system

Abstract

The system according to this embodiment aims to generate economic models based on standards and guidelines created by experts and to simulate the impact of policies and strategies. [Solution] The system according to the embodiment comprises a generation unit, a simulation unit, a report generation unit, and a decision support unit. The generation unit generates an economic model based on standards and guidelines created by experts. The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The decision support unit supports decision-making based on the report generated by the report generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of 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, when generating an economic model and simulating the influence of policies, the knowledge and criteria of experts have not been fully utilized, so there is room for improvement in accuracy and reliability.

[0005] The system according to the embodiment aims to generate an economic model based on criteria and guidelines created by experts and simulate the influence of policies and strategies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a simulation unit, a report generation unit, and a decision support unit. The generation unit generates an economic model based on standards and guidelines created by experts. The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The decision support unit supports decision-making based on the report generated by the report generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate economic models based on standards and guidelines created by experts and simulate the impact of policies and strategies. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 system according to an embodiment of the present invention is a system that uses AI to generate an economic model that takes into account various factors directly related to the SDGs (Sustainable Development Goals), and simulates the impact of policies and strategies. This system generates an economic model based on criteria and guidelines created by experts, simulates the impact of various policies and strategies, and generates a detailed report to support decision-making. For example, the system generates an economic model based on criteria and guidelines created by experts. In this process, while based on previous data, it is updated sequentially to include new trends and changes. For example, it can analyze past economic and environmental data to generate an economic model that reflects current trends and future predictions. Next, the system simulates the impact of applying various policies and strategies based on the generated economic model. For example, it can estimate the impact of a certain policy on economic growth or its effect on environmental protection. This makes it possible to identify the most effective means and reduce the risk of unforeseen circumstances. Furthermore, the system generates a detailed report based on the simulation results. This report includes the policies and strategies applied, their impact on the goals, and the expected future results. For example, it can specifically show the impact of a certain policy on economic growth or its effect on environmental protection. Based on the generated reports, decision-makers can make impactful, data-driven decisions. For example, they can select and implement the most effective policies based on simulation results. This makes it possible to formulate and implement effective strategies for achieving the SDGs. This mechanism provides simulation services for economic models toward achieving the SDGs, contributing to the promotion of a sustainable society. For example, it makes it possible to formulate and implement policies that balance economic growth and environmental protection. It also makes it possible to further accelerate the achievement of the SDGs across society. As a result, the system can formulate and implement effective strategies toward achieving the SDGs.

[0029] The system according to the embodiment comprises a generation unit, a simulation unit, a report generation unit, and a decision support unit. The generation unit generates an economic model based on criteria and guidelines created by experts. The generation unit analyzes, for example, historical economic and environmental data to generate an economic model that reflects current trends and future predictions. The generation unit is updated sequentially, for example, to include new trends and changes, while still being based on historical data. The generation unit generates an economic model based on, for example, criteria and guidelines created by experts. The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. The simulation unit estimates, for example, the impact of a certain policy on economic growth and its effect on environmental protection. The simulation unit simulates the impact of applying various policies and strategies. The simulation unit identifies, for example, the most effective means and reduces the risk of unforeseen circumstances. The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The report generation unit generates a detailed report that includes, for example, the policies and strategies applied, their impact on the objectives, and the expected future results. The report generation unit generates a detailed report based on simulation results, for example. The report generation unit specifically shows, for example, the impact of a certain policy on economic growth or its effect on environmental protection. The decision support unit supports decision-making based on the report generated by the report generation unit. The decision support unit supports decision-makers in making data-driven decisions based on the generated report, for example. The decision support unit supports selecting and implementing the most effective policy based on simulation results, for example. Thus, the system according to the embodiment can generate economic models based on expert standards and guidelines, simulate the impact of policies and strategies, and generate detailed reports to support decision-making.

[0030] The generation unit generates economic models based on standards and guidelines created by experts. For example, it analyzes historical economic and environmental data to generate economic models that reflect current trends and future predictions. Specifically, the generation unit collects economic indicators, market data, and environmental data over the past several decades and analyzes this data using statistical methods and machine learning algorithms. For example, it incorporates economic indicators such as GDP growth rate, unemployment rate, inflation rate, and trade balance as time-series data and extracts economic trends and patterns from this data. In addition, it collects environmental data such as temperature, precipitation, and CO2 emissions to model the interaction between economic activity and the environment. Based on this data, the generation unit generates economic models that reflect the current economic and environmental conditions. Furthermore, the generation unit is continuously updated to include new trends and changes. For example, it incorporates factors that affect the economy in real time, such as technological innovation, policy changes, and changes in international affairs, and updates the model. As a result, the generation unit can always provide economic models that reflect the latest information and make highly accurate predictions. The generation unit generates economic models based on standards and guidelines created by experts, thus providing highly reliable models.

[0031] The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. For example, the simulation unit estimates the impact of a particular policy on economic growth or its effect on environmental protection. Specifically, the simulation unit applies different policies and strategies to the generated economic model and analyzes the results. For example, it simulates various policy options such as tax reform, public investment, deregulation, and environmental protection policies, and evaluates the impact of each policy on economic growth, employment, inflation, and the environment. The simulation unit uses methods such as numerical simulation and Monte Carlo methods to quantitatively evaluate the effects of policies. This allows for a probabilistic evaluation of policy effects and the selection of the optimal policy while considering risks and uncertainties. Furthermore, the simulation unit identifies the most effective means and conducts scenario analysis to reduce risks from unforeseen events. For example, it evaluates the risks from unforeseen events such as economic crises and natural disasters and simulates countermeasures against these risks. In this way, the simulation unit can comprehensively evaluate the impact of policies and strategies and support optimal decision-making.

[0032] The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The report generation unit generates a detailed report that includes, for example, the policies and strategies applied, their impact on the objectives, and the expected future outcomes. Specifically, the report generation unit uses graphs, charts, and tables to visually display the simulation results in an easy-to-understand manner. For example, it creates line graphs showing the effects of policies, bar graphs showing fluctuations in economic indicators, and heatmaps showing risk assessments. The report also includes detailed explanations of the policy's background and objectives, the simulation's assumptions, and the interpretation of the results. This provides decision-makers with the information necessary to accurately understand the simulation results and make appropriate judgments. Furthermore, the report generation unit generates a report that includes recommendations and suggestions based on the simulation results. For example, it evaluates the impact of a specific policy on economic growth and presents recommended policy options based on the results. It also evaluates the effects on environmental protection and makes recommendations for achieving sustainable economic growth. In this way, the report generation unit provides a detailed report based on simulation results, supporting decision-makers in making data-driven decisions.

[0033] The decision support unit supports decision-making based on reports generated by the report generation unit. For example, the decision support unit supports decision-makers in making data-driven decisions based on the generated reports. Specifically, the decision support unit analyzes the content of the reports and assists in selecting the most effective policies and strategies. For example, based on simulation results, it proposes optimal policy options to promote economic growth and provides information for decision-makers to evaluate those options. It also supports policy selection that takes environmental protection and social justice into consideration, and supports decision-making to achieve sustainable development. Furthermore, based on simulation results, the decision support unit supports the selection and implementation of the most effective policies. For example, it evaluates the risks and uncertainties associated with policy implementation and proposes measures for risk management. It also monitors the implementation status of policies and provides feedback for corrections and improvements as needed. In this way, the decision support unit can support data-driven decision-making and provide information to maximize the effectiveness of policies. By supporting decision-makers in selecting and implementing optimal policies based on reports generated by the report generation unit, the decision support unit can enhance the overall effectiveness of the system.

[0034] The system includes a data collection unit. The data collection unit can collect the necessary data. For example, the data collection unit collects economic data, environmental data, social data, etc. For example, the data collection unit collects government data, corporate data, research data, etc. For example, the data collection unit integrates and collects data from different data sources. This allows the data collection unit to collect the necessary data.

[0035] The generation unit can generate economic models that are continuously updated to incorporate new trends and changes, while still being based on previous data. For example, the generation unit analyzes historical economic and environmental data to generate economic models that reflect current trends and future predictions. For example, the generation unit generates economic models based on criteria and guidelines created by experts. The generation unit is continuously updated to incorporate new trends and changes, while still being based on historical data. This allows the generation unit to continuously update its economic models to incorporate new trends and changes.

[0036] The simulation unit can estimate the economic, environmental, and social impacts of applying various policies and strategies. For example, the simulation unit can estimate the impact of a particular policy on economic growth or its effect on environmental protection. For example, the simulation unit can simulate the effects of applying various policies and strategies. For example, the simulation unit can identify the most effective measures and mitigate risks from unforeseen circumstances. This allows the simulation unit to estimate the impact of policies and strategies.

[0037] The report generation unit can generate detailed reports that include the policies and strategies applied, their impact on the objectives, and the expected future outcomes. For example, the report generation unit can generate detailed reports based on simulation results. For example, the report generation unit can specifically show the impact of a particular policy on economic growth or its effect on environmental protection. The report generation unit can generate detailed reports that include the policies and strategies applied, their impact on the objectives, and the expected future outcomes. This allows the report generation unit to generate detailed reports.

[0038] The decision support unit can support decision-makers in making data-driven decisions based on the generated reports. For example, the decision support unit can support decision-makers in making data-driven decisions based on the generated reports. For example, the decision support unit can support decision-makers in selecting and implementing the most effective policies based on simulation results. In this way, the decision support unit can support decision-makers in making data-driven decisions. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can take the generated reports as input and support decision-making using an AI model that supports decision-making.

[0039] The data collection unit can collect the necessary data. For example, the data collection unit collects economic data, environmental data, social data, etc. The data collection unit collects government data, corporate data, research data, etc. The data collection unit integrates and collects data from different data sources, for example. This allows the data collection unit to collect the necessary data. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can collect data using an AI model that integrates and collects data from different data sources.

[0040] The data collection unit can sequentially update data to include new trends and changes. For example, the data collection unit analyzes historical economic and environmental data and collects data that reflects current trends and future predictions. For example, the data collection unit collects data based on standards and guidelines created by experts. For example, the data collection unit is sequentially updated to include new trends and changes, even while based on historical data. This allows the data collection unit to sequentially update data to include new trends and changes. 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 update data using an AI model that sequentially updates data to include new trends and changes.

[0041] The report generation unit includes a visualization unit that visualizes the generated report. The visualization unit can visualize the generated report. The visualization unit visualizes the report using, for example, graphs, charts, infographics, etc. The visualization unit visually displays the generated report, for example. The visualization unit uses, for example, color coding and icons to highlight important data points. In this way, the visualization unit can visualize the generated report.

[0042] The generation unit can reflect expert opinions in real time and continuously update the generated economic model. For example, if an expert provides new data, the generation AI immediately reflects it in the economic model and generates the latest model. For example, if an expert proposes a policy change, the generation AI incorporates that change into the economic model and updates it. For example, if an expert discovers a new trend, the generation AI takes that trend into account and updates the economic model. In this way, the generation unit can reflect expert opinions in real time and continuously update the economic model. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can update the model using an AI model that takes expert opinions as input and updates the economic model.

[0043] The generation unit can simultaneously generate multiple models by considering different scenarios when generating economic models. For example, the generation unit can use a generation AI to consider different economic policy scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different environmental protection scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different social impact scenarios and generate multiple economic models based on each scenario. This allows the generation unit to simultaneously generate multiple models by considering different scenarios. 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 generate models using an AI model that takes different scenarios as input and generates multiple economic models.

[0044] The generation unit can generate different economic models by considering the characteristics of each region when generating economic models. For example, the generation unit's generating AI considers regional economic data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional environmental data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional social data and generates an optimal economic model for each region. In this way, the generation unit can generate different models by considering the characteristics of each region. 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 generate models using an AI model that takes regional characteristics as input and generates different economic models.

[0045] The generation unit can improve the accuracy of the economic model by referring to similar past cases when generating the economic model. For example, the generation unit's generating AI can refer to past economic data and improve the accuracy of the model based on similar economic conditions. For example, the generation unit's generating AI can refer to past environmental data and improve the accuracy of the model based on similar environmental conditions. For example, the generation unit's generating AI can refer to past social data and improve the accuracy of the model based on similar social conditions. In this way, the generation unit can improve the accuracy of the model by referring to similar past cases. 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 improve the accuracy of the model by using an AI model that takes similar past cases as input and improves the accuracy of the model.

[0046] The simulation unit can try different combinations of policies and strategies during simulation and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of economic policies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of environmental protection strategies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of social policies and identify the optimal combination. In this way, the simulation unit can try different combinations of policies and strategies and identify the optimal combination. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can take different combinations of policies and strategies as input and identify the optimal combination using an AI model that identifies the optimal combination.

[0047] The simulation unit can perform simulations across multiple timeframes, taking long-term effects into consideration. For example, the simulation unit can use a generating AI to perform simulations across short-term, medium-term, and long-term timeframes and evaluate the effects of each. For example, the simulation unit can use a generating AI to simulate economic growth across different timeframes and identify the optimal policy. For example, the simulation unit can use a generating AI to simulate environmental protection effects across different timeframes and identify the optimal strategy. This allows the simulation unit to perform simulations across multiple timeframes, taking long-term effects into consideration. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations using an AI model that takes different timeframes as input.

[0048] The simulation unit can perform simulations while considering data from different regions and countries. For example, the simulation unit's generating AI can consider economic data from different regions and perform simulations optimized for each region. For example, the simulation unit's generating AI can consider environmental data from different countries and perform simulations optimized for each country. For example, the simulation unit's generating AI can consider social data from different regions and perform simulations optimized for each region. In this way, the simulation unit can perform simulations while considering data from different regions and countries. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations using an AI model that takes data from different regions and countries as input.

[0049] The simulation unit can improve the accuracy of the simulation by referring to relevant external data during the simulation. For example, the simulation unit can improve the accuracy of the simulation by having a generating AI refer to real-time economic data. For example, the simulation unit can improve the accuracy of the simulation by having a generating AI refer to the latest environmental data. For example, the simulation unit can improve the accuracy of the simulation by having a generating AI refer to the latest social data. In this way, the simulation unit can improve the accuracy of the simulation by referring to relevant external data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can improve accuracy by using an AI model that takes relevant external data as input and improves the accuracy of the simulation.

[0050] The report generation unit can add visual elements to highlight important points when generating reports. For example, the report generation unit may add graphs or charts so that the generating AI highlights important data points. For example, the report generation unit may use color coding or icons so that the generating AI highlights the impact of important policies. For example, the report generation unit may add animations or effects so that the generating AI highlights important trends. In this way, the report generation unit can add visual elements to highlight important points. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit may add elements using an AI model that adds visual elements to highlight important points.

[0051] The report generation unit can generate reports in different formats and provide them to users according to their needs. For example, the report generation unit can use a generation AI to generate a report in PDF format and provide it to the user. For example, the report generation unit can use a generation AI to generate a report in Excel format and provide it to the user. For example, the report generation unit can use a generation AI to generate a report in Web format and provide it to the user. In this way, the report generation unit can generate reports in different formats and provide them to users according to their needs. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can generate reports using an AI model that generates reports in different formats.

[0052] The report generation unit can add elements that show progress when generating a report by comparing it to past reports. For example, the report generation unit can use a generating AI to compare past reports with current reports and add a graph that shows progress. For example, the report generation unit can use a generating AI to compare the impact of past policies with the impact of current policies and add a chart that shows progress. For example, the report generation unit can use a generating AI to compare past trends with current trends and add an icon that shows progress. In this way, the report generation unit can add elements that show progress by comparing it to past reports. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can use an AI model that takes past reports and current reports as input and adds elements that show progress to add elements.

[0053] The report generation unit can enrich the report content by referencing relevant external data during report generation. For example, the report generation unit's generating AI may reference the latest economic data to enrich the report content. For example, the report generation unit's generating AI may reference the latest environmental data to enrich the report content. For example, the report generation unit's generating AI may reference the latest social data to enrich the report content. In this way, the report generation unit can enrich the report content by referencing relevant external data. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can enrich the content using an AI model that takes relevant external data as input and enriches the report content.

[0054] The decision support unit can provide the optimal support method by referring to past decision history when providing decision support. For example, the decision support unit's generating AI can refer to past decision history and provide the optimal support method based on similar situations. For example, the decision support unit's generating AI can refer to past successful decisions and propose a method. For example, the decision support unit's generating AI can refer to past failed decisions and propose a way to avoid them. In this way, the decision support unit can provide the optimal support method by referring to past decision history. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can provide a support method using an AI model that takes past decision history as input and provides the optimal support method.

[0055] The decision support unit can present different scenarios during decision support, allowing the user to choose. For example, the decision support unit can use a generating AI to present different economic policy scenarios and allow the user to choose. For example, the decision support unit can use a generating AI to present different environmental protection scenarios and allow the user to choose. For example, the decision support unit can use a generating AI to present different social policy scenarios and allow the user to choose. In this way, the decision support unit can present different scenarios and allow the user to choose. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can take different scenarios as input and present scenarios using an AI model that presents scenarios.

[0056] The decision support unit can improve the accuracy of its support by referring to relevant external data during decision support. For example, the decision support unit's generating AI can refer to the latest economic data to improve the accuracy of its decision. For example, the decision support unit's generating AI can refer to the latest environmental data to improve the accuracy of its decision. For example, the decision support unit's generating AI can refer to the latest social data to improve the accuracy of its decision. In this way, the decision support unit can improve the accuracy of its support by referring to relevant external data. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can improve accuracy by using an AI model that takes relevant external data as input and improves the accuracy of its support.

[0057] The decision support unit can provide analysis results from different perspectives during decision-making, thereby supporting the user's decision-making from multiple angles. For example, the decision support unit can use a generative AI to provide analysis results from an economic perspective to support decision-making. For example, the decision support unit can use a generative AI to provide analysis results from an environmental perspective to support decision-making. For example, the decision support unit can use a generative AI to provide analysis results from a social perspective to support decision-making. In this way, the decision support unit can provide analysis results from different perspectives and support the user's decision-making from multiple angles. Some or all of the above-described processes in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can use an AI model that takes analysis results from different perspectives as input and provides analysis results to provide analysis results.

[0058] The data collection unit can integrate and collect data from different data sources during data collection. For example, the data collection unit may use a generative AI to integrate and collect economic data, environmental data, and social data. For example, the data collection unit may use a generative AI to integrate and collect data from different regions. For example, the data collection unit may use a generative AI to integrate and collect data from different timeframes. This allows the data collection unit to integrate and collect data from different data sources. 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 collect data using an AI model that takes data from different data sources as input and integrates and collects the data.

[0059] The data collection unit can evaluate the quality of the collected data and filter it as needed during data collection. For example, the data collection unit can use a generative AI to evaluate the reliability of the collected data and filter out unreliable data. For example, the data collection unit can use a generative AI to evaluate the consistency of the collected data and filter out inconsistent data. For example, the data collection unit can use a generative AI to evaluate the recency of the collected data and filter out outdated data. In this way, the data collection unit can evaluate the quality of the collected data and filter it as needed. 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 filter the data using an AI model that takes the quality of the collected data as input, evaluates the quality, and performs filtering.

[0060] The data collection unit can collect data while considering the characteristics of each region. For example, the data collection unit uses a generating AI to consider regional economic data and collect the data best suited to each region. For example, the data collection unit uses a generating AI to consider regional environmental data and collect the data best suited to each region. For example, the data collection unit uses a generating AI to consider regional social data and collect the data best suited to each region. In this way, the data collection unit can collect data while considering the characteristics of each region. 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 collect data using an AI model that takes regional characteristics as input and collects data.

[0061] The data collection unit can detect outliers by comparing current data with past collected data during data collection. For example, the data collection unit can use a generating AI to compare current economic data with past economic data and detect outliers. For example, the data collection unit can use a generating AI to compare current environmental data with past environmental data and detect outliers. For example, the data collection unit can use a generating AI to compare current social data with past social data and detect outliers. In this way, the data collection unit can detect outliers by comparing current data with past collected data. 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 use an AI model that detects outliers, taking past collected data as input, to detect outliers.

[0062] The visualization unit can add visual effects to highlight important data points during visualization. For example, the visualization unit may use color coding or icons to highlight important data points using a generative AI. For example, the visualization unit may add animations or effects to highlight important trends using a generative AI. For example, the visualization unit may add graphs or charts to highlight the impact of important policies using a generative AI. In this way, the visualization unit can add visual effects to highlight important data points. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may add effects using an AI model that adds visual effects to highlight important data points.

[0063] The visualization unit can integrate different datasets into a single visualization during the visualization process. For example, the visualization unit can use a generative AI to integrate and visualize economic, environmental, and social data. For example, the visualization unit can use a generative AI to integrate and visualize data from different regions. For example, the visualization unit can use a generative AI to integrate and visualize data from different timeframes. In this way, the visualization unit can integrate different datasets into a single visualization. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualizations using an AI model that takes different datasets as input and integrates and visualizes the data.

[0064] The visualization unit can simultaneously display data from different perspectives during visualization. For example, the visualization unit can use a generative AI to simultaneously display data from an economic perspective and data from an environmental perspective. For example, the visualization unit can use a generative AI to simultaneously display data from a social perspective and data from an economic perspective. For example, the visualization unit can use a generative AI to simultaneously display data from an environmental perspective and data from a social perspective. This allows the visualization unit to simultaneously display data from different perspectives. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use an AI model that takes data from different perspectives as input and displays the data simultaneously to perform the display.

[0065] The visualization unit can add elements that show trends by comparing them with past data during visualization. For example, the visualization unit can use a generative AI to compare past economic data with current economic data and add a graph that shows the trend. For example, the visualization unit can use a generative AI to compare past environmental data with current environmental data and add a chart that shows the trend. For example, the visualization unit can use a generative AI to compare past social data with current social data and add an icon that shows the trend. In this way, the visualization unit can add elements that show trends by comparing them with past data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can add elements using an AI model that takes past data as input and adds elements that show trends.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The generation unit can reflect expert opinions in real time and continuously update the generated economic model. For example, if an expert provides new data, the generation AI immediately reflects it in the economic model and generates the latest model. For example, if an expert proposes a policy change, the generation AI incorporates that change into the economic model and updates it. For example, if an expert discovers a new trend, the generation AI takes that trend into account and updates the economic model. In this way, the generation unit can reflect expert opinions in real time and continuously update the economic model. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can update the model using an AI model that takes expert opinions as input and updates the economic model.

[0068] The generation unit can simultaneously generate multiple models by considering different scenarios when generating economic models. For example, the generation unit can use a generation AI to consider different economic policy scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different environmental protection scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different social impact scenarios and generate multiple economic models based on each scenario. This allows the generation unit to simultaneously generate multiple models by considering different scenarios. 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 generate models using an AI model that takes different scenarios as input and generates multiple economic models.

[0069] The generation unit can generate different economic models by considering the characteristics of each region when generating economic models. For example, the generation unit's generating AI considers regional economic data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional environmental data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional social data and generates an optimal economic model for each region. In this way, the generation unit can generate different models by considering the characteristics of each region. 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 generate models using an AI model that takes regional characteristics as input and generates different economic models.

[0070] The generation unit can improve the accuracy of the economic model by referring to similar past cases when generating the economic model. For example, the generation unit's generating AI can refer to past economic data and improve the accuracy of the model based on similar economic conditions. For example, the generation unit's generating AI can refer to past environmental data and improve the accuracy of the model based on similar environmental conditions. For example, the generation unit's generating AI can refer to past social data and improve the accuracy of the model based on similar social conditions. In this way, the generation unit can improve the accuracy of the model by referring to similar past cases. 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 improve the accuracy of the model by using an AI model that takes similar past cases as input and improves the accuracy of the model.

[0071] The simulation unit can try different combinations of policies and strategies during simulation and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of economic policies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of environmental protection strategies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of social policies and identify the optimal combination. In this way, the simulation unit can try different combinations of policies and strategies and identify the optimal combination. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can take different combinations of policies and strategies as input and identify the optimal combination using an AI model that identifies the optimal combination.

[0072] The simulation unit can perform simulations across multiple timeframes, taking long-term effects into consideration. For example, the simulation unit can use a generating AI to perform simulations across short-term, medium-term, and long-term timeframes and evaluate the effects of each. For example, the simulation unit can use a generating AI to simulate economic growth across different timeframes and identify the optimal policy. For example, the simulation unit can use a generating AI to simulate environmental protection effects across different timeframes and identify the optimal strategy. This allows the simulation unit to perform simulations across multiple timeframes, taking long-term effects into consideration. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations using an AI model that takes different timeframes as input.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The generation unit generates an economic model based on criteria and guidelines created by experts. The generation unit analyzes historical economic and environmental data to generate an economic model that reflects current trends and future predictions. The generation unit is continuously updated to include new trends and changes. Step 2: The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. The simulation unit estimates the impact of a particular policy on economic growth and its effect on environmental protection. The simulation unit identifies the most effective measures and reduces the risks of unforeseen circumstances. Step 3: The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The report generation unit generates a detailed report that includes the policies and strategies applied, their impact on the objectives, and the expected future outcomes. Step 4: The decision support unit supports decision-making based on the reports generated by the report generation unit. The decision support unit supports decision-makers in making data-driven decisions based on the generated reports. It supports the selection and implementation of the most effective policies based on the simulation results.

[0075] (Example of form 2) The system according to an embodiment of the present invention is a system that uses AI to generate an economic model that takes into account various factors directly related to the SDGs (Sustainable Development Goals), and simulates the impact of policies and strategies. This system generates an economic model based on criteria and guidelines created by experts, simulates the impact of various policies and strategies, and generates a detailed report to support decision-making. For example, the system generates an economic model based on criteria and guidelines created by experts. In this process, while based on previous data, it is updated sequentially to include new trends and changes. For example, it can analyze past economic and environmental data to generate an economic model that reflects current trends and future predictions. Next, the system simulates the impact of applying various policies and strategies based on the generated economic model. For example, it can estimate the impact of a certain policy on economic growth or its effect on environmental protection. This makes it possible to identify the most effective means and reduce the risk of unforeseen circumstances. Furthermore, the system generates a detailed report based on the simulation results. This report includes the policies and strategies applied, their impact on the goals, and the expected future results. For example, it can specifically show the impact of a certain policy on economic growth or its effect on environmental protection. Based on the generated reports, decision-makers can make impactful, data-driven decisions. For example, they can select and implement the most effective policies based on simulation results. This makes it possible to formulate and implement effective strategies for achieving the SDGs. This mechanism provides simulation services for economic models toward achieving the SDGs, contributing to the promotion of a sustainable society. For example, it makes it possible to formulate and implement policies that balance economic growth and environmental protection. It also makes it possible to further accelerate the achievement of the SDGs across society. As a result, the system can formulate and implement effective strategies toward achieving the SDGs.

[0076] The system according to the embodiment comprises a generation unit, a simulation unit, a report generation unit, and a decision support unit. The generation unit generates an economic model based on criteria and guidelines created by experts. The generation unit analyzes, for example, historical economic and environmental data to generate an economic model that reflects current trends and future predictions. The generation unit is updated sequentially, for example, to include new trends and changes, while still being based on historical data. The generation unit generates an economic model based on, for example, criteria and guidelines created by experts. The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. The simulation unit estimates, for example, the impact of a certain policy on economic growth and its effect on environmental protection. The simulation unit simulates the impact of applying various policies and strategies. The simulation unit identifies, for example, the most effective means and reduces the risk of unforeseen circumstances. The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The report generation unit generates a detailed report that includes, for example, the policies and strategies applied, their impact on the objectives, and the expected future results. The report generation unit generates a detailed report based on simulation results, for example. The report generation unit specifically shows, for example, the impact of a certain policy on economic growth or its effect on environmental protection. The decision support unit supports decision-making based on the report generated by the report generation unit. The decision support unit supports decision-makers in making data-driven decisions based on the generated report, for example. The decision support unit supports selecting and implementing the most effective policy based on simulation results, for example. Thus, the system according to the embodiment can generate economic models based on expert standards and guidelines, simulate the impact of policies and strategies, and generate detailed reports to support decision-making.

[0077] The generation unit generates economic models based on standards and guidelines created by experts. For example, it analyzes historical economic and environmental data to generate economic models that reflect current trends and future predictions. Specifically, the generation unit collects economic indicators, market data, and environmental data over the past several decades and analyzes this data using statistical methods and machine learning algorithms. For example, it incorporates economic indicators such as GDP growth rate, unemployment rate, inflation rate, and trade balance as time-series data and extracts economic trends and patterns from this data. In addition, it collects environmental data such as temperature, precipitation, and CO2 emissions to model the interaction between economic activity and the environment. Based on this data, the generation unit generates economic models that reflect the current economic and environmental conditions. Furthermore, the generation unit is continuously updated to include new trends and changes. For example, it incorporates factors that affect the economy in real time, such as technological innovation, policy changes, and changes in international affairs, and updates the model. As a result, the generation unit can always provide economic models that reflect the latest information and make highly accurate predictions. The generation unit generates economic models based on standards and guidelines created by experts, thus providing highly reliable models.

[0078] The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. For example, the simulation unit estimates the impact of a particular policy on economic growth or its effect on environmental protection. Specifically, the simulation unit applies different policies and strategies to the generated economic model and analyzes the results. For example, it simulates various policy options such as tax reform, public investment, deregulation, and environmental protection policies, and evaluates the impact of each policy on economic growth, employment, inflation, and the environment. The simulation unit uses methods such as numerical simulation and Monte Carlo methods to quantitatively evaluate the effects of policies. This allows for a probabilistic evaluation of policy effects and the selection of the optimal policy while considering risks and uncertainties. Furthermore, the simulation unit identifies the most effective means and conducts scenario analysis to reduce risks from unforeseen events. For example, it evaluates the risks from unforeseen events such as economic crises and natural disasters and simulates countermeasures against these risks. In this way, the simulation unit can comprehensively evaluate the impact of policies and strategies and support optimal decision-making.

[0079] The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The report generation unit generates a detailed report that includes, for example, the policies and strategies applied, their impact on the objectives, and the expected future outcomes. Specifically, the report generation unit uses graphs, charts, and tables to visually display the simulation results in an easy-to-understand manner. For example, it creates line graphs showing the effects of policies, bar graphs showing fluctuations in economic indicators, and heatmaps showing risk assessments. The report also includes detailed explanations of the policy's background and objectives, the simulation's assumptions, and the interpretation of the results. This provides decision-makers with the information necessary to accurately understand the simulation results and make appropriate judgments. Furthermore, the report generation unit generates a report that includes recommendations and suggestions based on the simulation results. For example, it evaluates the impact of a specific policy on economic growth and presents recommended policy options based on the results. It also evaluates the effects on environmental protection and makes recommendations for achieving sustainable economic growth. In this way, the report generation unit provides a detailed report based on simulation results, supporting decision-makers in making data-driven decisions.

[0080] The decision support unit supports decision-making based on reports generated by the report generation unit. For example, the decision support unit supports decision-makers in making data-driven decisions based on the generated reports. Specifically, the decision support unit analyzes the content of the reports and assists in selecting the most effective policies and strategies. For example, based on simulation results, it proposes optimal policy options to promote economic growth and provides information for decision-makers to evaluate those options. It also supports policy selection that takes environmental protection and social justice into consideration, and supports decision-making to achieve sustainable development. Furthermore, based on simulation results, the decision support unit supports the selection and implementation of the most effective policies. For example, it evaluates the risks and uncertainties associated with policy implementation and proposes measures for risk management. It also monitors the implementation status of policies and provides feedback for corrections and improvements as needed. In this way, the decision support unit can support data-driven decision-making and provide information to maximize the effectiveness of policies. By supporting decision-makers in selecting and implementing optimal policies based on reports generated by the report generation unit, the decision support unit can enhance the overall effectiveness of the system.

[0081] The system includes a data collection unit. The data collection unit can collect the necessary data. For example, the data collection unit collects economic data, environmental data, social data, etc. For example, the data collection unit collects government data, corporate data, research data, etc. For example, the data collection unit integrates and collects data from different data sources. This allows the data collection unit to collect the necessary data.

[0082] The generation unit can generate economic models that are continuously updated to incorporate new trends and changes, while still being based on previous data. For example, the generation unit analyzes historical economic and environmental data to generate economic models that reflect current trends and future predictions. For example, the generation unit generates economic models based on criteria and guidelines created by experts. The generation unit is continuously updated to incorporate new trends and changes, while still being based on historical data. This allows the generation unit to continuously update its economic models to incorporate new trends and changes.

[0083] The simulation unit can estimate the economic, environmental, and social impacts of applying various policies and strategies. For example, the simulation unit can estimate the impact of a particular policy on economic growth or its effect on environmental protection. For example, the simulation unit can simulate the effects of applying various policies and strategies. For example, the simulation unit can identify the most effective measures and mitigate risks from unforeseen circumstances. This allows the simulation unit to estimate the impact of policies and strategies.

[0084] The report generation unit can generate detailed reports that include the policies and strategies applied, their impact on the objectives, and the expected future outcomes. For example, the report generation unit can generate detailed reports based on simulation results. For example, the report generation unit can specifically show the impact of a particular policy on economic growth or its effect on environmental protection. The report generation unit can generate detailed reports that include the policies and strategies applied, their impact on the objectives, and the expected future outcomes. This allows the report generation unit to generate detailed reports.

[0085] The decision support unit can support decision-makers in making data-driven decisions based on the generated reports. For example, the decision support unit can support decision-makers in making data-driven decisions based on the generated reports. For example, the decision support unit can support decision-makers in selecting and implementing the most effective policies based on simulation results. In this way, the decision support unit can support decision-makers in making data-driven decisions. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can take the generated reports as input and support decision-making using an AI model that supports decision-making.

[0086] The data collection unit can collect the necessary data. For example, the data collection unit collects economic data, environmental data, social data, etc. The data collection unit collects government data, corporate data, research data, etc. The data collection unit integrates and collects data from different data sources, for example. This allows the data collection unit to collect the necessary data. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can collect data using an AI model that integrates and collects data from different data sources.

[0087] The data collection unit can sequentially update data to include new trends and changes. For example, the data collection unit analyzes historical economic and environmental data and collects data that reflects current trends and future predictions. For example, the data collection unit collects data based on standards and guidelines created by experts. For example, the data collection unit is sequentially updated to include new trends and changes, even while based on historical data. This allows the data collection unit to sequentially update data to include new trends and changes. 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 update data using an AI model that sequentially updates data to include new trends and changes.

[0088] The report generation unit includes a visualization unit that visualizes the generated report. The visualization unit can visualize the generated report. The visualization unit visualizes the report using, for example, graphs, charts, infographics, etc. The visualization unit visually displays the generated report, for example. The visualization unit uses, for example, color coding and icons to highlight important data points. In this way, the visualization unit can visualize the generated report.

[0089] The generation unit can estimate the user's emotions and adjust the timing of economic model generation based on the estimated user emotions. For example, if the user is stressed, the generation unit's generating AI will delay the generation of the economic model and wait until the user is relaxed. For example, if the user is relaxed, the generation unit's generating AI will immediately generate the economic model and provide it to the user quickly. For example, if the user is in a hurry, the generation unit's generating AI will prioritize the generation of the economic model and provide the results quickly. In this way, the generation unit can adjust the timing of economic model generation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the generation timing using an AI model that takes user emotion data as input and adjusts the timing of economic model generation.

[0090] The generation unit can reflect expert opinions in real time and continuously update the generated economic model. For example, if an expert provides new data, the generation AI immediately reflects it in the economic model and generates the latest model. For example, if an expert proposes a policy change, the generation AI incorporates that change into the economic model and updates it. For example, if an expert discovers a new trend, the generation AI takes that trend into account and updates the economic model. In this way, the generation unit can reflect expert opinions in real time and continuously update the economic model. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can update the model using an AI model that takes expert opinions as input and updates the economic model.

[0091] The generation unit can simultaneously generate multiple models by considering different scenarios when generating economic models. For example, the generation unit can use a generation AI to consider different economic policy scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different environmental protection scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different social impact scenarios and generate multiple economic models based on each scenario. This allows the generation unit to simultaneously generate multiple models by considering different scenarios. 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 generate models using an AI model that takes different scenarios as input and generates multiple economic models.

[0092] The generation unit can estimate the user's emotions and determine the priority of the economic models to generate based on the estimated user emotions. For example, if the user is stressed, the generation AI will prioritize generating high-priority models and postpone lower-priority models. If the user is relaxed, the generation AI will generate all models equally. If the user is in a hurry, the generation AI will prioritize generating the most important models. In this way, the generation unit can determine the priority of economic models based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can take user emotion data as input and determine the priority using an AI model that determines the priority of economic models.

[0093] The generation unit can generate different economic models by considering the characteristics of each region when generating economic models. For example, the generation unit's generating AI considers regional economic data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional environmental data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional social data and generates an optimal economic model for each region. In this way, the generation unit can generate different models by considering the characteristics of each region. 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 generate models using an AI model that takes regional characteristics as input and generates different economic models.

[0094] The generation unit can improve the accuracy of the economic model by referring to similar past cases when generating the economic model. For example, the generation unit's generating AI can refer to past economic data and improve the accuracy of the model based on similar economic conditions. For example, the generation unit's generating AI can refer to past environmental data and improve the accuracy of the model based on similar environmental conditions. For example, the generation unit's generating AI can refer to past social data and improve the accuracy of the model based on similar social conditions. In this way, the generation unit can improve the accuracy of the model by referring to similar past cases. 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 improve the accuracy of the model by using an AI model that takes similar past cases as input and improves the accuracy of the model.

[0095] The simulation unit can estimate the user's emotions and adjust the simulation parameters based on the estimated emotions. For example, if the user is stressed, the simulation unit can set the simulation parameters gently and display the results gently. For example, if the user is relaxed, the simulation unit can set the simulation parameters in detail and display the results in detail. For example, if the user is in a hurry, the simulation unit can simplify the simulation parameters and display the results quickly. In this way, the simulation unit can adjust the simulation parameters based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can take user emotion data as input and adjust the parameters using an AI model that adjusts the simulation parameters.

[0096] The simulation unit can try different combinations of policies and strategies during simulation and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of economic policies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of environmental protection strategies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of social policies and identify the optimal combination. In this way, the simulation unit can try different combinations of policies and strategies and identify the optimal combination. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can take different combinations of policies and strategies as input and identify the optimal combination using an AI model that identifies the optimal combination.

[0097] The simulation unit can perform simulations across multiple timeframes, taking long-term effects into consideration. For example, the simulation unit can use a generating AI to perform simulations across short-term, medium-term, and long-term timeframes and evaluate the effects of each. For example, the simulation unit can use a generating AI to simulate economic growth across different timeframes and identify the optimal policy. For example, the simulation unit can use a generating AI to simulate environmental protection effects across different timeframes and identify the optimal strategy. This allows the simulation unit to perform simulations across multiple timeframes, taking long-term effects into consideration. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations using an AI model that takes different timeframes as input.

[0098] The simulation unit can estimate the user's emotions and adjust the display order of the simulation results based on the estimated user emotions. For example, if the user is stressed, the simulation unit will display important results first and detailed results later. For example, if the user is relaxed, the simulation unit will display detailed results first and the overall picture later. For example, if the user is in a hurry, the simulation unit will display key results first and detailed results later. In this way, the simulation unit can adjust the display order of the simulation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can adjust the display order using an AI model that takes user emotion data as input and adjusts the display order of the simulation results.

[0099] The simulation unit can perform simulations while considering data from different regions and countries. For example, the simulation unit's generating AI can consider economic data from different regions and perform simulations optimized for each region. For example, the simulation unit's generating AI can consider environmental data from different countries and perform simulations optimized for each country. For example, the simulation unit's generating AI can consider social data from different regions and perform simulations optimized for each region. In this way, the simulation unit can perform simulations while considering data from different regions and countries. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations using an AI model that takes data from different regions and countries as input.

[0100] The simulation unit can improve the accuracy of the simulation by referring to relevant external data during the simulation. For example, the simulation unit can improve the accuracy of the simulation by having a generating AI refer to real-time economic data. For example, the simulation unit can improve the accuracy of the simulation by having a generating AI refer to the latest environmental data. For example, the simulation unit can improve the accuracy of the simulation by having a generating AI refer to the latest social data. In this way, the simulation unit can improve the accuracy of the simulation by referring to relevant external data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can improve accuracy by using an AI model that takes relevant external data as input and improves the accuracy of the simulation.

[0101] The report generation unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is stressed, the report generation unit generates a simple and highly visual report. For example, if the user is relaxed, the report generation unit generates a report containing detailed information. For example, if the user is in a hurry, the report generation unit generates a concise report that gets straight to the point. In this way, the report generation unit can adjust the report's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can take user emotion data as input and adjust the presentation using an AI model that adjusts the report's presentation.

[0102] The report generation unit can add visual elements to highlight important points when generating reports. For example, the report generation unit may add graphs or charts so that the generating AI highlights important data points. For example, the report generation unit may use color coding or icons so that the generating AI highlights the impact of important policies. For example, the report generation unit may add animations or effects so that the generating AI highlights important trends. In this way, the report generation unit can add visual elements to highlight important points. Some or all of the above processing in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit may add elements using an AI model that adds visual elements to highlight important points.

[0103] The report generation unit can generate reports in different formats and provide them to users according to their needs. For example, the report generation unit can use a generation AI to generate a report in PDF format and provide it to the user. For example, the report generation unit can use a generation AI to generate a report in Excel format and provide it to the user. For example, the report generation unit can use a generation AI to generate a report in Web format and provide it to the user. In this way, the report generation unit can generate reports in different formats and provide them to users according to their needs. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can generate reports using an AI model that generates reports in different formats.

[0104] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the report generation unit will generate a short, concise report. For example, if the user is relaxed, the report generation unit will generate a longer report containing detailed information. For example, if the user is in a hurry, the report generation unit will generate a concise, quick-to-read report. In this way, the report generation unit can adjust the length of the report based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can take user emotion data as input and adjust the length using an AI model that adjusts the length of the report.

[0105] The report generation unit can add elements that show progress when generating a report by comparing it to past reports. For example, the report generation unit can use a generating AI to compare past reports with current reports and add a graph that shows progress. For example, the report generation unit can use a generating AI to compare the impact of past policies with the impact of current policies and add a chart that shows progress. For example, the report generation unit can use a generating AI to compare past trends with current trends and add an icon that shows progress. In this way, the report generation unit can add elements that show progress by comparing it to past reports. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can use an AI model that takes past reports and current reports as input and adds elements that show progress to add elements.

[0106] The report generation unit can enrich the report content by referencing relevant external data during report generation. For example, the report generation unit's generating AI may reference the latest economic data to enrich the report content. For example, the report generation unit's generating AI may reference the latest environmental data to enrich the report content. For example, the report generation unit's generating AI may reference the latest social data to enrich the report content. In this way, the report generation unit can enrich the report content by referencing relevant external data. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can enrich the content using an AI model that takes relevant external data as input and enriches the report content.

[0107] The decision support unit can estimate the user's emotions and adjust the decision support method based on the estimated user emotions. For example, if the user is stressed, the decision support unit provides a simple and highly visible support method. For example, if the user is relaxed, the decision support unit provides a support method that includes detailed information. For example, if the user is in a hurry, the decision support unit provides a concise support method that gets straight to the point. In this way, the decision support unit can adjust the decision support method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can adjust the support method using an AI model that takes user emotion data as input and adjusts the decision support method.

[0108] The decision support unit can provide the optimal support method by referring to past decision history when providing decision support. For example, the decision support unit's generating AI can refer to past decision history and provide the optimal support method based on similar situations. For example, the decision support unit's generating AI can refer to past successful decisions and propose a method. For example, the decision support unit's generating AI can refer to past failed decisions and propose a way to avoid them. In this way, the decision support unit can provide the optimal support method by referring to past decision history. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can provide a support method using an AI model that takes past decision history as input and provides the optimal support method.

[0109] The decision support unit can present different scenarios during decision support, allowing the user to choose. For example, the decision support unit can use a generating AI to present different economic policy scenarios and allow the user to choose. For example, the decision support unit can use a generating AI to present different environmental protection scenarios and allow the user to choose. For example, the decision support unit can use a generating AI to present different social policy scenarios and allow the user to choose. In this way, the decision support unit can present different scenarios and allow the user to choose. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can take different scenarios as input and present scenarios using an AI model that presents scenarios.

[0110] The decision support unit can estimate the user's emotions and determine the priority of decisions based on the estimated emotions. For example, if the user is stressed, the decision support unit will postpone less important decisions and prioritize more important ones. For example, if the user is relaxed, the decision support unit will support all decisions equally. For example, if the user is in a hurry, the decision support unit will prioritize the most important decisions. In this way, the decision support unit can determine the priority of decisions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can take user emotion data as input and determine priorities using an AI model that determines the priority of decisions.

[0111] The decision support unit can improve the accuracy of its support by referring to relevant external data during decision support. For example, the decision support unit's generating AI can refer to the latest economic data to improve the accuracy of its decision. For example, the decision support unit's generating AI can refer to the latest environmental data to improve the accuracy of its decision. For example, the decision support unit's generating AI can refer to the latest social data to improve the accuracy of its decision. In this way, the decision support unit can improve the accuracy of its support by referring to relevant external data. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can improve accuracy by using an AI model that takes relevant external data as input and improves the accuracy of its support.

[0112] The decision support unit can provide analysis results from different perspectives during decision-making, thereby supporting the user's decision-making from multiple angles. For example, the decision support unit can use a generative AI to provide analysis results from an economic perspective to support decision-making. For example, the decision support unit can use a generative AI to provide analysis results from an environmental perspective to support decision-making. For example, the decision support unit can use a generative AI to provide analysis results from a social perspective to support decision-making. In this way, the decision support unit can provide analysis results from different perspectives and support the user's decision-making from multiple angles. Some or all of the above-described processes in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can use an AI model that takes analysis results from different perspectives as input and provides analysis results to provide analysis results.

[0113] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay data collection and wait until the user is relaxed. For example, if the user is relaxed, the data collection unit may immediately begin data collection. For example, if the user is in a hurry, the data collection unit may prioritize data collection. In this way, the data collection unit can adjust the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can take user emotion data as input and adjust the timing of data collection using an AI model.

[0114] The data collection unit can integrate and collect data from different data sources during data collection. For example, the data collection unit may use a generative AI to integrate and collect economic data, environmental data, and social data. For example, the data collection unit may use a generative AI to integrate and collect data from different regions. For example, the data collection unit may use a generative AI to integrate and collect data from different timeframes. This allows the data collection unit to integrate and collect data from different data sources. 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 collect data using an AI model that takes data from different data sources as input and integrates and collects the data.

[0115] The data collection unit can evaluate the quality of the collected data and filter it as needed during data collection. For example, the data collection unit can use a generative AI to evaluate the reliability of the collected data and filter out unreliable data. For example, the data collection unit can use a generative AI to evaluate the consistency of the collected data and filter out inconsistent data. For example, the data collection unit can use a generative AI to evaluate the recency of the collected data and filter out outdated data. In this way, the data collection unit can evaluate the quality of the collected data and filter it as needed. 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 filter the data using an AI model that takes the quality of the collected data as input, evaluates the quality, and performs filtering.

[0116] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data and postpone collecting low-priority data. For example, if the user is relaxed, the data collection unit will collect all data equally. For example, if the user is in a hurry, the data collection unit will prioritize collecting the most important data. In this way, the data collection unit can determine the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can determine the priority using an AI model that takes user emotion data as input and determines the priority of data to collect.

[0117] The data collection unit can collect data while considering the characteristics of each region. For example, the data collection unit uses a generating AI to consider regional economic data and collect the data best suited to each region. For example, the data collection unit uses a generating AI to consider regional environmental data and collect the data best suited to each region. For example, the data collection unit uses a generating AI to consider regional social data and collect the data best suited to each region. In this way, the data collection unit can collect data while considering the characteristics of each region. 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 collect data using an AI model that takes regional characteristics as input and collects data.

[0118] The data collection unit can detect outliers by comparing current data with past collected data during data collection. For example, the data collection unit can use a generating AI to compare current economic data with past economic data and detect outliers. For example, the data collection unit can use a generating AI to compare current environmental data with past environmental data and detect outliers. For example, the data collection unit can use a generating AI to compare current social data with past social data and detect outliers. In this way, the data collection unit can detect outliers by comparing current data with past collected data. 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 use an AI model that detects outliers, taking past collected data as input, to detect outliers.

[0119] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit provides a simple and highly visible visualization method. For example, if the user is relaxed, the visualization unit provides a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit provides a concise visualization method that gets straight to the point. Thus, the visualization unit can adjust the visualization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can take user emotion data as input and adjust the method using an AI model that adjusts the visualization method.

[0120] The visualization unit can add visual effects to highlight important data points during visualization. For example, the visualization unit may use color coding or icons to highlight important data points using a generative AI. For example, the visualization unit may add animations or effects to highlight important trends using a generative AI. For example, the visualization unit may add graphs or charts to highlight the impact of important policies using a generative AI. In this way, the visualization unit can add visual effects to highlight important data points. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit may add effects using an AI model that adds visual effects to highlight important data points.

[0121] The visualization unit can integrate different datasets into a single visualization during the visualization process. For example, the visualization unit can use a generative AI to integrate and visualize economic, environmental, and social data. For example, the visualization unit can use a generative AI to integrate and visualize data from different regions. For example, the visualization unit can use a generative AI to integrate and visualize data from different timeframes. In this way, the visualization unit can integrate different datasets into a single visualization. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualizations using an AI model that takes different datasets as input and integrates and visualizes the data.

[0122] The visualization unit can estimate the user's emotions and determine the priority of visualizations based on the estimated emotions. For example, if the user is stressed, the visualization unit will postpone less important visualizations and prioritize more important ones. For example, if the user is relaxed, the visualization unit will provide all visualizations equally. For example, if the user is in a hurry, the visualization unit will provide the most important visualizations with the highest priority. In this way, the visualization unit can determine the priority of visualizations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can take user emotion data as input and determine the priority using an AI model that determines the priority of visualizations.

[0123] The visualization unit can simultaneously display data from different perspectives during visualization. For example, the visualization unit can use a generative AI to simultaneously display data from an economic perspective and data from an environmental perspective. For example, the visualization unit can use a generative AI to simultaneously display data from a social perspective and data from an economic perspective. For example, the visualization unit can use a generative AI to simultaneously display data from an environmental perspective and data from a social perspective. This allows the visualization unit to simultaneously display data from different perspectives. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use an AI model that takes data from different perspectives as input and displays the data simultaneously to perform the display.

[0124] The visualization unit can add elements that show trends by comparing them with past data during visualization. For example, the visualization unit can use a generative AI to compare past economic data with current economic data and add a graph that shows the trend. For example, the visualization unit can use a generative AI to compare past environmental data with current environmental data and add a chart that shows the trend. For example, the visualization unit can use a generative AI to compare past social data with current social data and add an icon that shows the trend. In this way, the visualization unit can add elements that show trends by comparing them with past data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can add elements using an AI model that takes past data as input and adds elements that show trends.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The generation unit can estimate the user's emotions and adjust the timing of economic model generation based on the estimated user emotions. For example, if the user is stressed, the generation unit's generating AI will delay the generation of the economic model and wait until the user is relaxed. For example, if the user is relaxed, the generation unit's generating AI will immediately generate the economic model and provide it to the user quickly. For example, if the user is in a hurry, the generation unit's generating AI will prioritize the generation of the economic model and provide the results quickly. In this way, the generation unit can adjust the timing of economic model generation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the generation timing using an AI model that takes user emotion data as input and adjusts the timing of economic model generation.

[0127] The generation unit can reflect expert opinions in real time and continuously update the generated economic model. For example, if an expert provides new data, the generation AI immediately reflects it in the economic model and generates the latest model. For example, if an expert proposes a policy change, the generation AI incorporates that change into the economic model and updates it. For example, if an expert discovers a new trend, the generation AI takes that trend into account and updates the economic model. In this way, the generation unit can reflect expert opinions in real time and continuously update the economic model. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can update the model using an AI model that takes expert opinions as input and updates the economic model.

[0128] The generation unit can simultaneously generate multiple models by considering different scenarios when generating economic models. For example, the generation unit can use a generation AI to consider different economic policy scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different environmental protection scenarios and generate multiple economic models based on each scenario. For example, the generation unit can use a generation AI to consider different social impact scenarios and generate multiple economic models based on each scenario. This allows the generation unit to simultaneously generate multiple models by considering different scenarios. 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 generate models using an AI model that takes different scenarios as input and generates multiple economic models.

[0129] The generation unit can estimate the user's emotions and determine the priority of the economic models to generate based on the estimated user emotions. For example, if the user is stressed, the generation AI will prioritize generating high-priority models and postpone lower-priority models. If the user is relaxed, the generation AI will generate all models equally. If the user is in a hurry, the generation AI will prioritize generating the most important models. In this way, the generation unit can determine the priority of economic models based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can take user emotion data as input and determine the priority using an AI model that determines the priority of economic models.

[0130] The generation unit can generate different economic models by considering the characteristics of each region when generating economic models. For example, the generation unit's generating AI considers regional economic data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional environmental data and generates an optimal economic model for each region. For example, the generation unit's generating AI considers regional social data and generates an optimal economic model for each region. In this way, the generation unit can generate different models by considering the characteristics of each region. 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 generate models using an AI model that takes regional characteristics as input and generates different economic models.

[0131] The generation unit can improve the accuracy of the economic model by referring to similar past cases when generating the economic model. For example, the generation unit's generating AI can refer to past economic data and improve the accuracy of the model based on similar economic conditions. For example, the generation unit's generating AI can refer to past environmental data and improve the accuracy of the model based on similar environmental conditions. For example, the generation unit's generating AI can refer to past social data and improve the accuracy of the model based on similar social conditions. In this way, the generation unit can improve the accuracy of the model by referring to similar past cases. 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 improve the accuracy of the model by using an AI model that takes similar past cases as input and improves the accuracy of the model.

[0132] The simulation unit can estimate the user's emotions and adjust the simulation parameters based on the estimated emotions. For example, if the user is stressed, the simulation unit can set the simulation parameters gently and display the results gently. For example, if the user is relaxed, the simulation unit can set the simulation parameters in detail and display the results in detail. For example, if the user is in a hurry, the simulation unit can simplify the simulation parameters and display the results quickly. In this way, the simulation unit can adjust the simulation parameters based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can take user emotion data as input and adjust the parameters using an AI model that adjusts the simulation parameters.

[0133] The simulation unit can try different combinations of policies and strategies during simulation and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of economic policies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of environmental protection strategies and identify the optimal combination. For example, the simulation unit's generating AI can try different combinations of social policies and identify the optimal combination. In this way, the simulation unit can try different combinations of policies and strategies and identify the optimal combination. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can take different combinations of policies and strategies as input and identify the optimal combination using an AI model that identifies the optimal combination.

[0134] The simulation unit can perform simulations across multiple timeframes, taking long-term effects into consideration. For example, the simulation unit can use a generating AI to perform simulations across short-term, medium-term, and long-term timeframes and evaluate the effects of each. For example, the simulation unit can use a generating AI to simulate economic growth across different timeframes and identify the optimal policy. For example, the simulation unit can use a generating AI to simulate environmental protection effects across different timeframes and identify the optimal strategy. This allows the simulation unit to perform simulations across multiple timeframes, taking long-term effects into consideration. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can perform simulations using an AI model that takes different timeframes as input.

[0135] The simulation unit can estimate the user's emotions and adjust the display order of the simulation results based on the estimated user emotions. For example, if the user is stressed, the simulation unit will display important results first and detailed results later. For example, if the user is relaxed, the simulation unit will display detailed results first and the overall picture later. For example, if the user is in a hurry, the simulation unit will display key results first and detailed results later. In this way, the simulation unit can adjust the display order of the simulation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can adjust the display order using an AI model that takes user emotion data as input and adjusts the display order of the simulation results.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The generation unit generates an economic model based on criteria and guidelines created by experts. The generation unit analyzes historical economic and environmental data to generate an economic model that reflects current trends and future predictions. The generation unit is continuously updated to include new trends and changes. Step 2: The simulation unit simulates the impact of various policies and strategies based on the economic model generated by the generation unit. The simulation unit estimates the impact of a particular policy on economic growth and its effect on environmental protection. The simulation unit identifies the most effective measures and reduces the risks of unforeseen circumstances. Step 3: The report generation unit generates a detailed report based on the simulation results obtained by the simulation unit. The report generation unit generates a detailed report that includes the policies and strategies applied, their impact on the objectives, and the expected future outcomes. Step 4: The decision support unit supports decision-making based on the reports generated by the report generation unit. The decision support unit supports decision-makers in making data-driven decisions based on the generated reports. It supports the selection and implementation of the most effective policies based on the simulation results.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the generation unit, simulation unit, report generation unit, decision support unit, collection unit, and visualization unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The simulation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The report generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The decision support unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the generation unit, simulation unit, report generation unit, decision support unit, collection unit, and visualization unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The simulation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The report generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The decision support unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the generation unit, simulation unit, report generation unit, decision support unit, collection unit, and visualization unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The simulation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The report generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The decision support unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the generation unit, simulation unit, report generation unit, decision support unit, collection unit, and visualization unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The simulation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The report generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The decision support unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A generation unit that generates economic models based on standards and guidelines created by experts, A simulation unit that simulates the impact of various policies and strategies based on the economic model generated by the generation unit, A report generation unit generates a detailed report based on the simulation results obtained by the aforementioned simulation unit, The system includes a decision support unit that supports decision-making based on the report generated by the report generation unit. A system characterized by the following features. (Note 2) It includes a data collection unit for collecting data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is This generates an economic model that is continuously updated to incorporate new trends and changes, while still being based on previous data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, To estimate the economic, environmental, and social impacts of applying various policies and strategies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The report generation unit, Generate a detailed report that includes the policies and strategies applied, their impact on the objectives, and expected future outcomes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned decision support unit, Supports decision-makers in making data-driven decisions based on the generated reports. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Collect the necessary data. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned collection unit is We continuously update the data to include new trends and changes. The system described in Appendix 2, characterized by the features described herein. (Note 9) The report generation unit, It includes a visualization unit that visualizes the generated report. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates user sentiment and adjusts the timing of economic model generation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system incorporates expert opinions in real time and continuously updates the generated economic models. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating economic models, multiple models are generated simultaneously, taking different scenarios into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates user sentiment and determines the priority of economic models to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating economic models, different models are generated that take into account the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating economic models, we improve the accuracy of the models by referring to similar past cases. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation parameters based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned simulation unit, During the simulation, different combinations of policies and strategies are tried to identify the optimal combination. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, During the simulation, we will perform simulations across multiple timeframes to consider long-term effects. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts the display order of the simulation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During the simulation, the simulation will take into account data from different regions and countries. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During simulation, we reference relevant external data to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The report generation unit, When generating reports, add visual elements to highlight key points. The system described in Appendix 1, characterized by the features described herein. (Note 24) The report generation unit, When generating reports, we create reports in different formats and provide them according to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 25) The report generation unit, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The report generation unit, When generating a report, add elements that show progress compared to previous reports. The system described in Appendix 1, characterized by the features described herein. (Note 27) The report generation unit, When generating reports, the report content is enriched by referencing relevant external data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned decision support unit, It estimates the user's emotions and adjusts how decision-making is supported based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned decision support unit, When providing decision support, we refer to past decision history to offer the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned decision support unit, When supporting decision-making, present different scenarios and allow users to choose. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned decision support unit, It estimates user emotions and prioritizes decision-making based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned decision support unit, When providing decision support, referencing relevant external data improves the accuracy of the support. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned decision support unit, When supporting decision-making, we provide analysis results from different perspectives, offering multifaceted support for the user's decision-making process. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned collection unit is During data collection, data from different data sources is integrated and collected. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned collection unit is During data collection, the quality of the collected data is evaluated, and filtering is performed as needed. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned collection unit is When collecting data, the data is collected while taking into account the characteristics of each region. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned collection unit is During data collection, detect outliers by comparing them with previously collected data. The system described in Appendix 2, characterized by the features described herein. (Note 40) The visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The visualization unit, When visualizing, add visual effects to highlight important data points. The system described in Appendix 2, characterized by the features described herein. (Note 42) The visualization unit, When creating visualizations, integrate different datasets into a single visualization. The system described in Appendix 2, characterized by the features described herein. (Note 43) The visualization unit, It estimates the user's emotions and determines the priority of visualizations based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 44) The visualization unit, When visualizing, display data from different perspectives simultaneously. The system described in Appendix 2, characterized by the features described herein. (Note 45) The visualization unit, When visualizing, add elements that show trends by comparing with historical data. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0210] 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 generation unit that generates economic models based on standards and guidelines created by experts, A simulation unit that simulates the impact of various policies and strategies based on the economic model generated by the generation unit, A report generation unit generates a detailed report based on the simulation results obtained by the aforementioned simulation unit, The system includes a decision support unit that supports decision-making based on the report generated by the report generation unit. A system characterized by the following features.

2. It includes a data collection unit for collecting data. The system according to feature 1.

3. The generating unit is This generates an economic model that is continuously updated to incorporate new trends and changes, while still being based on previous data. The system according to feature 1.

4. The aforementioned simulation unit, To estimate the economic, environmental, and social impacts of applying various policies and strategies. The system according to feature 1.

5. The report generation unit, Generate a detailed report that includes the policies and strategies applied, their impact on the objectives, and expected future outcomes. The system according to feature 1.

6. The aforementioned decision support unit, Supports decision-makers in making data-driven decisions based on the generated reports. The system according to feature 1.

7. The aforementioned collection unit is Collect the necessary data. The system according to feature 2.

8. The aforementioned collection unit is We continuously update the data to include new trends and changes. The system according to feature 2.

9. The report generation unit, It includes a visualization unit that visualizes the generated report. The system according to feature 1.

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