system

Abstract

The system according to this embodiment aims to analyze economic data in real time and predict future economic trends. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a forecasting unit, and a provisioning unit. The collection unit collects economic data in real time. The analysis unit analyzes the data collected by the collection unit. The forecasting unit predicts future economic trends based on the results analyzed by the analysis unit. The provisioning unit provides the results predicted by the forecasting unit as information that can be used for policy proposals and risk management.

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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to analyze economic data in real time and predict future economic trends, and there is room for improvement in providing information for policy proposals and risk management.

[0005] The system according to the embodiment aims to analyze economic data in real time and predict future economic trends.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a forecasting unit, and a data provision unit. The data collection unit collects economic data in real time. The analysis unit analyzes the data collected by the data collection unit. The forecasting unit predicts future economic trends based on the results analyzed by the analysis unit. The data provision unit provides the results predicted by the forecasting unit as information that can be used for policy proposals and risk management. [Effects of the Invention]

[0007] The system according to this embodiment can analyze economic data in real time and predict future economic trends. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [First Embodiment] 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) An AI agent according to an embodiment of the present invention is a system that analyzes economic data in real time and predicts future economic trends. This AI agent collects economic data in real time and analyzes it using machine learning and deep learning technologies. Next, it predicts future economic trends based on the analysis results. These prediction results are provided as information that can be used for policy proposals and risk management. For example, it is expected to improve the accuracy of GDP growth rate predictions, increase the success rate of policy proposals for risk avoidance, and reduce economic instability. Furthermore, by customizing the user interface, it is possible to provide information tailored to the user's needs. This enables data support for economic policymakers to make realistic decisions and provides information that enables companies to respond quickly to market fluctuations. In addition, by using creative data visualizations, information is provided in a way that is easy for users to understand. This AI agent targets medium- and large-scale financial institutions, government agencies, and international organizations, and provides highly accurate economic forecasting and risk identification to solve the uncertainty of economic forecasting and the difficulty of risk management. With the globalization of the economy and the evolution of data science, the demand for advanced economic forecasting tools is increasing, and this AI agent supports better social decision-making by improving economic stability and predictability. This allows AI agents to analyze economic data in real time and predict future economic trends.

[0029] The AI ​​agent according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a data provision unit. The data collection unit collects economic data in real time. For example, the data collection unit automatically collects economic data from the internet. The data collection unit can also acquire data provided by government agencies and financial institutions in real time. Furthermore, the data collection unit can also collect data directly from the field using sensors and IoT devices. For example, the data collection unit collects economic indicators from publicly available databases on the internet. Data provided by government agencies and financial institutions can be acquired in real time via APIs. By using sensors and IoT devices, on-site economic activity can be monitored in real time and data can be collected. The analysis unit analyzes the collected data using machine learning and deep learning technologies. For example, the analysis unit analyzes data patterns using machine learning algorithms. Furthermore, the analysis unit can extract data features using deep learning technologies. Furthermore, the analysis unit can perform data anomaly detection and build prediction models. For example, the analysis unit analyzes data using machine learning algorithms such as linear regression and decision trees. By using deep learning technology, complex patterns in data can be extracted, enabling highly accurate analysis. Anomaly detection in the data allows for the early identification of abnormal economic activity and the implementation of countermeasures. The forecasting unit predicts future economic trends based on the analysis results. For example, the forecasting unit predicts trends in economic indicators using time series analysis. It can also predict future economic scenarios using simulation models. Furthermore, the forecasting unit can improve prediction accuracy by combining multiple prediction models. For example, the forecasting unit predicts trends in economic indicators using time series analysis methods such as ARIMA models and LSTMs. Using simulation models, future economic scenarios can be predicted under multiple conditions. Combining multiple prediction models can improve prediction accuracy. The provisioning unit provides the prediction results as information that can be used for policy proposals and risk management. For example, the provisioning unit provides the prediction results in report format.Furthermore, the service provider can display forecast results in real time through a dashboard. In addition, the service provider can notify users of important forecast results using an alert function. For example, the service provider can provide forecast results as a PDF report. The dashboard allows users to check forecast results in real time. The alert function allows users to be immediately notified when important forecast results occur. As a result, the AI ​​agent according to this embodiment can collect, analyze, and forecast economic data in real time, providing information that can be used for policy proposals and risk management.

[0030] The data collection unit collects economic data in real time. For example, it automatically collects economic data from the internet. Specifically, it uses web scraping technology to extract necessary data from economic websites and public databases. This includes economic indicators, stock prices, exchange rates, and trade data. The data collection unit can also obtain data from government agencies and financial institutions in real time. This includes obtaining data via APIs, such as interest rate information from central banks and economic reports from statistical bureaus. Furthermore, the data collection unit can collect data directly from the field using sensors and IoT devices. For example, in a manufacturing plant, sensors installed on the production line monitor operating conditions and production volume in real time and collect that data. This allows the data collection unit to collect a wide range of economic data from diverse sources and understand the situation in real time. The collected data is stored in a central database and managed so that the analysis and forecasting units can access it. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0031] The analysis unit analyzes collected data using machine learning and deep learning technologies. For example, the analysis unit analyzes data patterns using machine learning algorithms. Specifically, it uses algorithms such as linear regression, decision trees, random forests, and support vector machines (SVMs) to reveal data trends and correlations. The analysis unit can also extract data features using deep learning technologies. This includes models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are particularly effective for analyzing complex patterns and nonlinear relationships. Furthermore, the analysis unit can perform anomaly detection and build predictive models for data. For example, for anomaly detection, it uses autoencoders and clustering algorithms for anomaly detection to detect data that deviates from normal patterns early on. For building predictive models, it uses LSTM (Long Short-Term Memory) networks and ARIMA (Autoregressive Moving Average) models, which are specialized for analyzing time series data, to predict future trends in economic indicators. As a result, the analysis unit can analyze collected data quickly and accurately, which is useful for understanding economic conditions, detecting anomalies early, and making future predictions.

[0032] The forecasting unit predicts future economic trends based on the analysis results. For example, the forecasting unit predicts trends in economic indicators using time series analysis. Specifically, it uses time series analysis methods such as ARIMA models and LSTMs to predict future trends from past data. This allows for highly accurate prediction of fluctuations in economic indicators. The forecasting unit can also predict future economic scenarios using simulation models. For example, it uses Monte Carlo simulations to generate scenarios under multiple economic conditions and evaluate the probability of each scenario occurring. Furthermore, the forecasting unit can improve prediction accuracy by combining multiple prediction models. For example, it integrates prediction models using different algorithms through ensemble learning, leveraging the strengths of each model to improve prediction accuracy. This allows the forecasting unit to predict future economic trends from multiple perspectives and provide information useful for policy proposals and risk management. The forecasting unit can continuously revise its prediction results based on real-time updated data to respond to the latest situations. This allows the forecasting unit to always provide highly accurate risk predictions based on the latest information and support quick and appropriate responses.

[0033] The service provider offers forecast results as information that can be used for policy proposals and risk management. For example, the service provider provides forecast results in report format. Specifically, it generates forecast results as a PDF report and distributes it to relevant parties. The service provider can also display forecast results in real time through a dashboard. The dashboard has a web-based interface and can be accessed by users through a browser. The dashboard displays graphs and charts of forecast results, allowing users to intuitively grasp the information. Furthermore, the service provider can notify important forecast results using an alert function. For example, if a specific economic indicator exceeds the forecast value or if abnormal economic activity is detected, an alert is generated and notified to relevant parties via email or SMS. This allows the service provider to provide users with timely and appropriate information that can be used for policy proposals and risk management. In addition, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the information it provides. This allows the service provider to provide users with high-quality information and contribute to the optimization of economic activity and improved risk management.

[0034] The data collection unit can collect economic data in real time. For example, the data collection unit can automatically collect economic data from the internet. The data collection unit can also acquire data provided by government agencies and financial institutions in real time. Furthermore, the data collection unit can collect data directly from the field using sensors and IoT devices. For example, the data collection unit can collect economic indicators from publicly available databases on the internet. Data provided by government agencies and financial institutions can be acquired in real time via APIs. By using sensors and IoT devices, on-site economic activity can be monitored in real time and data can be collected. This allows for the acquisition of the latest information by collecting economic data in real time. Some or all of the above-mentioned processes in the data collection unit may be performed using AI, for example, or not. For example, when the data collection unit collects economic indicators from publicly available databases on the internet, it can use AI to optimize the timing of data collection.

[0035] The analysis unit can analyze collected data using machine learning and deep learning technologies. For example, the analysis unit can analyze data patterns using machine learning algorithms. It can also extract data features using deep learning technologies. Furthermore, the analysis unit can perform data anomaly detection and build predictive models. For example, the analysis unit can analyze data using machine learning algorithms such as linear regression and decision trees. By using deep learning technologies, complex data patterns can be extracted, enabling highly accurate analysis. By detecting data anomalies, abnormal economic activity can be identified early, allowing for countermeasures to be taken. Thus, the accuracy of data analysis is improved by using machine learning and deep learning technologies. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes data patterns using machine learning algorithms, it can use AI to optimize the algorithm parameters.

[0036] The forecasting unit can predict future economic trends based on the analysis results. For example, the forecasting unit can predict trends in economic indicators using time series analysis. The forecasting unit can also predict future economic scenarios using simulation models. Furthermore, the forecasting unit can improve prediction accuracy by combining multiple prediction models. For example, the forecasting unit can predict trends in economic indicators using time series analysis methods such as ARIMA models and LSTMs. By using simulation models, future economic scenarios can be predicted under multiple conditions. By combining multiple prediction models, the accuracy of the prediction can be improved. This allows for the provision of information useful for policy proposals and risk management by predicting future economic trends based on the analysis results. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, when the forecasting unit predicts trends in economic indicators using time series analysis, it can use AI to optimize the parameters of the prediction model.

[0037] The service provider can provide forecast results as information that can be used for policy proposals and risk management. For example, the service provider can provide forecast results in report format. The service provider can also display forecast results in real time through a dashboard. Furthermore, the service provider can notify important forecast results using an alert function. For example, the service provider can provide forecast results as a PDF report. By using the dashboard, users can check forecast results in real time. By using the alert function, users can be immediately notified when important forecast results occur. In this way, by providing forecast results as information that can be used for policy proposals and risk management, the service provider supports user decision-making. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, when the service provider provides forecast results in report format, it can use AI to automatically generate the content of the report.

[0038] The service provider can provide information tailored to user needs by customizing the user interface. For example, the service provider can change the layout of the user interface. The service provider can also select display items. Furthermore, the service provider can customize the information display format according to user preferences. For example, the service provider can change the dashboard layout to prioritize displaying information that the user needs. By selecting display items, users can see only the information they need. By customizing the information display format, users can receive information tailored to their preferences. Thus, customizing the user interface makes it possible to provide information tailored to user needs. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, when changing the layout of the user interface, the service provider can use AI to analyze the user's operation history and suggest the optimal layout.

[0039] The service provider can provide information using creative data visualizations. For example, the service provider can visually represent information using infographics. It can also dynamically display information using interactive charts. Furthermore, the service provider can use data visualizations to facilitate understanding of information. For example, by using infographics, the service provider can visually represent complex information in an easy-to-understand way. By using interactive charts, users can dynamically manipulate data and view detailed information. By using data visualizations, understanding of information is facilitated, and users can intuitively grasp the information. Thus, by using creative data visualizations, information can be provided in a way that is easy for users to understand. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, when generating infographics, the service provider can use AI to extract key points from the data and represent them in a visually easy-to-understand format.

[0040] The data collection unit can analyze past economic data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past collection history. The data collection unit can also optimize the collection method based on past collection history to perform efficient data collection. Furthermore, the data collection unit can analyze past collection history and determine the priority of data to be collected. For example, the data collection unit can identify the most effective collection timing from past collection history. Based on past collection history, it optimizes the collection method to perform efficient data collection. It analyzes past collection history and determines the priority of data to be collected. This makes it possible to select the optimal collection method and perform efficient data collection by analyzing past collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collection history into AI and use AI to select the optimal collection method.

[0041] The data collection unit can filter economic data based on specific economic indicators or market trends. For example, the data collection unit can collect only important data based on specific economic indicators. The data collection unit can also analyze market trends and prioritize the collection of highly relevant data. Furthermore, the data collection unit can adjust the filtering criteria for collected data in response to fluctuations in economic indicators. For example, the data collection unit can collect only important data based on specific economic indicators, analyze market trends and prioritize the collection of highly relevant data, and adjust the filtering criteria for collected data in response to fluctuations in economic indicators. This allows for the priority collection of important data by filtering based on specific economic indicators or market trends. 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 AI to filter important data based on specific economic indicators.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting economic data. For example, the data collection unit can prioritize the collection of regional economic data based on geographical location information. The data collection unit can also filter highly relevant data by considering geographical location information. Furthermore, the data collection unit can determine the priority of the data to be collected based on geographical location information. For example, the data collection unit can prioritize the collection of regional economic data based on geographical location information. It can filter highly relevant data by considering geographical location information. It can determine the priority of the data to be collected based on geographical location information. This allows for the efficient collection of regional economic data by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI and use AI to filter highly relevant data.

[0043] The data collection unit can analyze social media activity and collect relevant data when collecting economic data. For example, the data collection unit can analyze social media trends and collect relevant economic data. The data collection unit can also determine the priority of data to be collected based on social media activity. Furthermore, the data collection unit can filter social media data and collect important economic data. For example, the data collection unit analyzes social media trends and collects relevant economic data. Based on social media activity, it determines the priority of data to be collected. It filters social media data and collects important economic data. This allows for the efficient collection of relevant economic data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input social media trends into AI and use AI to collect relevant economic data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the economic data during the analysis. For example, the analysis unit can perform a detailed analysis on important economic data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the economic data. For example, the analysis unit can perform a detailed analysis on important economic data, a simplified analysis on less important data, and dynamically adjust the level of detail of the analysis according to the importance of the economic data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the economic data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the economic data into the AI ​​and use the AI ​​to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of economic data during analysis. For example, the analysis unit can apply a specific analysis algorithm to macroeconomic data. It can also apply a different analysis algorithm to microeconomic data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of economic data. For example, the analysis unit can apply a specific analysis algorithm to macroeconomic data, a different analysis algorithm to microeconomic data, or select the optimal analysis algorithm depending on the category of economic data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the category of economic data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of economic data into AI and use AI to select the optimal analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of economic data collection during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent economic data. The analysis unit can also determine the priority of analysis by referring to past economic data. Furthermore, the analysis unit can dynamically adjust the priority of analysis according to the timing of economic data collection. For example, the analysis unit prioritizes the analysis of the most recent economic data. It determines the priority of analysis by referring to past economic data. It dynamically adjusts the priority of analysis according to the timing of economic data collection. This enables efficient data analysis by determining the priority of analysis based on the timing of economic data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of economic data collection into AI and use AI to determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of economic data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant economic data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of economic data. Furthermore, the analysis unit can analyze the relevance of economic data and determine the optimal analysis order. For example, the analysis unit prioritizes the analysis of highly relevant economic data. It dynamically adjusts the order of analysis according to the relevance of economic data. It analyzes the relevance of economic data and determines the optimal analysis order. This enables efficient data analysis by adjusting the order of analysis based on the relevance of economic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of economic data into AI and use AI to adjust the order of analysis.

[0048] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of economic data during the prediction process. For example, the prediction unit can analyze the interrelationships of economic data and reflect them in the prediction model. The prediction unit can also improve the accuracy of its predictions by considering the interrelationships of economic data. Furthermore, the prediction unit can optimize the prediction model based on the interrelationships of economic data. For example, the prediction unit analyzes the interrelationships of economic data and reflects them in the prediction model. It improves the accuracy of its predictions by considering the interrelationships of economic data. It optimizes the prediction model based on the interrelationships of economic data. As a result, the accuracy of the predictions is improved by considering the interrelationships of economic data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships of economic data into AI and optimize the prediction model using AI.

[0049] The prediction unit can make predictions while considering the attribute information of the economic data submitters. For example, the prediction unit adjusts the prediction model based on the attribute information of the economic data submitters. The prediction unit can also improve the accuracy of the prediction by considering the submitter's attribute information. Furthermore, the prediction unit can customize the prediction results based on the submitter's attribute information. For example, the prediction unit adjusts the prediction model based on the attribute information of the economic data submitters. It improves the accuracy of the prediction by considering the submitter's attribute information. It customizes the prediction results based on the submitter's attribute information. As a result, the accuracy of the prediction is improved by considering the attribute information of the economic data submitters. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the attribute information of the economic data submitters into AI and adjust the prediction model using AI.

[0050] The forecasting unit can make predictions while considering the geographical distribution of economic data. For example, the forecasting unit can make regional economic forecasts based on geographical distribution. The forecasting unit can also improve the accuracy of predictions by considering geographical distribution. Furthermore, the forecasting unit can customize the prediction results based on geographical distribution. For example, the forecasting unit makes regional economic forecasts based on geographical distribution. It improves the accuracy of predictions by considering geographical distribution. It customizes the prediction results based on geographical distribution. This makes it possible to make regional economic forecasts by considering the geographical distribution of economic data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the geographical distribution of economic data into AI and make predictions using AI.

[0051] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on economic data during the prediction process. For example, the prediction unit can refer to relevant literature on economic data and reflect it in the prediction model. The prediction unit can also improve the accuracy of its predictions based on the relevant literature. Furthermore, the prediction unit can customize the prediction results by referring to relevant literature on economic data. For example, the prediction unit can refer to relevant literature on economic data and reflect it in the prediction model. It improves the accuracy of its predictions based on the relevant literature. It customizes the prediction results by referring to relevant literature on economic data. As a result, the accuracy of the predictions is improved by referring to relevant literature on economic data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature on economic data into AI and reflect it in the prediction model using AI.

[0052] The information delivery unit can select the optimal information delivery method by referring to the user's past usage history when providing information. For example, the information delivery unit selects the optimal information delivery method based on the user's past usage history. The information delivery unit can also determine the priority of information delivery by referring to past usage history. Furthermore, the information delivery unit can analyze the user's past usage history and propose the optimal information delivery method. For example, the information delivery unit selects the optimal information delivery method based on the user's past usage history. It determines the priority of information delivery by referring to past usage history. It analyzes the user's past usage history and proposes the optimal information delivery method. In this way, the optimal information delivery method can be selected by referring to the user's past usage history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past usage history into AI and use AI to select the optimal information delivery method.

[0053] The information delivery unit can customize the means of information delivery based on the user's current needs when providing information. For example, the information delivery unit can analyze the user's current needs and select the optimal means of information delivery. The information delivery unit can also customize the means of information delivery according to the user's needs. Furthermore, the information delivery unit can dynamically adjust the means of information delivery based on the user's current needs. For example, the information delivery unit analyzes the user's current needs and selects the optimal means of information delivery. It customizes the means of information delivery according to the user's needs. It dynamically adjusts the means of information delivery based on the user's current needs. This makes it possible to provide more appropriate information by customizing the means of information delivery based on the user's current needs. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's current needs into AI and use AI to customize the means of information delivery.

[0054] The information provider can select the optimal information provision method by considering the user's geographical location information when providing information. For example, the information provider can provide region-specific information based on the user's geographical location information. The information provider can also select the optimal information provision method by considering the geographical location information. Furthermore, the information provider can customize the means of information provision based on the geographical location information. For example, the information provider can provide region-specific information based on the user's geographical location information. It selects the optimal information provision method by considering the geographical location information. It customizes the means of information provision based on the geographical location information. This makes it possible to provide optimal information for each region by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's geographical location information into AI and use AI to select the optimal information provision method.

[0055] The information provider can analyze the user's social media activity and propose methods for providing information. For example, the provider can analyze the user's social media activity and propose the most suitable method of information provision. The provider can also customize the methods of information provision based on social media activity. Furthermore, the provider can analyze social media data and determine the priority of information provision. For example, the provider can analyze the user's social media activity and propose the most suitable method of information provision. It can customize the methods of information provision based on social media activity. It can analyze social media data and determine the priority of information provision. This allows the provider to propose the most suitable method of information provision by analyzing the user's social media activity. Some or all of the above processes in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity into AI and use AI to propose the most suitable method of information provision.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can focus on specific industries or sectors when collecting economic data. For example, the data collection unit can prioritize collecting data from the financial industry. It can also collect data related to specific sectors, such as manufacturing or services. Furthermore, the data collection unit can filter data related to specific industries or sectors, prioritizing the collection of important data. This allows for the efficient collection of more relevant data by focusing on specific industries or sectors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data related to specific industries or sectors into an AI and use the AI ​​to filter important data.

[0058] The analysis unit can perform analysis while considering the seasonality of economic data. For example, the analysis unit can perform seasonal adjustments on data affected by seasonality. The analysis unit can also analyze seasonality patterns and reflect them in the predictive model. Furthermore, the analysis unit can perform anomaly detection on data while considering the effects of seasonality. As a result, by considering the seasonality of economic data, more accurate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seasonally affected data into AI and perform seasonal adjustments using AI.

[0059] The forecasting unit can evaluate the reliability of economic data during forecasting and prioritize the use of highly reliable data. For example, the forecasting unit can evaluate the reliability of the data source and provider. It can also evaluate the consistency and accuracy of the data and select highly reliable data. Furthermore, the forecasting unit can exclude unreliable data to improve the accuracy of the forecast. By evaluating the reliability of economic data, it is possible to provide more accurate forecast results. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or not using AI. For example, the forecasting unit can input the reliability of the data into AI and use AI to select highly reliable data.

[0060] The information provider can adjust the level of detail of the information provided according to the user's level of expertise. For example, the provider can provide detailed information to economic experts. It can also provide simplified information to general users. Furthermore, the provider can adjust the display format of the information according to the user's level of expertise. By adjusting the level of detail of the information according to the user's level of expertise, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's level of expertise into AI and use AI to adjust the level of detail of the information.

[0061] The data collection unit can focus on specific time periods when collecting economic data. For example, it can prioritize collecting data during periods of high trading activity. It can also collect data during times when specific events or announcements are made. Furthermore, it can filter data related to specific time periods and prioritize the collection of important data. This allows for the efficient collection of more relevant data by focusing on specific time periods. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data related to specific time periods into an AI and use the AI ​​to filter important data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects economic data in real time. The data collection unit can, for example, automatically collect economic data from the internet. It can also acquire data provided by government agencies and financial institutions in real time. Furthermore, the data collection unit can collect data directly from the field using sensors and IoT devices. Step 2: The analysis unit analyzes the collected data using machine learning and deep learning technologies. For example, the analysis unit analyzes data patterns using machine learning algorithms. It can also extract data features using deep learning technologies. Furthermore, it can perform anomaly detection and build predictive models for the data. Step 3: The forecasting unit predicts future economic trends based on the analysis results. For example, the forecasting unit predicts trends in economic indicators using time series analysis. It can also predict future economic scenarios using simulation models. Furthermore, it can improve forecasting accuracy by combining multiple forecasting models. Step 4: The service provider provides the forecast results as information that can be used for policy proposals and risk management. For example, the service provider provides the forecast results in report format. It can also display the forecast results in real time through a dashboard. Furthermore, it can notify important forecast results using an alert function.

[0064] (Example of form 2) An AI agent according to an embodiment of the present invention is a system that analyzes economic data in real time and predicts future economic trends. This AI agent collects economic data in real time and analyzes it using machine learning and deep learning technologies. Next, it predicts future economic trends based on the analysis results. These prediction results are provided as information that can be used for policy proposals and risk management. For example, it is expected to improve the accuracy of GDP growth rate predictions, increase the success rate of policy proposals for risk avoidance, and reduce economic instability. Furthermore, by customizing the user interface, it is possible to provide information tailored to the user's needs. This enables data support for economic policymakers to make realistic decisions and provides information that enables companies to respond quickly to market fluctuations. In addition, by using creative data visualizations, information is provided in a way that is easy for users to understand. This AI agent targets medium- and large-scale financial institutions, government agencies, and international organizations, and provides highly accurate economic forecasting and risk identification to solve the uncertainty of economic forecasting and the difficulty of risk management. With the globalization of the economy and the evolution of data science, the demand for advanced economic forecasting tools is increasing, and this AI agent supports better social decision-making by improving economic stability and predictability. This allows AI agents to analyze economic data in real time and predict future economic trends.

[0065] The AI ​​agent according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a data provision unit. The data collection unit collects economic data in real time. For example, the data collection unit automatically collects economic data from the internet. The data collection unit can also acquire data provided by government agencies and financial institutions in real time. Furthermore, the data collection unit can also collect data directly from the field using sensors and IoT devices. For example, the data collection unit collects economic indicators from publicly available databases on the internet. Data provided by government agencies and financial institutions can be acquired in real time via APIs. By using sensors and IoT devices, on-site economic activity can be monitored in real time and data can be collected. The analysis unit analyzes the collected data using machine learning and deep learning technologies. For example, the analysis unit analyzes data patterns using machine learning algorithms. Furthermore, the analysis unit can extract data features using deep learning technologies. Furthermore, the analysis unit can perform data anomaly detection and build prediction models. For example, the analysis unit analyzes data using machine learning algorithms such as linear regression and decision trees. By using deep learning technology, complex patterns in data can be extracted, enabling highly accurate analysis. Anomaly detection in the data allows for the early identification of abnormal economic activity and the implementation of countermeasures. The forecasting unit predicts future economic trends based on the analysis results. For example, the forecasting unit predicts trends in economic indicators using time series analysis. It can also predict future economic scenarios using simulation models. Furthermore, the forecasting unit can improve prediction accuracy by combining multiple prediction models. For example, the forecasting unit predicts trends in economic indicators using time series analysis methods such as ARIMA models and LSTMs. Using simulation models, future economic scenarios can be predicted under multiple conditions. Combining multiple prediction models can improve prediction accuracy. The provisioning unit provides the prediction results as information that can be used for policy proposals and risk management. For example, the provisioning unit provides the prediction results in report format.Furthermore, the service provider can display forecast results in real time through a dashboard. In addition, the service provider can notify users of important forecast results using an alert function. For example, the service provider can provide forecast results as a PDF report. The dashboard allows users to check forecast results in real time. The alert function allows users to be immediately notified when important forecast results occur. As a result, the AI ​​agent according to this embodiment can collect, analyze, and forecast economic data in real time, providing information that can be used for policy proposals and risk management.

[0066] The data collection unit collects economic data in real time. For example, it automatically collects economic data from the internet. Specifically, it uses web scraping technology to extract necessary data from economic websites and public databases. This includes economic indicators, stock prices, exchange rates, and trade data. The data collection unit can also obtain data from government agencies and financial institutions in real time. This includes obtaining data via APIs, such as interest rate information from central banks and economic reports from statistical bureaus. Furthermore, the data collection unit can collect data directly from the field using sensors and IoT devices. For example, in a manufacturing plant, sensors installed on the production line monitor operating conditions and production volume in real time and collect that data. This allows the data collection unit to collect a wide range of economic data from diverse sources and understand the situation in real time. The collected data is stored in a central database and managed so that the analysis and forecasting units can access it. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0067] The analysis unit analyzes collected data using machine learning and deep learning technologies. For example, the analysis unit analyzes data patterns using machine learning algorithms. Specifically, it uses algorithms such as linear regression, decision trees, random forests, and support vector machines (SVMs) to reveal data trends and correlations. The analysis unit can also extract data features using deep learning technologies. This includes models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are particularly effective for analyzing complex patterns and nonlinear relationships. Furthermore, the analysis unit can perform anomaly detection and build predictive models for data. For example, for anomaly detection, it uses autoencoders and clustering algorithms for anomaly detection to detect data that deviates from normal patterns early on. For building predictive models, it uses LSTM (Long Short-Term Memory) networks and ARIMA (Autoregressive Moving Average) models, which are specialized for analyzing time series data, to predict future trends in economic indicators. As a result, the analysis unit can analyze collected data quickly and accurately, which is useful for understanding economic conditions, detecting anomalies early, and making future predictions.

[0068] The forecasting unit predicts future economic trends based on the analysis results. For example, the forecasting unit predicts trends in economic indicators using time series analysis. Specifically, it uses time series analysis methods such as ARIMA models and LSTMs to predict future trends from past data. This allows for highly accurate prediction of fluctuations in economic indicators. The forecasting unit can also predict future economic scenarios using simulation models. For example, it uses Monte Carlo simulations to generate scenarios under multiple economic conditions and evaluate the probability of each scenario occurring. Furthermore, the forecasting unit can improve prediction accuracy by combining multiple prediction models. For example, it integrates prediction models using different algorithms through ensemble learning, leveraging the strengths of each model to improve prediction accuracy. This allows the forecasting unit to predict future economic trends from multiple perspectives and provide information useful for policy proposals and risk management. The forecasting unit can continuously revise its prediction results based on real-time updated data to respond to the latest situations. This allows the forecasting unit to always provide highly accurate risk predictions based on the latest information and support quick and appropriate responses.

[0069] The service provider offers forecast results as information that can be used for policy proposals and risk management. For example, the service provider provides forecast results in report format. Specifically, it generates forecast results as a PDF report and distributes it to relevant parties. The service provider can also display forecast results in real time through a dashboard. The dashboard has a web-based interface and can be accessed by users through a browser. The dashboard displays graphs and charts of forecast results, allowing users to intuitively grasp the information. Furthermore, the service provider can notify important forecast results using an alert function. For example, if a specific economic indicator exceeds the forecast value or if abnormal economic activity is detected, an alert is generated and notified to relevant parties via email or SMS. This allows the service provider to provide users with timely and appropriate information that can be used for policy proposals and risk management. In addition, the service provider can collect user feedback and continuously improve the accuracy and usefulness of the information it provides. This allows the service provider to provide users with high-quality information and contribute to the optimization of economic activity and improved risk management.

[0070] The data collection unit can collect economic data in real time. For example, the data collection unit can automatically collect economic data from the internet. The data collection unit can also acquire data provided by government agencies and financial institutions in real time. Furthermore, the data collection unit can collect data directly from the field using sensors and IoT devices. For example, the data collection unit can collect economic indicators from publicly available databases on the internet. Data provided by government agencies and financial institutions can be acquired in real time via APIs. By using sensors and IoT devices, on-site economic activity can be monitored in real time and data can be collected. This allows for the acquisition of the latest information by collecting economic data in real time. Some or all of the above-mentioned processes in the data collection unit may be performed using AI, for example, or not. For example, when the data collection unit collects economic indicators from publicly available databases on the internet, it can use AI to optimize the timing of data collection.

[0071] The analysis unit can analyze collected data using machine learning and deep learning technologies. For example, the analysis unit can analyze data patterns using machine learning algorithms. It can also extract data features using deep learning technologies. Furthermore, the analysis unit can perform data anomaly detection and build predictive models. For example, the analysis unit can analyze data using machine learning algorithms such as linear regression and decision trees. By using deep learning technologies, complex data patterns can be extracted, enabling highly accurate analysis. By detecting data anomalies, abnormal economic activity can be identified early, allowing for countermeasures to be taken. Thus, the accuracy of data analysis is improved by using machine learning and deep learning technologies. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, when the analysis unit analyzes data patterns using machine learning algorithms, it can use AI to optimize the algorithm parameters.

[0072] The forecasting unit can predict future economic trends based on the analysis results. For example, the forecasting unit can predict trends in economic indicators using time series analysis. The forecasting unit can also predict future economic scenarios using simulation models. Furthermore, the forecasting unit can improve prediction accuracy by combining multiple prediction models. For example, the forecasting unit can predict trends in economic indicators using time series analysis methods such as ARIMA models and LSTMs. By using simulation models, future economic scenarios can be predicted under multiple conditions. By combining multiple prediction models, the accuracy of the prediction can be improved. This allows for the provision of information useful for policy proposals and risk management by predicting future economic trends based on the analysis results. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, when the forecasting unit predicts trends in economic indicators using time series analysis, it can use AI to optimize the parameters of the prediction model.

[0073] The service provider can provide forecast results as information that can be used for policy proposals and risk management. For example, the service provider can provide forecast results in report format. The service provider can also display forecast results in real time through a dashboard. Furthermore, the service provider can notify important forecast results using an alert function. For example, the service provider can provide forecast results as a PDF report. By using the dashboard, users can check forecast results in real time. By using the alert function, users can be immediately notified when important forecast results occur. In this way, by providing forecast results as information that can be used for policy proposals and risk management, the service provider supports user decision-making. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, when the service provider provides forecast results in report format, it can use AI to automatically generate the content of the report.

[0074] The service provider can provide information tailored to user needs by customizing the user interface. For example, the service provider can change the layout of the user interface. The service provider can also select display items. Furthermore, the service provider can customize the information display format according to user preferences. For example, the service provider can change the dashboard layout to prioritize displaying information that the user needs. By selecting display items, users can see only the information they need. By customizing the information display format, users can receive information tailored to their preferences. Thus, customizing the user interface makes it possible to provide information tailored to user needs. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, when changing the layout of the user interface, the service provider can use AI to analyze the user's operation history and suggest the optimal layout.

[0075] The service provider can provide information using creative data visualizations. For example, the service provider can visually represent information using infographics. It can also dynamically display information using interactive charts. Furthermore, the service provider can use data visualizations to facilitate understanding of information. For example, by using infographics, the service provider can visually represent complex information in an easy-to-understand way. By using interactive charts, users can dynamically manipulate data and view detailed information. By using data visualizations, understanding of information is facilitated, and users can intuitively grasp the information. Thus, by using creative data visualizations, information can be provided in a way that is easy for users to understand. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, when generating infographics, the service provider can use AI to extract key points from the data and represent them in a visually easy-to-understand format.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of economic data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect only important data. Conversely, if the user is relaxed, the data collection unit can increase the collection frequency and collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of important data in real time. For example, the data collection unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. If the user is stressed, the data collection frequency is reduced and only important data is collected. If the user is relaxed, the data collection frequency is increased and more detailed data is collected. If the user is in a hurry, important data is prioritized and collected in real time. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can capture the user's facial expressions with a camera, estimate emotions using generative AI, and adjust the timing of data collection.

[0077] The data collection unit can analyze past economic data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past collection history. The data collection unit can also optimize the collection method based on past collection history to perform efficient data collection. Furthermore, the data collection unit can analyze past collection history and determine the priority of data to be collected. For example, the data collection unit can identify the most effective collection timing from past collection history. Based on past collection history, it optimizes the collection method to perform efficient data collection. It analyzes past collection history and determines the priority of data to be collected. This makes it possible to select the optimal collection method and perform efficient data collection by analyzing past collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collection history into AI and use AI to select the optimal collection method.

[0078] The data collection unit can filter economic data based on specific economic indicators or market trends. For example, the data collection unit can collect only important data based on specific economic indicators. The data collection unit can also analyze market trends and prioritize the collection of highly relevant data. Furthermore, the data collection unit can adjust the filtering criteria for collected data in response to fluctuations in economic indicators. For example, the data collection unit can collect only important data based on specific economic indicators, analyze market trends and prioritize the collection of highly relevant data, and adjust the filtering criteria for collected data in response to fluctuations in economic indicators. This allows for the priority collection of important data by filtering based on specific economic indicators or market trends. 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 AI to filter important data based on specific economic indicators.

[0079] The data collection unit can estimate the user's emotions and prioritize the economic data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting important data in real time. For example, the data collection unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. If the user is stressed, it prioritizes collecting only important data. If the user is relaxed, it prioritizes collecting detailed data. If the user is in a hurry, it prioritizes collecting important data in real time. This allows for more appropriate data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can capture the user's facial expressions with a camera, estimate their emotions using generative AI, and determine the priority of the data to be collected.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting economic data. For example, the data collection unit can prioritize the collection of regional economic data based on geographical location information. The data collection unit can also filter highly relevant data by considering geographical location information. Furthermore, the data collection unit can determine the priority of the data to be collected based on geographical location information. For example, the data collection unit can prioritize the collection of regional economic data based on geographical location information. It can filter highly relevant data by considering geographical location information. It can determine the priority of the data to be collected based on geographical location information. This allows for the efficient collection of regional economic data by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI and use AI to filter highly relevant data.

[0081] The data collection unit can analyze social media activity and collect relevant data when collecting economic data. For example, the data collection unit can analyze social media trends and collect relevant economic data. The data collection unit can also determine the priority of data to be collected based on social media activity. Furthermore, the data collection unit can filter social media data and collect important economic data. For example, the data collection unit analyzes social media trends and collects relevant economic data. Based on social media activity, it determines the priority of data to be collected. It filters social media data and collects important economic data. This allows for the efficient collection of relevant economic data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input social media trends into AI and use AI to collect relevant economic data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. If the user is stressed, it provides a simple analysis result. If the user is relaxed, it provides a detailed analysis result. If the user is in a hurry, it provides a concise analysis result. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can capture the user's facial expressions with a camera, estimate their emotions using generative AI, and adjust the way the analysis is presented.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the economic data during the analysis. For example, the analysis unit can perform a detailed analysis on important economic data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the economic data. For example, the analysis unit can perform a detailed analysis on important economic data, a simplified analysis on less important data, and dynamically adjust the level of detail of the analysis according to the importance of the economic data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the economic data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the economic data into the AI ​​and use the AI ​​to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of economic data during analysis. For example, the analysis unit can apply a specific analysis algorithm to macroeconomic data. It can also apply a different analysis algorithm to microeconomic data. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of economic data. For example, the analysis unit can apply a specific analysis algorithm to macroeconomic data, a different analysis algorithm to microeconomic data, or select the optimal analysis algorithm depending on the category of economic data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the category of economic data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of economic data into AI and use AI to select the optimal analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a short analysis result. It can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. If the user is stressed, it provides a short analysis result. If the user is relaxed, it provides a detailed analysis result. If the user is in a hurry, it provides a concise analysis result. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can capture the user's facial expressions with a camera, estimate their emotions using generative AI, and adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the timing of economic data collection during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent economic data. The analysis unit can also determine the priority of analysis by referring to past economic data. Furthermore, the analysis unit can dynamically adjust the priority of analysis according to the timing of economic data collection. For example, the analysis unit prioritizes the analysis of the most recent economic data. It determines the priority of analysis by referring to past economic data. It dynamically adjusts the priority of analysis according to the timing of economic data collection. This enables efficient data analysis by determining the priority of analysis based on the timing of economic data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of economic data collection into AI and use AI to determine the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of economic data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant economic data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of economic data. Furthermore, the analysis unit can analyze the relevance of economic data and determine the optimal analysis order. For example, the analysis unit prioritizes the analysis of highly relevant economic data. It dynamically adjusts the order of analysis according to the relevance of economic data. It analyzes the relevance of economic data and determines the optimal analysis order. This enables efficient data analysis by adjusting the order of analysis based on the relevance of economic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of economic data into AI and use AI to adjust the order of analysis.

[0088] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is stressed, the prediction unit applies conservative prediction criteria. It can also apply detailed prediction criteria if the user is relaxed. Furthermore, if the user is in a hurry, it can apply rapid prediction criteria. For example, the prediction unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. If the user is stressed, it applies conservative prediction criteria. If the user is relaxed, it applies detailed prediction criteria. If the user is in a hurry, it applies rapid prediction criteria. This allows for more accurate prediction results by adjusting the prediction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can capture the user's facial expression with a camera, estimate their emotions using generative AI, and adjust the prediction criteria.

[0089] The prediction unit can improve the accuracy of its predictions by considering the interrelationships of economic data during the prediction process. For example, the prediction unit can analyze the interrelationships of economic data and reflect them in the prediction model. The prediction unit can also improve the accuracy of its predictions by considering the interrelationships of economic data. Furthermore, the prediction unit can optimize the prediction model based on the interrelationships of economic data. For example, the prediction unit analyzes the interrelationships of economic data and reflects them in the prediction model. It improves the accuracy of its predictions by considering the interrelationships of economic data. It optimizes the prediction model based on the interrelationships of economic data. As a result, the accuracy of the predictions is improved by considering the interrelationships of economic data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships of economic data into AI and optimize the prediction model using AI.

[0090] The prediction unit can make predictions while considering the attribute information of the economic data submitters. For example, the prediction unit adjusts the prediction model based on the attribute information of the economic data submitters. The prediction unit can also improve the accuracy of the prediction by considering the submitter's attribute information. Furthermore, the prediction unit can customize the prediction results based on the submitter's attribute information. For example, the prediction unit adjusts the prediction model based on the attribute information of the economic data submitters. It improves the accuracy of the prediction by considering the submitter's attribute information. It customizes the prediction results based on the submitter's attribute information. As a result, the accuracy of the prediction is improved by considering the attribute information of the economic data submitters. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the attribute information of the economic data submitters into AI and adjust the prediction model using AI.

[0091] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize displaying important prediction results. It can also display detailed prediction results if the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying concise prediction results. For example, the prediction unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. If the user is stressed, it prioritizes displaying important prediction results. If the user is relaxed, it displays detailed prediction results. If the user is in a hurry, it prioritizes displaying concise prediction results. This allows for more appropriate information to be provided by adjusting the display order of prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can capture the user's facial expression with a camera, estimate their emotions using generative AI, and adjust the display order of the prediction results.

[0092] The forecasting unit can make predictions while considering the geographical distribution of economic data. For example, the forecasting unit can make regional economic forecasts based on geographical distribution. The forecasting unit can also improve the accuracy of predictions by considering geographical distribution. Furthermore, the forecasting unit can customize the prediction results based on geographical distribution. For example, the forecasting unit makes regional economic forecasts based on geographical distribution. It improves the accuracy of predictions by considering geographical distribution. It customizes the prediction results based on geographical distribution. This makes it possible to make regional economic forecasts by considering the geographical distribution of economic data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the geographical distribution of economic data into AI and make predictions using AI.

[0093] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on economic data during the prediction process. For example, the prediction unit can refer to relevant literature on economic data and reflect it in the prediction model. The prediction unit can also improve the accuracy of its predictions based on the relevant literature. Furthermore, the prediction unit can customize the prediction results by referring to relevant literature on economic data. For example, the prediction unit can refer to relevant literature on economic data and reflect it in the prediction model. It improves the accuracy of its predictions based on the relevant literature. It customizes the prediction results by referring to relevant literature on economic data. As a result, the accuracy of the predictions is improved by referring to relevant literature on economic data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature on economic data into AI and reflect it in the prediction model using AI.

[0094] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider can select a simple method of information delivery. If the user is relaxed, the information provider can also select a detailed method of information delivery. Furthermore, if the user is in a hurry, the information provider can select a rapid method of information delivery. For example, the information provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. If the user is stressed, a simple method of information delivery is selected. If the user is relaxed, a detailed method of information delivery is selected. If the user is in a hurry, a rapid method of information delivery is selected. This allows for more appropriate information delivery by adjusting the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can capture the user's facial expressions with a camera, estimate their emotions using generative AI, and adjust the method of information provision accordingly.

[0095] The information delivery unit can select the optimal information delivery method by referring to the user's past usage history when providing information. For example, the information delivery unit selects the optimal information delivery method based on the user's past usage history. The information delivery unit can also determine the priority of information delivery by referring to past usage history. Furthermore, the information delivery unit can analyze the user's past usage history and propose the optimal information delivery method. For example, the information delivery unit selects the optimal information delivery method based on the user's past usage history. It determines the priority of information delivery by referring to past usage history. It analyzes the user's past usage history and proposes the optimal information delivery method. In this way, the optimal information delivery method can be selected by referring to the user's past usage history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past usage history into AI and use AI to select the optimal information delivery method.

[0096] The information delivery unit can customize the means of information delivery based on the user's current needs when providing information. For example, the information delivery unit can analyze the user's current needs and select the optimal means of information delivery. The information delivery unit can also customize the means of information delivery according to the user's needs. Furthermore, the information delivery unit can dynamically adjust the means of information delivery based on the user's current needs. For example, the information delivery unit analyzes the user's current needs and selects the optimal means of information delivery. It customizes the means of information delivery according to the user's needs. It dynamically adjusts the means of information delivery based on the user's current needs. This makes it possible to provide more appropriate information by customizing the means of information delivery based on the user's current needs. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's current needs into AI and use AI to customize the means of information delivery.

[0097] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is stressed, the information provider will prioritize providing important information. If the user is relaxed, the information provider can also provide detailed information. Furthermore, if the user is in a hurry, the information provider can prioritize providing concise information. For example, the information provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. If the user is stressed, important information will be prioritized. If the user is relaxed, detailed information will be provided. If the user is in a hurry, concise information will be prioritized. This allows for more appropriate information provision by determining the priority of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can capture a user's facial expression with a camera, use generative AI to estimate their emotions, and determine the priority of information provision.

[0098] The information provider can select the optimal information provision method by considering the user's geographical location information when providing information. For example, the information provider can provide region-specific information based on the user's geographical location information. The information provider can also select the optimal information provision method by considering the geographical location information. Furthermore, the information provider can customize the means of information provision based on the geographical location information. For example, the information provider can provide region-specific information based on the user's geographical location information. It selects the optimal information provision method by considering the geographical location information. It customizes the means of information provision based on the geographical location information. This makes it possible to provide optimal information for each region by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's geographical location information into AI and use AI to select the optimal information provision method.

[0099] The information provider can analyze the user's social media activity and propose methods for providing information. For example, the provider can analyze the user's social media activity and propose the most suitable method of information provision. The provider can also customize the methods of information provision based on social media activity. Furthermore, the provider can analyze social media data and determine the priority of information provision. For example, the provider can analyze the user's social media activity and propose the most suitable method of information provision. It can customize the methods of information provision based on social media activity. It can analyze social media data and determine the priority of information provision. This allows the provider to propose the most suitable method of information provision by analyzing the user's social media activity. Some or all of the above processes in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's social media activity into AI and use AI to propose the most suitable method of information provision.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, important data is prioritized for analysis. If the user is relaxed, detailed data is prioritized for analysis. If the user is in a hurry, concise data is prioritized for analysis. By adjusting the priority of analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can capture the user's facial expression with a camera, estimate emotions using generative AI, and determine the priority of analysis.

[0102] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the user is stressed, the frequency of information delivery is reduced and only important information is provided. If the user is relaxed, the frequency of information delivery is increased and detailed information is provided. If the user is in a hurry, important information is prioritized and provided in real time. This allows for more appropriate information delivery by adjusting the timing of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can capture the user's facial expression with a camera, estimate their emotions using generative AI, and adjust the timing of information delivery.

[0103] The prediction unit can estimate the user's emotions and adjust the level of detail in the prediction based on the estimated emotions. For example, if the user is stressed, it provides a simplified prediction result. If the user is relaxed, it provides a detailed prediction result. If the user is in a hurry, it provides a concise prediction result. By adjusting the level of detail in the prediction according to the user's emotions, more appropriate prediction results can be provided. 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can capture the user's facial expression with a camera, estimate the emotion using generative AI, and adjust the level of detail in the prediction.

[0104] The information provider can estimate the user's emotions and adjust the format of information delivery based on the estimated emotions. For example, if the user is stressed, information is provided in a simple format. If the user is relaxed, information is provided in a detailed format. If the user is in a hurry, information is provided in a concise format. By adjusting the format of information delivery according to the user's emotions, more appropriate information can be provided. 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 information provider may be performed using AI, or not using AI. For example, the information provider can capture the user's facial expression with a camera, estimate their emotions using generative AI, and adjust the format of information delivery.

[0105] The prediction unit can estimate the user's emotions and adjust the frequency of predictions based on the estimated emotions. For example, if the user is stressed, the frequency of predictions is reduced and only important predictions are provided. If the user is relaxed, the frequency of predictions is increased and detailed predictions are provided. If the user is in a hurry, important predictions are prioritized and provided in real time. By adjusting the frequency of predictions according to the user's emotions, more appropriate prediction results can be provided. 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can capture the user's facial expression with a camera, estimate emotions using generative AI, and adjust the frequency of predictions.

[0106] The data collection unit can focus on specific industries or sectors when collecting economic data. For example, the data collection unit can prioritize collecting data from the financial industry. It can also collect data related to specific sectors, such as manufacturing or services. Furthermore, the data collection unit can filter data related to specific industries or sectors, prioritizing the collection of important data. This allows for the efficient collection of more relevant data by focusing on specific industries or sectors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data related to specific industries or sectors into an AI and use the AI ​​to filter important data.

[0107] The analysis unit can perform analysis while considering the seasonality of economic data. For example, the analysis unit can perform seasonal adjustments on data affected by seasonality. The analysis unit can also analyze seasonality patterns and reflect them in the predictive model. Furthermore, the analysis unit can perform anomaly detection on data while considering the effects of seasonality. As a result, by considering the seasonality of economic data, more accurate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seasonally affected data into AI and perform seasonal adjustments using AI.

[0108] The forecasting unit can evaluate the reliability of economic data during forecasting and prioritize the use of highly reliable data. For example, the forecasting unit can evaluate the reliability of the data source and provider. It can also evaluate the consistency and accuracy of the data and select highly reliable data. Furthermore, the forecasting unit can exclude unreliable data to improve the accuracy of the forecast. By evaluating the reliability of economic data, it is possible to provide more accurate forecast results. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or not using AI. For example, the forecasting unit can input the reliability of the data into AI and use AI to select highly reliable data.

[0109] The information provider can adjust the level of detail of the information provided according to the user's level of expertise. For example, the provider can provide detailed information to economic experts. It can also provide simplified information to general users. Furthermore, the provider can adjust the display format of the information according to the user's level of expertise. By adjusting the level of detail of the information according to the user's level of expertise, it becomes possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's level of expertise into AI and use AI to adjust the level of detail of the information.

[0110] The data collection unit can focus on specific time periods when collecting economic data. For example, it can prioritize collecting data during periods of high trading activity. It can also collect data during times when specific events or announcements are made. Furthermore, it can filter data related to specific time periods and prioritize the collection of important data. This allows for the efficient collection of more relevant data by focusing on specific time periods. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data related to specific time periods into an AI and use the AI ​​to filter important data.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects economic data in real time. The data collection unit can, for example, automatically collect economic data from the internet. It can also acquire data provided by government agencies and financial institutions in real time. Furthermore, the data collection unit can collect data directly from the field using sensors and IoT devices. Step 2: The analysis unit analyzes the collected data using machine learning and deep learning technologies. For example, the analysis unit analyzes data patterns using machine learning algorithms. It can also extract data features using deep learning technologies. Furthermore, it can perform anomaly detection and build predictive models for the data. Step 3: The forecasting unit predicts future economic trends based on the analysis results. For example, the forecasting unit predicts trends in economic indicators using time series analysis. It can also predict future economic scenarios using simulation models. Furthermore, it can improve forecasting accuracy by combining multiple forecasting models. Step 4: The service provider provides the forecast results as information that can be used for policy proposals and risk management. For example, the service provider provides the forecast results in report format. It can also display the forecast results in real time through a dashboard. Furthermore, it can notify important forecast results using an alert function.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects economic data in real time using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future economic trends using time series analysis and simulation models. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the prediction results in report format or dashboard format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and data provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects economic data in real time using the camera 42 and sensors of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future economic trends using time series analysis and simulation models. The data provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the prediction results in report format or dashboard format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 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.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the 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.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects economic data in real time using the camera 42 and sensors of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future economic trends using time series analysis and simulation models. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the prediction results in report format or dashboard format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, prediction unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects economic data in real time using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future economic trends using time series analysis and simulation models. The provision unit is implemented in the control unit 46A of the robot 414 and provides the prediction results in report format or dashboard format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that collects economic data in real time, An analysis unit analyzes the data collected by the aforementioned collection unit, A forecasting unit that predicts future economic trends based on the results of the analysis performed by the aforementioned analysis unit, A provisioning unit provides the results predicted by the aforementioned prediction unit as information that can be used for policy proposals and risk management, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect economic data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data using machine learning and deep learning techniques. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Predicting future economic trends based on analysis results The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The forecast results will be provided as information that can be used for policy proposals and risk management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, By customizing the user interface, we provide information tailored to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing information using creative data visualizations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user sentiment and adjust the timing of economic data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past economic data collection history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting economic data, filtering is performed based on specific economic indicators or market trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the economic data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting economic data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting economic data, analyze social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the economic data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analytical algorithms are applied depending on the category of economic data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the economic data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the economic data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, consider the interrelationships of economic data to improve the accuracy of the forecast. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When making predictions, the attribute information of the economic data submitters is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, the geographical distribution of economic data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When making predictions, we improve the accuracy of our forecasts by referring to relevant literature on economic data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, the system selects the most suitable method of information delivery by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, customize the means of information delivery based on the user's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we analyze users' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects economic data in real time, An analysis unit analyzes the data collected by the aforementioned collection unit, A forecasting unit that predicts future economic trends based on the results of the analysis performed by the aforementioned analysis unit, A provisioning unit provides the results predicted by the aforementioned prediction unit as information that can be used for policy proposals and risk management, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, We analyze the collected data using machine learning and deep learning techniques. The system according to feature 1.

3. The prediction unit, Predicting future economic trends based on analysis results The system according to feature 1.

4. The aforementioned supply unit is, The forecast results will be provided as information that can be used for policy proposals and risk management. The system according to feature 1.

5. The aforementioned supply unit is, By customizing the user interface, we provide information tailored to the user's needs. The system according to feature 1.

6. The aforementioned supply unit is, Providing information using creative data visualizations. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of economic data collection based on the estimated user sentiment. The system according to feature 1.

Images