generative AI learns from past success stories and the latest market trends to generate innovative ideas tailored to the characteristics of the region. For example, it simulates the feasibility and economic impact of generated ideas and develops concrete implementation plans. Furthermore, it incorporates the opinions of diverse stakeholders, such as local residents, businesses, and government agencies, and supports consensus building. This supports the revitalization of local economies and job creation, and realizes sustainable development in the region. As a result, smart village creators can propose new industries and business models that utilize untapped local resources.

[0071] The smart village creator according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and an evaluation unit. The data collection unit collects local data. Local data includes, but is not limited to, demographic data, economic data, and environmental data. The data collection unit collects, for example, data on local natural resources, cultural assets, human resources, and infrastructure. The data collection unit can acquire data from, for example, sensors or databases. The data collection unit can also collect data through surveys and interviews with local residents and businesses. For example, the data collection unit collects data on local natural resources using sensors. The data collection unit can acquire data on local cultural assets from databases. The data collection unit can also collect data through surveys and interviews with local residents and businesses. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the collected data using statistical analysis. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit can also analyze the data using generative AI. For example, the analysis unit can analyze regional demographics using statistical analysis. The analysis unit can analyze regional economic data using machine learning algorithms. The analysis unit can also analyze regional environmental data using generative AI. The generation unit generates ideas based on the analysis results obtained by the analysis unit. These generated ideas include, but are not limited to, business ideas and technical ideas. For example, the generation unit can generate innovative ideas tailored to the characteristics of the region using generative AI. The generation unit can also learn from past success stories and the latest market trends to generate ideas tailored to the characteristics of the region. Furthermore, the generation unit can use generative AI to generate ideas that uncover the potential value of the region. For example, the generation unit can use generative AI to generate business ideas that utilize regional tourism resources. The generation unit can use generative AI to generate technical ideas that utilize regional agricultural products. The generation unit can also use generative AI to generate ideas that utilize regional cultural assets.The evaluation unit evaluates the feasibility of the ideas generated by the generation unit. The evaluation of feasibility includes, but is not limited to, technical feasibility and economic feasibility. For example, the evaluation unit may evaluate the technical feasibility of the generated ideas. The evaluation unit may also evaluate the economic feasibility of the generated ideas. Furthermore, the evaluation unit may also evaluate the social feasibility of the generated ideas. For example, the evaluation unit may evaluate the technical feasibility of the generated ideas using simulation. The evaluation unit may evaluate the economic feasibility of the generated ideas using cost-benefit analysis. The evaluation unit may also evaluate the social feasibility of the generated ideas using questionnaire surveys. This allows the smart village creator according to the embodiment to propose new industries and business models that utilize underutilized local resources. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit may use an AI model to evaluate the feasibility of the ideas generated by the generation unit.

[0072] The data collection unit collects local data. This data includes, but is not limited to, demographic, economic, and environmental data. For example, the unit collects data on local natural resources, cultural assets, human resources, and infrastructure. Specifically, it collects data on local natural resources using sensors. For instance, it can install underground sensors and weather sensors to monitor soil quality, water quality, and weather data in real time. This allows for a detailed understanding of changes in the local natural environment. Furthermore, data on cultural assets can be obtained from databases. For example, information on local historical buildings and traditional events can be collected from digital archives to serve as foundational data for evaluating the cultural value of the region. The data collection unit can also collect data through surveys and interviews with local residents and businesses. For example, it can conduct surveys of local residents regarding their living environment and local issues to gather their needs and opinions. It can also conduct interviews with local businesses regarding their economic activities and business environment to understand the current state and challenges of the local economy. This allows the data collection unit to collect comprehensive local data from diverse data sources and gain a multifaceted understanding of the region's current situation. The collected data is centrally managed in a central database, making it accessible to the analysis and generation departments. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis department analyzes the data collected by the data collection department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, the analysis department analyzes collected data using statistical analysis. For example, to analyze regional demographics, statistical data such as age distribution, birth rates, and death rates can be used to predict future population trends. Machine learning algorithms can also be used to analyze data. For example, sales data and employment data can be used to predict regional economic growth patterns and changes in industrial structure. Furthermore, generative AI can be used to analyze data. For example, weather data and environmental sensor data can be used to predict the impacts of climate change and environmental risks when analyzing regional environmental data. Generative AI can process large amounts of data quickly and extract complex patterns and trends, enabling a detailed understanding of regional characteristics and challenges. This allows the analysis department to analyze collected data from multiple perspectives and accurately grasp the current situation and future risks of the region. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical economic data, it is possible to identify growth patterns and risk factors for specific industries and formulate future countermeasures. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and strategy formulation, thereby improving the reliability and security of the entire system.

[0074] The generation unit generates ideas based on the analysis results obtained by the analysis unit. These generated ideas include, but are not limited to, business ideas and technical ideas. Specifically, the generation unit uses generational AI to generate innovative ideas tailored to regional characteristics. For example, to generate business ideas utilizing regional tourism resources, it can learn from past success stories and the latest market trends to propose tourism plans and event planning tailored to regional characteristics. Furthermore, to generate technical ideas utilizing regional agricultural products, it can learn from the latest information on agricultural and processing technologies to propose new products and services utilizing regional specialties. In addition, the generation unit can generate ideas utilizing regional cultural assets. For example, it can propose tourism and educational programs utilizing regional traditional crafts and cultural events to enhance the cultural value of the region. Based on a large amount of data, the generational AI can learn from past success stories and the latest trends to generate optimal ideas tailored to regional characteristics. This allows the generation unit to maximize the use of underutilized regional resources and propose new industries and business models. Furthermore, the generation unit can collaborate with the evaluation unit to concretize the generated ideas in order to assess their feasibility. This allows the generation unit to quickly generate innovative ideas tailored to local characteristics, thereby contributing to regional development.

[0075] The evaluation unit assesses the feasibility of the ideas generated by the generation unit. This feasibility assessment includes, but is not limited to, technical and economic feasibility. Specifically, the evaluation unit can assess the technical feasibility of the generated ideas. For example, it can evaluate the technical challenges and necessary equipment for introducing new agricultural technologies and determine feasibility. It can also assess the economic feasibility of the generated ideas. For example, it can evaluate the profitability and cost structure of a new business model and determine whether it is economically sustainable. Furthermore, the evaluation unit can assess the social feasibility of the generated ideas. For example, it can use surveys to evaluate whether a new tourism program will be accepted by local residents and tourists and determine its social acceptance. The evaluation unit can use methods such as simulations, cost-benefit analyses, and surveys to perform these evaluations. For example, it can evaluate technical feasibility using simulations and economic feasibility using cost-benefit analyses. It can also evaluate social feasibility using surveys and reflect the opinions of local residents and stakeholders. Furthermore, the evaluation unit can also use AI models for evaluation. For example, to evaluate the feasibility of ideas generated by the generation unit, an AI model can be used to assess technical challenges and economic risks, enabling a rapid and accurate evaluation. This allows the evaluation unit to comprehensively assess the feasibility of the generated ideas and select the optimal ideas to contribute to regional development.

[0076] The opinion gathering department can collect opinions from stakeholders. For example, it can collect opinions from stakeholders such as local residents, businesses, and government agencies. The opinion gathering department can collect opinions through methods such as surveys and interviews. It can also collect opinions using online platforms. For example, the opinion gathering department can collect opinions from local residents through surveys. It can collect opinions from businesses through interviews. It can also collect opinions from government agencies through online platforms. This allows for proposals that reflect diverse opinions from the community by collecting the opinions of stakeholders. Some or all of the above-described processes in the opinion gathering department may be performed using AI, or not. For example, the opinion gathering department can input the results of surveys and interviews into an AI model and have the AI ​​perform the opinion collection.

[0077] The consensus-building unit can analyze opinions and support consensus building. For example, the consensus-building unit can analyze collected opinions and provide information for consensus building. The consensus-building unit can support consensus building through, for example, voting or discussion. The consensus-building unit can also support consensus building using an online platform. For example, the consensus-building unit can analyze collected opinions using statistical analysis. The consensus-building unit can support consensus building through voting. The consensus-building unit can also support consensus building through discussion. The consensus-building unit can also support consensus building using an online platform. This allows for smoother regional consensus building by analyzing opinions and supporting consensus building. Some or all of the above-described processes in the consensus-building unit may be performed using, for example, AI, or not using AI. For example, the consensus-building unit can input collected opinions into an AI model and have the AI ​​perform the analysis of the opinions.

[0078] The planning department can translate proposed ideas into concrete implementation plans. For example, the planning department can formulate schedules and resource allocations based on proposed ideas. The planning department can formulate implementation plans using project management tools, for example. The planning department can also formulate implementation plans using online platforms. For example, the planning department can formulate schedules based on proposed ideas. The planning department can formulate resource allocations based on proposed ideas. The planning department can also formulate implementation plans using project management tools, for example. The planning department can also formulate implementation plans using online platforms. This allows for the formulation of feasible plans by translating proposed ideas into concrete implementation plans. Some or all of the above processes in the planning department may be performed using AI, for example, or not. For example, the planning department can input proposed ideas into an AI model and have the AI ​​formulate the implementation plan.

[0079] The learning unit can continuously learn and improve the accuracy of its suggestions. For example, the learning unit can learn using historical data or newly collected data. The learning unit can also learn using machine learning algorithms. Furthermore, the learning unit can learn using generative AI. For example, the learning unit can learn using historical data. The learning unit can learn using newly collected data. The learning unit can also learn using machine learning algorithms. The learning unit can also learn using generative AI. This continuous learning improves the accuracy of suggestions, enabling more appropriate suggestions. Some or all of the above-described processes in the learning unit may be performed using AI, or not. For example, the learning unit can input historical data into an AI model and have the AI ​​perform the learning.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also adjust the timing of data collection to match the user's schedule if the user is busy. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is busy, the data collection unit can also adjust the timing of data collection to match the user's schedule. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The data collection unit can analyze past data collection history and select an efficient collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. Furthermore, the data collection unit can identify patterns in collection methods based on past data collection history and select the optimal method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. The data collection unit can also identify patterns in collection methods based on past data collection history and select the optimal method. This enables efficient data collection by analyzing past data collection history and selecting the optimal method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into an AI model and have the AI ​​select the collection method.

[0082] The data collection unit can filter data based on local seasons and events during data collection. For example, the data collection unit can change the type of data collected depending on the local season. The data collection unit can prioritize the collection of relevant data based on local event information. The data collection unit can also adjust the timing of data collection based on seasons and events. For example, the data collection unit can change the type of data collected depending on the local season. The data collection unit can prioritize the collection of relevant data based on local event information. The data collection unit can also adjust the timing of data collection based on seasons and events. This allows for the collection of highly relevant data by filtering data based on local seasons and events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input local season and event information into an AI model and have the AI ​​perform the data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting high-importance data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. The data collection unit can also reduce the amount of data collected to alleviate the burden if the user is stressed. For example, if the user is excited, the data collection unit will prioritize collecting high-importance data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is stressed, the data collection unit can also reduce the amount of data collected to alleviate the burden. This enables efficient data collection by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the region. The data collection unit can set the scope of data collection considering the geographical location information. The data collection unit can also determine the type of data to collect based on the geographical location information. For example, the data collection unit can prioritize the collection of highly relevant data based on the geographical location information of the region. The data collection unit can set the scope of data collection considering the geographical location information. The data collection unit can also determine the type of data to collect based on the geographical location information. This allows for the efficient collection of highly relevant data by considering the geographical location information of the region. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the region into an AI model and have the AI ​​perform data collection.

[0085] The data collection unit can analyze local social media activity and collect relevant data during data collection. For example, the data collection unit can analyze local social media activity and collect relevant data. The data collection unit can determine the type of data to collect based on social media trends. Furthermore, the data collection unit can adjust the frequency of data collection according to the level of social media activity. This allows for efficient collection of relevant data by analyzing local social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input local social media activity data into an AI model and have the AI ​​perform the data collection.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can also provide analysis results using visually stimulating graphs and charts. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can also provide analysis results using visually stimulating graphs and charts. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a concise analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a concise analysis on low-importance data. The analysis unit can also determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an environmental analysis algorithm to natural resource data. For example, the analysis unit can apply a historical value analysis algorithm to cultural asset data. For example, the analysis unit can apply a skill matching algorithm to human resource data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide the analysis using visually stimulating graphs and charts. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can also provide the analysis using visually stimulating graphs and charts. This allows the analysis unit to provide the user with the most optimal analysis by adjusting the length of the analysis according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis according to the data collection period. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis according to the data collection period. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection period into the generative AI and have the generative AI perform the determination of the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0092] The generation unit can estimate the user's emotions and adjust the way the generated ideas are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed ideas. If the user is in a hurry, the generation unit can provide concise ideas that get straight to the point. Furthermore, if the user is excited, the generation unit can provide visually stimulating ideas. This allows the system to provide ideas that are easy for the user to understand by adjusting the way ideas are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] The generation unit can adjust the level of detail generated based on the importance of the data when generating ideas. For example, the generation unit can generate detailed ideas based on high-importance data. The generation unit can generate concise ideas based on low-importance data. The generation unit can also determine the priority of the ideas to be generated according to the importance of the data. For example, the generation unit can generate detailed ideas based on high-importance data. The generation unit can generate concise ideas based on low-importance data. The generation unit can also determine the priority of the ideas to be generated according to the importance of the data. This makes efficient idea generation possible by adjusting the level of detail generated based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the generation.

[0094] The generation unit can apply different generation algorithms depending on the data category when generating ideas. For example, the generation unit can generate ideas related to environmental protection based on natural resource data. The generation unit can generate ideas related to the tourism industry based on cultural asset data. The generation unit can also generate ideas related to education and training based on human resource data. For example, the generation unit can generate ideas related to environmental protection based on natural resource data. The generation unit can generate ideas related to the tourism industry based on cultural asset data. The generation unit can also generate ideas related to education and training based on human resource data. By applying different generation algorithms depending on the data category, more appropriate ideas can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI execute the application of the generation algorithm.

[0095] The generation unit can estimate the user's emotions and adjust the length of the ideas it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide short, concise ideas. If the user is relaxed, the generation unit can provide detailed ideas. Furthermore, if the user is excited, the generation unit can provide visually stimulating ideas. This allows the system to provide the user with the most suitable ideas by adjusting the length of the ideas according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0096] The generation unit can determine the generation priority based on the data collection timing when generating ideas. For example, the generation unit can prioritize generating ideas based on the latest data. The generation unit can generate ideas based on the latest data while referring to past data. The generation unit can also determine the priority of ideas to generate depending on the data collection timing. For example, the generation unit can prioritize generating ideas based on the latest data. The generation unit can generate ideas based on the latest data while referring to past data. The generation unit can also determine the priority of ideas to generate depending on the data collection timing. This allows for the priority generation of ideas based on the latest data by determining the generation priority based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the data collection timing into the generation AI and have the generation AI determine the generation priority.

[0097] The generation unit can adjust the order of idea generation based on the relevance of the data. For example, the generation unit can prioritize generating ideas based on highly relevant data. The generation unit can postpone generating ideas based on less relevant data. The generation unit can also adjust the order of ideas to be generated according to the relevance of the data. For example, the generation unit can prioritize generating ideas based on highly relevant data. The generation unit can postpone generating ideas based on less relevant data. The generation unit can also adjust the order of ideas to be generated according to the relevance of the data. This allows for efficient idea generation by adjusting the order of generation based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of generation.

[0098] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide detailed simulation results. If the user is in a hurry, the simulation unit can provide concise simulation results that get straight to the point. Furthermore, if the user is excited, the simulation unit can provide simulation results using visually stimulating graphs or charts. For example, if the user is relaxed, the simulation unit can provide detailed simulation results. If the user is in a hurry, the simulation unit can provide concise simulation results that get straight to the point. If the user is excited, the simulation unit can also provide simulation results using visually stimulating graphs or charts. This allows the system to provide the optimal simulation results for the user by adjusting the simulation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0099] The simulation unit can select an efficient simulation method by referring to past simulation results during the simulation. For example, the simulation unit can select the optimal simulation method based on past simulation results. The simulation unit can analyze past simulation results, find areas for improvement in the simulation method, and optimize it. Furthermore, the simulation unit can find patterns in simulation methods based on past simulation results and select the optimal method. For example, the simulation unit can select the optimal simulation method based on past simulation results. The simulation unit can analyze past simulation results, find areas for improvement in the simulation method, and optimize it. The simulation unit can also find patterns in simulation methods based on past simulation results and select the optimal method. This makes it possible to select the optimal simulation method by referring to past simulation results, enabling efficient simulation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input past simulation results into an AI model and have the AI ​​perform the selection of the simulation method.

[0100] The simulation unit can customize the simulation methods based on regional characteristics during the simulation. For example, the simulation unit can customize the simulation methods according to regional characteristics. The simulation unit can set simulation parameters considering regional characteristics. The simulation unit can also adjust the simulation results based on regional characteristics. For example, the simulation unit can customize the simulation methods according to regional characteristics. The simulation unit can set simulation parameters considering regional characteristics. The simulation unit can also adjust the simulation results based on regional characteristics. By customizing the simulation methods based on regional characteristics, more appropriate simulation results can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input regional characteristics into an AI model and have the AI ​​perform the customization of the simulation methods.

[0101] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is excited, the simulation unit will prioritize high-priority simulations. If the user is relaxed, the simulation unit will prioritize detailed simulations. The simulation unit can also reduce the amount of simulations to alleviate the burden if the user is stressed. For example, if the user is excited, the simulation unit will prioritize high-priority simulations. If the user is relaxed, the simulation unit will prioritize detailed simulations. If the user is stressed, the simulation unit can also reduce the amount of simulations to alleviate the burden. This enables efficient simulation by determining the priority of simulations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0102] The simulation unit can select the optimal simulation method during simulation, taking into account the geographical location information of the region. For example, the simulation unit can select the optimal simulation method based on the geographical location information of the region. The simulation unit can set the simulation range, taking into account the geographical location information. The simulation unit can also set the simulation parameters based on the geographical location information. For example, the simulation unit can select the optimal simulation method based on the geographical location information of the region. The simulation unit can set the simulation range, taking into account the geographical location information. The simulation unit can also set the simulation parameters, taking into account the geographical location information of the region. This enables efficient simulation by selecting the simulation method while considering the geographical location information of the region. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the geographical location information of the region into an AI model and have the AI ​​perform the selection of the simulation method.

[0103] The simulation unit can analyze local social media activity during simulation and propose simulation methods. For example, the simulation unit can analyze local social media activity and propose simulation methods. The simulation unit can determine simulation methods based on social media trends. The simulation unit can also adjust simulation methods according to the volume of social media activity. For example, the simulation unit can analyze local social media activity and propose simulation methods. The simulation unit can determine simulation methods based on social media trends. The simulation unit can also adjust simulation methods according to the volume of social media activity. This allows for the proposal of more appropriate simulation methods by analyzing local social media activity. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input local social media activity data into an AI model and have the AI ​​execute the proposal of simulation methods.

[0104] The opinion gathering unit can estimate the user's emotions and adjust the timing of opinion gathering based on the estimated emotions. For example, if the user is stressed, the opinion gathering unit can reduce the frequency of opinion gathering to lessen the user's burden. If the user is relaxed, the opinion gathering unit can increase the frequency of opinion gathering to collect more detailed opinions. Furthermore, if the user is busy, the opinion gathering unit can adjust the timing of opinion gathering to match the user's schedule. For example, if the user is stressed, the opinion gathering unit can reduce the frequency of opinion gathering to lessen the user's burden. If the user is relaxed, the opinion gathering unit can increase the frequency of opinion gathering to collect more detailed opinions. If the user is busy, the opinion gathering unit can also adjust the timing of opinion gathering to match the user's schedule. This allows for efficient opinion gathering by adjusting the timing of opinion gathering according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The opinion collection unit can analyze past opinion collection history and select the optimal collection method. For example, the opinion collection unit can identify and apply the most efficient collection method from past opinion collection history. The opinion collection unit can analyze past opinion collection history, find areas for improvement in collection methods, and optimize them. Furthermore, the opinion collection unit can identify patterns in collection methods based on past opinion collection history and select the optimal method. For example, the opinion collection unit can identify and apply the most efficient collection method from past opinion collection history. The opinion collection unit can analyze past opinion collection history, find areas for improvement in collection methods, and optimize them. The opinion collection unit can also identify patterns in collection methods based on past opinion collection history and select the optimal method. This makes it possible to select the optimal collection method and collect opinions efficiently by analyzing past opinion collection history. Some or all of the above processes in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input past opinion collection history into an AI model and have the AI ​​select the collection method.

[0106] The opinion collection unit can estimate the user's emotions and determine the priority of opinions to collect based on the estimated emotions. For example, if the user is excited, the opinion collection unit will prioritize collecting high-importance opinions. If the user is relaxed, the opinion collection unit will prioritize collecting detailed opinions. Furthermore, if the user is stressed, the opinion collection unit can reduce the amount of opinions collected to alleviate the burden. For example, if the user is excited, the opinion collection unit will prioritize collecting high-importance opinions. If the user is relaxed, the opinion collection unit will prioritize collecting detailed opinions. If the user is stressed, the opinion collection unit can reduce the amount of opinions collected to alleviate the burden. This enables efficient opinion collection by determining the priority of opinions to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0107] The opinion collection unit can prioritize collecting highly relevant opinions by considering the geographical location information of the region. For example, the opinion collection unit can prioritize collecting highly relevant opinions based on the geographical location information of the region. The opinion collection unit can set the scope of opinion collection considering the geographical location information. The opinion collection unit can also determine the types of opinions to collect based on the geographical location information. For example, the opinion collection unit can prioritize collecting highly relevant opinions based on the geographical location information of the region. The opinion collection unit can set the scope of opinion collection considering the geographical location information. The opinion collection unit can also determine the types of opinions to collect based on the geographical location information. This allows for the efficient collection of highly relevant opinions by considering the geographical location information of the region. Some or all of the above processing in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input the geographical location information of the region into an AI model and have the AI ​​perform the opinion collection.

[0108] The consensus-building unit can estimate the user's emotions and adjust the consensus-building method based on the estimated emotions. For example, if the user is relaxed, the consensus-building unit can provide a detailed consensus-building method. If the user is in a hurry, the consensus-building unit can provide a concise consensus-building method that gets straight to the point. Furthermore, if the user is excited, the consensus-building unit can also provide a consensus-building method using visually stimulating graphs and charts. For example, if the user is relaxed, the consensus-building unit can provide a detailed consensus-building method. If the user is in a hurry, the consensus-building unit can provide a concise consensus-building method that gets straight to the point. If the user is excited, the consensus-building unit can also provide a consensus-building method using visually stimulating graphs and charts. This allows the consensus-building unit to provide the optimal consensus-building method for the user by adjusting the consensus-building method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the consensus building unit may be performed using AI, for example, or without AI. For example, the consensus building unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0109] The consensus-building unit can select the optimal method by referring to past consensus-building history during consensus-building. For example, the consensus-building unit can select the optimal method based on past consensus-building history. The consensus-building unit can analyze past consensus-building history, find areas for improvement in the method, and optimize it. Furthermore, the consensus-building unit can also find patterns in the method based on past consensus-building history and select the optimal method. For example, the consensus-building unit can select the optimal method based on past consensus-building history. The consensus-building unit can analyze past consensus-building history, find areas for improvement in the method, and optimize it. The consensus-building unit can also find patterns in the method based on past consensus-building history and select the optimal method. This makes it possible to select the optimal method by referring to past consensus-building history and achieve efficient consensus-building. Some or all of the above-described processes in the consensus-building unit may be performed using AI, for example, or without using AI. For example, the consensus-building unit can input past consensus-building history into an AI model and have the AI ​​perform the method selection.

[0110] The consensus-building unit can estimate the user's emotions and determine the priority of consensus-building based on the estimated user emotions. For example, if the user is excited, the consensus-building unit will prioritize high-priority consensus-building. If the user is relaxed, the consensus-building unit will prioritize detailed consensus-building. Furthermore, if the user is stressed, the consensus-building unit can reduce the amount of consensus-building to alleviate the burden. For example, if the user is excited, the consensus-building unit will prioritize high-priority consensus-building. If the user is relaxed, the consensus-building unit will prioritize detailed consensus-building. If the user is stressed, the consensus-building unit can reduce the amount of consensus-building to alleviate the burden. This enables efficient consensus-building by determining the priority of consensus-building according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the consensus-building unit may be performed using AI, for example, or without AI. For example, the consensus building unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0111] The consensus-building unit can select the optimal method when forming a consensus, taking into account the geographical location information of the region. For example, the consensus-building unit can select the optimal method based on the geographical location information of the region. The consensus-building unit can set the scope of consensus formation, taking into account the geographical location information. The consensus-building unit can also set the parameters of consensus formation, taking into account the geographical location information. This enables efficient consensus formation by selecting a consensus-building method that takes into account the geographical location information of the region. Some or all of the above-described processes in the consensus-building unit may be performed using AI, for example, or without using AI. For example, the consensus-building unit can input the geographical location information of the region into an AI model and have the AI ​​select a consensus-building method.

[0112] The planning unit can estimate the user's emotions and adjust the planning method based on the estimated emotions. For example, if the user is relaxed, the planning unit can provide a detailed planning method. If the user is in a hurry, the planning unit can provide a concise planning method that gets straight to the point. Furthermore, if the user is excited, the planning unit can provide a planning method using visually stimulating graphs and charts. For example, if the user is relaxed, the planning unit can provide a detailed planning method. If the user is in a hurry, the planning unit can provide a concise planning method that gets straight to the point. If the user is excited, the planning unit can also provide a planning method using visually stimulating graphs and charts. This allows the system to provide the optimal planning method for the user by adjusting the planning method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the planning department may be performed using AI, for example, or without AI. For example, the planning department can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0113] The planning department can select the optimal method by referring to past planning history when formulating a plan. For example, the planning department can select the optimal method based on past planning history. The planning department can analyze past planning history to find areas for improvement in the methods and optimize them. Furthermore, the planning department can also find patterns in the methods based on past planning history and select the optimal method. For example, the planning department can select the optimal method based on past planning history. The planning department can analyze past planning history to find areas for improvement in the methods and optimize them. The planning department can also find patterns in the methods based on past planning history and select the optimal method. This makes it possible to select the optimal method by referring to past planning history and to formulate a plan efficiently. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input past planning history into an AI model and have the AI ​​perform the method selection.

[0114] The planning unit can estimate the user's emotions and determine the priority of planning based on the estimated emotions. For example, if the user is excited, the planning unit will prioritize high-priority planning. If the user is relaxed, the planning unit will prioritize detailed planning. Furthermore, if the user is stressed, the planning unit can reduce the amount of planning required to alleviate the burden. For example, if the user is excited, the planning unit will prioritize high-priority planning. If the user is relaxed, the planning unit will prioritize detailed planning. If the user is stressed, the planning unit can reduce the amount of planning required to alleviate the burden. This enables efficient planning by determining the priority of planning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning department can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The planning department can select the optimal method when formulating a plan, taking into account the geographical location information of the region. For example, the planning department can select the optimal method based on the geographical location information of the region. The planning department can set the scope of the plan, taking into account the geographical location information. The planning department can also set the parameters of the plan based on the geographical location information. For example, the planning department can select the optimal method based on the geographical location information of the region. The planning department can set the scope of the plan, taking into account the geographical location information. The planning department can also set the parameters of the plan, taking into account the geographical location information of the region. This makes efficient plan formulation possible by selecting a plan formulation method while taking into account the geographical location information of the region. Some or all of the above processes in the planning department may be performed using AI, for example, or without using AI. For example, the planning department can input the geographical location information of the region into an AI model and have the AI ​​select a plan formulation method.

[0116] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can provide detailed training data. If the user is in a hurry, the learning unit can provide concise training data that gets straight to the point. Furthermore, if the user is excited, the learning unit can provide training data using visually stimulating graphs and charts. For example, if the user is relaxed, the learning unit can provide detailed training data. If the user is in a hurry, the learning unit can provide concise training data that gets straight to the point. If the user is excited, the learning unit can also provide training data using visually stimulating graphs and charts. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0117] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data to find areas for improvement in the algorithm and optimize it. The learning unit can also find algorithmic patterns based on past learning data and select the optimal method. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can analyze past learning data to find areas for improvement in the algorithm and optimize it. The learning unit can also find algorithmic patterns based on past learning data and select the optimal method. This allows for efficient learning by selecting the optimal learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into an AI model and have the AI ​​perform the optimization of the learning algorithm.

[0118] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit can increase the learning frequency and conduct detailed learning. If the user is in a hurry, the learning unit can decrease the learning frequency and conduct concise learning. Furthermore, if the user is excited, the learning unit can use visually stimulating graphs and charts for learning. For example, if the user is relaxed, the learning unit can increase the learning frequency and conduct detailed learning. If the user is in a hurry, the learning unit can decrease the learning frequency and conduct concise learning. If the user is excited, the learning unit can also use visually stimulating graphs and charts for learning. This allows for efficient learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0119] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can weight the training data based on the latest data. The learning unit can also weight the training data based on the latest data while referring to past data. Furthermore, the learning unit can adjust the weighting of the training data according to the data collection timing. For example, the learning unit can weight the training data based on the latest data. The learning unit can also weight the training data based on the latest data while referring to past data. The learning unit can also adjust the weighting of the training data according to the data collection timing. This enables efficient training by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the data collection timing into an AI model and have the AI ​​perform the training data weighting.

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

[0121] The data collection unit can estimate the emotions of local residents when collecting local data and adjust the data collection method based on those emotions. For example, if residents are feeling anxious, the unit can reduce the frequency of data collection to lessen their burden. Conversely, if residents are interested, the unit can increase the frequency of data collection to collect more detailed data. Furthermore, if residents are busy, the unit can adjust the timing of data collection to match their schedules. In this way, by adjusting the data collection method according to residents' emotions, the burden on residents is reduced and efficient data collection becomes possible.

[0122] The opinion gathering department can use a sentiment estimation function to determine the priority of opinions to collect from local residents and businesses. For example, if residents are agitated, high-importance opinions will be prioritized. If residents are relaxed, detailed opinions will be prioritized. Furthermore, if residents are stressed, the amount of opinions collected will be reduced to alleviate their burden. This allows for efficient opinion gathering by prioritizing opinions according to residents' emotions.

[0123] The consensus-building unit can analyze opinions collected from local residents and businesses and adjust the consensus-building method using sentiment estimation when providing information for consensus building. For example, if residents are relaxed, it can provide a detailed consensus-building method. If residents are in a hurry, it can provide a concise consensus-building method that gets straight to the point. Furthermore, if residents are agitated, it can provide a consensus-building method using visually stimulating graphs and charts. In this way, by adjusting the consensus-building method according to the emotions of the residents, it can provide the most optimal consensus-building method for them.

[0124] The planning department can use emotion estimation to adjust the planning method when translating proposed ideas into concrete implementation plans. For example, if residents are relaxed, a detailed planning method can be provided. If residents are in a hurry, a concise planning method that gets straight to the point can be provided. Furthermore, if residents are excited, a planning method using visually stimulating graphs and charts can be provided. In this way, by adjusting the planning method according to the residents' emotions, the optimal planning method can be provided for the residents.

[0125] The learning unit can continuously learn and improve the accuracy of its suggestions by using emotion estimation capabilities to select training data. For example, if a resident is relaxed, it can provide detailed training data. If a resident is in a hurry, it can provide concise training data that gets straight to the point. If a resident is excited, it can provide training data using visually stimulating graphs and charts. This allows for efficient learning by selecting training data according to the resident's emotions.

[0126] The data collection unit can filter local data based on local seasons and events. For example, it can change the type of data collected depending on the local season. Based on local event information, the data collection unit can prioritize the collection of relevant data. Furthermore, the data collection unit can adjust the timing of data collection based on seasons and events. This allows for the collection of highly relevant data by filtering data based on local seasons and events.

[0127] The data collection unit can analyze past data collection history and select the most efficient collection method. For example, it can identify and apply the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. Furthermore, the data collection unit can identify patterns in collection methods based on past data collection history and select the optimal method. As a result, by analyzing past data collection history, the optimal collection method can be selected, enabling efficient data collection.

[0128] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the region during data collection. For example, it can prioritize the collection of highly relevant data based on the geographical location information of the region. The data collection unit can set the scope of data collection considering the geographical location information. Furthermore, the data collection unit can also determine the type of data to collect based on the geographical location information. As a result, by collecting data while considering the geographical location information of the region, highly relevant data can be collected efficiently.

[0129] The data collection unit can analyze local social media activity and collect relevant data during the data collection process. For example, it can analyze local social media activity and collect relevant data. The data collection unit can determine the type of data to collect based on social media trends. Furthermore, the data collection unit can adjust the frequency of data collection according to the level of social media activity. This allows for the efficient collection of relevant data by analyzing local social media activity.

[0130] The analysis department can apply different analysis algorithms depending on the data category during analysis. For example, it can apply an environmental analysis algorithm to natural resource data, a historical value analysis algorithm to cultural asset data, and a skills matching algorithm to human resource data. By applying different analysis algorithms depending on the data category, it can provide more appropriate analysis results.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects local data. Local data includes demographic data, economic data, environmental data, natural resources, cultural assets, human resources, and infrastructure. The data collection unit can acquire data from sensors and databases, as well as through surveys and interviews with local residents and businesses. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, the analysis unit can use statistical analysis to analyze regional demographics, machine learning algorithms to analyze regional economic data, and generative AI to analyze regional environmental data. Step 3: The generation unit generates ideas based on the analysis results obtained by the analysis unit. The generated ideas include business ideas and technical ideas. The generation unit can generate innovative ideas tailored to the characteristics of the region using generation AI, and can generate ideas tailored to the characteristics of the region by learning from past success stories and the latest market trends. Step 4: The evaluation unit assesses the feasibility of the ideas generated by the generation unit. Feasibility assessment includes technical feasibility, economic feasibility, and social feasibility. The evaluation unit can assess technical feasibility using simulations, economic feasibility using cost-benefit analysis, and social feasibility using surveys.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, opinion gathering unit, consensus building unit, planning unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can acquire data from the sensors and database of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas using generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the feasibility of the generated ideas. The opinion gathering unit collects opinions from stakeholders using the control unit 46A of the smart device 14. The consensus building unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions to support consensus building. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the proposed ideas into a concrete implementation plan. The learning unit is implemented by the specific processing unit 290 of the data processing device 12, and continuously learns to improve the accuracy of the suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

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

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, evaluation unit, opinion gathering unit, consensus building unit, planning unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can acquire data from the sensors and database of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas using generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the feasibility of the generated ideas. The opinion gathering unit collects opinions from stakeholders using the control unit 46A of the smart glasses 214. The consensus building unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions to support consensus building. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the proposed ideas into a concrete implementation plan. The learning unit is implemented by the specific processing unit 290 of the data processing device 12, and continuously learns to improve the accuracy of the suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

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

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, opinion gathering unit, consensus building unit, planning unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can acquire data from the sensors and database of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas using generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the feasibility of the generated ideas. The opinion gathering unit collects opinions from stakeholders using the control unit 46A of the headset terminal 314. The consensus building unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions to support consensus building. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the proposed ideas into a concrete implementation plan. The learning unit is implemented by the specific processing unit 290 of the data processing device 12, and continuously learns to improve the accuracy of the suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

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

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, evaluation unit, opinion gathering unit, consensus building unit, planning unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can acquire data from the robot 414's sensors or database. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas using generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the feasibility of the generated ideas. The opinion gathering unit collects opinions from stakeholders using the control unit 46A of the robot 414. The consensus building unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions to support consensus building. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the proposed ideas into a concrete implementation plan. The learning unit is implemented by the specific processing unit 290 of the data processing device 12, and continuously learns to improve the accuracy of the suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) The data collection department collects local data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates ideas based on the analysis results obtained by the aforementioned analysis unit, The system includes an evaluation unit that evaluates the feasibility of ideas generated by the generation unit. A system characterized by the following features. (Note 2) It includes a department for collecting opinions from stakeholders. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a consensus-building unit that analyzes opinions and supports consensus building. The system described in Appendix 1, characterized by the features described herein. (Note 4) The department is equipped to develop a planning division that translates proposed ideas into concrete implementation plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a learning unit that continuously learns and improves the accuracy of its suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history and select the most efficient collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filter it based on local seasons and events. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze local social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts how ideas are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating ideas, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating ideas, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the ideas generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating ideas, prioritize the generation based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating ideas, adjust the generation order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During the simulation, past simulation results are referenced to select the most efficient simulation method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During the simulation, the simulation method is customized based on the characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by considering the geographical location information of the region. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned simulation unit, During the simulation, we will analyze local social media activity and propose simulation methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned opinion collection department, We estimate the user's emotions and adjust the timing of opinion collection based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned opinion collection department, Analyze past opinion collection history and select the most suitable collection method. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned opinion collection department, It estimates user sentiment and determines the priority of opinions to collect based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned opinion collection department, When collecting opinions, we prioritize collecting opinions that are highly relevant, taking into account the geographical location of the region. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned consensus building unit, It estimates user emotions and adjusts the consensus-building method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned consensus building unit, When reaching a consensus, the optimal method is selected by referring to past consensus-building history. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned consensus building unit, It estimates user emotions and determines consensus-building priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned consensus building unit, When reaching a consensus, the most suitable method will be selected, taking into account the geographical location information of the region. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned planning department, We estimate user emotions and adjust the planning method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned planning department, When formulating a plan, refer to past planning history to select the most suitable method. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned planning department, We estimate user emotions and determine planning priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned planning department, When formulating a plan, the most suitable method will be selected, taking into account the geographical location information of the region. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The data collection department collects local data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates ideas based on the analysis results obtained by the aforementioned analysis unit, The system includes an evaluation unit that evaluates the feasibility of ideas generated by the generation unit. A system characterized by the following features.

2. It includes a department for collecting opinions from stakeholders. The system according to feature 1.

3. It includes a consensus-building unit that analyzes opinions and supports consensus building. The system according to feature 1.

4. The department is equipped to develop a planning division that translates proposed ideas into concrete implementation plans. The system according to feature 1.

5. It features a learning unit that continuously learns and improves the accuracy of its suggestions. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze past data collection history and select the most efficient collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filter it based on local seasons and events. The system according to feature 1.

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