system

Abstract

The system according to this embodiment aims to ensure transparency and fairness in policy-making and to prioritize the interests of the entire nation. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an opinion collection unit, an opinion analysis unit, and an execution unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates policy options based on the information analyzed by the analysis unit. The opinion collection unit makes the policies generated by the generation unit public and collects opinions. The opinion analysis unit analyzes the opinions collected by the opinion collection unit and modifies or improves the policies. The execution unit makes the final policy decision and puts it into action.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prejudice and personal interests affect policy decisions, lacking transparency and fairness.

[0005] The system according to the embodiment aims to ensure transparency and fairness in policy decisions and prioritize the interests of the entire nation.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a generation unit, an opinion collection unit, an opinion analysis unit, and an execution unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates policy options based on the information analyzed by the analysis unit. The opinion collection unit makes the policies generated by the generation unit public and collects opinions. The opinion analysis unit analyzes the opinions collected by the opinion collection unit and modifies or improves the policies. The execution unit makes the final policy decision and puts it into action. [Effects of the Invention]

[0007] The system according to this embodiment ensures transparency and fairness in policy-making and prioritizes the interests of the entire nation. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0016] [First Embodiment] 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 political management system according to an embodiment of the present invention is a system that uses AI to enhance the transparency and fairness of politics. This political management system collects vast amounts of data in order to make policy decisions that are not influenced by individual political beliefs and that prioritize the interests of the entire nation. Next, it analyzes the collected data and extracts the information necessary for policy decisions. An AI algorithm is used for the analysis to grasp the correlations and trends of the data. Next, based on the extracted information, it generates policy options. The AI ​​evaluates the advantages and disadvantages of each option and proposes the optimal policy, prioritizing the interests of the entire nation. This proposal is made through a transparent decision-making process, eliminating bias and personal interests. Furthermore, the proposed policy is made public to the public, and opinions are collected. The AI ​​analyzes the collected opinions and modifies or improves the policy. This process realizes highly reliable political management. Finally, the final policy decision is made by the AI ​​and put into action. This system provides a new form of leadership that does not depend on political bases or name recognition, and realizes fair political management that prioritizes the interests of the entire nation. For example, a political management system collects data such as economic indicators, social statistics, international relations, and environmental data. Next, it analyzes the collected data to understand its correlations and trends. Then, based on the analyzed information, it generates policy options. The AI ​​evaluates the merits and demerits of each option and proposes the optimal policy, prioritizing the interests of the entire nation. Next, it makes the proposed policies public and collects opinions. The AI ​​analyzes the collected opinions and modifies or improves the policies. Finally, it makes a final policy decision and puts it into action. In this way, the political management system can achieve fair political management that prioritizes the interests of the entire nation.

[0029] The political management system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an opinion collection unit, an opinion analysis unit, and an execution unit. The collection unit collects data. The collection unit collects data such as economic indicators, social statistics, international relations, and environmental data. The collection unit collects data provided by government agencies and international organizations, for example. The collection unit can also collect publicly available data on the internet and data from social media. For example, the collection unit obtains economic indicators from a government agency's database. The collection unit collects international relations data from reports of international organizations. The collection unit collects social statistics data from social media posts. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, grasps the correlation and trends of the data. The analysis unit analyzes the data using statistical analysis and machine learning, for example. The analysis unit can use correlation coefficients and regression analysis to grasp the correlation of the data. The analysis unit can use time series analysis and moving averages to grasp the trends of the data. For example, the analysis unit analyzes the relationship between economic indicators and social statistics using correlation coefficients. The analysis unit uses regression analysis to grasp trends in international relations data. The analysis unit uses time series analysis to analyze fluctuations in environmental data. The generation unit generates policy options based on the information analyzed by the analysis unit. The generation unit, for example, evaluates the advantages and disadvantages of each option and proposes the optimal policy while prioritizing the interests of the entire nation. The generation unit evaluates the advantages and disadvantages of each option using cost-benefit analysis and risk assessment. The generation unit can consider economic, social, and environmental benefits in order to evaluate the interests of the entire nation. For example, the generation unit uses cost-benefit analysis to evaluate the advantages and disadvantages of economic policies. The generation unit uses risk assessment to evaluate the advantages and disadvantages of social policies. The generation unit proposes environmental policies considering economic, social, and environmental benefits. The opinion gathering unit makes the policies generated by the generation unit public and collects opinions. The opinion gathering unit makes the policies public through government websites and media, for example. The opinion gathering department can collect public opinion through surveys and interviews. For example, the opinion gathering department can publish policies on the government website and solicit opinions from the public.The Opinion Gathering Department announces policies through the media and gathers public opinion. The Opinion Gathering Department collects public opinion by conducting surveys. The Opinion Analysis Department analyzes the opinions collected by the Opinion Gathering Department and modifies or improves policies. The Opinion Analysis Department analyzes collected opinions using methods such as text analysis and sentiment analysis. The Opinion Analysis Department can extract important opinions from the collected opinions and modify or improve policies. For example, the Opinion Analysis Department uses text analysis to classify public opinion. The Opinion Analysis Department uses sentiment analysis to understand the sentiment behind public opinion. The Opinion Analysis Department extracts important opinions and modifies or improves policies. The Implementation Department makes the final policy decision and puts it into action. The Implementation Department, for example, formulates a policy implementation plan and puts it into action. The Implementation Department can monitor the status of policy implementation and make adjustments as needed. For example, the Implementation Department formulates a policy implementation plan and puts it into action. The Implementation Department monitors the status of policy implementation and makes adjustments as needed. As a result, the political management system according to this embodiment can efficiently carry out the process from data collection to policy decision-making and implementation.

[0030] The data collection unit collects data. For example, it collects economic indicators, social statistics, international relations data, and environmental data. Specifically, it collects data provided by government agencies and international organizations. For example, it obtains economic indicators from government databases and international relations data from reports of international organizations. The data collection unit can also collect publicly available data from the internet and social media. For example, it collects social statistics data from social media posts and environmental data from various sensors and observation agencies. The data collection unit centrally manages this data and stores it in a database that is updated in real time. Furthermore, the data collection unit performs data validation and filtering to ensure data reliability and accuracy. For example, it detects data duplication and omissions and supplements or corrects them as needed. The data collection unit also adjusts the frequency and timing of data collection to ensure that the latest information is always available. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and strengthen the data infrastructure of the entire system. Furthermore, the data collection unit can collaborate with other systems and departments to share and integrate data. For example, the collected data is made accessible to the analysis and generation units and used for policy formulation and evaluation. The data collection unit also takes data security and privacy protection into consideration and establishes an appropriate management system. This allows the data collection unit to provide highly reliable data and improve the overall performance and reliability of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit identifies data correlations and trends. Specifically, the analysis unit uses statistical analysis and machine learning to analyze data. For instance, it uses correlation coefficients and regression analysis to understand data correlations, and time series analysis and moving averages to understand data trends. The analysis unit can use correlation coefficients to analyze the relationship between economic indicators and social statistics. It can also use regression analysis to understand trends in international relations data. Furthermore, it can use time series analysis to analyze fluctuations in environmental data. Based on these analysis results, the analysis unit can detect data patterns and anomalies, which can be used to formulate and evaluate policies. For example, the analysis unit can analyze fluctuation patterns in economic indicators to evaluate the effectiveness of economic policies. It can also analyze trends in social statistics data to identify areas for improvement in social policies. Furthermore, it can detect anomalies in environmental data and conduct risk assessments of environmental policies. The analysis unit can also visualize these analysis results and provide them in an easy-to-understand format. For example, it can use graphs and charts to visually represent data trends and correlations. Furthermore, the analysis department will provide analysis results in the form of reports and dashboards, making them easily accessible to stakeholders. This will enable the analysis department to effectively utilize the data analysis results to support policy formulation and evaluation. In addition, the analysis department will continuously improve the accuracy of its analysis methods and algorithms, enabling it to provide more accurate analysis results.

[0032] The generation unit generates policy options based on the information analyzed by the analysis unit. For example, the generation unit evaluates the advantages and disadvantages of each option and proposes the optimal policy, prioritizing the interests of the entire nation. Specifically, the generation unit uses cost-benefit analysis and risk assessment to evaluate the advantages and disadvantages of each option. For example, cost-benefit analysis can be used to evaluate the advantages and disadvantages of economic policies. Risk assessment can be used to evaluate the advantages and disadvantages of social policies. Furthermore, the generation unit can propose environmental policies considering economic, social, and environmental benefits. Based on these evaluation results, the generation unit selects and proposes the optimal policy. For example, the generation unit evaluates economic policy options and proposes the most effective policy. It also evaluates social policy options and proposes the least risky policy. Furthermore, it evaluates environmental policy options and proposes the most sustainable policy. The generation unit provides these policy options in an easy-to-understand format, making them easily comprehensible to stakeholders. For example, it presents the advantages and disadvantages of policies in tables and graphs for easy comparison. The generation unit can also simulate policy options and predict future impacts. This allows the Generative Division to propose optimal policies and support policy-making while prioritizing the interests of the entire nation. Furthermore, the Generative Division can continuously review policy options and respond flexibly to the latest information and circumstances.

[0033] The Opinion Gathering Department makes the policies generated by the Policy Generation Department public and collects opinions from the public. Specifically, the Opinion Gathering Department publishes policies through government websites and media. For example, it can publish policies on government websites and solicit opinions from the public. It can also announce policies through the media and collect public opinions. Furthermore, the Opinion Gathering Department can collect public opinions through surveys and interviews. For example, it can conduct surveys to collect public opinions and conduct interviews to obtain detailed opinions. The Opinion Gathering Department centrally manages these opinions and stores them in a database. Furthermore, the Opinion Gathering Department verifies and filters opinions to ensure their reliability and representativeness. For example, it detects duplicate or inappropriate opinions and removes them as necessary. The Opinion Gathering Department also adjusts the frequency and timing of opinion collection to ensure that the latest opinions are always available. This allows the Opinion Gathering Department to efficiently collect diverse opinions and use them to improve policies. Furthermore, the Opinion Gathering Department can collaborate with other systems and departments to share and integrate opinions. For example, collected opinions will be made accessible to the opinion analysis and implementation departments and used for policy modification and implementation. Furthermore, the opinion collection department will take into consideration the protection of opinion privacy and establish an appropriate management system. This will enable the opinion collection department to provide highly reliable opinions and improve the overall performance and reliability of the system.

[0034] The Opinion Analysis Department analyzes opinions collected by the Opinion Collection Department and uses this analysis to revise and improve policies. Specifically, the Opinion Analysis Department analyzes collected opinions using text analysis and sentiment analysis. For example, it can classify public opinions using text analysis and understand the sentiment behind those opinions using sentiment analysis. Based on these analysis results, the Opinion Analysis Department extracts important opinions and uses them to revise and improve policies. For example, it can classify public opinions using text analysis and identify areas for policy improvement. It can also understand the sentiment behind public opinions using sentiment analysis and evaluate the likelihood of policy acceptance. Furthermore, the Opinion Analysis Department can visualize the analysis results and provide them in an easy-to-understand format. For example, it can visually represent opinion classifications and sentiment trends using graphs and charts. The Opinion Analysis Department also provides analysis results as reports and dashboards, making them easily accessible to stakeholders. This allows the Opinion Analysis Department to effectively utilize collected opinions and support policy revision and improvement. In addition, the Opinion Analysis Department can continuously improve the accuracy of its analysis methods and algorithms to provide more accurate analysis results. This allows the opinion analysis department to accurately reflect public opinion in policy revisions and improvements, thereby maximizing the effectiveness of those policies.

[0035] The implementation department makes the final policy decisions and puts them into action. Specifically, the implementation department formulates and implements policy implementation plans. For example, it can formulate and implement policy implementation plans. The implementation department can also monitor the status of policy implementation and make revisions as needed. For example, it can monitor the status of policy implementation and make revisions as needed. The implementation department provides these implementation plans in an easy-to-understand format so that stakeholders can easily comprehend them. For example, it can present the implementation plans in tables and graphs to make it easier to compare progress. The implementation department can also simulate the status of policy implementation and predict future impacts. This allows the implementation department to effectively manage the status of policy implementation and maximize the effectiveness of the policy. Furthermore, the implementation department can continuously improve the accuracy of implementation methods and processes to provide more accurate implementation results. This allows the implementation department to accurately grasp the status of policy implementation and respond flexibly as needed. Furthermore, the implementation department can collect feedback on policy implementation and use it to improve implementation methods and processes. For example, it can collect feedback on policy implementation and identify areas for improvement in implementation methods and processes. Furthermore, the implementation unit can collect data on policy implementation and evaluate its progress. This allows the implementation unit to effectively manage policy implementation and maximize its effectiveness.

[0036] The data collection unit can collect data such as economic indicators, social statistics, international relations, and environmental data. For example, the data collection unit can collect data provided by government agencies and international organizations. The data collection unit can also collect publicly available data on the internet and data from social media. For example, the data collection unit can obtain economic indicators from government agency databases. The data collection unit can collect international relations data from reports of international organizations. The data collection unit can collect social statistics data from social media posts. By collecting diverse data, the information available for policy making is enriched. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media post data into a generating AI and have the generating AI perform the process of extracting relevant information from the post data.

[0037] The analysis unit can grasp the correlations and trends of the collected data. The analysis unit analyzes the data using, for example, statistical analysis and machine learning. The analysis unit can use correlation coefficients and regression analysis to grasp the correlations of the data. The analysis unit can use time series analysis and moving averages to grasp the trends of the data. For example, the analysis unit uses correlation coefficients to analyze the relationship between economic indicators and social statistics. The analysis unit uses regression analysis to grasp the trends in international relations data. The analysis unit uses time series analysis to analyze fluctuations in environmental data. By grasping the correlations and trends of the data, more accurate policy decisions become possible. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of the correlations and trends of the data.

[0038] The generation unit can evaluate the advantages and disadvantages of each option and propose the optimal policy while prioritizing the interests of the entire nation. For example, the generation unit can evaluate the advantages and disadvantages of each option using cost-benefit analysis and risk assessment. To evaluate the interests of the entire nation, the generation unit can consider economic, social, and environmental benefits. For example, the generation unit can evaluate the advantages and disadvantages of economic policies using cost-benefit analysis. The generation unit can evaluate the advantages and disadvantages of social policies using risk assessment. The generation unit can propose environmental policies considering economic, social, and environmental benefits. This enables policy proposals that prioritize the interests of the entire nation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have a generating AI perform the evaluation of the advantages and disadvantages of each option, and the generating AI can propose the optimal policy based on the evaluation results.

[0039] The Opinion Gathering Department can disclose proposed policies to the public and collect their opinions. The Opinion Gathering Department can disclose policies through government websites and media, for example. The Opinion Gathering Department can collect public opinions through surveys and interviews. For example, the Opinion Gathering Department can disclose policies on government websites and solicit opinions from the public. The Opinion Gathering Department can announce policies through the media and collect public opinions. The Opinion Gathering Department can collect public opinions by conducting surveys. This improves the transparency and fairness of policies by collecting public opinions. Some or all of the above processes in the Opinion Gathering Department may be carried out using AI, for example, or not using AI. For example, the Opinion Gathering Department can input the collected opinions into a generating AI and have the generating AI perform an analysis of the opinions.

[0040] The Opinion Analysis Unit can analyze collected opinions and revise or improve policies. For example, the Opinion Analysis Unit can analyze collected opinions using text analysis and sentiment analysis. The Opinion Analysis Unit can extract important opinions from the collected opinions and revise or improve policies. For example, the Opinion Analysis Unit can classify public opinions using text analysis. The Opinion Analysis Unit can grasp the sentiment behind public opinions using sentiment analysis. The Opinion Analysis Unit extracts important opinions and revises or improves policies. This makes it possible to revise and improve policies that reflect public opinions. Some or all of the above processing in the Opinion Analysis Unit may be performed using AI, for example, or without AI. For example, the Opinion Analysis Unit can input collected opinions into a generating AI and have the generating AI perform the analysis of the opinions.

[0041] The implementation unit can make final policy decisions and put them into action. For example, the implementation unit can formulate a policy implementation plan and put it into action. The implementation unit can monitor the status of policy implementation and make adjustments as needed. For example, the implementation unit can formulate a policy implementation plan and put it into action. The implementation unit can monitor the status of policy implementation and make adjustments as needed. This ensures that final policy decisions and their implementation are carried out efficiently. Some or all of the above processes in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can have a generating AI formulate a policy implementation plan, and the generating AI can then implement the policy based on that plan.

[0042] The data collection unit can analyze past policy decision history and select the optimal data collection method. For example, the data collection unit can analyze data collection methods used in past policy decisions and reuse successful methods. The data collection unit can optimize the timing and frequency of data collection based on past policy decision history. The data collection unit can identify problems with data collection in past policy decisions and implement improvement measures. For example, the data collection unit can analyze data collection methods used in past policy decisions and reuse successful methods. The data collection unit can optimize the timing and frequency of data collection based on past policy decision history. The data collection unit can identify problems with data collection in past policy decisions and implement improvement measures. This makes it possible to select the optimal data collection method by utilizing past policy decision history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past policy decision history into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter data based on current public interest and social trends during data collection. For example, the data collection unit can prioritize collecting data on topics of high public interest. The data collection unit can analyze social media trends and collect relevant data. The data collection unit can filter information from specific data sources based on public interest. For example, the data collection unit can prioritize collecting data on topics of high public interest. The data collection unit can analyze social media trends and collect relevant data. The data collection unit can filter information from specific data sources based on public interest. This enables data collection based on public interest and social trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input social media trend data into a generating AI and have the generating AI perform the filtering of relevant data.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of citizens during data collection. For example, if a problem occurs in a particular area, the data collection unit will prioritize the collection of data from that area. The data collection unit can collect information from relevant data sources based on geographical location information. The data collection unit can analyze citizens' movement patterns and collect relevant data. For example, if a problem occurs in a particular area, the data collection unit will prioritize the collection of data from that area. The data collection unit will collect information from relevant data sources based on geographical location information. The data collection unit will analyze citizens' movement patterns and collect relevant data. This enables the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0045] The data collection unit can analyze citizens' social media activities and collect relevant data during data collection. For example, the data collection unit can collect public opinion on social media and reflect it in policy decisions. The data collection unit can analyze social media trends and collect relevant data. The data collection unit can collect specific data based on public interest on social media. For example, the data collection unit can collect public opinion on social media and reflect it in policy decisions. The data collection unit analyzes social media trends and collects relevant data. The data collection unit collects specific data based on public interest on social media. This enables data collection based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posting data into a generating AI and have the generating AI collect relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For less important data, the analysis unit can perform a simplified analysis. The analysis unit can optimize the analysis resources according to the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For less important data, the analysis unit can perform a simplified analysis. The analysis unit can optimize the analysis resources according to the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm using an economic model to economic data. The analysis unit can apply an analysis algorithm using a statistical model to social statistical data. The analysis unit can apply an analysis algorithm using an environmental model to environmental data. For example, the analysis unit can apply an analysis algorithm using an economic model to economic data. The analysis unit can apply an analysis algorithm using a statistical model to social statistical data. The analysis unit can apply an analysis algorithm using an environmental model to environmental data. This makes it possible to apply analysis algorithms according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to understand the situation in real time. The analysis unit can analyze historical data to grasp long-term trends. The analysis unit can optimize the analysis resources according to the data collection timing. For example, the analysis unit can prioritize the analysis of the latest data to understand the situation in real time. The analysis unit can analyze historical data to grasp long-term trends. The analysis unit optimizes the analysis resources according to the data collection timing. This makes it possible to determine the analysis priority based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly grasp the situation. The analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can optimize the analysis resources according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly grasp the situation. The analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can optimize the analysis resources according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0050] The generation unit can adjust the level of detail based on the importance of each policy option when generating policy options. For example, the generation unit can provide detailed information for policy options with high importance. For policy options with low importance, the generation unit can provide simplified information. The generation unit can optimize the level of detail of the information according to the importance of each option. For example, the generation unit can provide detailed information for policy options with high importance. For policy options with low importance, the generation unit can provide simplified information. The generation unit can optimize the level of detail of the information according to the importance of each option. This makes it possible to adjust the level of detail according to the importance of each option. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of each option into the generation AI and have the generation AI perform the level of detail adjustment.

[0051] The generation unit can apply different generation algorithms depending on the category of the policy options when generating them. For example, the generation unit can apply a generation algorithm using an economic model to economic policies. For social policies, it can apply a generation algorithm using a social model. For environmental policies, it can apply a generation algorithm using an environmental model. This makes it possible to apply a generation algorithm according to the category of the options. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the options into a generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0052] The generation unit can determine priorities based on the submission timing of policy options when generating them. For example, the generation unit can prioritize the generation of policy options that are urgent. The generation unit can adjust the priority of policy options according to the submission timing. The generation unit can optimize the resources used to generate policy options based on the submission timing. For example, the generation unit can prioritize the generation of policy options that are urgent. The generation unit can adjust the priority of policy options according to the submission timing. The generation unit can optimize the resources used to generate policy options based on the submission timing. This makes it possible to determine the priority of policy options based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing of options into a generation AI and have the generation AI perform the priority determination.

[0053] The generation unit can adjust the order of policy options based on their relevance when generating them. For example, the generation unit can prioritize generating highly relevant policy options. The generation unit can postpone generating less relevant policy options. The generation unit can optimize the generation resources according to the relevance of the options. For example, the generation unit prioritizes generating highly relevant policy options. The generation unit postpones generating less relevant policy options. The generation unit optimizes the generation resources according to the relevance of the options. This makes it possible to adjust the order based on the relevance of the options. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the options into a generation AI and have the generation AI perform the order adjustment.

[0054] The opinion collection unit can select the optimal collection method by referring to the public's past opinion submission history when collecting opinions. For example, the opinion collection unit can analyze past opinion submission history and reuse successful collection methods. The opinion collection unit can optimize the timing and frequency of opinion collection based on past opinion submission history. The opinion collection unit can identify problems in past opinion submissions and implement improvement measures. For example, the opinion collection unit can analyze past opinion submission history and reuse successful collection methods. The opinion collection unit can optimize the timing and frequency of opinion collection based on past opinion submission history. The opinion collection unit can identify problems in past opinion submissions and implement improvement measures. This makes it possible to select the optimal opinion collection method by utilizing past opinion submission history. Some or all of the above processes in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input past opinion submission history into a generating AI and have the generating AI select the optimal opinion collection method.

[0055] The opinion collection unit can prioritize collecting highly relevant opinions by considering the geographical location information of citizens when collecting opinions. For example, if a problem occurs in a particular region, the opinion collection unit will prioritize collecting opinions from that region. The opinion collection unit can collect relevant opinions based on geographical location information. The opinion collection unit can analyze citizens' movement patterns and collect relevant opinions. For example, if a problem occurs in a particular region, the opinion collection unit will prioritize collecting opinions from that region. The opinion collection unit collects relevant opinions based on geographical location information. The opinion collection unit analyzes citizens' movement patterns and collects relevant opinions. This makes it possible to collect highly relevant opinions based on geographical location information. Some or all of the above processing in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input geographical location information into a generating AI and have the generating AI collect highly relevant opinions.

[0056] The opinion analysis unit can adjust the level of detail of the analysis based on the importance of the opinions during opinion analysis. For example, the opinion analysis unit can perform a detailed analysis on opinions with high importance, and a simplified analysis on opinions with low importance. The opinion analysis unit can optimize the analysis resources according to the importance of the opinions. For example, the opinion analysis unit can perform a detailed analysis on opinions with high importance, and a simplified analysis on opinions with low importance. The opinion analysis unit can optimize the analysis resources according to the importance of the opinions. This makes it possible to adjust the level of detail of the analysis according to the importance of the opinions. Some or all of the above processing in the opinion analysis unit may be performed using AI, for example, or without using AI. For example, the opinion analysis unit can input the importance of the opinions into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0057] The opinion analysis unit can apply different analysis algorithms depending on the category of the opinion during opinion analysis. For example, the opinion analysis unit can apply an analysis algorithm using an economic model to opinions on economics. For opinions on society, it can apply an analysis algorithm using a social model. For opinions on the environment, it can apply an analysis algorithm using an environmental model. This makes it possible to apply an analysis algorithm according to the category of the opinion. Some or all of the above processing in the opinion analysis unit may be performed using AI, for example, or without AI. For example, the opinion analysis unit can input the category of the opinion into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0058] The opinion analysis unit can determine the priority of analysis based on when the opinions were submitted. For example, the opinion analysis unit can prioritize the analysis of the latest opinions to grasp the situation in real time. The opinion analysis unit can analyze past opinions to grasp long-term trends. The opinion analysis unit can optimize the analysis resources according to when the opinions were submitted. For example, the opinion analysis unit can prioritize the analysis of the latest opinions to grasp the situation in real time. The opinion analysis unit can analyze past opinions to grasp long-term trends. The opinion analysis unit can optimize the analysis resources according to when the opinions were submitted. This makes it possible to determine the priority of analysis based on when the opinions were submitted. Some or all of the above processes in the opinion analysis unit may be performed using AI, for example, or without using AI. For example, the opinion analysis unit can input the timing of opinion submissions into a generating AI and have the generating AI perform the determination of the analysis priority.

[0059] The opinion analysis unit can adjust the order of analysis based on the relevance of opinions during opinion analysis. For example, the opinion analysis unit can prioritize the analysis of highly relevant opinions to quickly grasp the situation. The opinion analysis unit can postpone the analysis of less relevant opinions to perform efficient analysis. The opinion analysis unit can optimize the analysis resources according to the relevance of opinions. For example, the opinion analysis unit can prioritize the analysis of highly relevant opinions to quickly grasp the situation. The opinion analysis unit can postpone the analysis of less relevant opinions to perform efficient analysis. The opinion analysis unit can optimize the analysis resources according to the relevance of opinions. This makes it possible to adjust the order of analysis based on the relevance of opinions. Some or all of the above processing in the opinion analysis unit may be performed using AI, for example, or without AI. For example, the opinion analysis unit can input the relevance of opinions into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0060] The implementation unit can select the optimal implementation method by referring to past implementation history when implementing a policy. For example, the implementation unit can analyze past policy implementation history and reuse successful methods. The implementation unit can optimize the timing and frequency of implementation based on past policy implementation history. The implementation unit can identify problems in past policy implementations and implement improvement measures. For example, the implementation unit can analyze past policy implementation history and reuse successful methods. The implementation unit can optimize the timing and frequency of implementation based on past policy implementation history. The implementation unit can identify problems in past policy implementations and implement improvement measures. This makes it possible to select the optimal policy implementation method by utilizing past implementation history. Some or all of the above processes in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input past implementation history into a generating AI and have the generating AI select the optimal implementation method.

[0061] The implementation unit can select the optimal implementation method when implementing policies, taking into account the geographical location information of citizens. For example, if a problem occurs in a particular region, the implementation unit will prioritize the implementation of policies for that region. The implementation unit can implement relevant policies based on geographical location information. The implementation unit can analyze the movement patterns of citizens and implement relevant policies. For example, if a problem occurs in a particular region, the implementation unit will prioritize the implementation of policies for that region. The implementation unit will implement relevant policies based on geographical location information. The implementation unit will analyze the movement patterns of citizens and implement relevant policies. This makes it possible to select the optimal implementation method based on the geographical location information of citizens. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input geographical location information into a generating AI and have the generating AI select the optimal implementation method.

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

[0063] The analysis unit can visualize the results of data analysis and present them to the public in an easy-to-understand manner. For example, the analysis unit can visualize data correlations and trends using graphs and charts. The analysis unit can provide interactive dashboards, allowing the public to freely manipulate data and view details. Through data visualization, the analysis unit can make it easier for the public to understand the basis for policy decisions. For example, the analysis unit can display trends in economic indicators as line graphs, allowing the public to grasp economic trends at a glance. The analysis unit can display social statistics data as pie charts, visually showing the proportion of each category. The analysis unit can display environmental data as heatmaps, allowing for an intuitive understanding of environmental conditions in each region. In this way, data visualization can promote public understanding and engagement.

[0064] The generation unit can simulate policy options and predict the impact of each option. For example, the generation unit can simulate the impact of economic policies using economic models. The generation unit can simulate the impact of social policies using social models. The generation unit can simulate the impact of environmental policies using environmental models. For example, the generation unit can simulate economic policies and predict their impact on GDP and unemployment rates. The generation unit can simulate social policies and predict their impact on social welfare and education. The generation unit can simulate environmental policies and predict their impact on greenhouse gas emissions and biodiversity. This allows for a prior evaluation of the impact of policy options and the proposal of optimal policies.

[0065] The Opinion Collection Department can ensure anonymity when collecting public opinion. For example, when conducting online surveys, the Opinion Collection Department will not collect personal information of respondents. The Opinion Collection Department can allow anonymous submission of opinions. The Opinion Collection Department can take measures to protect the privacy of citizens during the opinion collection process. For example, the Opinion Collection Department can anonymize online survey responses so that individuals cannot be identified. The Opinion Collection Department can provide an anonymous option on opinion submission forms so that citizens can freely submit their opinions. The Opinion Collection Department can protect the privacy of citizens by encrypting data and controlling access during the opinion collection process. This will provide an environment in which citizens can submit their opinions with peace of mind.

[0066] The opinion analysis unit can cluster collected opinions and extract common themes and topics. For example, it can use text mining techniques to extract frequently occurring keywords from the opinions. The opinion analysis unit can classify opinions by theme and understand the trends in opinions for each theme. The opinion analysis unit can visualize the clustering results and present them to the public in an easy-to-understand manner. For example, the opinion analysis unit can use text mining techniques to extract frequently occurring keywords from opinions on economic policy. The opinion analysis unit classifies opinions by theme and divides them into categories such as economic policy, social policy, and environmental policy. The opinion analysis unit visualizes the clustering results in graphs and charts to show the trends in opinions to the public. This allows for the effective analysis of collected opinions and helps in the modification and improvement of policies.

[0067] The implementation team can monitor the status of policy implementation in real time and make the progress public. For example, the implementation team can publish the status of policy implementation on an online platform so that the public can check the progress. The implementation team can regularly update data on policy implementation to provide the latest information. The implementation team can collect feedback from the public on the status of policy implementation and make corrections as needed. For example, the implementation team can publish the status of policy implementation on an online platform so that the public can check the progress. The implementation team can regularly update data on policy implementation to provide the latest information. The implementation team can collect feedback from the public on the status of policy implementation and make corrections as needed. This makes the status of policy implementation transparent and gains the trust of the public.

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

[0069] Step 1: The data collection unit collects data. The data collection unit collects data such as economic indicators, social statistics, international relations, and environmental data. The data collection unit collects data provided by government agencies and international organizations, for example. The data collection unit can also collect publicly available data on the internet and data from social media. For example, the data collection unit obtains economic indicators from government agency databases. The data collection unit collects international relations data from reports of international organizations. The data collection unit collects social statistics data from social media posts. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit, for example, identifies correlations and trends in the data. The analysis unit analyzes the data using statistical analysis and machine learning, for example. The analysis unit can use correlation coefficients and regression analysis to identify correlations in the data. The analysis unit can use time series analysis and moving averages to identify trends in the data. For example, the analysis unit uses correlation coefficients to analyze the relationship between economic indicators and social statistics. The analysis unit uses regression analysis to identify trends in international relations data. The analysis unit uses time series analysis to analyze fluctuations in environmental data. Step 3: The generation unit generates policy options based on the information analyzed by the analysis unit. The generation unit, for example, evaluates the merits and demerits of each option and proposes the optimal policy, prioritizing the interests of the entire nation. The generation unit evaluates the merits and demerits of each option using, for example, cost-benefit analysis and risk assessment. The generation unit can consider economic, social, and environmental benefits in order to evaluate the interests of the entire nation. For example, the generation unit evaluates the merits and demerits of economic policies using cost-benefit analysis. The generation unit evaluates the merits and demerits of social policies using risk assessment. The generation unit proposes environmental policies, taking into account economic, social, and environmental benefits. Step 4: The Opinion Gathering Department makes the policies generated by the Generation Department public and collects opinions. The Opinion Gathering Department may, for example, publish the policies through the government website or media. The Opinion Gathering Department can also collect public opinions through surveys and interviews. For example, the Opinion Gathering Department may publish the policies on the government website and solicit opinions from the public. The Opinion Gathering Department may announce the policies through the media and collect public opinions. The Opinion Gathering Department may conduct surveys to collect public opinions. Step 5: The Opinion Analysis Department analyzes the opinions collected by the Opinion Collection Department and modifies or improves policies. The Opinion Analysis Department analyzes the collected opinions using methods such as text analysis and sentiment analysis. The Opinion Analysis Department can extract important opinions from the collected opinions and modify or improve policies. For example, the Opinion Analysis Department uses text analysis to classify public opinions. The Opinion Analysis Department uses sentiment analysis to understand the sentiment behind public opinions. The Opinion Analysis Department extracts important opinions and modifies or improves policies. Step 6: The implementation department makes the final policy decision and puts it into action. For example, the implementation department formulates and implements a policy implementation plan. The implementation department can monitor the status of policy implementation and make adjustments as needed. For example, the implementation department formulates and implements a policy implementation plan. The implementation department monitors the status of policy implementation and makes adjustments as needed.

[0070] (Example of form 2) The political management system according to an embodiment of the present invention is a system that uses AI to enhance the transparency and fairness of politics. This political management system collects vast amounts of data in order to make policy decisions that are not influenced by individual political beliefs and that prioritize the interests of the entire nation. Next, it analyzes the collected data and extracts the information necessary for policy decisions. An AI algorithm is used for the analysis to grasp the correlations and trends of the data. Next, based on the extracted information, it generates policy options. The AI ​​evaluates the advantages and disadvantages of each option and proposes the optimal policy, prioritizing the interests of the entire nation. This proposal is made through a transparent decision-making process, eliminating bias and personal interests. Furthermore, the proposed policy is made public to the public, and opinions are collected. The AI ​​analyzes the collected opinions and modifies or improves the policy. This process realizes highly reliable political management. Finally, the final policy decision is made by the AI ​​and put into action. This system provides a new form of leadership that does not depend on political bases or name recognition, and realizes fair political management that prioritizes the interests of the entire nation. For example, a political management system collects data such as economic indicators, social statistics, international relations, and environmental data. Next, it analyzes the collected data to understand its correlations and trends. Then, based on the analyzed information, it generates policy options. The AI ​​evaluates the merits and demerits of each option and proposes the optimal policy, prioritizing the interests of the entire nation. Next, it makes the proposed policies public and collects opinions. The AI ​​analyzes the collected opinions and modifies or improves the policies. Finally, it makes a final policy decision and puts it into action. In this way, the political management system can achieve fair political management that prioritizes the interests of the entire nation.

[0071] The political management system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an opinion collection unit, an opinion analysis unit, and an execution unit. The collection unit collects data. The collection unit collects data such as economic indicators, social statistics, international relations, and environmental data. The collection unit collects data provided by government agencies and international organizations, for example. The collection unit can also collect publicly available data on the internet and data from social media. For example, the collection unit obtains economic indicators from a government agency's database. The collection unit collects international relations data from reports of international organizations. The collection unit collects social statistics data from social media posts. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, grasps the correlation and trends of the data. The analysis unit analyzes the data using statistical analysis and machine learning, for example. The analysis unit can use correlation coefficients and regression analysis to grasp the correlation of the data. The analysis unit can use time series analysis and moving averages to grasp the trends of the data. For example, the analysis unit analyzes the relationship between economic indicators and social statistics using correlation coefficients. The analysis unit uses regression analysis to grasp trends in international relations data. The analysis unit uses time series analysis to analyze fluctuations in environmental data. The generation unit generates policy options based on the information analyzed by the analysis unit. The generation unit, for example, evaluates the advantages and disadvantages of each option and proposes the optimal policy while prioritizing the interests of the entire nation. The generation unit evaluates the advantages and disadvantages of each option using cost-benefit analysis and risk assessment. The generation unit can consider economic, social, and environmental benefits in order to evaluate the interests of the entire nation. For example, the generation unit uses cost-benefit analysis to evaluate the advantages and disadvantages of economic policies. The generation unit uses risk assessment to evaluate the advantages and disadvantages of social policies. The generation unit proposes environmental policies considering economic, social, and environmental benefits. The opinion gathering unit makes the policies generated by the generation unit public and collects opinions. The opinion gathering unit makes the policies public through government websites and media, for example. The opinion gathering department can collect public opinion through surveys and interviews. For example, the opinion gathering department can publish policies on the government website and solicit opinions from the public.The Opinion Gathering Department announces policies through the media and gathers public opinion. The Opinion Gathering Department collects public opinion by conducting surveys. The Opinion Analysis Department analyzes the opinions collected by the Opinion Gathering Department and modifies or improves policies. The Opinion Analysis Department analyzes collected opinions using methods such as text analysis and sentiment analysis. The Opinion Analysis Department can extract important opinions from the collected opinions and modify or improve policies. For example, the Opinion Analysis Department uses text analysis to classify public opinion. The Opinion Analysis Department uses sentiment analysis to understand the sentiment behind public opinion. The Opinion Analysis Department extracts important opinions and modifies or improves policies. The Implementation Department makes the final policy decision and puts it into action. The Implementation Department, for example, formulates a policy implementation plan and puts it into action. The Implementation Department can monitor the status of policy implementation and make adjustments as needed. For example, the Implementation Department formulates a policy implementation plan and puts it into action. The Implementation Department monitors the status of policy implementation and makes adjustments as needed. As a result, the political management system according to this embodiment can efficiently carry out the process from data collection to policy decision-making and implementation.

[0072] The data collection unit collects data. For example, it collects economic indicators, social statistics, international relations data, and environmental data. Specifically, it collects data provided by government agencies and international organizations. For example, it obtains economic indicators from government databases and international relations data from reports of international organizations. The data collection unit can also collect publicly available data from the internet and social media. For example, it collects social statistics data from social media posts and environmental data from various sensors and observation agencies. The data collection unit centrally manages this data and stores it in a database that is updated in real time. Furthermore, the data collection unit performs data validation and filtering to ensure data reliability and accuracy. For example, it detects data duplication and omissions and supplements or corrects them as needed. The data collection unit also adjusts the frequency and timing of data collection to ensure that the latest information is always available. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and strengthen the data infrastructure of the entire system. Furthermore, the data collection unit can collaborate with other systems and departments to share and integrate data. For example, the collected data is made accessible to the analysis and generation units and used for policy formulation and evaluation. The data collection unit also takes data security and privacy protection into consideration and establishes an appropriate management system. This allows the data collection unit to provide highly reliable data and improve the overall performance and reliability of the system.

[0073] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit identifies data correlations and trends. Specifically, the analysis unit uses statistical analysis and machine learning to analyze data. For instance, it uses correlation coefficients and regression analysis to understand data correlations, and time series analysis and moving averages to understand data trends. The analysis unit can use correlation coefficients to analyze the relationship between economic indicators and social statistics. It can also use regression analysis to understand trends in international relations data. Furthermore, it can use time series analysis to analyze fluctuations in environmental data. Based on these analysis results, the analysis unit can detect data patterns and anomalies, which can be used to formulate and evaluate policies. For example, the analysis unit can analyze fluctuation patterns in economic indicators to evaluate the effectiveness of economic policies. It can also analyze trends in social statistics data to identify areas for improvement in social policies. Furthermore, it can detect anomalies in environmental data and conduct risk assessments of environmental policies. The analysis unit can also visualize these analysis results and provide them in an easy-to-understand format. For example, it can use graphs and charts to visually represent data trends and correlations. Furthermore, the analysis department will provide analysis results in the form of reports and dashboards, making them easily accessible to stakeholders. This will enable the analysis department to effectively utilize the data analysis results to support policy formulation and evaluation. In addition, the analysis department will continuously improve the accuracy of its analysis methods and algorithms, enabling it to provide more accurate analysis results.

[0074] The generation unit generates policy options based on the information analyzed by the analysis unit. For example, the generation unit evaluates the advantages and disadvantages of each option and proposes the optimal policy, prioritizing the interests of the entire nation. Specifically, the generation unit uses cost-benefit analysis and risk assessment to evaluate the advantages and disadvantages of each option. For example, cost-benefit analysis can be used to evaluate the advantages and disadvantages of economic policies. Risk assessment can be used to evaluate the advantages and disadvantages of social policies. Furthermore, the generation unit can propose environmental policies considering economic, social, and environmental benefits. Based on these evaluation results, the generation unit selects and proposes the optimal policy. For example, the generation unit evaluates economic policy options and proposes the most effective policy. It also evaluates social policy options and proposes the least risky policy. Furthermore, it evaluates environmental policy options and proposes the most sustainable policy. The generation unit provides these policy options in an easy-to-understand format, making them easily comprehensible to stakeholders. For example, it presents the advantages and disadvantages of policies in tables and graphs for easy comparison. The generation unit can also simulate policy options and predict future impacts. This allows the Generative Division to propose optimal policies and support policy-making while prioritizing the interests of the entire nation. Furthermore, the Generative Division can continuously review policy options and respond flexibly to the latest information and circumstances.

[0075] The Opinion Gathering Department makes the policies generated by the Policy Generation Department public and collects opinions from the public. Specifically, the Opinion Gathering Department publishes policies through government websites and media. For example, it can publish policies on government websites and solicit opinions from the public. It can also announce policies through the media and collect public opinions. Furthermore, the Opinion Gathering Department can collect public opinions through surveys and interviews. For example, it can conduct surveys to collect public opinions and conduct interviews to obtain detailed opinions. The Opinion Gathering Department centrally manages these opinions and stores them in a database. Furthermore, the Opinion Gathering Department verifies and filters opinions to ensure their reliability and representativeness. For example, it detects duplicate or inappropriate opinions and removes them as necessary. The Opinion Gathering Department also adjusts the frequency and timing of opinion collection to ensure that the latest opinions are always available. This allows the Opinion Gathering Department to efficiently collect diverse opinions and use them to improve policies. Furthermore, the Opinion Gathering Department can collaborate with other systems and departments to share and integrate opinions. For example, collected opinions will be made accessible to the opinion analysis and implementation departments and used for policy modification and implementation. Furthermore, the opinion collection department will take into consideration the protection of opinion privacy and establish an appropriate management system. This will enable the opinion collection department to provide highly reliable opinions and improve the overall performance and reliability of the system.

[0076] The Opinion Analysis Department analyzes opinions collected by the Opinion Collection Department and uses this analysis to revise and improve policies. Specifically, the Opinion Analysis Department analyzes collected opinions using text analysis and sentiment analysis. For example, it can classify public opinions using text analysis and understand the sentiment behind those opinions using sentiment analysis. Based on these analysis results, the Opinion Analysis Department extracts important opinions and uses them to revise and improve policies. For example, it can classify public opinions using text analysis and identify areas for policy improvement. It can also understand the sentiment behind public opinions using sentiment analysis and evaluate the likelihood of policy acceptance. Furthermore, the Opinion Analysis Department can visualize the analysis results and provide them in an easy-to-understand format. For example, it can visually represent opinion classifications and sentiment trends using graphs and charts. The Opinion Analysis Department also provides analysis results as reports and dashboards, making them easily accessible to stakeholders. This allows the Opinion Analysis Department to effectively utilize collected opinions and support policy revision and improvement. In addition, the Opinion Analysis Department can continuously improve the accuracy of its analysis methods and algorithms to provide more accurate analysis results. This allows the opinion analysis department to accurately reflect public opinion in policy revisions and improvements, thereby maximizing the effectiveness of those policies.

[0077] The implementation department makes the final policy decisions and puts them into action. Specifically, the implementation department formulates and implements policy implementation plans. For example, it can formulate and implement policy implementation plans. The implementation department can also monitor the status of policy implementation and make revisions as needed. For example, it can monitor the status of policy implementation and make revisions as needed. The implementation department provides these implementation plans in an easy-to-understand format so that stakeholders can easily comprehend them. For example, it can present the implementation plans in tables and graphs to make it easier to compare progress. The implementation department can also simulate the status of policy implementation and predict future impacts. This allows the implementation department to effectively manage the status of policy implementation and maximize the effectiveness of the policy. Furthermore, the implementation department can continuously improve the accuracy of implementation methods and processes to provide more accurate implementation results. This allows the implementation department to accurately grasp the status of policy implementation and respond flexibly as needed. Furthermore, the implementation department can collect feedback on policy implementation and use it to improve implementation methods and processes. For example, it can collect feedback on policy implementation and identify areas for improvement in implementation methods and processes. Furthermore, the implementation unit can collect data on policy implementation and evaluate its progress. This allows the implementation unit to effectively manage policy implementation and maximize its effectiveness.

[0078] The data collection unit can collect data such as economic indicators, social statistics, international relations, and environmental data. For example, the data collection unit can collect data provided by government agencies and international organizations. The data collection unit can also collect publicly available data on the internet and data from social media. For example, the data collection unit can obtain economic indicators from government agency databases. The data collection unit can collect international relations data from reports of international organizations. The data collection unit can collect social statistics data from social media posts. By collecting diverse data, the information available for policy making is enriched. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media post data into a generating AI and have the generating AI perform the process of extracting relevant information from the post data.

[0079] The analysis unit can grasp the correlations and trends of the collected data. The analysis unit analyzes the data using, for example, statistical analysis and machine learning. The analysis unit can use correlation coefficients and regression analysis to grasp the correlations of the data. The analysis unit can use time series analysis and moving averages to grasp the trends of the data. For example, the analysis unit uses correlation coefficients to analyze the relationship between economic indicators and social statistics. The analysis unit uses regression analysis to grasp the trends in international relations data. The analysis unit uses time series analysis to analyze fluctuations in environmental data. By grasping the correlations and trends of the data, more accurate policy decisions become possible. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of the correlations and trends of the data.

[0080] The generation unit can evaluate the advantages and disadvantages of each option and propose the optimal policy while prioritizing the interests of the entire nation. For example, the generation unit can evaluate the advantages and disadvantages of each option using cost-benefit analysis and risk assessment. To evaluate the interests of the entire nation, the generation unit can consider economic, social, and environmental benefits. For example, the generation unit can evaluate the advantages and disadvantages of economic policies using cost-benefit analysis. The generation unit can evaluate the advantages and disadvantages of social policies using risk assessment. The generation unit can propose environmental policies considering economic, social, and environmental benefits. This enables policy proposals that prioritize the interests of the entire nation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can have a generating AI perform the evaluation of the advantages and disadvantages of each option, and the generating AI can propose the optimal policy based on the evaluation results.

[0081] The Opinion Gathering Department can disclose proposed policies to the public and collect their opinions. The Opinion Gathering Department can disclose policies through government websites and media, for example. The Opinion Gathering Department can collect public opinions through surveys and interviews. For example, the Opinion Gathering Department can disclose policies on government websites and solicit opinions from the public. The Opinion Gathering Department can announce policies through the media and collect public opinions. The Opinion Gathering Department can collect public opinions by conducting surveys. This improves the transparency and fairness of policies by collecting public opinions. Some or all of the above processes in the Opinion Gathering Department may be carried out using AI, for example, or not using AI. For example, the Opinion Gathering Department can input the collected opinions into a generating AI and have the generating AI perform an analysis of the opinions.

[0082] The Opinion Analysis Unit can analyze collected opinions and revise or improve policies. For example, the Opinion Analysis Unit can analyze collected opinions using text analysis and sentiment analysis. The Opinion Analysis Unit can extract important opinions from the collected opinions and revise or improve policies. For example, the Opinion Analysis Unit can classify public opinions using text analysis. The Opinion Analysis Unit can grasp the sentiment behind public opinions using sentiment analysis. The Opinion Analysis Unit extracts important opinions and revises or improves policies. This makes it possible to revise and improve policies that reflect public opinions. Some or all of the above processing in the Opinion Analysis Unit may be performed using AI, for example, or without AI. For example, the Opinion Analysis Unit can input collected opinions into a generating AI and have the generating AI perform the analysis of the opinions.

[0083] The implementation unit can make final policy decisions and put them into action. For example, the implementation unit can formulate a policy implementation plan and put it into action. The implementation unit can monitor the status of policy implementation and make adjustments as needed. For example, the implementation unit can formulate a policy implementation plan and put it into action. The implementation unit can monitor the status of policy implementation and make adjustments as needed. This ensures that final policy decisions and their implementation are carried out efficiently. Some or all of the above processes in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can have a generating AI formulate a policy implementation plan, and the generating AI can then implement the policy based on that plan.

[0084] The data collection unit can estimate public sentiment and adjust the timing of data collection based on the estimated public sentiment. For example, during periods of unstable public sentiment, the data collection unit increases the frequency of data collection to grasp the situation in real time. During periods of stable public sentiment, the data collection unit conducts regular data collection to grasp long-term trends. During events that evoke heightened public sentiment, the data collection unit can prioritize the collection of specific data to analyze the impact of the event. For example, during periods of unstable public sentiment, the data collection unit increases the frequency of data collection to grasp the situation in real time. During periods of stable public sentiment, the data collection unit conducts regular data collection to grasp long-term trends. During events that evoke heightened public sentiment, the data collection unit prioritizes the collection of specific data to analyze the impact of the event. This makes it possible to adjust the timing of data collection according to public sentiment. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the sentiment data of citizens into a generating AI and have the generating AI perform sentiment estimation.

[0085] The data collection unit can analyze past policy decision history and select the optimal data collection method. For example, the data collection unit can analyze data collection methods used in past policy decisions and reuse successful methods. The data collection unit can optimize the timing and frequency of data collection based on past policy decision history. The data collection unit can identify problems with data collection in past policy decisions and implement improvement measures. For example, the data collection unit can analyze data collection methods used in past policy decisions and reuse successful methods. The data collection unit can optimize the timing and frequency of data collection based on past policy decision history. The data collection unit can identify problems with data collection in past policy decisions and implement improvement measures. This makes it possible to select the optimal data collection method by utilizing past policy decision history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past policy decision history into a generating AI and have the generating AI select the optimal data collection method.

[0086] The data collection unit can filter data based on current public interest and social trends during data collection. For example, the data collection unit can prioritize collecting data on topics of high public interest. The data collection unit can analyze social media trends and collect relevant data. The data collection unit can filter information from specific data sources based on public interest. For example, the data collection unit can prioritize collecting data on topics of high public interest. The data collection unit can analyze social media trends and collect relevant data. The data collection unit can filter information from specific data sources based on public interest. This enables data collection based on public interest and social trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input social media trend data into a generating AI and have the generating AI perform the filtering of relevant data.

[0087] The data collection unit can estimate public sentiment and determine the priority of data to collect based on the estimated public sentiment. For example, during periods of unstable public sentiment, the data collection unit can prioritize the collection of economic data. During periods of stable public sentiment, the data collection unit can prioritize the collection of social statistical data. During events that evoke heightened public sentiment, the data collection unit can prioritize the collection of event-related data. For example, during periods of unstable public sentiment, the data collection unit can prioritize the collection of economic data. During periods of stable public sentiment, the data collection unit can prioritize the collection of social statistical data. During events that evoke heightened public sentiment, the data collection unit can prioritize the collection of event-related data. This makes it possible to prioritize data according to public sentiment. Sentiment estimation is achieved using sentiment estimation functions, for example, using an sentiment engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the sentiment data of citizens into a generating AI and have the generating AI perform sentiment estimation.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of citizens during data collection. For example, if a problem occurs in a particular area, the data collection unit will prioritize the collection of data from that area. The data collection unit can collect information from relevant data sources based on geographical location information. The data collection unit can analyze citizens' movement patterns and collect relevant data. For example, if a problem occurs in a particular area, the data collection unit will prioritize the collection of data from that area. The data collection unit will collect information from relevant data sources based on geographical location information. The data collection unit will analyze citizens' movement patterns and collect relevant data. This enables the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0089] The data collection unit can analyze citizens' social media activities and collect relevant data during data collection. For example, the data collection unit can collect public opinion on social media and reflect it in policy decisions. The data collection unit can analyze social media trends and collect relevant data. The data collection unit can collect specific data based on public interest on social media. For example, the data collection unit can collect public opinion on social media and reflect it in policy decisions. The data collection unit analyzes social media trends and collects relevant data. The data collection unit collects specific data based on public interest on social media. This enables data collection based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posting data into a generating AI and have the generating AI collect relevant data.

[0090] The analysis unit can estimate public sentiment and adjust the data analysis method based on the estimated public sentiment. For example, during periods of unstable public sentiment, the analysis unit can perform rapid data analysis to grasp the situation in real time. During periods of stable public sentiment, the analysis unit can perform detailed data analysis to grasp long-term trends. During events that evoke heightened public sentiment, the analysis unit can prioritize specific data analysis to analyze the impact of the event. For example, during periods of unstable public sentiment, the analysis unit can perform rapid data analysis to grasp the situation in real time. During periods of stable public sentiment, the analysis unit can perform detailed data analysis to grasp long-term trends. During events that evoke heightened public sentiment, the analysis unit can prioritize specific data analysis to analyze the impact of the event. This makes it possible to adjust the data analysis method according to public sentiment. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input national sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For less important data, the analysis unit can perform a simplified analysis. The analysis unit can optimize the analysis resources according to the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For less important data, the analysis unit can perform a simplified analysis. The analysis unit can optimize the analysis resources according to the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm using an economic model to economic data. The analysis unit can apply an analysis algorithm using a statistical model to social statistical data. The analysis unit can apply an analysis algorithm using an environmental model to environmental data. For example, the analysis unit can apply an analysis algorithm using an economic model to economic data. The analysis unit can apply an analysis algorithm using a statistical model to social statistical data. The analysis unit can apply an analysis algorithm using an environmental model to environmental data. This makes it possible to apply analysis algorithms according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0093] The analysis unit can estimate public sentiment and adjust the display method of the analysis results based on the estimated public sentiment. For example, during periods of unstable public sentiment, the analysis unit can provide a simple and highly visible display method. During periods of stable public sentiment, the analysis unit can provide a display method that includes detailed information. During events that evoke heightened public sentiment, the analysis unit can provide a visually stimulating display method. For example, during periods of unstable public sentiment, the analysis unit provides a simple and highly visible display method. During periods of stable public sentiment, the analysis unit provides a display method that includes detailed information. During events that evoke heightened public sentiment, the analysis unit provides a visually stimulating display method. This makes it possible to adjust the display method of the analysis results according to public sentiment. Sentiment estimation is achieved using a sentiment estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input national sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0094] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data to understand the situation in real time. The analysis unit can analyze historical data to grasp long-term trends. The analysis unit can optimize the analysis resources according to the data collection timing. For example, the analysis unit can prioritize the analysis of the latest data to understand the situation in real time. The analysis unit can analyze historical data to grasp long-term trends. The analysis unit optimizes the analysis resources according to the data collection timing. This makes it possible to determine the analysis priority based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly grasp the situation. The analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can optimize the analysis resources according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to quickly grasp the situation. The analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can optimize the analysis resources according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0096] The generation unit can estimate public sentiment and adjust the presentation of policy options based on the estimated public sentiment. For example, during periods of unstable public sentiment, the generation unit can provide a simple and easily understandable presentation. During periods of stable public sentiment, the generation unit can provide a presentation that includes detailed information. During events that evoke heightened public sentiment, the generation unit can provide a visually stimulating presentation. For example, during periods of unstable public sentiment, the generation unit provides a simple and easily understandable presentation. During periods of stable public sentiment, the generation unit provides a presentation that includes detailed information. During events that evoke heightened public sentiment, the generation unit provides a visually stimulating presentation. This makes it possible to adjust the presentation of policy options according to public sentiment. Sentiment estimation is achieved using a sentiment estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input national sentiment data into a generation AI and have the generation AI perform sentiment estimation.

[0097] The generation unit can adjust the level of detail based on the importance of each policy option when generating policy options. For example, the generation unit can provide detailed information for policy options with high importance. For policy options with low importance, the generation unit can provide simplified information. The generation unit can optimize the level of detail of the information according to the importance of each option. For example, the generation unit can provide detailed information for policy options with high importance. For policy options with low importance, the generation unit can provide simplified information. The generation unit can optimize the level of detail of the information according to the importance of each option. This makes it possible to adjust the level of detail according to the importance of each option. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of each option into the generation AI and have the generation AI perform the level of detail adjustment.

[0098] The generation unit can apply different generation algorithms depending on the category of the policy options when generating them. For example, the generation unit can apply a generation algorithm using an economic model to economic policies. For social policies, it can apply a generation algorithm using a social model. For environmental policies, it can apply a generation algorithm using an environmental model. This makes it possible to apply a generation algorithm according to the category of the options. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the options into a generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0099] The generation unit can estimate public sentiment and adjust the length of policy options based on the estimated public sentiment. For example, during periods of unstable public sentiment, the generation unit can provide short, concise policy options. During periods of stable public sentiment, the generation unit can provide longer policy options that include detailed explanations. During events that evoke heightened public sentiment, the generation unit can provide visually stimulating policy options. For example, during periods of unstable public sentiment, the generation unit provides short, concise policy options. During periods of stable public sentiment, the generation unit provides longer policy options that include detailed explanations. During events that evoke heightened public sentiment, the generation unit provides visually stimulating policy options. This makes it possible to adjust the length of policy options according to public sentiment. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input national sentiment data into a generation AI and have the generation AI perform sentiment estimation.

[0100] The generation unit can determine priorities based on the submission timing of policy options when generating them. For example, the generation unit can prioritize the generation of policy options that are urgent. The generation unit can adjust the priority of policy options according to the submission timing. The generation unit can optimize the resources used to generate policy options based on the submission timing. For example, the generation unit can prioritize the generation of policy options that are urgent. The generation unit can adjust the priority of policy options according to the submission timing. The generation unit can optimize the resources used to generate policy options based on the submission timing. This makes it possible to determine the priority of policy options based on the submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing of options into a generation AI and have the generation AI perform the priority determination.

[0101] The generation unit can adjust the order of policy options based on their relevance when generating them. For example, the generation unit can prioritize generating highly relevant policy options. The generation unit can postpone generating less relevant policy options. The generation unit can optimize the generation resources according to the relevance of the options. For example, the generation unit prioritizes generating highly relevant policy options. The generation unit postpones generating less relevant policy options. The generation unit optimizes the generation resources according to the relevance of the options. This makes it possible to adjust the order based on the relevance of the options. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the options into a generation AI and have the generation AI perform the order adjustment.

[0102] The opinion gathering department can estimate public sentiment and adjust its opinion gathering methods based on that estimation. For example, during periods of unstable public sentiment, the opinion gathering department can conduct rapid opinion gathering to grasp the situation in real time. During periods of stable public sentiment, the opinion gathering department can conduct detailed opinion gathering to grasp long-term trends. During events that evoke heightened public sentiment, the opinion gathering department can prioritize gathering specific opinions and analyze the impact of those events. For example, during periods of unstable public sentiment, the opinion gathering department can conduct rapid opinion gathering to grasp the situation in real time. During periods of stable public sentiment, the opinion gathering department can conduct detailed opinion gathering to grasp long-term trends. During events that evoke heightened public sentiment, the opinion gathering department can prioritize gathering specific opinions and analyze the impact of those events. This makes it possible to adjust opinion gathering methods according to public sentiment. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the opinion collection unit may be performed using AI, or not using AI. For example, the opinion collection unit may input public sentiment data into the generative AI and have the generative AI perform sentiment estimation.

[0103] The opinion collection unit can select the optimal collection method by referring to the public's past opinion submission history when collecting opinions. For example, the opinion collection unit can analyze past opinion submission history and reuse successful collection methods. The opinion collection unit can optimize the timing and frequency of opinion collection based on past opinion submission history. The opinion collection unit can identify problems in past opinion submissions and implement improvement measures. For example, the opinion collection unit can analyze past opinion submission history and reuse successful collection methods. The opinion collection unit can optimize the timing and frequency of opinion collection based on past opinion submission history. The opinion collection unit can identify problems in past opinion submissions and implement improvement measures. This makes it possible to select the optimal opinion collection method by utilizing past opinion submission history. Some or all of the above processes in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input past opinion submission history into a generating AI and have the generating AI select the optimal opinion collection method.

[0104] The opinion gathering department can estimate public sentiment and determine the priority of opinion gathering based on the estimated public sentiment. For example, during periods of unstable public sentiment, the opinion gathering department will prioritize collecting important opinions. During periods of stable public sentiment, the opinion gathering department can collect a wide range of opinions. During events that evoke heightened public sentiment, the opinion gathering department can prioritize collecting event-related opinions. For example, during periods of unstable public sentiment, the opinion gathering department will prioritize collecting important opinions. During periods of stable public sentiment, the opinion gathering department will collect a wide range of opinions. During events that evoke heightened public sentiment, the opinion gathering department will prioritize collecting event-related opinions. This makes it possible to determine the priority of opinion gathering in accordance with public sentiment. Sentiment estimation is achieved using sentiment estimation functions, for example, using an sentiment engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the opinion collection unit may be carried out using AI, for example, or without AI. For example, the opinion collection unit can input public sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0105] The opinion collection unit can prioritize collecting highly relevant opinions by considering the geographical location information of citizens when collecting opinions. For example, if a problem occurs in a particular region, the opinion collection unit will prioritize collecting opinions from that region. The opinion collection unit can collect relevant opinions based on geographical location information. The opinion collection unit can analyze citizens' movement patterns and collect relevant opinions. For example, if a problem occurs in a particular region, the opinion collection unit will prioritize collecting opinions from that region. The opinion collection unit collects relevant opinions based on geographical location information. The opinion collection unit analyzes citizens' movement patterns and collects relevant opinions. This makes it possible to collect highly relevant opinions based on geographical location information. Some or all of the above processing in the opinion collection unit may be performed using AI, for example, or without AI. For example, the opinion collection unit can input geographical location information into a generating AI and have the generating AI collect highly relevant opinions.

[0106] The opinion analysis unit can estimate public sentiment and adjust its opinion analysis methods based on the estimated public sentiment. For example, during periods of unstable public sentiment, the opinion analysis unit can perform rapid opinion analysis to grasp the situation in real time. During periods of stable public sentiment, the opinion analysis unit can perform detailed opinion analysis to grasp long-term trends. During events that evoke heightened public sentiment, the opinion analysis unit can prioritize specific opinion analyses to analyze the impact of the event. For example, during periods of unstable public sentiment, the opinion analysis unit can perform rapid opinion analysis to grasp the situation in real time. During periods of stable public sentiment, the opinion analysis unit can perform detailed opinion analysis to grasp long-term trends. During events that evoke heightened public sentiment, the opinion analysis unit can prioritize specific opinion analyses to analyze the impact of the event. This makes it possible to adjust the opinion analysis methods according to public sentiment. Sentiment estimation is achieved using sentiment estimation functions, such as sentiment engines or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the opinion analysis unit may be performed using AI, or not using AI. For example, the opinion analysis unit may input public sentiment data into the generating AI and have the generating AI perform sentiment estimation.

[0107] The opinion analysis unit can adjust the level of detail of the analysis based on the importance of the opinions during opinion analysis. For example, the opinion analysis unit can perform a detailed analysis on opinions with high importance, and a simplified analysis on opinions with low importance. The opinion analysis unit can optimize the analysis resources according to the importance of the opinions. For example, the opinion analysis unit can perform a detailed analysis on opinions with high importance, and a simplified analysis on opinions with low importance. The opinion analysis unit can optimize the analysis resources according to the importance of the opinions. This makes it possible to adjust the level of detail of the analysis according to the importance of the opinions. Some or all of the above processing in the opinion analysis unit may be performed using AI, for example, or without using AI. For example, the opinion analysis unit can input the importance of the opinions into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0108] The opinion analysis unit can apply different analysis algorithms depending on the category of the opinion during opinion analysis. For example, the opinion analysis unit can apply an analysis algorithm using an economic model to opinions on economics. For opinions on society, it can apply an analysis algorithm using a social model. For opinions on the environment, it can apply an analysis algorithm using an environmental model. This makes it possible to apply an analysis algorithm according to the category of the opinion. Some or all of the above processing in the opinion analysis unit may be performed using AI, for example, or without AI. For example, the opinion analysis unit can input the category of the opinion into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0109] The opinion analysis unit can estimate public sentiment and adjust the display method of the opinion analysis results based on the estimated public sentiment. For example, during periods of unstable public sentiment, the opinion analysis unit can provide a simple and highly visible display method. During periods of stable public sentiment, the opinion analysis unit can provide a display method that includes detailed information. During events that evoke heightened public sentiment, the opinion analysis unit can provide a visually stimulating display method. For example, during periods of unstable public sentiment, the opinion analysis unit provides a simple and highly visible display method. During periods of stable public sentiment, the opinion analysis unit provides a display method that includes detailed information. During events that evoke heightened public sentiment, the opinion analysis unit provides a visually stimulating display method. This makes it possible to adjust the display method of the opinion analysis results according to public sentiment. Sentiment estimation is achieved using a sentiment estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the opinion analysis unit may be performed using AI, for example, or without AI. For example, the opinion analysis unit can input public sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0110] The opinion analysis unit can determine the priority of analysis based on when the opinions were submitted. For example, the opinion analysis unit can prioritize the analysis of the latest opinions to grasp the situation in real time. The opinion analysis unit can analyze past opinions to grasp long-term trends. The opinion analysis unit can optimize the analysis resources according to when the opinions were submitted. For example, the opinion analysis unit can prioritize the analysis of the latest opinions to grasp the situation in real time. The opinion analysis unit can analyze past opinions to grasp long-term trends. The opinion analysis unit can optimize the analysis resources according to when the opinions were submitted. This makes it possible to determine the priority of analysis based on when the opinions were submitted. Some or all of the above processes in the opinion analysis unit may be performed using AI, for example, or without using AI. For example, the opinion analysis unit can input the timing of opinion submissions into a generating AI and have the generating AI perform the determination of the analysis priority.

[0111] The opinion analysis unit can adjust the order of analysis based on the relevance of opinions during opinion analysis. For example, the opinion analysis unit can prioritize the analysis of highly relevant opinions to quickly grasp the situation. The opinion analysis unit can postpone the analysis of less relevant opinions to perform efficient analysis. The opinion analysis unit can optimize the analysis resources according to the relevance of opinions. For example, the opinion analysis unit can prioritize the analysis of highly relevant opinions to quickly grasp the situation. The opinion analysis unit can postpone the analysis of less relevant opinions to perform efficient analysis. The opinion analysis unit can optimize the analysis resources according to the relevance of opinions. This makes it possible to adjust the order of analysis based on the relevance of opinions. Some or all of the above processing in the opinion analysis unit may be performed using AI, for example, or without AI. For example, the opinion analysis unit can input the relevance of opinions into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0112] The implementation unit can estimate public sentiment and adjust policy implementation methods based on the estimated public sentiment. For example, during periods of unstable public sentiment, the implementation unit can implement policies rapidly and grasp the situation in real time. During periods of stable public sentiment, the implementation unit can implement policies in detail and grasp long-term trends. During events that evoke heightened public sentiment, the implementation unit can prioritize specific policy implementations and analyze the impact of the events. For example, during periods of unstable public sentiment, the implementation unit can implement policies rapidly and grasp the situation in real time. During periods of stable public sentiment, the implementation unit can implement policies in detail and grasp long-term trends. During events that evoke heightened public sentiment, the implementation unit can prioritize specific policy implementations and analyze the impact of the events. This makes it possible to adjust policy implementation methods in accordance with public sentiment. Sentiment estimation is achieved using sentiment estimation functions, for example, using an sentiment engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input national sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0113] The implementation unit can select the optimal implementation method by referring to past implementation history when implementing a policy. For example, the implementation unit can analyze past policy implementation history and reuse successful methods. The implementation unit can optimize the timing and frequency of implementation based on past policy implementation history. The implementation unit can identify problems in past policy implementations and implement improvement measures. For example, the implementation unit can analyze past policy implementation history and reuse successful methods. The implementation unit can optimize the timing and frequency of implementation based on past policy implementation history. The implementation unit can identify problems in past policy implementations and implement improvement measures. This makes it possible to select the optimal policy implementation method by utilizing past implementation history. Some or all of the above processes in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input past implementation history into a generating AI and have the generating AI select the optimal implementation method.

[0114] The implementation unit can estimate public sentiment and determine policy implementation priorities based on the estimated public sentiment. For example, during periods of unstable public sentiment, the implementation unit will prioritize important policies. During periods of stable public sentiment, the implementation unit can implement a wide range of policies. During events that evoke heightened public sentiment, the implementation unit can prioritize event-related policies. For example, during periods of unstable public sentiment, the implementation unit will prioritize important policies. During periods of stable public sentiment, the implementation unit will implement a wide range of policies. During events that evoke heightened public sentiment, the implementation unit will prioritize event-related policies. This makes it possible to prioritize policy implementation in accordance with public sentiment. Sentiment estimation is achieved using a sentiment estimation function, for example, using a sentiment engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the execution unit can input citizens' sentiment data into a generating AI and have the generating AI perform sentiment estimation.

[0115] The implementation unit can select the optimal implementation method when implementing policies, taking into account the geographical location information of citizens. For example, if a problem occurs in a particular region, the implementation unit will prioritize the implementation of policies for that region. The implementation unit can implement relevant policies based on geographical location information. The implementation unit can analyze the movement patterns of citizens and implement relevant policies. For example, if a problem occurs in a particular region, the implementation unit will prioritize the implementation of policies for that region. The implementation unit will implement relevant policies based on geographical location information. The implementation unit will analyze the movement patterns of citizens and implement relevant policies. This makes it possible to select the optimal implementation method based on the geographical location information of citizens. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can input geographical location information into a generating AI and have the generating AI select the optimal implementation method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can visualize the results of data analysis and present them to the public in an easy-to-understand manner. For example, the analysis unit can visualize data correlations and trends using graphs and charts. The analysis unit can provide interactive dashboards, allowing the public to freely manipulate data and view details. Through data visualization, the analysis unit can make it easier for the public to understand the basis for policy decisions. For example, the analysis unit can display trends in economic indicators as line graphs, allowing the public to grasp economic trends at a glance. The analysis unit can display social statistics data as pie charts, visually showing the proportion of each category. The analysis unit can display environmental data as heatmaps, allowing for an intuitive understanding of environmental conditions in each region. In this way, data visualization can promote public understanding and engagement.

[0118] The generation unit can simulate policy options and predict the impact of each option. For example, the generation unit can simulate the impact of economic policies using economic models. The generation unit can simulate the impact of social policies using social models. The generation unit can simulate the impact of environmental policies using environmental models. For example, the generation unit can simulate economic policies and predict their impact on GDP and unemployment rates. The generation unit can simulate social policies and predict their impact on social welfare and education. The generation unit can simulate environmental policies and predict their impact on greenhouse gas emissions and biodiversity. This allows for a prior evaluation of the impact of policy options and the proposal of optimal policies.

[0119] The Opinion Collection Department can ensure anonymity when collecting public opinion. For example, when conducting online surveys, the Opinion Collection Department will not collect personal information of respondents. The Opinion Collection Department can allow anonymous submission of opinions. The Opinion Collection Department can take measures to protect the privacy of citizens during the opinion collection process. For example, the Opinion Collection Department can anonymize online survey responses so that individuals cannot be identified. The Opinion Collection Department can provide an anonymous option on opinion submission forms so that citizens can freely submit their opinions. The Opinion Collection Department can protect the privacy of citizens by encrypting data and controlling access during the opinion collection process. This will provide an environment in which citizens can submit their opinions with peace of mind.

[0120] The opinion analysis unit can cluster collected opinions and extract common themes and topics. For example, it can use text mining techniques to extract frequently occurring keywords from the opinions. The opinion analysis unit can classify opinions by theme and understand the trends in opinions for each theme. The opinion analysis unit can visualize the clustering results and present them to the public in an easy-to-understand manner. For example, the opinion analysis unit can use text mining techniques to extract frequently occurring keywords from opinions on economic policy. The opinion analysis unit classifies opinions by theme and divides them into categories such as economic policy, social policy, and environmental policy. The opinion analysis unit visualizes the clustering results in graphs and charts to show the trends in opinions to the public. This allows for the effective analysis of collected opinions and helps in the modification and improvement of policies.

[0121] The implementation team can monitor the status of policy implementation in real time and make the progress public. For example, the implementation team can publish the status of policy implementation on an online platform so that the public can check the progress. The implementation team can regularly update data on policy implementation to provide the latest information. The implementation team can collect feedback from the public on the status of policy implementation and make corrections as needed. For example, the implementation team can publish the status of policy implementation on an online platform so that the public can check the progress. The implementation team can regularly update data on policy implementation to provide the latest information. The implementation team can collect feedback from the public on the status of policy implementation and make corrections as needed. This makes the status of policy implementation transparent and gains the trust of the public.

[0122] The data collection unit can estimate public sentiment and adjust its data collection methods based on that estimation. For example, during periods of unstable public sentiment, the unit can collect data rapidly to understand the situation in real time. During periods of stable public sentiment, the unit can collect detailed data to grasp long-term trends. During events that evoke heightened public sentiment, the unit can prioritize the collection of specific data and analyze the impact of those events. For example, during periods of unstable public sentiment, the unit can collect data rapidly to understand the situation in real time. During periods of stable public sentiment, the unit can collect detailed data to grasp long-term trends. During events that evoke heightened public sentiment, the unit can prioritize the collection of specific data and analyze the impact of those events. This allows for adjustments to data collection methods in accordance with public sentiment.

[0123] The analysis unit can estimate public sentiment and determine the priority of data analysis based on that estimated sentiment. For example, during periods of unstable public sentiment, the analysis unit prioritizes the analysis of important data. During periods of stable public sentiment, the analysis unit can analyze a wide range of data. During events that evoke heightened public sentiment, the analysis unit can prioritize the analysis of event-related data. For example, during periods of unstable public sentiment, the analysis unit prioritizes the analysis of important data. During periods of stable public sentiment, the analysis unit analyzes a wide range of data. During events that evoke heightened public sentiment, the analysis unit prioritizes the analysis of event-related data. This makes it possible to prioritize data analysis according to public sentiment.

[0124] The generation unit can estimate public sentiment and adjust the presentation method of policy options based on the estimated public sentiment. For example, during periods of unstable public sentiment, the generation unit can provide a simple and highly visible presentation method. During periods of stable public sentiment, the generation unit can provide a presentation method that includes detailed information. During events that evoke heightened public sentiment, the generation unit can provide a visually stimulating presentation method. This makes it possible to adjust the presentation method of policy options according to public sentiment.

[0125] The opinion gathering department can estimate public sentiment and adjust the timing of opinion gathering based on that estimation. For example, during periods of unstable public sentiment, the opinion gathering department can conduct rapid opinion gathering to grasp the situation in real time. During periods of stable public sentiment, the opinion gathering department can conduct regular opinion gathering to grasp long-term trends. During events that evoke heightened public sentiment, the opinion gathering department can prioritize specific opinion gathering and analyze the impact of those events. For example, during periods of unstable public sentiment, the opinion gathering department can conduct rapid opinion gathering to grasp the situation in real time. During periods of stable public sentiment, the opinion gathering department can conduct regular opinion gathering to grasp long-term trends. During events that evoke heightened public sentiment, the opinion gathering department can prioritize specific opinion gathering and analyze the impact of those events. This makes it possible to adjust the timing of opinion gathering in accordance with public sentiment.

[0126] The implementation team can estimate public sentiment and adjust the timing of policy implementation based on that estimation. For example, during periods of unstable public sentiment, the implementation team can implement policies rapidly and grasp the situation in real time. During periods of stable public sentiment, the implementation team can implement policies in detail and grasp long-term trends. During events that heighten public sentiment, the implementation team can prioritize specific policy implementations and analyze the impact of those events. For example, during periods of unstable public sentiment, the implementation team can implement policies rapidly and grasp the situation in real time. During periods of stable public sentiment, the implementation team can implement policies in detail and grasp long-term trends. During events that heighten public sentiment, the implementation team can prioritize specific policy implementations and analyze the impact of those events. This makes it possible to adjust the timing of policy implementation in accordance with public sentiment.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data. The data collection unit collects data such as economic indicators, social statistics, international relations, and environmental data. The data collection unit collects data provided by government agencies and international organizations, for example. The data collection unit can also collect publicly available data on the internet and data from social media. For example, the data collection unit obtains economic indicators from government agency databases. The data collection unit collects international relations data from reports of international organizations. The data collection unit collects social statistics data from social media posts. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit, for example, identifies correlations and trends in the data. The analysis unit analyzes the data using statistical analysis and machine learning, for example. The analysis unit can use correlation coefficients and regression analysis to identify correlations in the data. The analysis unit can use time series analysis and moving averages to identify trends in the data. For example, the analysis unit uses correlation coefficients to analyze the relationship between economic indicators and social statistics. The analysis unit uses regression analysis to identify trends in international relations data. The analysis unit uses time series analysis to analyze fluctuations in environmental data. Step 3: The generation unit generates policy options based on the information analyzed by the analysis unit. The generation unit, for example, evaluates the merits and demerits of each option and proposes the optimal policy, prioritizing the interests of the entire nation. The generation unit evaluates the merits and demerits of each option using, for example, cost-benefit analysis and risk assessment. The generation unit can consider economic, social, and environmental benefits in order to evaluate the interests of the entire nation. For example, the generation unit evaluates the merits and demerits of economic policies using cost-benefit analysis. The generation unit evaluates the merits and demerits of social policies using risk assessment. The generation unit proposes environmental policies, taking into account economic, social, and environmental benefits. Step 4: The Opinion Gathering Department makes the policies generated by the Generation Department public and collects opinions. The Opinion Gathering Department may, for example, publish the policies through the government website or media. The Opinion Gathering Department can also collect public opinions through surveys and interviews. For example, the Opinion Gathering Department may publish the policies on the government website and solicit opinions from the public. The Opinion Gathering Department may announce the policies through the media and collect public opinions. The Opinion Gathering Department may conduct surveys to collect public opinions. Step 5: The Opinion Analysis Department analyzes the opinions collected by the Opinion Collection Department and modifies or improves policies. The Opinion Analysis Department analyzes the collected opinions using methods such as text analysis and sentiment analysis. The Opinion Analysis Department can extract important opinions from the collected opinions and modify or improve policies. For example, the Opinion Analysis Department uses text analysis to classify public opinions. The Opinion Analysis Department uses sentiment analysis to understand the sentiment behind public opinions. The Opinion Analysis Department extracts important opinions and modifies or improves policies. Step 6: The implementation department makes the final policy decision and puts it into action. For example, the implementation department formulates and implements a policy implementation plan. The implementation department can monitor the status of policy implementation and make adjustments as needed. For example, the implementation department formulates and implements a policy implementation plan. The implementation department monitors the status of policy implementation and makes adjustments as needed.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, opinion collection unit, opinion analysis unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data such as economic indicators and social statistics. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates policy options based on the analyzed information. The opinion collection unit is implemented by the control unit 46A of the smart device 14 and makes the generated policy public and collects opinions. The opinion analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions and modifies or improves the policy. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes the final policy decision and puts it into action. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, opinion collection unit, opinion analysis unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data such as economic indicators and social statistics. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates policy options based on the analyzed information. The opinion collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 and makes the generated policy public and collects opinions. The opinion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions and modifies or improves the policy. The execution unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes the final policy decision and puts it into action. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, opinion collection unit, opinion analysis unit, and execution unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data such as economic indicators and social statistics. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates policy options based on the analyzed information. The opinion collection unit is implemented by, for example, the control unit 46A of the headset terminal 314 and makes the generated policies public and collects opinions. The opinion analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions and modifies or improves the policies. The execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes the final policy decision and puts it into action. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[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 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.

[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 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).

[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] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, opinion collection unit, opinion analysis unit, and execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data such as economic indicators and social statistics. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates policy options based on the analyzed information. The opinion collection unit is implemented by, for example, the control unit 46A of the robot 414 and makes the generated policies public and collects opinions. The opinion analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected opinions and modifies or improves the policies. The execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes the final policy decision and puts it into action. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates policy options based on the information analyzed by the aforementioned analysis unit, The opinion gathering unit makes the policies generated by the aforementioned generation unit public and collects opinions from the public, The opinion analysis department analyzes the opinions collected by the aforementioned opinion collection department and makes revisions and improvements to policies, It comprises an implementation unit that makes the final policy decisions and puts them into action. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as economic indicators, social statistics, international relations, and environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Understand the correlations and trends in the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We evaluate the advantages and disadvantages of each option and propose the optimal policy while prioritizing the interests of the entire nation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned opinion collection department, The proposed policies will be made public, and opinions will be collected. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned opinion analysis unit, We will analyze the collected opinions and revise or improve policies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The execution unit is, Make a final policy decision and put it into action. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate public sentiment and adjust the timing of data collection based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past policy-making history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the current interests and social trends of the public. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate public sentiment and prioritize the data to collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the collection of highly relevant data will be prioritized, taking into account the geographical location information of the citizens. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze the social media activity of citizens and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate public sentiment and adjust the data analysis methods based on the estimated public sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We estimate public sentiment and adjust the display method of the analysis results based on the estimated public sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate public sentiment and adjust the way policy options are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating policy options, adjust the level of detail based on the importance of each option. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating policy options, different generation algorithms are applied depending on the category of the option. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Estimate public sentiment and adjust the length of policy options based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating policy options, prioritization is determined based on when the options were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating policy options, adjust the order based on the relevance of the options. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned opinion collection department, We estimate public sentiment and adjust our opinion-gathering methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned opinion collection department, When collecting opinions, the most suitable collection method will be selected by referring to the public's past opinion submission history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned opinion collection department, Estimate public sentiment and determine priorities for opinion gathering based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned opinion collection department, When collecting opinions, we will prioritize collecting opinions that are highly relevant, taking into account the geographical location of citizens. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned opinion analysis unit, We estimate public sentiment and adjust the opinion analysis method based on the estimated public sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned opinion analysis unit, When analyzing opinions, adjust the level of detail based on the importance of each opinion. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned opinion analysis unit, When analyzing opinions, different analysis algorithms are applied depending on the category of the opinion. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned opinion analysis unit, We estimate public sentiment and adjust the display method of opinion analysis results based on the estimated public sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned opinion analysis unit, When analyzing opinions, the priority of the analysis is determined based on when the opinions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned opinion analysis unit, When analyzing opinions, adjust the order of analysis based on the relevance of the opinions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The execution unit is, Estimate public sentiment and adjust policy implementation methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The execution unit is, When implementing policies, the optimal implementation method is selected by referring to past implementation history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The execution unit is, Estimate public sentiment and determine policy implementation priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The execution unit is, When implementing policies, the optimal implementation method will be selected by considering the geographical location information of the citizens. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 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 generation unit generates policy options based on the information analyzed by the aforementioned analysis unit, The opinion gathering unit makes the policies generated by the aforementioned generation unit public and collects opinions from the public, The opinion analysis department analyzes the opinions collected by the aforementioned opinion collection department and makes revisions and improvements to policies, It comprises an implementation unit that makes the final policy decisions and puts them into action. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as economic indicators, social statistics, international relations, and environmental data. The system according to feature 1.

3. The aforementioned analysis unit, Understand the correlations and trends in the collected data. The system according to feature 1.

4. The generating unit is We evaluate the advantages and disadvantages of each option and propose the optimal policy while prioritizing the interests of the entire nation. The system according to feature 1.

5. The aforementioned opinion collection department, The proposed policies will be made public, and opinions will be collected. The system according to feature 1.

6. The aforementioned opinion analysis unit, We will analyze the collected opinions and revise or improve policies. The system according to feature 1.

7. The execution unit is, Make a final policy decision and put it into action. The system according to feature 1.

8. The aforementioned collection unit is We estimate public sentiment and adjust the timing of data collection based on that estimated sentiment. The system according to feature 1.

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