system

Abstract

The system according to this embodiment aims to identify social issues and prioritize and implement effective countermeasures. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a problem identification unit, a countermeasure prioritization unit, a policy proposal unit, and a project execution unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The problem identification unit identifies social problems based on the analysis results obtained by the analysis unit. The countermeasure prioritization unit prioritizes countermeasures based on the problems identified by the problem identification unit. The policy proposal unit makes policy proposals based on the countermeasures prioritized by the countermeasure prioritization unit. The project execution unit executes projects based on the policies proposed by the policy proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional technology, there is a problem that the process of identifying social issues, prioritizing effective countermeasures, and implementing them is complex and difficult to perform efficiently.

[0005] The system according to the embodiment aims to identify social issues, prioritize effective countermeasures, and implement them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a problem identification unit, a countermeasure prioritization unit, a policy proposal unit, and a project execution unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The problem identification unit identifies social problems based on the analysis results obtained by the analysis unit. The countermeasure prioritization unit prioritizes countermeasures based on the problems identified by the problem identification unit. The policy proposal unit makes policy proposals based on the countermeasures prioritized by the countermeasure prioritization unit. The project execution unit executes projects based on the policies proposed by the policy proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify social issues and prioritize and implement effective countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The integrated solution platform according to an embodiment of the present invention is a system that deploys a comprehensive solution platform to impoverished cities, solves fundamental problems without using its own capital, and builds continuously prosperous socio-economic activities. This platform collects and analyzes data in fields such as economics, politics, healthcare, agriculture, IT, and education to identify social issues and prioritize countermeasures. It makes policy proposals with AI and experts and executes projects. For example, AI collects and analyzes data from impoverished cities. Next, AI identifies social issues and prioritizes countermeasures. For example, in the fields of healthcare, agriculture, and education, AI breaks down issues and plans. Furthermore, AI and experts collaborate to make policy proposals and execute projects. To secure funding and revenue, it raises funds from social impact investment funds, crowdfunding, and from governments and international organizations. It raises funds without generating its own capital and secures revenue through the proceeds from project execution. This platform starts with generating case studies in Small Start and builds up successful cases in Town, City, State, and Country. This expands fundraising and revenue generation, realizing sustainable urban development. This enables the integrated solutions platform to provide comprehensive solutions to impoverished cities, solve fundamental problems without using its own capital, and build sustainable, prosperous socio-economic activities.

[0029] The integrated solution platform according to this embodiment comprises a data collection unit, an analysis unit, a problem identification unit, a countermeasure prioritization unit, a policy proposal unit, and a project execution unit. The data collection unit collects data. The data collection unit can collect data in fields such as economics, politics, medicine, agriculture, IT, and education. The data collection unit can acquire data from sensors and databases, for example. The data collection unit can also collect information from social media and online forums. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using AI, for example. The AI ​​can use technologies such as deep learning and natural language processing. The problem identification unit identifies social issues based on the analysis results obtained by the analysis unit. The problem identification unit identifies issues using AI, for example. The AI ​​can use data mining and machine learning algorithms, for example. The countermeasure prioritization unit prioritizes countermeasures based on the issues identified by the problem identification unit. The countermeasure prioritization unit prioritizes countermeasures using AI, for example. The AI ​​can prioritize countermeasures based on criteria such as impact, urgency, and cost. The Policy Proposal Department makes policy proposals based on measures prioritized by the Measure Prioritization Department. The Policy Proposal Department makes policy proposals using, for example, AI. The AI ​​can be used for methods such as drafting bills and creating budget proposals. The Project Execution Department executes projects based on the policies proposed by the Policy Proposal Department. The Project Execution Department executes projects using, for example, AI. The AI ​​can be used for methods such as project progress management and resource optimization. As a result, the integrated solution platform according to the embodiment can provide comprehensive solutions to impoverished cities, solve fundamental problems without using its own capital, and build continuously prosperous socio-economic activities.

[0030] The data collection unit collects data. For example, it can collect data in fields such as economics, politics, healthcare, agriculture, IT, and education. Specifically, in the economic field, it collects macroeconomic indicators such as GDP growth rate, unemployment rate, and inflation rate, as well as corporate financial data and consumer purchasing behavior data. In the political field, it collects data such as election results, policy-making processes, and parliamentary minutes. In the healthcare field, it collects hospital patient data, medical equipment operating status, and drug usage data. In the agricultural field, it collects crop growth status, weather data, and soil nutrient status. In the IT field, it collects network traffic data, server operating status, and security incident logs. In the education field, it collects student performance data, attendance rates, and data measuring the effectiveness of educational programs. The data collection unit acquires data from sources such as sensors and databases. Sensors are diverse, including environmental sensors, medical sensors, and agricultural sensors, and can collect data in real time. Databases are acquired from databases held by government agencies, companies, and research institutions. Furthermore, the data collection unit can also gather information from social media and online forums. From social media, it collects user posts, comments, and trend information, while from online forums, it collects expert opinions and discussion content. This allows the data collection unit to gather a wide range of data from diverse data sources and build a comprehensive database. In addition, to ensure data quality, the data collection unit can perform data cleaning and normalization to provide reliable data.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. The Analysis Department uses AI, for example, to analyze the data. AI can utilize technologies such as deep learning and natural language processing. Specifically, it can use deep learning to analyze image and audio data, performing pattern recognition and anomaly detection. It can use natural language processing to extract meaning from text data, performing sentiment analysis and topic modeling. Furthermore, AI can use big data analysis techniques to extract useful information from large amounts of data, revealing trends and correlations. For example, it can analyze economic data to predict economic trends and changes in consumer purchasing behavior. It can analyze medical data to evaluate disease occurrence patterns and treatment effectiveness. It can analyze agricultural data to predict crop growth and yield. It can analyze IT data to understand network traffic patterns and security incident trends. It can analyze educational data to evaluate the effectiveness of educational programs and student learning performance. This allows the Analysis Department to analyze collected data from multiple perspectives and provide useful insights for solving social issues. Furthermore, the analysis department can use data visualization techniques to display analysis results in an easy-to-understand manner, enabling stakeholders to intuitively grasp them. For example, graphs, charts, and heatmaps can be used to visually indicate data trends and outliers. This allows the analysis department to effectively communicate data analysis results and support decision-making.

[0032] The Issue Identification Unit identifies social issues based on the analysis results obtained by the Analysis Unit. The Issue Identification Unit identifies issues using, for example, AI. AI can use, for example, data mining or machine learning algorithms. Specifically, it uses data mining techniques to extract potential issues and problems from large amounts of data. For example, economic data can identify economic imbalances or stagnant growth in specific regions or industries. Medical data can identify rising incidence rates of specific diseases or shortages of medical resources. Agricultural data can identify poor crop growth or the impact of climate change. IT data can identify increasing security threats or degrading system performance. Educational data can identify declining academic achievement or widening educational disparities. It can also use machine learning algorithms to analyze data patterns and trends and predict future issues. For example, economic data can be analyzed to predict the risk of future economic recession. Medical data can be analyzed to predict future disease outbreaks. Agricultural data can be analyzed to predict the risk of future food shortages. IT data can be analyzed to predict future increases in security threats. Educational data can be analyzed to predict widening educational disparities in the future. This allows the Issue Identification Department to identify not only current issues but also future issues, providing information to enable early countermeasures. Furthermore, the Issue Identification Department can evaluate the impact and urgency of the identified issues and prioritize them. This enables the Issue Identification Department to respond quickly to the most important issues and implement effective countermeasures.

[0033] The countermeasure prioritization unit prioritizes countermeasures based on the issues identified by the issue identification unit. The countermeasure prioritization unit uses AI, for example, to prioritize countermeasures. AI can prioritize countermeasures based on criteria such as impact, urgency, and cost. Specifically, impact assessment considers the magnitude of the impact a particular issue has on society as a whole. For example, for economic issues, it evaluates the impact on GDP and employment; for healthcare issues, it evaluates the impact on patient health and medical resources. Urgency assessment determines whether the issue needs to be addressed urgently. For example, emergencies such as natural disasters and pandemics require a rapid response. Cost assessment considers the resources and expenses necessary to implement countermeasures. For example, infrastructure development and the introduction of medical equipment incur significant costs, making cost-effectiveness assessment crucial. Based on these criteria, AI can calculate the priority of each countermeasure and identify the most effective one. Furthermore, the countermeasure prioritization unit can use simulation technology to examine multiple countermeasure scenarios and select the optimal countermeasure. For example, it can simulate different combinations of countermeasures and their implementation timings to select the most effective one. Furthermore, the priority setting unit can periodically review priorities and respond to the latest situation. This allows the priority setting unit to always select the most optimal solution and support effective problem-solving.

[0034] The Policy Proposal Department makes policy proposals based on measures prioritized by the Measure Prioritization Department. The Policy Proposal Department uses AI, for example, to make policy proposals. AI can be used in methods such as drafting bills and creating budget proposals. Specifically, AI analyzes past policy data and successful examples of bills to generate optimal policy proposals. For example, in economic policy, it analyzes the effects of past economic stimulus measures and tax reforms to propose the most effective policies. In healthcare policy, it analyzes the effects of past healthcare system reforms and vaccination programs to propose the most effective policies. In agricultural policy, it analyzes the effects of past agricultural support measures and disaster countermeasures to propose the most effective policies. In IT policy, it analyzes the effects of past cybersecurity measures and digital infrastructure development to propose the most effective policies. In education policy, it analyzes the effects of past education reforms and scholarship programs to propose the most effective policies. Furthermore, AI can use simulation technology to evaluate the effects of proposed policies in advance. For example, it can perform simulations of economic policies to predict the impact on GDP and employment. It can perform simulations of healthcare policies to predict the impact on patient health and medical resources. It can perform simulations of agricultural policies to predict the impact on crop growth and yield. We conduct simulations of IT policies to predict their impact on network security and performance. We also conduct simulations of education policies to predict their impact on student learning performance and educational disparities. This allows the policy proposal department to make evidence-based policy proposals and support effective policy implementation.

[0035] The Project Execution Department executes projects based on policies proposed by the Policy Proposal Department. The Project Execution Department uses AI, for example, to execute projects. AI can be used for methods such as project progress management and resource optimization. Specifically, AI manages project schedules and monitors the progress of each task in real time. For example, in construction projects, it manages construction progress and material procurement to prevent delays and cost overruns. In medical projects, it manages patient treatment plans and the allocation of medical resources to ensure efficient medical care. In agricultural projects, it manages crop cultivation plans and harvest schedules to ensure optimal harvest timing. In IT projects, it manages system development schedules and resource allocation to support project success. In educational projects, it manages the implementation schedule of educational programs and resource allocation to ensure effective educational delivery. Furthermore, AI optimizes resources to maximize project efficiency. For example, it plans the optimal allocation of human resources and the efficient use of materials to reduce project costs. AI also manages risks to increase the probability of project success. For example, it identifies project risk factors and plans risk mitigation measures. This allows the project execution department to efficiently manage project progress using AI and lead it to success. Furthermore, the project execution department can evaluate project results and identify areas for improvement for future projects. This enables the project execution department to continuously improve and achieve more effective project execution.

[0036] The fundraising department can raise funds. For example, it can raise funds through crowdfunding. It can also raise funds by applying for government subsidies. Furthermore, it can raise funds through social impact investment funds. For example, the fundraising department can use a crowdfunding platform to publish project details and raise funds from supporters. When applying for government subsidies, the fundraising department clarifies the project's objectives and the amount of funding required and submits an application. When using social impact investment funds, the fundraising department explains the project's social impact and profitability to investors and raises funds. This allows the fundraising department to secure funds to execute projects without using its own capital. Some or all of the above processes in the fundraising department may be performed using AI or not. For example, the fundraising department can input data from a crowdfunding platform into an AI, which can then suggest the most suitable fundraising method.

[0037] The revenue generation unit can generate revenue. For example, the revenue generation unit can generate revenue through the sale of goods. It can also generate revenue through the provision of services. Furthermore, the revenue generation unit can generate revenue through investment returns. For example, the revenue generation unit can generate revenue by selling goods produced in the project. In the case of service provision, the revenue generation unit can generate revenue by setting a fee for the services provided in the project. In the case of investment returns, the revenue generation unit can generate additional revenue by reinvesting a portion of the project's revenue. This allows the revenue generation unit to generate revenue necessary for sustainable project operation. Some or all of the above processes in the revenue generation unit may be performed using AI or not. For example, the revenue generation unit can input sales data of goods into AI, which can then propose an optimal sales strategy.

[0038] The success story generation unit can generate success stories. For example, the success story generation unit can generate a success story when a project is completed. It can also generate a success story when a goal is achieved. Furthermore, it can generate a success story when a social impact is achieved. For example, if a project is completed on schedule, the success story generation unit records that completion as a success story. In the case of goal achievement, if the project's goals are achieved, the success story generation unit records that achievement as a success story. In the case of social impact, the success story generation unit evaluates the impact the project had on society and records that impact as a success story. This allows the success story generation unit to generate success stories that can be easily expanded to other regions. Some or all of the above-described processes in the success story generation unit may be performed using AI or not. For example, the success story generation unit can input project progress data into AI, and the AI ​​can automatically generate success stories.

[0039] The data collection unit can collect data in fields such as economics, politics, healthcare, agriculture, IT, and education. For example, in the economic field, it can collect data such as GDP and unemployment rates, and in the healthcare field, it can collect data such as the number of hospitals and access to healthcare. The data collection unit can obtain data from government statistical databases, for example. It can also collect information from social media and online forums. For example, the data collection unit can analyze social media posts to understand local conditions. It can also collect opinions and comments from online forums and incorporate them into the data. This allows the data collection unit to collect data from diverse fields and conduct comprehensive analysis. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into AI, which can then analyze and collect the data.

[0040] The analysis department can analyze data collected by the collection department using AI. For example, the analysis department can analyze data using deep learning. Deep learning can learn from large amounts of data and identify patterns and trends. The analysis department can also analyze text data using natural language processing. Natural language processing is a technique that extracts meaning and sentiment from text data. Furthermore, the analysis department can analyze data using machine learning algorithms. Machine learning algorithms can build predictive models from data and predict future trends. For example, the analysis department can use deep learning to analyze economic data and identify economic trends. It can use natural language processing to analyze social media posts and understand local sentiment. It can use machine learning algorithms to analyze medical data and predict hospital demand. In this way, the analysis department can analyze collected data with high accuracy and provide a foundation for identifying social issues.

[0041] The problem identification unit can identify social issues based on the analysis results obtained by the analysis unit. For example, the problem identification unit can identify issues using AI. AI can extract important patterns and trends from data using data mining techniques. The problem identification unit can also predict social issues from data using machine learning algorithms. For example, the problem identification unit can analyze economic data to identify the causes of poverty, analyze medical data to identify problems with access to healthcare, and analyze educational data to identify problems with educational inequality. In this way, the problem identification unit can identify social issues based on the analysis results and provide a foundation for taking effective countermeasures.

[0042] The countermeasure prioritization unit can prioritize countermeasures based on the issues identified by the issue identification unit. The countermeasure prioritization unit can prioritize countermeasures using, for example, AI. The AI ​​can evaluate and prioritize countermeasures based on criteria such as impact, urgency, and cost. For example, the countermeasure prioritization unit will prioritize countermeasures with high impact, high urgency, and low cost. In this way, the countermeasure prioritization unit can prioritize countermeasures to achieve efficient problem solving.

[0043] The Policy Proposal Department can make policy proposals based on measures prioritized by the Measure Prioritization Department. The Policy Proposal Department can make policy proposals using, for example, AI. The AI ​​can make policy proposals using methods such as drafting bills and creating budget proposals. For example, the Policy Proposal Department can draft bills based on measures with a high impact. It can create budget proposals based on measures with a high urgency. It can propose policies based on measures with a low cost. In this way, the Policy Proposal Department can make proposals to implement effective policies.

[0044] The Project Execution Department can execute projects based on policies proposed by the Policy Proposal Department. The Project Execution Department can, for example, use AI to execute projects. AI can execute projects using methods such as project progress management and resource optimization. For example, the Project Execution Department can monitor project progress in real time and reallocate resources as needed. As the project progresses, it can assess risks and take appropriate measures. This allows the Project Execution Department to execute projects in a way that ensures their effectiveness.

[0045] The data collection unit can customize its collection methods to take into account the local cultural background and social circumstances. For example, the data collection unit may use appropriate language and expressions based on the local cultural background when collecting data. It may also adjust the method and timing of data collection considering the local social circumstances. During specific local events or festivals, data collection may be temporarily suspended. This allows the data collection unit to collect data in a way that is appropriate for the characteristics of the region. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the local cultural background and social circumstances into an AI, which can then suggest the optimal collection method.

[0046] The data collection unit can receive real-time feedback during data collection and dynamically adjust the collection method. For example, the data collection unit can receive user feedback during data collection and immediately change the collection method. Based on the real-time feedback, it can adjust the type and amount of data to be collected. Based on the feedback, it can dynamically change the timing and frequency of data collection. This allows the data collection unit to perform efficient data collection based on real-time feedback. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user feedback data into AI, which can then suggest the optimal collection method.

[0047] The data collection unit can optimize the collection range by considering the geographical characteristics of the region during data collection. For example, the data collection unit can set the collection range based on the geographical characteristics of the region and collect data efficiently. It can dynamically change the collection range considering geographical characteristics. It can customize the collection method according to the geographical characteristics of the region. This allows the data collection unit to perform efficient data collection according to the geographical characteristics of the region. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the geographical characteristics of the region into the AI, and the AI ​​can propose the optimal collection range.

[0048] The data collection unit can integrate and collect information from social media and online forums during data collection. For example, the data collection unit can collect posts from social media to understand local conditions. It can also collect opinions and comments from online forums and incorporate them into the data. By integrating data from social media and online forums, it can collect comprehensive information. This allows the data collection unit to perform comprehensive data collection by integrating information from social media and online forums. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into AI, which can then analyze and collect the data.

[0049] The analysis unit can identify trends and build predictive models by comparing current data with past data during analysis. For example, the analysis unit can identify current trends and make future predictions based on past data. It compares past and current data to identify patterns of change. Based on past data, it builds predictive models and predicts future trends. This allows the analysis unit to make future predictions by comparing with past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into AI, which can then identify trends and build predictive models.

[0050] The analysis unit can integrate different data sources and perform analysis from multiple perspectives during the analysis process. For example, the analysis unit can integrate different data sources and perform comprehensive analysis. It can combine information from different data sources and perform analysis from multiple perspectives. It can provide more accurate analysis results based on different data sources. In this way, the analysis unit can perform comprehensive analysis by integrating different data sources. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data sources into AI, and the AI ​​can integrate and analyze the data.

[0051] The analysis department can customize the analysis results by taking into account the local economic and political situation during the analysis. For example, the analysis department can adjust the analysis results by considering the local economic situation. It can customize the analysis results by considering the local political situation. It can optimize the analysis results based on the local economic and political situation. This allows the analysis department to provide analysis results tailored to the characteristics of the region, enabling more appropriate measures. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input data on the local economic and political situation into AI, which can then provide optimal analysis results.

[0052] The analysis department can improve the accuracy of its analysis by referring to relevant research papers and reports during the analysis process. For example, the analysis department can improve the accuracy of its analysis by referring to relevant research papers. It can supplement the analysis results based on relevant reports. It can increase the reliability of its analysis by referring to research papers and reports. In this way, the reliability of the analysis department's analysis can be increased by referring to relevant research papers and reports. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input data from relevant research papers and reports into an AI, which can then improve the accuracy of the analysis.

[0053] The problem identification unit can improve the accuracy of problem identification by referring to past success and failure cases when identifying problems. For example, the problem identification unit can improve the accuracy of problem identification by referring to past success cases. It can improve the method of problem identification based on past failure cases. It can improve the accuracy of problem identification by referring to success and failure cases. In this way, the problem identification unit can improve the accuracy of problem identification by referring to past cases. Some or all of the above processing in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on past success and failure cases into AI, and the AI ​​can improve the accuracy of problem identification.

[0054] The problem identification unit can improve its accuracy by incorporating feedback from local experts and residents during the problem identification process. For example, the problem identification unit can improve the accuracy of problem identification based on feedback from local experts. It can improve its problem identification method by incorporating opinions from local residents. It can improve the accuracy of problem identification by referring to feedback from experts and residents. In this way, the problem identification unit improves the accuracy of problem identification by incorporating feedback from local experts and residents. Some or all of the above processes in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input feedback data from local experts and residents into AI, which can then improve the accuracy of identification.

[0055] The problem identification unit can customize its problem identification method by considering the geographical characteristics and cultural background of the region. For example, the problem identification unit adjusts the problem identification method based on the geographical characteristics of the region. It customizes the problem identification method by considering the cultural background of the region. It optimizes the problem identification method according to the geographical characteristics and cultural background. This allows the problem identification unit to identify problems that are appropriate to the characteristics of the region. Some or all of the above processes in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on the geographical characteristics and cultural background of the region into the AI, which can then propose the optimal identification method.

[0056] The problem identification unit can improve the accuracy of problem identification by referring to relevant policies and regulations when identifying problems. For example, the problem identification unit can improve the accuracy of problem identification by referring to relevant policies. It can improve the method of problem identification based on relevant laws and regulations. It can improve the accuracy of problem identification by referring to policies and laws and regulations. In this way, the problem identification unit improves the accuracy of problem identification by referring to relevant policies and laws and regulations. Some or all of the above processing in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on relevant policies and laws and regulations into AI, which can improve the accuracy of identification.

[0057] The countermeasure prioritization unit can optimize priorities by evaluating the effectiveness of past countermeasures when prioritizing countermeasures. For example, the countermeasure prioritization unit evaluates the effectiveness of past countermeasures and prioritizes the implementation of the most effective countermeasures. Based on past countermeasure failure cases, it re-evaluates priorities. It analyzes the effectiveness of past countermeasures and optimizes priorities. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by evaluating the effectiveness of past countermeasures. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on past countermeasures into AI, and the AI ​​can evaluate their effectiveness and optimize priorities.

[0058] The countermeasure prioritization unit can determine priorities by considering local resources and infrastructure conditions when prioritizing countermeasures. For example, the countermeasure prioritization unit considers local resources and prioritizes the implementation of the most effective countermeasures. Based on the local infrastructure conditions, it re-evaluates the prioritization. Considering resources and infrastructure conditions, it optimizes the prioritization. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by considering local resources and infrastructure conditions. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on local resources and infrastructure conditions into AI, and the AI ​​can propose the optimal prioritization.

[0059] The countermeasure prioritization unit can optimize priorities by considering the local economic and political situation when prioritizing countermeasures. For example, the countermeasure prioritization unit considers the local economic situation and prioritizes the implementation of the most effective countermeasure. Based on the local political situation, it re-evaluates the prioritization. Considering the economic and political situation, it optimizes the prioritization. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasure by considering the local economic and political situation. Some or all of the above processing in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal prioritization.

[0060] The countermeasure prioritization unit can determine priorities by referring to relevant research papers and reports when prioritizing countermeasures. For example, the countermeasure prioritization unit can refer to relevant research papers and prioritize the implementation of the most effective countermeasures. Based on relevant reports, it re-evaluates the priorities. By referring to research papers and reports, it optimizes the priorities. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by referring to relevant research papers and reports. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data from relevant research papers and reports into AI, and the AI ​​can propose the optimal priorities.

[0061] The policy proposal department can optimize its proposals by evaluating the effectiveness of past policies. For example, the policy proposal department can evaluate the effectiveness of past policies and make the most effective proposal. It can re-evaluate proposals based on past policy failures. It can analyze the effectiveness of past policies and optimize proposals. In this way, the policy proposal department can make the most effective proposals by evaluating the effectiveness of past policies. Some or all of the above processes in the policy proposal department may be performed using AI or not. For example, the policy proposal department can input data on past policies into AI, which can then evaluate their effectiveness and optimize the proposals.

[0062] The policy proposal department can improve its proposals by incorporating feedback from local experts and residents. For example, the policy proposal department can improve its proposals based on feedback from local experts, incorporate opinions from local residents to improve the proposals, and refer to feedback from experts and residents to enhance the proposals. In this way, the policy proposal department can improve the accuracy of its proposals by incorporating feedback from local experts and residents. Some or all of the above processes in the policy proposal department may be performed using AI, or not. For example, the policy proposal department can input feedback data from local experts and residents into AI, which can then improve the proposals.

[0063] The policy proposal department can customize its proposals when submitting them, taking into account the geographical characteristics and cultural background of the region. For example, the policy proposal department can adjust the proposal based on the geographical characteristics of the region, customize the proposal considering the cultural background of the region, and optimize the proposal according to the geographical characteristics and cultural background. This allows the policy proposal department to make policy proposals that are tailored to the characteristics of the region. Some or all of the above processes in the policy proposal department may be performed using AI, or not. For example, the policy proposal department can input data on the geographical characteristics and cultural background of the region into an AI, which can then provide the most suitable proposal.

[0064] The policy proposal department can improve its proposals by referring to relevant policies and regulations when making policy proposals. For example, the policy proposal department can improve its proposals by referring to relevant policies. It can improve its proposals based on relevant laws and regulations. It can enhance its proposals by referring to policies and laws and regulations. In this way, the accuracy of the policy proposal department's proposals can be improved by referring to relevant policies and laws and regulations. Some or all of the above processes in the policy proposal department may be performed using AI or not. For example, the policy proposal department can input data on relevant policies and laws and regulations into AI, which can then improve the proposals.

[0065] The project execution unit can improve execution accuracy by referring to past project success and failure cases during project execution. For example, the project execution unit can improve project execution accuracy by referring to past success cases. It can improve execution methods based on past failure cases. It can improve execution accuracy by referring to success and failure cases. In this way, the project execution unit can improve the accuracy of project execution by referring to past cases. Some or all of the above processes in the project execution unit may be performed using AI or not. For example, the project execution unit can input data on past success and failure cases into AI, and the AI ​​can improve execution accuracy.

[0066] The project execution unit can optimize its execution method by considering local resources and infrastructure conditions during project execution. For example, the project execution unit considers local resources and executes the project in the most effective way. It re-evaluates the execution method based on the local infrastructure conditions. It optimizes the execution method by considering resources and infrastructure conditions. In this way, the project execution unit can execute the project in the most effective way by considering local resources and infrastructure conditions. Some or all of the above processes in the project execution unit may be performed using AI or not. For example, the project execution unit can input data on local resources and infrastructure conditions into AI, and the AI ​​can propose the optimal execution method.

[0067] The project execution unit can optimize its execution methods by considering the local economic and political situation during project execution. For example, the project execution unit can execute the project in the most effective way, taking into account the local economic situation. It can re-evaluate the execution method based on the local political situation. It can optimize the execution method by considering the economic and political situation. In this way, the project execution unit can execute the project in the most effective way by considering the local economic and political situation. Some or all of the above processes in the project execution unit may be performed using AI, or not. For example, the project execution unit can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal execution method.

[0068] The project execution unit can improve execution accuracy by referring to relevant research papers and reports during project execution. For example, the project execution unit can improve project execution accuracy by referring to relevant research papers. Based on relevant reports, it can improve execution methods. By referring to research papers and reports, it can increase execution accuracy. In this way, the accuracy of project execution can be improved by the project execution unit by referring to relevant research papers and reports. Some or all of the above processes in the project execution unit may be performed using AI or not. For example, the project execution unit can input data from relevant research papers and reports into AI, which can then improve execution accuracy.

[0069] The fundraising department can optimize its fundraising methods by referring to past successful and unsuccessful fundraising cases. For example, the fundraising department can optimize fundraising methods by referring to past successes. It can improve fundraising methods based on past failures. By referring to successes and failures, the fundraising department optimizes its fundraising methods by referring to past cases. Some or all of the above processes in the fundraising department may be performed using AI or not. For example, the fundraising department can input data on past successes and failures into an AI, which can then optimize the fundraising methods.

[0070] The fundraising department can optimize its fundraising methods by considering the local economic and political situation. For example, the fundraising department can consider the local economic situation and raise funds in the most effective way. It can re-evaluate the fundraising methods based on the local political situation. It can optimize the fundraising methods by considering the economic and political situation. In this way, the fundraising department can raise funds in the most effective way by considering the local economic and political situation. Some or all of the above processes in the fundraising department may be performed using AI or not. For example, the fundraising department can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal fundraising method.

[0071] The revenue generation unit can optimize its methods when generating revenue by referring to past success and failure cases. For example, the revenue generation unit can optimize its revenue generation methods by referring to past success cases. It can improve its methods based on past failure cases. It can optimize its methods by referring to success and failure cases. In this way, the revenue generation unit optimizes its revenue generation methods by referring to past cases. Some or all of the above processes in the revenue generation unit may be performed using AI or not. For example, the revenue generation unit can input data on past success and failure cases into AI, and the AI ​​can optimize the methods.

[0072] The revenue generation unit can optimize its methods for generating revenue by considering the local economic and political situation. For example, the revenue generation unit may generate revenue in the most effective way, taking into account the local economic situation. It may then re-evaluate the method based on the local political situation. By considering the economic and political situation, it can optimize the method. This allows the revenue generation unit to generate revenue in the most effective way by considering the local economic and political situation. Some or all of the above processes in the revenue generation unit may be performed using AI or not. For example, the revenue generation unit can input data on the local economic and political situation into an AI, which can then propose the optimal method.

[0073] The success story generation unit can improve its generation accuracy by referring to past success and failure stories when generating success stories. For example, the success story generation unit can improve generation accuracy by referring to past success stories. It can improve the generation method based on past failure stories. It can improve generation accuracy by referring to success and failure stories. In this way, the success story generation unit can improve the accuracy of generating success stories by referring to past cases. Some or all of the above processing in the success story generation unit may be performed using AI or not. For example, the success story generation unit can input data on past success and failure stories into AI, and the AI ​​can improve generation accuracy.

[0074] The success story generation unit can optimize its generation method by considering the local economic and political situation when generating success stories. For example, the success story generation unit generates success stories in the most effective way, taking into account the local economic situation. It re-evaluates the generation method based on the local political situation. It optimizes the generation method by considering the economic and political situation. In this way, the success story generation unit can generate success stories in the most effective way by considering the local economic and political situation. Some or all of the above processing in the success story generation unit may be performed using AI or not. For example, the success story generation unit can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal generation method.

[0075] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0076] The data collection unit can customize its collection methods to take into account the local cultural background and social circumstances. For example, the data collection unit may use appropriate language and expressions based on the local cultural background when collecting data. It may also adjust the method and timing of data collection considering the local social circumstances. During specific local events or festivals, data collection may be temporarily suspended. This allows the data collection unit to collect data in a way that is appropriate for the characteristics of the region. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the local cultural background and social circumstances into an AI, which can then suggest the optimal collection method.

[0077] The analysis unit can identify trends and build predictive models by comparing current data with past data during analysis. For example, the analysis unit can identify current trends and make future predictions based on past data. It compares past and current data to identify patterns of change. Based on past data, it builds predictive models and predicts future trends. This allows the analysis unit to make future predictions by comparing with past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into AI, which can then identify trends and build predictive models.

[0078] The problem identification unit can improve the accuracy of problem identification by referring to past success and failure cases when identifying problems. For example, the problem identification unit can improve the accuracy of problem identification by referring to past success cases. It can improve the method of problem identification based on past failure cases. It can improve the accuracy of problem identification by referring to success and failure cases. In this way, the problem identification unit can improve the accuracy of problem identification by referring to past cases. Some or all of the above processing in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on past success and failure cases into AI, and the AI ​​can improve the accuracy of problem identification.

[0079] The countermeasure prioritization unit can optimize priorities by evaluating the effectiveness of past countermeasures when prioritizing countermeasures. For example, the countermeasure prioritization unit evaluates the effectiveness of past countermeasures and prioritizes the implementation of the most effective countermeasures. Based on past countermeasure failure cases, it re-evaluates priorities. It analyzes the effectiveness of past countermeasures and optimizes priorities. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by evaluating the effectiveness of past countermeasures. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on past countermeasures into AI, and the AI ​​can evaluate their effectiveness and optimize priorities.

[0080] The policy proposal department can optimize its proposals by evaluating the effectiveness of past policies. For example, the policy proposal department can evaluate the effectiveness of past policies and make the most effective proposal. It can re-evaluate proposals based on past policy failures. It can analyze the effectiveness of past policies and optimize proposals. In this way, the policy proposal department can make the most effective proposals by evaluating the effectiveness of past policies. Some or all of the above processes in the policy proposal department may be performed using AI or not. For example, the policy proposal department can input data on past policies into AI, which can then evaluate their effectiveness and optimize the proposals.

[0081] The following briefly describes the processing flow for example form 1.

[0082] Step 1: The data collection unit collects data. The data collection unit can collect data in fields such as economics, politics, medicine, agriculture, IT, and education. The data collection unit can acquire data from sensors and databases, for example. It can also collect information from social media and online forums. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. The AI ​​can use technologies such as deep learning and natural language processing. Step 3: The problem identification unit identifies social issues based on the analysis results obtained by the analysis unit. The problem identification unit identifies issues using, for example, AI. The AI ​​can use, for example, data mining or machine learning algorithms. Step 4: The countermeasure prioritization unit prioritizes countermeasures based on the issues identified by the issue identification unit. The countermeasure prioritization unit prioritizes countermeasures using, for example, AI. The AI ​​can prioritize countermeasures based on criteria such as impact, urgency, and cost. Step 5: The policy proposal department makes policy proposals based on the measures prioritized by the measures prioritization department. The policy proposal department makes policy proposals using, for example, AI. The AI ​​can be used for methods such as drafting legislation or creating budget proposals. Step 6: The Project Execution Department executes the project based on the policies proposed by the Policy Proposal Department. The Project Execution Department executes the project using, for example, AI. The AI ​​can be used for methods such as project progress management and resource optimization.

[0083] (Example of form 2) The integrated solution platform according to an embodiment of the present invention is a system that deploys a comprehensive solution platform to impoverished cities, solves fundamental problems without using its own capital, and builds continuously prosperous socio-economic activities. This platform collects and analyzes data in fields such as economics, politics, healthcare, agriculture, IT, and education to identify social issues and prioritize countermeasures. It makes policy proposals with AI and experts and executes projects. For example, AI collects and analyzes data from impoverished cities. Next, AI identifies social issues and prioritizes countermeasures. For example, in the fields of healthcare, agriculture, and education, AI breaks down issues and plans. Furthermore, AI and experts collaborate to make policy proposals and execute projects. To secure funding and revenue, it raises funds from social impact investment funds, crowdfunding, and from governments and international organizations. It raises funds without generating its own capital and secures revenue through the proceeds from project execution. This platform starts with generating case studies in Small Start and builds up successful cases in Town, City, State, and Country. This expands fundraising and revenue generation, realizing sustainable urban development. This enables the integrated solutions platform to provide comprehensive solutions to impoverished cities, solve fundamental problems without using its own capital, and build sustainable, prosperous socio-economic activities.

[0084] The integrated solution platform according to this embodiment comprises a data collection unit, an analysis unit, a problem identification unit, a countermeasure prioritization unit, a policy proposal unit, and a project execution unit. The data collection unit collects data. The data collection unit can collect data in fields such as economics, politics, medicine, agriculture, IT, and education. The data collection unit can acquire data from sensors and databases, for example. The data collection unit can also collect information from social media and online forums. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using AI, for example. The AI ​​can use technologies such as deep learning and natural language processing. The problem identification unit identifies social issues based on the analysis results obtained by the analysis unit. The problem identification unit identifies issues using AI, for example. The AI ​​can use data mining and machine learning algorithms, for example. The countermeasure prioritization unit prioritizes countermeasures based on the issues identified by the problem identification unit. The countermeasure prioritization unit prioritizes countermeasures using AI, for example. The AI ​​can prioritize countermeasures based on criteria such as impact, urgency, and cost. The Policy Proposal Department makes policy proposals based on measures prioritized by the Measure Prioritization Department. The Policy Proposal Department makes policy proposals using, for example, AI. The AI ​​can be used for methods such as drafting bills and creating budget proposals. The Project Execution Department executes projects based on the policies proposed by the Policy Proposal Department. The Project Execution Department executes projects using, for example, AI. The AI ​​can be used for methods such as project progress management and resource optimization. As a result, the integrated solution platform according to the embodiment can provide comprehensive solutions to impoverished cities, solve fundamental problems without using its own capital, and build continuously prosperous socio-economic activities.

[0085] The data collection unit collects data. For example, it can collect data in fields such as economics, politics, healthcare, agriculture, IT, and education. Specifically, in the economic field, it collects macroeconomic indicators such as GDP growth rate, unemployment rate, and inflation rate, as well as corporate financial data and consumer purchasing behavior data. In the political field, it collects data such as election results, policy-making processes, and parliamentary minutes. In the healthcare field, it collects hospital patient data, medical equipment operating status, and drug usage data. In the agricultural field, it collects crop growth status, weather data, and soil nutrient status. In the IT field, it collects network traffic data, server operating status, and security incident logs. In the education field, it collects student performance data, attendance rates, and data measuring the effectiveness of educational programs. The data collection unit acquires data from sources such as sensors and databases. Sensors are diverse, including environmental sensors, medical sensors, and agricultural sensors, and can collect data in real time. Databases are acquired from databases held by government agencies, companies, and research institutions. Furthermore, the data collection unit can also gather information from social media and online forums. From social media, it collects user posts, comments, and trend information, while from online forums, it collects expert opinions and discussion content. This allows the data collection unit to gather a wide range of data from diverse data sources and build a comprehensive database. In addition, to ensure data quality, the data collection unit can perform data cleaning and normalization to provide reliable data.

[0086] The Analysis Department analyzes the data collected by the Data Collection Department. The Analysis Department uses AI, for example, to analyze the data. AI can utilize technologies such as deep learning and natural language processing. Specifically, it can use deep learning to analyze image and audio data, performing pattern recognition and anomaly detection. It can use natural language processing to extract meaning from text data, performing sentiment analysis and topic modeling. Furthermore, AI can use big data analysis techniques to extract useful information from large amounts of data, revealing trends and correlations. For example, it can analyze economic data to predict economic trends and changes in consumer purchasing behavior. It can analyze medical data to evaluate disease occurrence patterns and treatment effectiveness. It can analyze agricultural data to predict crop growth and yield. It can analyze IT data to understand network traffic patterns and security incident trends. It can analyze educational data to evaluate the effectiveness of educational programs and student learning performance. This allows the Analysis Department to analyze collected data from multiple perspectives and provide useful insights for solving social issues. Furthermore, the analysis department can use data visualization techniques to display analysis results in an easy-to-understand manner, enabling stakeholders to intuitively grasp them. For example, graphs, charts, and heatmaps can be used to visually indicate data trends and outliers. This allows the analysis department to effectively communicate data analysis results and support decision-making.

[0087] The Issue Identification Unit identifies social issues based on the analysis results obtained by the Analysis Unit. The Issue Identification Unit identifies issues using, for example, AI. AI can use, for example, data mining or machine learning algorithms. Specifically, it uses data mining techniques to extract potential issues and problems from large amounts of data. For example, economic data can identify economic imbalances or stagnant growth in specific regions or industries. Medical data can identify rising incidence rates of specific diseases or shortages of medical resources. Agricultural data can identify poor crop growth or the impact of climate change. IT data can identify increasing security threats or degrading system performance. Educational data can identify declining academic achievement or widening educational disparities. It can also use machine learning algorithms to analyze data patterns and trends and predict future issues. For example, economic data can be analyzed to predict the risk of future economic recession. Medical data can be analyzed to predict future disease outbreaks. Agricultural data can be analyzed to predict the risk of future food shortages. IT data can be analyzed to predict future increases in security threats. Educational data can be analyzed to predict widening educational disparities in the future. This allows the Issue Identification Department to identify not only current issues but also future issues, providing information to enable early countermeasures. Furthermore, the Issue Identification Department can evaluate the impact and urgency of the identified issues and prioritize them. This enables the Issue Identification Department to respond quickly to the most important issues and implement effective countermeasures.

[0088] The countermeasure prioritization unit prioritizes countermeasures based on the issues identified by the issue identification unit. The countermeasure prioritization unit uses AI, for example, to prioritize countermeasures. AI can prioritize countermeasures based on criteria such as impact, urgency, and cost. Specifically, impact assessment considers the magnitude of the impact a particular issue has on society as a whole. For example, for economic issues, it evaluates the impact on GDP and employment; for healthcare issues, it evaluates the impact on patient health and medical resources. Urgency assessment determines whether the issue needs to be addressed urgently. For example, emergencies such as natural disasters and pandemics require a rapid response. Cost assessment considers the resources and expenses necessary to implement countermeasures. For example, infrastructure development and the introduction of medical equipment incur significant costs, making cost-effectiveness assessment crucial. Based on these criteria, AI can calculate the priority of each countermeasure and identify the most effective one. Furthermore, the countermeasure prioritization unit can use simulation technology to examine multiple countermeasure scenarios and select the optimal countermeasure. For example, it can simulate different combinations of countermeasures and their implementation timings to select the most effective one. Furthermore, the priority setting unit can periodically review priorities and respond to the latest situation. This allows the priority setting unit to always select the most optimal solution and support effective problem-solving.

[0089] The Policy Proposal Department makes policy proposals based on measures prioritized by the Measure Prioritization Department. The Policy Proposal Department uses AI, for example, to make policy proposals. AI can be used in methods such as drafting bills and creating budget proposals. Specifically, AI analyzes past policy data and successful examples of bills to generate optimal policy proposals. For example, in economic policy, it analyzes the effects of past economic stimulus measures and tax reforms to propose the most effective policies. In healthcare policy, it analyzes the effects of past healthcare system reforms and vaccination programs to propose the most effective policies. In agricultural policy, it analyzes the effects of past agricultural support measures and disaster countermeasures to propose the most effective policies. In IT policy, it analyzes the effects of past cybersecurity measures and digital infrastructure development to propose the most effective policies. In education policy, it analyzes the effects of past education reforms and scholarship programs to propose the most effective policies. Furthermore, AI can use simulation technology to evaluate the effects of proposed policies in advance. For example, it can perform simulations of economic policies to predict the impact on GDP and employment. It can perform simulations of healthcare policies to predict the impact on patient health and medical resources. It can perform simulations of agricultural policies to predict the impact on crop growth and yield. We conduct simulations of IT policies to predict their impact on network security and performance. We also conduct simulations of education policies to predict their impact on student learning performance and educational disparities. This allows the policy proposal department to make evidence-based policy proposals and support effective policy implementation.

[0090] The Project Execution Department executes projects based on policies proposed by the Policy Proposal Department. The Project Execution Department uses AI, for example, to execute projects. AI can be used for methods such as project progress management and resource optimization. Specifically, AI manages project schedules and monitors the progress of each task in real time. For example, in construction projects, it manages construction progress and material procurement to prevent delays and cost overruns. In medical projects, it manages patient treatment plans and the allocation of medical resources to ensure efficient medical care. In agricultural projects, it manages crop cultivation plans and harvest schedules to ensure optimal harvest timing. In IT projects, it manages system development schedules and resource allocation to support project success. In educational projects, it manages the implementation schedule of educational programs and resource allocation to ensure effective educational delivery. Furthermore, AI optimizes resources to maximize project efficiency. For example, it plans the optimal allocation of human resources and the efficient use of materials to reduce project costs. AI also manages risks to increase the probability of project success. For example, it identifies project risk factors and plans risk mitigation measures. This allows the project execution department to efficiently manage project progress using AI and lead it to success. Furthermore, the project execution department can evaluate project results and identify areas for improvement for future projects. This enables the project execution department to continuously improve and achieve more effective project execution.

[0091] The fundraising department can raise funds. For example, it can raise funds through crowdfunding. It can also raise funds by applying for government subsidies. Furthermore, it can raise funds through social impact investment funds. For example, the fundraising department can use a crowdfunding platform to publish project details and raise funds from supporters. When applying for government subsidies, the fundraising department clarifies the project's objectives and the amount of funding required and submits an application. When using social impact investment funds, the fundraising department explains the project's social impact and profitability to investors and raises funds. This allows the fundraising department to secure funds to execute projects without using its own capital. Some or all of the above processes in the fundraising department may be performed using AI or not. For example, the fundraising department can input data from a crowdfunding platform into an AI, which can then suggest the most suitable fundraising method.

[0092] The revenue generation unit can generate revenue. For example, the revenue generation unit can generate revenue through the sale of goods. It can also generate revenue through the provision of services. Furthermore, the revenue generation unit can generate revenue through investment returns. For example, the revenue generation unit can generate revenue by selling goods produced in the project. In the case of service provision, the revenue generation unit can generate revenue by setting a fee for the services provided in the project. In the case of investment returns, the revenue generation unit can generate additional revenue by reinvesting a portion of the project's revenue. This allows the revenue generation unit to generate revenue necessary for sustainable project operation. Some or all of the above processes in the revenue generation unit may be performed using AI or not. For example, the revenue generation unit can input sales data of goods into AI, which can then propose an optimal sales strategy.

[0093] The success story generation unit can generate success stories. For example, the success story generation unit can generate a success story when a project is completed. It can also generate a success story when a goal is achieved. Furthermore, it can generate a success story when a social impact is achieved. For example, if a project is completed on schedule, the success story generation unit records that completion as a success story. In the case of goal achievement, if the project's goals are achieved, the success story generation unit records that achievement as a success story. In the case of social impact, the success story generation unit evaluates the impact the project had on society and records that impact as a success story. This allows the success story generation unit to generate success stories that can be easily expanded to other regions. Some or all of the above-described processes in the success story generation unit may be performed using AI or not. For example, the success story generation unit can input project progress data into AI, and the AI ​​can automatically generate success stories.

[0094] The data collection unit can collect data in fields such as economics, politics, healthcare, agriculture, IT, and education. For example, in the economic field, it can collect data such as GDP and unemployment rates, and in the healthcare field, it can collect data such as the number of hospitals and access to healthcare. The data collection unit can obtain data from government statistical databases, for example. It can also collect information from social media and online forums. For example, the data collection unit can analyze social media posts to understand local conditions. It can also collect opinions and comments from online forums and incorporate them into the data. This allows the data collection unit to collect data from diverse fields and conduct comprehensive analysis. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into AI, which can then analyze and collect the data.

[0095] The analysis department can analyze data collected by the collection department using AI. For example, the analysis department can analyze data using deep learning. Deep learning can learn from large amounts of data and identify patterns and trends. The analysis department can also analyze text data using natural language processing. Natural language processing is a technique that extracts meaning and sentiment from text data. Furthermore, the analysis department can analyze data using machine learning algorithms. Machine learning algorithms can build predictive models from data and predict future trends. For example, the analysis department can use deep learning to analyze economic data and identify economic trends. It can use natural language processing to analyze social media posts and understand local sentiment. It can use machine learning algorithms to analyze medical data and predict hospital demand. In this way, the analysis department can analyze collected data with high accuracy and provide a foundation for identifying social issues.

[0096] The problem identification unit can identify social issues based on the analysis results obtained by the analysis unit. For example, the problem identification unit can identify issues using AI. AI can extract important patterns and trends from data using data mining techniques. The problem identification unit can also predict social issues from data using machine learning algorithms. For example, the problem identification unit can analyze economic data to identify the causes of poverty, analyze medical data to identify problems with access to healthcare, and analyze educational data to identify problems with educational inequality. In this way, the problem identification unit can identify social issues based on the analysis results and provide a foundation for taking effective countermeasures.

[0097] The countermeasure prioritization unit can prioritize countermeasures based on the issues identified by the issue identification unit. The countermeasure prioritization unit can prioritize countermeasures using, for example, AI. The AI ​​can evaluate and prioritize countermeasures based on criteria such as impact, urgency, and cost. For example, the countermeasure prioritization unit will prioritize countermeasures with high impact, high urgency, and low cost. In this way, the countermeasure prioritization unit can prioritize countermeasures to achieve efficient problem solving.

[0098] The Policy Proposal Department can make policy proposals based on measures prioritized by the Measure Prioritization Department. The Policy Proposal Department can make policy proposals using, for example, AI. The AI ​​can make policy proposals using methods such as drafting bills and creating budget proposals. For example, the Policy Proposal Department can draft bills based on measures with a high impact. It can create budget proposals based on measures with a high urgency. It can propose policies based on measures with a low cost. In this way, the Policy Proposal Department can make proposals to implement effective policies.

[0099] The Project Execution Department can execute projects based on policies proposed by the Policy Proposal Department. The Project Execution Department can, for example, use AI to execute projects. AI can execute projects using methods such as project progress management and resource optimization. For example, the Project Execution Department can monitor project progress in real time and reallocate resources as needed. As the project progresses, it can assess risks and take appropriate measures. This allows the Project Execution Department to execute projects in a way that ensures their effectiveness.

[0100] 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 reduces the frequency of data collection to lessen the user's burden. If the user is relaxed, it collects detailed data to obtain more information. If the user is in a hurry, it prioritizes collecting only important data and processes it quickly. In this way, the data collection unit can adjust the timing of data collection according to the user's emotions and reduce the user's burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The data collection unit can customize its collection methods to take into account the local cultural background and social circumstances. For example, the data collection unit may use appropriate language and expressions based on the local cultural background when collecting data. It may also adjust the method and timing of data collection considering the local social circumstances. During specific local events or festivals, data collection may be temporarily suspended. This allows the data collection unit to collect data in a way that is appropriate for the characteristics of the region. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the local cultural background and social circumstances into an AI, which can then suggest the optimal collection method.

[0102] The data collection unit can receive real-time feedback during data collection and dynamically adjust the collection method. For example, the data collection unit can receive user feedback during data collection and immediately change the collection method. Based on the real-time feedback, it can adjust the type and amount of data to be collected. Based on the feedback, it can dynamically change the timing and frequency of data collection. This allows the data collection unit to perform efficient data collection based on real-time feedback. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user feedback data into AI, which can then suggest the optimal collection method.

[0103] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, it will prioritize collecting detailed data. If the user is in a hurry, it will prioritize collecting data that can be collected quickly. In this way, the data collection unit can prioritize data according to the user's emotions and prioritize the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The data collection unit can optimize the collection range by considering the geographical characteristics of the region during data collection. For example, the data collection unit can set the collection range based on the geographical characteristics of the region and collect data efficiently. It can dynamically change the collection range considering geographical characteristics. It can customize the collection method according to the geographical characteristics of the region. This allows the data collection unit to perform efficient data collection according to the geographical characteristics of the region. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the geographical characteristics of the region into the AI, and the AI ​​can propose the optimal collection range.

[0105] The data collection unit can integrate and collect information from social media and online forums during data collection. For example, the data collection unit can collect posts from social media to understand local conditions. It can also collect opinions and comments from online forums and incorporate them into the data. By integrating data from social media and online forums, it can collect comprehensive information. This allows the data collection unit to perform comprehensive data collection by integrating information from social media and online forums. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media data into AI, which can then analyze and collect the data.

[0106] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visual presentation. If the user is relaxed, it provides a presentation that includes detailed information. If the user is in a hurry, it provides a presentation that gets straight to the point. In this way, the analysis unit can provide a presentation of analysis results that is appropriate to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The analysis unit can identify trends and build predictive models by comparing current data with past data during analysis. For example, the analysis unit can identify current trends and make future predictions based on past data. It compares past and current data to identify patterns of change. Based on past data, it builds predictive models and predicts future trends. This allows the analysis unit to make future predictions by comparing with past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into AI, which can then identify trends and build predictive models.

[0108] The analysis unit can integrate different data sources and perform analysis from multiple perspectives during the analysis process. For example, the analysis unit can integrate different data sources and perform comprehensive analysis. It can combine information from different data sources and perform analysis from multiple perspectives. It can provide more accurate analysis results based on different data sources. In this way, the analysis unit can perform comprehensive analysis by integrating different data sources. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data sources into AI, and the AI ​​can integrate and analyze the data.

[0109] The analysis unit can estimate the user's emotions and prioritize analyses based on those emotions. For example, if the user is stressed, the analysis unit will prioritize only the most important analyses. If the user is relaxed, it will prioritize detailed analyses. If the user is in a hurry, it will prioritize analyses that yield quick results. This allows the analysis unit to prioritize analyses according to the user's emotions and prioritize important analyses. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The analysis department can customize the analysis results by taking into account the local economic and political situation during the analysis. For example, the analysis department can adjust the analysis results by considering the local economic situation. It can customize the analysis results by considering the local political situation. It can optimize the analysis results based on the local economic and political situation. This allows the analysis department to provide analysis results tailored to the characteristics of the region, enabling more appropriate measures. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input data on the local economic and political situation into AI, which can then provide optimal analysis results.

[0111] The analysis department can improve the accuracy of its analysis by referring to relevant research papers and reports during the analysis process. For example, the analysis department can improve the accuracy of its analysis by referring to relevant research papers. It can supplement the analysis results based on relevant reports. It can increase the reliability of its analysis by referring to research papers and reports. In this way, the reliability of the analysis department's analysis can be increased by referring to relevant research papers and reports. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input data from relevant research papers and reports into an AI, which can then improve the accuracy of the analysis.

[0112] The problem identification unit can estimate the user's emotions and adjust the problem identification method based on the estimated user emotions. For example, if the user is nervous, the problem identification unit identifies the problem in a simple and highly visible way. If the user is relaxed, it identifies the problem in a way that includes detailed information. If the user is in a hurry, it provides a way to quickly identify the problem. In this way, the problem identification unit can provide a problem identification method that is appropriate to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The problem identification unit can improve the accuracy of problem identification by referring to past success and failure cases when identifying problems. For example, the problem identification unit can improve the accuracy of problem identification by referring to past success cases. It can improve the method of problem identification based on past failure cases. It can improve the accuracy of problem identification by referring to success and failure cases. In this way, the problem identification unit can improve the accuracy of problem identification by referring to past cases. Some or all of the above processing in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on past success and failure cases into AI, and the AI ​​can improve the accuracy of problem identification.

[0114] The problem identification unit can improve its accuracy by incorporating feedback from local experts and residents during the problem identification process. For example, the problem identification unit can improve the accuracy of problem identification based on feedback from local experts. It can improve its problem identification method by incorporating opinions from local residents. It can improve the accuracy of problem identification by referring to feedback from experts and residents. In this way, the problem identification unit improves the accuracy of problem identification by incorporating feedback from local experts and residents. Some or all of the above processes in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input feedback data from local experts and residents into AI, which can then improve the accuracy of identification.

[0115] The issue identification unit can estimate the user's emotions and determine the priority of issues to identify based on the estimated emotions. For example, if the user is stressed, the issue identification unit will prioritize identifying only important issues. If the user is relaxed, it will prioritize identifying detailed issues. If the user is in a hurry, it will prioritize identifying issues that can be identified quickly. In this way, the issue identification unit can determine the priority of issues according to the user's emotions and prioritize identifying important issues. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The problem identification unit can customize its problem identification method by considering the geographical characteristics and cultural background of the region. For example, the problem identification unit adjusts the problem identification method based on the geographical characteristics of the region. It customizes the problem identification method by considering the cultural background of the region. It optimizes the problem identification method according to the geographical characteristics and cultural background. This allows the problem identification unit to identify problems that are appropriate to the characteristics of the region. Some or all of the above processes in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on the geographical characteristics and cultural background of the region into the AI, which can then propose the optimal identification method.

[0117] The problem identification unit can improve the accuracy of problem identification by referring to relevant policies and regulations when identifying problems. For example, the problem identification unit can improve the accuracy of problem identification by referring to relevant policies. It can improve the method of problem identification based on relevant laws and regulations. It can improve the accuracy of problem identification by referring to policies and laws and regulations. In this way, the problem identification unit improves the accuracy of problem identification by referring to relevant policies and laws and regulations. Some or all of the above processing in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on relevant policies and laws and regulations into AI, which can improve the accuracy of identification.

[0118] The priority prioritization unit can estimate the user's emotions and adjust the priority of countermeasures based on the estimated emotions. For example, if the user is stressed, the priority prioritization unit will prioritize only important countermeasures. If the user is relaxed, it will prioritize detailed countermeasures. If the user is in a hurry, it will prioritize countermeasures that can be implemented quickly. In this way, the priority prioritization unit can adjust the priority of countermeasures according to the user's emotions and prioritize important countermeasures. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The countermeasure prioritization unit can optimize priorities by evaluating the effectiveness of past countermeasures when prioritizing countermeasures. For example, the countermeasure prioritization unit evaluates the effectiveness of past countermeasures and prioritizes the implementation of the most effective countermeasures. Based on past countermeasure failure cases, it re-evaluates priorities. It analyzes the effectiveness of past countermeasures and optimizes priorities. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by evaluating the effectiveness of past countermeasures. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on past countermeasures into AI, and the AI ​​can evaluate their effectiveness and optimize priorities.

[0120] The countermeasure prioritization unit can determine priorities by considering local resources and infrastructure conditions when prioritizing countermeasures. For example, the countermeasure prioritization unit considers local resources and prioritizes the implementation of the most effective countermeasures. Based on the local infrastructure conditions, it re-evaluates the prioritization. Considering resources and infrastructure conditions, it optimizes the prioritization. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by considering local resources and infrastructure conditions. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on local resources and infrastructure conditions into AI, and the AI ​​can propose the optimal prioritization.

[0121] The action prioritization unit can estimate the user's emotions and adjust the order in which actions are taken based on the estimated emotions. For example, if the user is stressed, the action prioritization unit will prioritize only important actions. If the user is relaxed, it will prioritize detailed actions. If the user is in a hurry, it will prioritize actions that can be taken quickly. In this way, the action prioritization unit can adjust the order in which actions are taken according to the user's emotions and prioritize important actions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The countermeasure prioritization unit can optimize priorities by considering the local economic and political situation when prioritizing countermeasures. For example, the countermeasure prioritization unit considers the local economic situation and prioritizes the implementation of the most effective countermeasure. Based on the local political situation, it re-evaluates the prioritization. Considering the economic and political situation, it optimizes the prioritization. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasure by considering the local economic and political situation. Some or all of the above processing in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal prioritization.

[0123] The countermeasure prioritization unit can determine priorities by referring to relevant research papers and reports when prioritizing countermeasures. For example, the countermeasure prioritization unit can refer to relevant research papers and prioritize the implementation of the most effective countermeasures. Based on relevant reports, it re-evaluates the priorities. By referring to research papers and reports, it optimizes the priorities. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by referring to relevant research papers and reports. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data from relevant research papers and reports into AI, and the AI ​​can propose the optimal priorities.

[0124] The policy proposal department can estimate the user's emotions and adjust the way policy proposals are presented based on those emotions. For example, if the user is tense, the policy proposal department will provide a simple and highly visible presentation. If the user is relaxed, it will provide a presentation that includes detailed information. If the user is in a hurry, it will provide a presentation that gets straight to the point. In this way, the policy proposal department can provide a presentation of policy proposals that is tailored to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The policy proposal department can optimize its proposals by evaluating the effectiveness of past policies. For example, the policy proposal department can evaluate the effectiveness of past policies and make the most effective proposal. It can re-evaluate proposals based on past policy failures. It can analyze the effectiveness of past policies and optimize proposals. In this way, the policy proposal department can make the most effective proposals by evaluating the effectiveness of past policies. Some or all of the above processes in the policy proposal department may be performed using AI or not. For example, the policy proposal department can input data on past policies into AI, which can then evaluate their effectiveness and optimize the proposals.

[0126] The policy proposal department can improve its proposals by incorporating feedback from local experts and residents. For example, the policy proposal department can improve its proposals based on feedback from local experts, incorporate opinions from local residents to improve the proposals, and refer to feedback from experts and residents to enhance the proposals. In this way, the policy proposal department can improve the accuracy of its proposals by incorporating feedback from local experts and residents. Some or all of the above processes in the policy proposal department may be performed using AI, or not. For example, the policy proposal department can input feedback data from local experts and residents into AI, which can then improve the proposals.

[0127] The policy proposal department can estimate the user's emotions and prioritize policy proposals based on those emotions. For example, if the user is stressed, the policy proposal department will prioritize only important proposals. If the user is relaxed, it will prioritize detailed proposals. If the user is in a hurry, it will prioritize proposals that can be implemented quickly. In this way, the policy proposal department can prioritize policy proposals according to the user's emotions and prioritize important proposals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The policy proposal department can customize its proposals when submitting them, taking into account the geographical characteristics and cultural background of the region. For example, the policy proposal department can adjust the proposal based on the geographical characteristics of the region, customize the proposal considering the cultural background of the region, and optimize the proposal according to the geographical characteristics and cultural background. This allows the policy proposal department to make policy proposals that are tailored to the characteristics of the region. Some or all of the above processes in the policy proposal department may be performed using AI, or not. For example, the policy proposal department can input data on the geographical characteristics and cultural background of the region into an AI, which can then provide the most suitable proposal.

[0129] The policy proposal department can improve its proposals by referring to relevant policies and regulations when making policy proposals. For example, the policy proposal department can improve its proposals by referring to relevant policies. It can improve its proposals based on relevant laws and regulations. It can enhance its proposals by referring to policies and laws and regulations. In this way, the accuracy of the policy proposal department's proposals can be improved by referring to relevant policies and laws and regulations. Some or all of the above processes in the policy proposal department may be performed using AI or not. For example, the policy proposal department can input data on relevant policies and laws and regulations into AI, which can then improve the proposals.

[0130] The project execution unit can estimate the user's emotions and adjust the project execution method based on the estimated emotions. For example, if the user is tense, the project execution unit will execute the project in a simple and highly visible manner. If the user is relaxed, it will execute the project in a way that includes detailed information. If the user is in a hurry, it will provide a way to execute the project quickly. In this way, the project execution unit can provide a project execution method that is tailored to the user's emotions and deepen its understanding of the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The project execution unit can improve execution accuracy by referring to past project success and failure cases during project execution. For example, the project execution unit can improve project execution accuracy by referring to past success cases. It can improve execution methods based on past failure cases. It can improve execution accuracy by referring to success and failure cases. In this way, the project execution unit can improve the accuracy of project execution by referring to past cases. Some or all of the above processes in the project execution unit may be performed using AI or not. For example, the project execution unit can input data on past success and failure cases into AI, and the AI ​​can improve execution accuracy.

[0132] The project execution unit can optimize its execution method by considering local resources and infrastructure conditions during project execution. For example, the project execution unit considers local resources and executes the project in the most effective way. It re-evaluates the execution method based on the local infrastructure conditions. It optimizes the execution method by considering resources and infrastructure conditions. In this way, the project execution unit can execute the project in the most effective way by considering local resources and infrastructure conditions. Some or all of the above processes in the project execution unit may be performed using AI or not. For example, the project execution unit can input data on local resources and infrastructure conditions into AI, and the AI ​​can propose the optimal execution method.

[0133] The project execution unit can estimate the user's emotions and adjust the execution order of projects based on the estimated emotions. For example, if the user is stressed, the project execution unit will prioritize executing only important projects. If the user is relaxed, it will prioritize executing detailed projects. If the user is in a hurry, it will prioritize executing projects that can be completed quickly. In this way, the project execution unit can adjust the execution order of projects according to the user's emotions and prioritize important projects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The project execution unit can optimize its execution methods by considering the local economic and political situation during project execution. For example, the project execution unit can execute the project in the most effective way, taking into account the local economic situation. It can re-evaluate the execution method based on the local political situation. It can optimize the execution method by considering the economic and political situation. In this way, the project execution unit can execute the project in the most effective way by considering the local economic and political situation. Some or all of the above processes in the project execution unit may be performed using AI, or not. For example, the project execution unit can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal execution method.

[0135] The project execution unit can improve execution accuracy by referring to relevant research papers and reports during project execution. For example, the project execution unit can improve project execution accuracy by referring to relevant research papers. Based on relevant reports, it can improve execution methods. By referring to research papers and reports, it can increase execution accuracy. In this way, the accuracy of project execution can be improved by the project execution unit by referring to relevant research papers and reports. Some or all of the above processes in the project execution unit may be performed using AI or not. For example, the project execution unit can input data from relevant research papers and reports into AI, which can then improve execution accuracy.

[0136] The fundraising department can estimate the user's emotions and adjust the fundraising method based on the estimated emotions. For example, if the user is stressed, the fundraising department will raise funds in a simple and highly visible way. If the user is relaxed, it will raise funds in a way that includes detailed information. If the user is in a hurry, it will provide a way to raise funds quickly. In this way, the fundraising department can provide fundraising methods that are tailored to the user's emotions and deepen its understanding of the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0137] The fundraising department can optimize its fundraising methods by referring to past successful and unsuccessful fundraising cases. For example, the fundraising department can optimize fundraising methods by referring to past successes. It can improve fundraising methods based on past failures. By referring to successes and failures, the fundraising department optimizes its fundraising methods by referring to past cases. Some or all of the above processes in the fundraising department may be performed using AI or not. For example, the fundraising department can input data on past successes and failures into an AI, which can then optimize the fundraising methods.

[0138] The fundraising department can estimate the user's emotions and determine fundraising priorities based on those emotions. For example, if the user is stressed, the fundraising department will prioritize only important fundraising. If the user is relaxed, it will prioritize detailed fundraising. If the user is in a hurry, it will prioritize methods for rapid fundraising. This allows the fundraising department to determine fundraising priorities according to the user's emotions and prioritize important fundraising. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0139] The fundraising department can optimize its fundraising methods by considering the local economic and political situation. For example, the fundraising department can consider the local economic situation and raise funds in the most effective way. It can re-evaluate the fundraising methods based on the local political situation. It can optimize the fundraising methods by considering the economic and political situation. In this way, the fundraising department can raise funds in the most effective way by considering the local economic and political situation. Some or all of the above processes in the fundraising department may be performed using AI or not. For example, the fundraising department can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal fundraising method.

[0140] The revenue generation unit can estimate the user's emotions and adjust the revenue generation method based on the estimated emotions. For example, if the user is tense, the revenue generation unit will generate revenue in a simple and visually clear way. If the user is relaxed, it will generate revenue in a way that includes detailed information. If the user is in a hurry, it will provide a way to generate revenue quickly. In this way, the revenue generation unit can provide revenue generation methods that are tailored to the user's emotions and deepen its understanding of the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0141] The revenue generation unit can optimize its methods when generating revenue by referring to past success and failure cases. For example, the revenue generation unit can optimize its revenue generation methods by referring to past success cases. It can improve its methods based on past failure cases. It can optimize its methods by referring to success and failure cases. In this way, the revenue generation unit optimizes its revenue generation methods by referring to past cases. Some or all of the above processes in the revenue generation unit may be performed using AI or not. For example, the revenue generation unit can input data on past success and failure cases into AI, and the AI ​​can optimize the methods.

[0142] The revenue generation unit can estimate the user's emotions and determine revenue generation priorities based on those estimated emotions. For example, if the user is stressed, the revenue generation unit will prioritize only important revenue generation. If the user is relaxed, it will prioritize detailed revenue generation. If the user is in a hurry, it will prioritize methods for rapid revenue generation. In this way, the revenue generation unit can determine revenue generation priorities according to the user's emotions and prioritize important revenue generation. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0143] The revenue generation unit can optimize its methods for generating revenue by considering the local economic and political situation. For example, the revenue generation unit may generate revenue in the most effective way, taking into account the local economic situation. It may then re-evaluate the method based on the local political situation. By considering the economic and political situation, it can optimize the method. This allows the revenue generation unit to generate revenue in the most effective way by considering the local economic and political situation. Some or all of the above processes in the revenue generation unit may be performed using AI or not. For example, the revenue generation unit can input data on the local economic and political situation into an AI, which can then propose the optimal method.

[0144] The success story generation unit can estimate the user's emotions and adjust the success story generation method based on the estimated user emotions. For example, if the user is nervous, the success story generation unit generates success stories in a simple and highly visible way. If the user is relaxed, it generates success stories in a way that includes detailed information. If the user is in a hurry, it provides a method for generating success stories quickly. In this way, the success story generation unit can provide a success story generation method that is tailored to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0145] The success story generation unit can improve its generation accuracy by referring to past success and failure stories when generating success stories. For example, the success story generation unit can improve generation accuracy by referring to past success stories. It can improve the generation method based on past failure stories. It can improve generation accuracy by referring to success and failure stories. In this way, the success story generation unit can improve the accuracy of generating success stories by referring to past cases. Some or all of the above processing in the success story generation unit may be performed using AI or not. For example, the success story generation unit can input data on past success and failure stories into AI, and the AI ​​can improve generation accuracy.

[0146] The success story generation unit can estimate the user's emotions and prioritize success stories based on those emotions. For example, if the user is stressed, the success story generation unit will prioritize generating only important success stories. If the user is relaxed, it will prioritize generating detailed success stories. If the user is in a hurry, it will prioritize methods that generate success stories quickly. In this way, the success story generation unit can prioritize success stories according to the user's emotions and generate important success stories first. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0147] The success story generation unit can optimize its generation method by considering the local economic and political situation when generating success stories. For example, the success story generation unit generates success stories in the most effective way, taking into account the local economic situation. It re-evaluates the generation method based on the local political situation. It optimizes the generation method by considering the economic and political situation. In this way, the success story generation unit can generate success stories in the most effective way by considering the local economic and political situation. Some or all of the above processing in the success story generation unit may be performed using AI or not. For example, the success story generation unit can input data on the local economic and political situation into AI, and the AI ​​can propose the optimal generation method.

[0148] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0149] 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 reduces the frequency of data collection to lessen the user's burden. If the user is relaxed, it collects detailed data to obtain more information. If the user is in a hurry, it prioritizes collecting only important data and processes it quickly. In this way, the data collection unit can adjust the timing of data collection according to the user's emotions and reduce the user's burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0150] The data collection unit can customize its collection methods to take into account the local cultural background and social circumstances. For example, the data collection unit may use appropriate language and expressions based on the local cultural background when collecting data. It may also adjust the method and timing of data collection considering the local social circumstances. During specific local events or festivals, data collection may be temporarily suspended. This allows the data collection unit to collect data in a way that is appropriate for the characteristics of the region. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the local cultural background and social circumstances into an AI, which can then suggest the optimal collection method.

[0151] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visual presentation. If the user is relaxed, it provides a presentation that includes detailed information. If the user is in a hurry, it provides a presentation that gets straight to the point. In this way, the analysis unit can provide a presentation of analysis results that is appropriate to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0152] The analysis unit can identify trends and build predictive models by comparing current data with past data during analysis. For example, the analysis unit can identify current trends and make future predictions based on past data. It compares past and current data to identify patterns of change. Based on past data, it builds predictive models and predicts future trends. This allows the analysis unit to make future predictions by comparing with past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past data into AI, which can then identify trends and build predictive models.

[0153] The problem identification unit can estimate the user's emotions and adjust the problem identification method based on the estimated user emotions. For example, if the user is nervous, the problem identification unit identifies the problem in a simple and highly visible way. If the user is relaxed, it identifies the problem in a way that includes detailed information. If the user is in a hurry, it provides a way to quickly identify the problem. In this way, the problem identification unit can provide a problem identification method that is appropriate to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0154] The problem identification unit can improve the accuracy of problem identification by referring to past success and failure cases when identifying problems. For example, the problem identification unit can improve the accuracy of problem identification by referring to past success cases. It can improve the method of problem identification based on past failure cases. It can improve the accuracy of problem identification by referring to success and failure cases. In this way, the problem identification unit can improve the accuracy of problem identification by referring to past cases. Some or all of the above processing in the problem identification unit may be performed using AI or not. For example, the problem identification unit can input data on past success and failure cases into AI, and the AI ​​can improve the accuracy of problem identification.

[0155] The priority prioritization unit can estimate the user's emotions and adjust the priority of countermeasures based on the estimated emotions. For example, if the user is stressed, the priority prioritization unit will prioritize only important countermeasures. If the user is relaxed, it will prioritize detailed countermeasures. If the user is in a hurry, it will prioritize countermeasures that can be implemented quickly. In this way, the priority prioritization unit can adjust the priority of countermeasures according to the user's emotions and prioritize important countermeasures. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0156] The countermeasure prioritization unit can optimize priorities by evaluating the effectiveness of past countermeasures when prioritizing countermeasures. For example, the countermeasure prioritization unit evaluates the effectiveness of past countermeasures and prioritizes the implementation of the most effective countermeasures. Based on past countermeasure failure cases, it re-evaluates priorities. It analyzes the effectiveness of past countermeasures and optimizes priorities. In this way, the countermeasure prioritization unit can prioritize the implementation of the most effective countermeasures by evaluating the effectiveness of past countermeasures. Some or all of the above processes in the countermeasure prioritization unit may be performed using AI or not. For example, the countermeasure prioritization unit can input data on past countermeasures into AI, and the AI ​​can evaluate their effectiveness and optimize priorities.

[0157] The policy proposal department can estimate the user's emotions and adjust the way policy proposals are presented based on those emotions. For example, if the user is tense, the policy proposal department will provide a simple and highly visible presentation. If the user is relaxed, it will provide a presentation that includes detailed information. If the user is in a hurry, it will provide a presentation that gets straight to the point. In this way, the policy proposal department can provide a presentation of policy proposals that is tailored to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0158] The policy proposal department can optimize its proposals by evaluating the effectiveness of past policies. For example, the policy proposal department can evaluate the effectiveness of past policies and make the most effective proposal. It can re-evaluate proposals based on past policy failures. It can analyze the effectiveness of past policies and optimize proposals. In this way, the policy proposal department can make the most effective proposals by evaluating the effectiveness of past policies. Some or all of the above processes in the policy proposal department may be performed using AI or not. For example, the policy proposal department can input data on past policies into AI, which can then evaluate their effectiveness and optimize the proposals.

[0159] The following briefly describes the processing flow for example form 2.

[0160] Step 1: The data collection unit collects data. The data collection unit can collect data in fields such as economics, politics, medicine, agriculture, IT, and education. The data collection unit can acquire data from sensors and databases, for example. It can also collect information from social media and online forums. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. The AI ​​can use technologies such as deep learning and natural language processing. Step 3: The problem identification unit identifies social issues based on the analysis results obtained by the analysis unit. The problem identification unit identifies issues using, for example, AI. The AI ​​can use, for example, data mining or machine learning algorithms. Step 4: The countermeasure prioritization unit prioritizes countermeasures based on the issues identified by the issue identification unit. The countermeasure prioritization unit prioritizes countermeasures using, for example, AI. The AI ​​can prioritize countermeasures based on criteria such as impact, urgency, and cost. Step 5: The policy proposal department makes policy proposals based on the measures prioritized by the measures prioritization department. The policy proposal department makes policy proposals using, for example, AI. The AI ​​can be used for methods such as drafting legislation or creating budget proposals. Step 6: The Project Execution Department executes the project based on the policies proposed by the Policy Proposal Department. The Project Execution Department executes the project using, for example, AI. The AI ​​can be used for methods such as project progress management and resource optimization.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, problem identification unit, countermeasure prioritization unit, policy proposal unit, project execution unit, fundraising unit, revenue generation unit, and success story generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit acquires data from the sensors and database of the smart device 14. The analysis unit analyzes the data using AI by the identification processing unit 290 of the data processing unit 12. The problem identification unit identifies social issues by the identification processing unit 290 of the data processing unit 12. The countermeasure prioritization unit prioritizes countermeasures by the identification processing unit 290 of the data processing unit 12. The policy proposal unit makes policy proposals by the identification processing unit 290 of the data processing unit 12. The project execution unit executes the project by the identification processing unit 290 of the data processing unit 12. The fundraising unit raises funds using the crowdfunding platform of the smart device 14. The revenue generation unit manages product sales data by the control unit 46A of the smart device 14 and secures revenue. The success case generation unit generates success cases using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the data collection unit, analysis unit, problem identification unit, countermeasure prioritization unit, policy proposal unit, project execution unit, fundraising unit, revenue generation unit, and success story generation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires data from the sensors and database of the smart glasses 214. The analysis unit analyzes the data using AI by the identification processing unit 290 of the data processing unit 12. The problem identification unit identifies social issues by the identification processing unit 290 of the data processing unit 12. The countermeasure prioritization unit prioritizes countermeasures by the identification processing unit 290 of the data processing unit 12. The policy proposal unit makes policy proposals by the identification processing unit 290 of the data processing unit 12. The project execution unit executes the project by the identification processing unit 290 of the data processing unit 12. The fundraising unit raises funds using the crowdfunding platform of the smart glasses 214. The revenue generation unit manages product sales data by the control unit 46A of the smart glasses 214 and secures revenue. The success case generation unit generates success cases using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0181] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] Each of the multiple elements described above, including the data collection unit, analysis unit, problem identification unit, countermeasure prioritization unit, policy proposal unit, project execution unit, fundraising unit, revenue generation unit, and success story generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit acquires data from the sensors and database of the headset terminal 314. The analysis unit analyzes the data using AI by the identification processing unit 290 of the data processing unit 12. The problem identification unit identifies social issues by the identification processing unit 290 of the data processing unit 12. The countermeasure prioritization unit prioritizes countermeasures by the identification processing unit 290 of the data processing unit 12. The policy proposal unit makes policy proposals by the identification processing unit 290 of the data processing unit 12. The project execution unit executes the project by the identification processing unit 290 of the data processing unit 12. The fundraising unit raises funds using the crowdfunding platform of the headset terminal 314. The revenue generation unit manages product sales data by the control unit 46A of the headset terminal 314 and secures revenue. The success case generation unit generates success cases using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0197] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] Each of the multiple elements described above, including the data collection unit, analysis unit, problem identification unit, countermeasure prioritization unit, policy proposal unit, project execution unit, fundraising unit, revenue generation unit, and success story generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit acquires data from the robot 414's sensors and database. The analysis unit analyzes the data using AI by the identification processing unit 290 of the data processing unit 12. The problem identification unit identifies social issues by the identification processing unit 290 of the data processing unit 12. The countermeasure prioritization unit prioritizes countermeasures by the identification processing unit 290 of the data processing unit 12. The policy proposal unit makes policy proposals by the identification processing unit 290 of the data processing unit 12. The project execution unit executes the project by the identification processing unit 290 of the data processing unit 12. The fundraising unit raises funds using the robot 414's crowdfunding platform. The revenue generation unit manages product sales data by the control unit 46A of the robot 414 and secures revenue. The success case generation unit generates success cases using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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."

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A problem identification unit identifies social issues based on the analysis results obtained by the aforementioned analysis unit, A countermeasure prioritization unit that prioritizes countermeasures based on the issues identified by the aforementioned issue identification unit, A policy proposal unit that makes policy proposals based on the measures prioritized by the aforementioned measures prioritization unit, The project includes a project execution unit that executes projects based on policies proposed by the aforementioned policy proposal unit. A system characterized by the following features. (Note 2) It has a fundraising department that handles fundraising. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a revenue-securing unit to ensure profits are generated. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a success story generation unit to generate success stories. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We collect data in fields such as economics, politics, medicine, agriculture, IT, and education. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is The data collected by the aforementioned collection unit is analyzed using AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned problem identification unit, Based on the analysis results obtained by the aforementioned analysis unit, social issues are identified. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned countermeasure prioritization unit is: Based on the issues identified by the aforementioned issue identification unit, countermeasures are prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned policy proposal department, Policy proposals are made based on the measures prioritized by the aforementioned measures prioritization unit. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned project execution unit, The project will be executed based on the policies proposed by the aforementioned policy proposal department. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is When collecting data, the collection method is customized to take into account the local cultural background and social circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, receive real-time feedback and dynamically adjust the collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned collection unit is When collecting data, the collection range is optimized considering the geographical characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During data collection, information from social media and online forums is integrated and collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, trends are identified by comparing them with historical data, and predictive models are built. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, different data sources are integrated to perform analysis from multiple perspectives. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During the analysis, the results are customized by taking into account the local economic and political situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, we refer to relevant research papers and reports to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned problem identification unit, We estimate user emotions and adjust the problem identification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned problem identification unit, When identifying problems, we improve the accuracy of problem identification by referring to past success stories and failure stories. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned problem identification unit, When identifying problems, incorporate feedback from local experts and residents to improve the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned problem identification unit, Estimate user emotions and prioritize the identified issues based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned problem identification unit, When identifying issues, customize the identification method by taking into account the geographical characteristics and cultural background of the region. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned problem identification unit, When identifying issues, referencing relevant policies and regulations improves the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned countermeasure prioritization unit is: It estimates user sentiment and adjusts the priority of countermeasures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned countermeasure prioritization unit is: When prioritizing countermeasures, evaluate the effectiveness of past measures to optimize prioritization. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned countermeasure prioritization unit is: When prioritizing countermeasures, the priority should be determined by considering local resources and infrastructure conditions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned countermeasure prioritization unit is: It estimates the user's emotions and adjusts the order in which countermeasures are implemented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Appendix 33) The countermeasure prioritization unit optimizes the priority by considering the economic and political situations of the region when assigning the countermeasure priority. The system according to Appendix 1, characterized in that. (Appendix 34) The countermeasure prioritization unit determines the priority by referring to relevant research papers and reports when assigning the countermeasure priority. The system according to Appendix 1, characterized in that. (Appendix 35) The policy proposal unit estimates the user's sentiment and adjusts the expression method of the policy proposal based on the estimated user sentiment. The system according to Appendix 1, characterized in that. (Appendix 36) The policy proposal unit evaluates the effects of past policies and optimizes the proposal content when making the policy proposal. The system according to Appendix 1, characterized in that. (Appendix 37) The policy proposal unit incorporates feedback from local experts and residents to improve the proposal content when making the policy proposal. The system according to Appendix 1, characterized in that. (Appendix 38) The policy proposal unit estimates the user's sentiment and determines the priority of the policy proposal based on the estimated user sentiment. The system according to Appendix 1, characterized in that. (Appendix 39) The policy proposal unit customizes the proposal content by considering the geographical characteristics and cultural background of the region when making the policy proposal. The system according to Appendix 1, characterized in that. (Appendix 40) The policy proposal unit refers to relevant policies and regulations to improve the proposal content when making the policy proposal. The system according to Appendix 1, characterized in that. (Note 41) The aforementioned project execution unit, Estimate user emotions and adjust project execution based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned project execution unit, When executing a project, we improve execution accuracy by referring to past project success and failure stories. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned project execution unit, When executing a project, optimize the execution method by considering local resources and infrastructure conditions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned project execution unit, It estimates user sentiment and adjusts the execution order of projects based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned project execution unit, When executing a project, the execution method should be optimized by considering the local economic and political situation. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned project execution unit, When executing a project, refer to relevant research papers and reports to improve execution accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned fundraising department, It estimates user sentiment and adjusts fundraising methods based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 48) The aforementioned fundraising department, When raising funds, we optimize our fundraising methods by referring to past success and failure stories. The system according to appendix 2, characterized in that... (Appendix 49) The said financing department estimates the user's emotion and determines the priority of financing based on the estimated user emotion The system according to appendix 2, characterized in that... (Appendix 50) The said financing department optimizes the financing method by considering the regional economic situation and political situation during financing The system according to appendix 2, characterized in that... (Appendix 51) The said revenue ensuring department estimates the user's emotion and adjusts the revenue ensuring method based on the estimated user emotion The system according to appendix 3, characterized in that... (Appendix 52) The said revenue ensuring department optimizes the method by referring to past successful and failed revenue ensuring cases during revenue ensuring The system according to appendix 3, characterized in that... (Appendix 53) The said revenue ensuring department estimates the user's emotion and determines the priority of revenue ensuring based on the estimated user emotion The system according to appendix 3, characterized in that... (Appendix 54) The said revenue ensuring department optimizes the method by considering the regional economic situation and political situation during revenue ensuring The system according to appendix 3, characterized in that... (Appendix 55) The said successful case generating department estimates the user's emotion and adjusts the successful case generating method based on the estimated user emotion The system according to appendix 4, characterized in that... (Appendix 56) The said successful case generating department improves the generation accuracy by referring to past successful and failed cases during successful case generation The system described in Appendix 4, characterized by the features described herein. (Note 57) The aforementioned success story generation unit, It estimates user emotions and prioritizes success stories based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 58) The aforementioned success story generation unit, When generating success stories, the generation method is optimized by taking into account the local economic and political situation. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0233] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A problem identification unit identifies social issues based on the analysis results obtained by the aforementioned analysis unit, A countermeasure prioritization unit that prioritizes countermeasures based on the issues identified by the aforementioned issue identification unit, A policy proposal unit that makes policy proposals based on the measures prioritized by the aforementioned measures prioritization unit, The project includes a project execution unit that executes projects based on policies proposed by the aforementioned policy proposal unit. A system characterized by the following features.

2. It has a fundraising department that handles fundraising. The system according to feature 1.

3. It includes a revenue-securing unit to ensure profits are generated. The system according to feature 1.

4. Equipped with a success story generation unit to generate success stories. The system according to feature 1.

5. The aforementioned collection unit is We collect data in fields such as economics, politics, medicine, agriculture, IT, and education. The system according to feature 1.

6. The aforementioned analysis unit is The data collected by the aforementioned collection unit is analyzed using AI. The system according to feature 1.

7. The aforementioned problem identification unit is Based on the analysis results obtained by the aforementioned analysis unit, social issues are identified. The system according to feature 1.

8. The aforementioned countermeasure prioritization unit, Based on the issues identified by the aforementioned issue identification unit, countermeasures are prioritized. The system according to feature 1.

9. The aforementioned policy proposal department, Policy proposals are made based on the measures prioritized by the aforementioned measures prioritization unit. The system according to feature 1.

10. The aforementioned project execution unit, The project will be executed based on the policies proposed by the aforementioned policy proposal department. The system according to feature 1.

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