System

Abstract

An object of a system according to an embodiment is to accurately evaluate a market trend and a risk in real estate investment and to increase the certainty of investment determination.SOLUTION: A system includes a collection unit, an analysis unit, a prediction unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit performs prediction based on the analysis result obtained by the analysis unit. The evaluation unit performs risk evaluation based on the prediction result obtained by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method 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 a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult to accurately assess market trends and risks in real estate investment, leading to uncertainty in investment decisions.

[0005] The system according to the embodiment aims to accurately assess market trends and risks in real estate investments and increase the reliability of investment decisions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit makes a prediction based on the analysis result obtained by the analysis unit. The evaluation unit makes a risk assessment based on the prediction result obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately assess market trends and risks in real estate investments, thereby increasing the reliability of investment decisions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A real estate investment analysis system according to an embodiment of the present invention uses a generative AI to analyze market trends, forecasts, and risk. This system helps investors make sound investment decisions and minimizes investment risk. For example, the real estate investment analysis system collects data, and the generative AI analyzes market trends. Next, the generative AI makes predictions and finally performs risk assessment. For example, the system analyzes data such as past real estate price fluctuations, transaction volumes, and economic indicators to predict future market trends. Next, the generative AI predicts future real estate prices and transaction volumes based on the analysis results. For example, the system predicts the rise and fall of real estate prices in a specific region and suggests appropriate investment timing to investors. Finally, the generative AI evaluates investment risk based on the prediction results and provides advice to investors to minimize risk. For example, the system evaluates risk factors (such as economic conditions, political risk, and natural disaster risk) in a specific region and suggests strategies to investors to avoid risk. In this way, the real estate investment analysis system helps investors make sound investment decisions and minimizes investment risk. In this way, the real estate investment analysis system helps investors make sound investment decisions and minimizes investment risk. For example, the system can support successful real estate investments for everyone, from beginners to veterans.

[0029] A real estate investment analysis system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and an evaluation unit. The collection unit collects data. Examples of the data include, but are not limited to, real estate data, economic indicator data, and transaction data. The collection unit collects data such as past real estate price fluctuations, transaction volume, and economic indicators. The collection unit can also collect data taking into account the data source, collection frequency, and collection method. For example, the collection unit can collect data on real estate price fluctuations over the past five years. The collection unit can also collect data on monthly and annual transaction volume. The collection unit can also collect economic indicator data such as GDP, unemployment rate, and inflation rate. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis is performed based on, for example, but is not limited to, the analysis algorithm used and the purpose of the analysis. For example, the analysis unit analyzes market trends based on the collected data. The analysis unit can also analyze trends in price increases and transaction volume increases. The analysis unit can also analyze correlations between data using the generation AI. For example, the analysis unit can analyze the correlation between real estate prices and transaction volume. The prediction unit uses the generation AI to make predictions based on the analysis results obtained by the analysis unit. The predictions are made, for example, based on a prediction model and a prediction period, but are not limited to these examples. For example, the prediction unit predicts future real estate prices and transaction volume based on the analysis results. The prediction unit can also predict increases or decreases in real estate prices in a specific area. Furthermore, the prediction unit can predict future market trends using the generation AI. For example, the prediction unit can optimize the prediction algorithm by comparing past prediction results with actual results. The evaluation unit uses the generation AI to perform risk assessment based on the prediction results obtained by the prediction unit. The risk assessment is made, for example, based on evaluation criteria and types of risk factors, but is not limited to these examples. For example, the evaluation unit evaluates investment risk based on the prediction results and provides investors with advice to reduce the risk. The evaluation unit can also perform risk assessment taking into account factors such as economic conditions, political risks, and natural disaster risks.Furthermore, the evaluation unit can use the generative AI to improve the accuracy of risk assessment. For example, the evaluation unit can compare past risk assessment results with actual results to optimize the evaluation algorithm. This allows the real estate investment analysis system according to the embodiment to consistently perform processes from data collection to analysis, prediction, and risk assessment. For example, investors can make reliable investment decisions and minimize investment risks.

[0030] The collection unit can collect data on past real estate price fluctuations, transaction volumes, and economic indicators. The collection unit, for example, collects data on past real estate price fluctuations. For example, the collection unit can collect data on real estate price fluctuations over the past five years. The collection unit can also collect transaction volume data. For example, the collection unit can collect data on monthly transaction volumes and annual transaction volumes. The collection unit can also collect data on economic indicators. For example, the collection unit can collect economic indicator data such as GDP, unemployment rate, and inflation rate. This allows for more accurate analysis of market trends by collecting past data. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input data on past real estate price fluctuations into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze market trends based on the collected data. The analysis unit, for example, analyzes market trends based on the collected data. For example, the analysis unit can analyze trends such as rising prices and increasing transaction volumes. The analysis unit can also analyze correlations in data using the generation AI. For example, the analysis unit can analyze the correlation between real estate prices and transaction volumes. Furthermore, the analysis unit can improve the accuracy of data analysis using the generation AI. For example, the analysis unit can combine different analysis algorithms to evaluate analysis results from multiple perspectives. This makes it possible to analyze market trends and provide basic data for predicting future market trends. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI perform market trend analysis.

[0032] The prediction unit can predict future real estate prices and transaction volumes based on the analysis results. The prediction unit, for example, can predict future real estate prices and transaction volumes based on the analysis results. For example, the prediction unit can predict increases or decreases in real estate prices in a specific area. The prediction unit can also predict future market trends using a generation AI. For example, the prediction unit can optimize a prediction algorithm by comparing past prediction results with actual results. Furthermore, the prediction unit can improve the accuracy of predictions by combining different prediction models. For example, the prediction unit can combine machine learning models and statistical models to improve the accuracy of predictions. This allows future real estate prices and transaction volumes to be predicted, thereby suggesting appropriate investment timing to investors. Some or all of the above-described processing in the prediction unit may be performed using, or without, the generation AI. For example, the prediction unit can input the analysis results into the generation AI and have the generation AI execute predictions of future real estate prices and transaction volumes.

[0033] The evaluation unit can evaluate investment risk based on the prediction results and provide investors with advice to reduce the risk. For example, the evaluation unit can evaluate investment risk based on the prediction results and provide investors with advice to reduce the risk. For example, the evaluation unit can evaluate investment risk based on a risk score. The evaluation unit can also evaluate risk by taking into account factors such as economic conditions, political risks, and natural disaster risks. For example, the evaluation unit can perform risk evaluation by referring to economic indicators such as economic growth rate and unemployment rate. Furthermore, the evaluation unit can use a generation AI to improve the accuracy of risk evaluation. For example, the evaluation unit can optimize the evaluation algorithm by comparing past risk evaluation results with actual results. This can support investor risk management by evaluating investment risk and providing advice to minimize risk. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input prediction results into the generation AI and have the generation AI perform an investment risk evaluation.

[0034] The evaluation unit can perform risk assessment by taking into account factors such as economic conditions, political risks, and natural disaster risks. The evaluation unit can perform risk assessment by taking into account factors such as economic conditions, political risks, and natural disaster risks. For example, the evaluation unit can perform risk assessment by referring to economic indicators such as economic growth rate and unemployment rate. The evaluation unit can also perform risk assessment by taking into account political risks such as the risk of a change in government or the risk of policy change. Furthermore, the evaluation unit can perform risk assessment by taking into account natural disaster risks such as earthquake risk and flood risk. This enables more comprehensive risk assessment by taking into account a variety of risk factors. Some or all of the above-described processing in the evaluation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the evaluation unit can input data on economic conditions, political risks, and natural disaster risks into the generation AI and have the generation AI perform a risk assessment.

[0035] The collection unit can evaluate the reliability of past data and prioritize collecting highly reliable data. The collection unit, for example, evaluates the source of past data and prioritizes collecting data from highly reliable data sources. The collection unit can also evaluate the consistency of data and prioritize collecting consistent data. Furthermore, the collection unit can evaluate the frequency of data updates and prioritize collecting the latest data. This allows for the prioritized collection of highly reliable data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can have the generation AI evaluate the reliability of past data and have the generation AI collect highly reliable data.

[0036] When collecting data, the collection unit can select the type of data to collect taking into account the characteristics of each region. For example, the collection unit collects different data in urban and rural areas to reflect the characteristics of each region. The collection unit can also select the type of data to collect taking into account the economic situation of the region. Furthermore, the collection unit can select the type of data to collect taking into account the characteristics of the real estate market of the region. This makes it possible to collect more appropriate data by taking into account the characteristics of each region. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can have the generation AI evaluate the characteristics of each region and have the generation AI select the type of data to collect.

[0037] The collection unit can update the collected data to reflect real-time market trends when collecting data. The collection unit, for example, collects real-time real estate transaction data and reflects market trends. The collection unit can also collect real-time economic indicators and reflect market trends. Furthermore, the collection unit can collect real-time news articles and reflect market trends. This allows the latest data to be collected by reflecting real-time market trends. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit may cause a generation AI to collect real-time market trend data and cause the generation AI to update the data.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's investment history. For example, the collection unit can prioritize collecting data on areas in which the user has invested in the past. The collection unit can also prioritize collecting data on real estate types in which the user has invested in the past. Furthermore, the collection unit can prioritize collecting data related to the user's investment strategy. This makes it possible to prioritize collecting highly relevant data by taking into account the user's investment history. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's investment history data into the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can collect information from social media and news articles when collecting data. For example, the collection unit collects real estate-related posts on social media to understand trends. The collection unit can also collect information about the real estate market from news articles. Furthermore, the collection unit can collect user opinions on social media and analyze market trends. In this way, by collecting information from social media and news articles, it is possible to understand the latest market trends. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause a generation AI to collect data from social media and news articles and cause the generation AI to collect information.

[0040] The collection unit can customize the collection method by reflecting user feedback when collecting data. For example, the collection unit can adjust the type of data to be collected based on user feedback. The collection unit can also adjust the frequency of data collection based on user feedback. Furthermore, the collection unit can improve the data collection method based on user feedback. This allows the collection method to be customized by reflecting user feedback, and more appropriate data to be collected. Some or all of the above-described processing in the collection unit can be performed, for example, using or without using the generation AI. For example, the collection unit can input user feedback data into the generation AI and have the generation AI customize the collection method.

[0041] The analysis unit can improve the accuracy of the analysis by taking into account data correlations during analysis. The analysis unit can, for example, analyze the correlation between real estate prices and transaction volumes to improve accuracy. The analysis unit can also analyze the correlation between economic indicators and the real estate market to improve accuracy. Furthermore, the analysis unit can analyze the correlation between market characteristics for each region and real estate prices to improve accuracy. In this way, by taking data correlations into account, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI analyze the data correlations and have the generation AI improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can combine different analysis algorithms to evaluate the analysis results from multiple perspectives. The analysis unit can evaluate the analysis results by combining, for example, a machine learning algorithm and statistical analysis. The analysis unit can also evaluate the analysis results by combining deep learning and regression analysis. Furthermore, the analysis unit can evaluate the analysis results by combining clustering and time series analysis. This allows the analysis results to be evaluated from multiple perspectives by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to combine different analysis algorithms and perform a multifaceted evaluation of the analysis results.

[0043] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit, for example, adjusts the parameters of the algorithm based on past analysis results. The analysis unit can also improve the accuracy of the algorithm based on past analysis results. Furthermore, the analysis unit can update the learning data of the algorithm based on past analysis results. This makes it possible to optimize the analysis algorithm and improve the accuracy of the analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have the generation AI refer to past analysis results and cause the generation AI to optimize the analysis algorithm.

[0044] The analysis unit can perform the analysis taking into account the market characteristics of each region. The analysis unit can perform the analysis taking into account, for example, the different market characteristics between urban and rural areas. The analysis unit can also perform the analysis taking into account the economic situation of the region. Furthermore, the analysis unit can perform the analysis taking into account the characteristics of the real estate market of the region. By taking into account the market characteristics of each region, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input market characteristic data for each region into the generation AI and have the generation AI perform the analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related economic indicators and political situations. The analysis unit can improve the accuracy of the analysis by referring to economic indicators such as economic growth rate and unemployment rate, for example. The analysis unit can also improve the accuracy of the analysis by referring to political situations such as political risks and policy changes. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to international economic trends. In this way, the accuracy of the analysis can be improved by referring to related economic indicators and political situations. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input data on economic indicators and political situations into the generation AI and have the generation AI improve the accuracy of the analysis.

[0046] During analysis, the analysis unit can customize the analysis results based on the user's investment strategy. The analysis unit customizes the analysis results based on, for example, the user's risk tolerance. The analysis unit can also customize the analysis results based on the user's investment goals. Furthermore, the analysis unit can customize the analysis results based on the user's investment period. This allows the analysis results to be customized based on the user's investment strategy, thereby providing optimal information for the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's investment strategy data into the generation AI and have the generation AI customize the analysis results.

[0047] During prediction, the prediction unit can optimize the prediction algorithm by comparing past prediction results with actual results. For example, the prediction unit can compare past prediction results with actual real estate prices and optimize the algorithm. The prediction unit can also compare past prediction results with actual transaction volumes and optimize the algorithm. Furthermore, the prediction unit can compare past prediction results with actual market trends and optimize the algorithm. In this way, by comparing past prediction results with actual results, the prediction algorithm can be optimized and the accuracy of prediction can be improved. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the prediction unit can have the generation AI compare past prediction results with actual results and cause the generation AI to optimize the prediction algorithm.

[0048] The prediction unit can improve the accuracy of prediction by combining different prediction models during prediction. The prediction unit can improve the accuracy of prediction by, for example, combining a machine learning model and a statistical model. The prediction unit can also improve the accuracy of prediction by combining a deep learning model and a regression model. Furthermore, the prediction unit can improve the accuracy of prediction by combining a clustering model and a time series model. In this way, the accuracy of prediction can be improved by combining different prediction models. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can cause the generation AI to combine different prediction models and improve the accuracy of prediction.

[0049] The prediction unit can make predictions taking into account market characteristics for each region. The prediction unit can make predictions taking into account, for example, different market characteristics between urban and rural areas. The prediction unit can also make predictions taking into account the economic situation of the region. Furthermore, the prediction unit can make predictions taking into account the characteristics of the real estate market in the region. By taking into account the market characteristics for each region, more accurate prediction results can be provided. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input market characteristic data for each region into the generation AI and have the generation AI execute the prediction.

[0050] The prediction unit can improve the accuracy of the prediction by referring to related economic indicators and political situations when making predictions. The prediction unit can improve the accuracy of the prediction by referring to economic indicators such as economic growth rate and unemployment rate, for example. The prediction unit can also improve the accuracy of the prediction by referring to political situations such as political risks and policy changes. Furthermore, the prediction unit can improve the accuracy of the prediction by referring to international economic trends. This allows the accuracy of the prediction to be improved by referring to related economic indicators and political situations. Some or all of the above-mentioned processing in the prediction unit can be performed using, or without, the generation AI, for example. For example, the prediction unit can input data on economic indicators and political situations into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0051] The prediction unit can customize the prediction result based on the user's investment strategy when making a prediction. The prediction unit customizes the prediction result based on, for example, the user's risk tolerance. The prediction unit can also customize the prediction result based on the user's investment goals. Furthermore, the prediction unit can customize the prediction result based on the user's investment period. This allows the prediction result to be customized based on the user's investment strategy, thereby providing optimal information for the user. Some or all of the above-described processing in the prediction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the user's investment strategy data into the generation AI and have the generation AI customize the prediction result.

[0052] The prediction unit can improve the accuracy of the prediction by integrating information from different data sources during prediction. For example, the prediction unit can improve the accuracy of the prediction by integrating real estate transaction data and economic indicator data. The prediction unit can also improve the accuracy of the prediction by integrating social media data and news article data. Furthermore, the prediction unit can improve the accuracy of the prediction by integrating regional market data and international economic data. In this way, by integrating information from different data sources, the accuracy of the prediction can be improved. Some or all of the above-described processing in the prediction unit can be performed using, or without, the generation AI. For example, the prediction unit can have the generation AI integrate information from different data sources and cause the generation AI to improve the accuracy of the prediction.

[0053] During risk assessment, the assessment unit can optimize the assessment algorithm by comparing past risk assessment results with actual results. The assessment unit, for example, compares past risk assessment results with actual real estate prices and optimizes the algorithm. The assessment unit can also compare past risk assessment results with actual transaction volumes and optimize the algorithm. The assessment unit can also compare past risk assessment results with actual market trends and optimize the algorithm. In this way, by comparing past risk assessment results with actual results, the assessment algorithm can be optimized and the accuracy of risk assessment can be improved. Some or all of the above-mentioned processing in the assessment unit may be performed, for example, using or without the generation AI. For example, the assessment unit can have the generation AI compare past risk assessment results with actual results and cause the generation AI to optimize the assessment algorithm.

[0054] During risk assessment, the assessment unit can perform a comprehensive risk assessment by combining different risk factors. The assessment unit can, for example, perform a comprehensive risk assessment by combining economic risk and political risk. The assessment unit can also perform a comprehensive risk assessment by combining natural disaster risk and market risk. Furthermore, the assessment unit can perform a comprehensive risk assessment by combining risk factors for each region. This allows a comprehensive risk assessment to be performed by combining different risk factors. Some or all of the above-described processing in the assessment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the assessment unit can input data on different risk factors into the generation AI and have the generation AI perform a comprehensive risk assessment.

[0055] The evaluation unit can perform risk assessment by taking into account the risk characteristics of each region. For example, the evaluation unit performs the assessment by taking into account different risk characteristics between urban and rural areas. The evaluation unit can also perform risk assessment by taking into account the economic situation of the region. Furthermore, the evaluation unit can perform risk assessment by taking into account the risk of natural disasters in the region. This allows for a more accurate risk assessment to be provided by taking into account the risk characteristics of each region. Some or all of the above-described processing in the evaluation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input risk characteristic data for each region into the generation AI and have the generation AI perform the risk assessment.

[0056] The evaluation unit can improve the accuracy of the assessment by referring to related economic indicators and political situations during risk assessment. The evaluation unit can improve the accuracy of the assessment by referring to economic indicators such as economic growth rate and unemployment rate, for example. The evaluation unit can also improve the accuracy of the assessment by referring to political situations such as political risks and policy changes. Furthermore, the evaluation unit can improve the accuracy of the assessment by referring to international economic trends. In this way, the accuracy of the assessment can be improved by referring to related economic indicators and political situations. Some or all of the above-mentioned processing in the evaluation unit can be performed using, or without, the generation AI, for example. For example, the evaluation unit can input data on economic indicators and political situations into the generation AI and cause the generation AI to improve the accuracy of the assessment.

[0057] The evaluation unit can customize the evaluation results based on the user's investment strategy during risk evaluation. The evaluation unit customizes the evaluation results based on, for example, the user's risk tolerance. The evaluation unit can also customize the evaluation results based on the user's investment goals. Furthermore, the evaluation unit can customize the evaluation results based on the user's investment period. This makes it possible to provide optimal information for the user by customizing the evaluation results based on the user's investment strategy. Some or all of the above-described processing in the evaluation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input the user's investment strategy data into the generation AI and have the generation AI customize the evaluation results.

[0058] The evaluation unit can improve the accuracy of the evaluation by integrating information from different data sources during risk evaluation. For example, the evaluation unit can improve the accuracy of the evaluation by integrating real estate transaction data and economic indicator data. The evaluation unit can also improve the accuracy of the evaluation by integrating social media data and news article data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by integrating regional market data and international economic data. In this way, by integrating information from different data sources, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using or without the generation AI. For example, the evaluation unit can cause the generation AI to integrate information from different data sources and improve the accuracy of the evaluation.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting data, the collection unit can prioritize collecting highly relevant data taking into consideration the user's investment goals. For example, if the user is aiming for short-term profits, the collection unit can prioritize collecting data related to short-term market trends. Also, if the user is aiming for long-term asset formation, the collection unit can prioritize collecting data related to long-term market trends. Furthermore, if the user is interested in a specific region, detailed data related to that region can be prioritized. This allows for more appropriate investment decisions to be supported by collecting highly relevant data according to the user's investment goals.

[0061] When assessing risk, the assessment unit can customize the assessment results by taking into account the user's investment history. For example, the assessment unit can perform risk assessment based on the user's successful investment patterns in the past. The assessment unit can also provide advice to help the user avoid investment patterns that have failed in the past. Furthermore, the assessment unit can perform risk assessment based on the user's investment strategy and propose the optimal risk management method for the user. In this way, by taking into account the user's investment history, a more personalized risk assessment can be provided.

[0062] During data collection, the collection unit can collect real-time user behavior data and adjust the timing of data collection based on the user's behavior patterns. For example, the frequency of data collection can be increased during times when the user frequently trades, or reduced during times when the user does not trade, thereby reducing the load on the system. Furthermore, important data can be collected preferentially based on the user's behavior patterns. This allows for more efficient data collection by adjusting the timing of data collection according to the user's behavior patterns.

[0063] The forecasting unit can improve the accuracy of the forecast by integrating information from different data sources during the forecasting process. For example, real estate transaction data and economic indicator data can be integrated to improve the accuracy of the forecast. Also, social media data and news article data can be integrated to improve the accuracy of the forecast. Furthermore, regional market data and international economic data can be integrated to improve the accuracy of the forecast. In this way, by integrating information from different data sources, the accuracy of the forecast can be improved.

[0064] The collection unit can customize the collection method by reflecting user feedback when collecting data. For example, the type of data to be collected can be adjusted based on user feedback. The frequency of data collection can also be adjusted based on user feedback. Furthermore, the data collection method can be improved based on user feedback. In this way, by reflecting user feedback, the collection method can be customized and more appropriate data can be collected.

[0065] During analysis, the analysis unit can combine different analysis algorithms to evaluate the analysis results from multiple angles. For example, the analysis results can be evaluated by combining a machine learning algorithm and statistical analysis. The analysis results can also be evaluated by combining deep learning and regression analysis. Furthermore, the analysis results can be evaluated by combining clustering and time series analysis. In this way, by combining different analysis algorithms, the analysis results can be evaluated from multiple angles.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department collects data. This data includes real estate data, economic indicator data, transaction data, etc. The collection department collects data such as past real estate price fluctuations, transaction volume, and economic indicators. The collection department can also collect data taking into consideration the data source, collection frequency, collection method, etc. For example, the collection department collects data on real estate price fluctuations over the past five years, monthly transaction volume, annual transaction volume, and economic indicator data such as GDP, unemployment rate, and inflation rate. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed based on the analysis algorithm used and the purpose of the analysis. For example, the analysis unit analyzes market trends based on the collected data, and analyzes trends of rising prices and increasing transaction volumes, as well as correlations between data. For example, it analyzes the correlation between real estate prices and transaction volumes. Step 3: The prediction unit uses the generative AI to make predictions based on the analysis results obtained by the analysis unit. Predictions are made based on the prediction model and the forecast period. For example, based on the analysis results, future real estate prices and transaction volumes, increases and decreases in real estate prices in specific areas, and future market trends are predicted. The prediction algorithm can also be optimized by comparing past prediction results with actual results. Step 4: The evaluation unit uses the generation AI to perform risk assessment based on the prediction results obtained by the prediction unit. Risk assessment is performed based on the evaluation criteria and type of risk factor. For example, investment risk is assessed based on the prediction results and advice is provided to investors to reduce the risk. Risk assessment can also take into account factors such as economic conditions, political risk, and natural disaster risk. The assessment algorithm can also be optimized by comparing past risk assessment results with actual results.

[0068] (Example 2) A real estate investment analysis system according to an embodiment of the present invention uses a generative AI to analyze market trends, forecasts, and risk. This system helps investors make sound investment decisions and minimizes investment risk. For example, the real estate investment analysis system collects data, and the generative AI analyzes market trends. Next, the generative AI makes predictions and finally performs risk assessment. For example, the system analyzes data such as past real estate price fluctuations, transaction volumes, and economic indicators to predict future market trends. Next, the generative AI predicts future real estate prices and transaction volumes based on the analysis results. For example, the system predicts the rise and fall of real estate prices in a specific region and suggests appropriate investment timing to investors. Finally, the generative AI evaluates investment risk based on the prediction results and provides advice to investors to minimize risk. For example, the system evaluates risk factors (such as economic conditions, political risk, and natural disaster risk) in a specific region and suggests strategies to investors to avoid risk. In this way, the real estate investment analysis system helps investors make sound investment decisions and minimizes investment risk. In this way, the real estate investment analysis system helps investors make sound investment decisions and minimizes investment risk. For example, the system can support successful real estate investments for everyone, from beginners to veterans.

[0069] A real estate investment analysis system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and an evaluation unit. The collection unit collects data. Examples of the data include, but are not limited to, real estate data, economic indicator data, and transaction data. The collection unit collects data such as past real estate price fluctuations, transaction volume, and economic indicators. The collection unit can also collect data taking into account the data source, collection frequency, and collection method. For example, the collection unit can collect data on real estate price fluctuations over the past five years. The collection unit can also collect data on monthly and annual transaction volume. The collection unit can also collect economic indicator data such as GDP, unemployment rate, and inflation rate. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis is performed based on, for example, but is not limited to, the analysis algorithm used and the purpose of the analysis. For example, the analysis unit analyzes market trends based on the collected data. The analysis unit can also analyze trends in price increases and transaction volume increases. The analysis unit can also analyze correlations between data using the generation AI. For example, the analysis unit can analyze the correlation between real estate prices and transaction volume. The prediction unit uses the generation AI to make predictions based on the analysis results obtained by the analysis unit. The predictions are made, for example, based on a prediction model and a prediction period, but are not limited to these examples. For example, the prediction unit predicts future real estate prices and transaction volume based on the analysis results. The prediction unit can also predict increases or decreases in real estate prices in a specific area. Furthermore, the prediction unit can predict future market trends using the generation AI. For example, the prediction unit can optimize the prediction algorithm by comparing past prediction results with actual results. The evaluation unit uses the generation AI to perform risk assessment based on the prediction results obtained by the prediction unit. The risk assessment is made, for example, based on evaluation criteria and types of risk factors, but is not limited to these examples. For example, the evaluation unit evaluates investment risk based on the prediction results and provides investors with advice to reduce the risk. The evaluation unit can also perform risk assessment taking into account factors such as economic conditions, political risks, and natural disaster risks.Furthermore, the evaluation unit can use the generative AI to improve the accuracy of risk assessment. For example, the evaluation unit can compare past risk assessment results with actual results to optimize the evaluation algorithm. This allows the real estate investment analysis system according to the embodiment to consistently perform processes from data collection to analysis, prediction, and risk assessment. For example, investors can make reliable investment decisions and minimize investment risks.

[0070] The collection unit can collect data on past real estate price fluctuations, transaction volumes, and economic indicators. The collection unit, for example, collects data on past real estate price fluctuations. For example, the collection unit can collect data on real estate price fluctuations over the past five years. The collection unit can also collect transaction volume data. For example, the collection unit can collect data on monthly transaction volumes and annual transaction volumes. The collection unit can also collect data on economic indicators. For example, the collection unit can collect economic indicator data such as GDP, unemployment rate, and inflation rate. This allows for more accurate analysis of market trends by collecting past data. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input data on past real estate price fluctuations into the generation AI and have the generation AI collect the data.

[0071] The analysis unit can analyze market trends based on the collected data. The analysis unit, for example, analyzes market trends based on the collected data. For example, the analysis unit can analyze trends such as rising prices and increasing transaction volumes. The analysis unit can also analyze correlations in data using the generation AI. For example, the analysis unit can analyze the correlation between real estate prices and transaction volumes. Furthermore, the analysis unit can improve the accuracy of data analysis using the generation AI. For example, the analysis unit can combine different analysis algorithms to evaluate analysis results from multiple perspectives. This makes it possible to analyze market trends and provide basic data for predicting future market trends. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI perform market trend analysis.

[0072] The prediction unit can predict future real estate prices and transaction volumes based on the analysis results. The prediction unit, for example, can predict future real estate prices and transaction volumes based on the analysis results. For example, the prediction unit can predict increases or decreases in real estate prices in a specific area. The prediction unit can also predict future market trends using a generation AI. For example, the prediction unit can optimize a prediction algorithm by comparing past prediction results with actual results. Furthermore, the prediction unit can improve the accuracy of predictions by combining different prediction models. For example, the prediction unit can combine machine learning models and statistical models to improve the accuracy of predictions. This allows future real estate prices and transaction volumes to be predicted, thereby suggesting appropriate investment timing to investors. Some or all of the above-described processing in the prediction unit may be performed using, or without, the generation AI. For example, the prediction unit can input the analysis results into the generation AI and have the generation AI execute predictions of future real estate prices and transaction volumes.

[0073] The evaluation unit can evaluate investment risk based on the prediction results and provide investors with advice to reduce the risk. For example, the evaluation unit can evaluate investment risk based on the prediction results and provide investors with advice to reduce the risk. For example, the evaluation unit can evaluate investment risk based on a risk score. The evaluation unit can also evaluate risk by taking into account factors such as economic conditions, political risks, and natural disaster risks. For example, the evaluation unit can perform risk evaluation by referring to economic indicators such as economic growth rate and unemployment rate. Furthermore, the evaluation unit can use a generation AI to improve the accuracy of risk evaluation. For example, the evaluation unit can optimize the evaluation algorithm by comparing past risk evaluation results with actual results. This can support investor risk management by evaluating investment risk and providing advice to minimize risk. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input prediction results into the generation AI and have the generation AI perform an investment risk evaluation.

[0074] The evaluation unit can perform risk assessment by taking into account factors such as economic conditions, political risks, and natural disaster risks. The evaluation unit can perform risk assessment by taking into account factors such as economic conditions, political risks, and natural disaster risks. For example, the evaluation unit can perform risk assessment by referring to economic indicators such as economic growth rate and unemployment rate. The evaluation unit can also perform risk assessment by taking into account political risks such as the risk of a change in government or the risk of policy change. Furthermore, the evaluation unit can perform risk assessment by taking into account natural disaster risks such as earthquake risk and flood risk. This enables more comprehensive risk assessment by taking into account a variety of risk factors. Some or all of the above-described processing in the evaluation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the evaluation unit can input data on economic conditions, political risks, and natural disaster risks into the generation AI and have the generation AI perform a risk assessment.

[0075] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important data and quickly analyze it. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0076] The collection unit can evaluate the reliability of past data and prioritize collecting highly reliable data. The collection unit, for example, evaluates the source of past data and prioritizes collecting data from highly reliable data sources. The collection unit can also evaluate the consistency of data and prioritize collecting consistent data. Furthermore, the collection unit can evaluate the frequency of data updates and prioritize collecting the latest data. This allows for the prioritized collection of highly reliable data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can have the generation AI evaluate the reliability of past data and have the generation AI collect highly reliable data.

[0077] When collecting data, the collection unit can select the type of data to collect taking into account the characteristics of each region. For example, the collection unit collects different data in urban and rural areas to reflect the characteristics of each region. The collection unit can also select the type of data to collect taking into account the economic situation of the region. Furthermore, the collection unit can select the type of data to collect taking into account the characteristics of the real estate market of the region. This makes it possible to collect more appropriate data by taking into account the characteristics of each region. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can have the generation AI evaluate the characteristics of each region and have the generation AI select the type of data to collect.

[0078] The collection unit can update the collected data to reflect real-time market trends when collecting data. The collection unit, for example, collects real-time real estate transaction data and reflects market trends. The collection unit can also collect real-time economic indicators and reflect market trends. Furthermore, the collection unit can collect real-time news articles and reflect market trends. This allows the latest data to be collected by reflecting real-time market trends. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit may cause a generation AI to collect real-time market trend data and cause the generation AI to update the data.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit can prioritize collecting only important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This reduces the burden on the user by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's investment history. For example, the collection unit can prioritize collecting data on areas in which the user has invested in the past. The collection unit can also prioritize collecting data on real estate types in which the user has invested in the past. Furthermore, the collection unit can prioritize collecting data related to the user's investment strategy. This makes it possible to prioritize collecting highly relevant data by taking into account the user's investment history. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's investment history data into the generation AI and cause the generation AI to collect highly relevant data.

[0081] The collection unit can collect information from social media and news articles when collecting data. For example, the collection unit collects real estate-related posts on social media to understand trends. The collection unit can also collect information about the real estate market from news articles. Furthermore, the collection unit can collect user opinions on social media and analyze market trends. In this way, by collecting information from social media and news articles, it is possible to understand the latest market trends. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can cause a generation AI to collect data from social media and news articles and cause the generation AI to collect information.

[0082] The collection unit can customize the collection method by reflecting user feedback when collecting data. For example, the collection unit can adjust the type of data to be collected based on user feedback. The collection unit can also adjust the frequency of data collection based on user feedback. Furthermore, the collection unit can improve the data collection method based on user feedback. This allows the collection method to be customized by reflecting user feedback, and more appropriate data to be collected. Some or all of the above-described processing in the collection unit can be performed, for example, using or without using the generation AI. For example, the collection unit can input user feedback data into the generation AI and have the generation AI customize the collection method.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. By adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0084] The analysis unit can improve the accuracy of the analysis by taking into account data correlations during analysis. The analysis unit can, for example, analyze the correlation between real estate prices and transaction volumes to improve accuracy. The analysis unit can also analyze the correlation between economic indicators and the real estate market to improve accuracy. Furthermore, the analysis unit can analyze the correlation between market characteristics for each region and real estate prices to improve accuracy. In this way, by taking data correlations into account, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI analyze the data correlations and have the generation AI improve the accuracy of the analysis.

[0085] During analysis, the analysis unit can combine different analysis algorithms to evaluate the analysis results from multiple perspectives. The analysis unit can evaluate the analysis results by combining, for example, a machine learning algorithm and statistical analysis. The analysis unit can also evaluate the analysis results by combining deep learning and regression analysis. Furthermore, the analysis unit can evaluate the analysis results by combining clustering and time series analysis. This allows the analysis results to be evaluated from multiple perspectives by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to combine different analysis algorithms and perform a multifaceted evaluation of the analysis results.

[0086] During analysis, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit, for example, adjusts the parameters of the algorithm based on past analysis results. The analysis unit can also improve the accuracy of the algorithm based on past analysis results. Furthermore, the analysis unit can update the learning data of the algorithm based on past analysis results. This makes it possible to optimize the analysis algorithm and improve the accuracy of the analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have the generation AI refer to past analysis results and cause the generation AI to optimize the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, when the user is stressed, the analysis unit can prioritize displaying only important analysis results. Furthermore, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can quickly display analysis results. This allows for prioritized provision of important information to the user by prioritizing the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0088] The analysis unit can perform the analysis taking into account the market characteristics of each region. The analysis unit can perform the analysis taking into account, for example, the different market characteristics between urban and rural areas. The analysis unit can also perform the analysis taking into account the economic situation of the region. Furthermore, the analysis unit can perform the analysis taking into account the characteristics of the real estate market of the region. By taking into account the market characteristics of each region, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input market characteristic data for each region into the generation AI and have the generation AI perform the analysis.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related economic indicators and political situations. The analysis unit can improve the accuracy of the analysis by referring to economic indicators such as economic growth rate and unemployment rate, for example. The analysis unit can also improve the accuracy of the analysis by referring to political situations such as political risks and policy changes. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to international economic trends. In this way, the accuracy of the analysis can be improved by referring to related economic indicators and political situations. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input data on economic indicators and political situations into the generation AI and have the generation AI improve the accuracy of the analysis.

[0090] During analysis, the analysis unit can customize the analysis results based on the user's investment strategy. The analysis unit customizes the analysis results based on, for example, the user's risk tolerance. The analysis unit can also customize the analysis results based on the user's investment goals. Furthermore, the analysis unit can customize the analysis results based on the user's investment period. This allows the analysis results to be customized based on the user's investment strategy, thereby providing optimal information for the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's investment strategy data into the generation AI and have the generation AI customize the analysis results.

[0091] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a display method that focuses on the main points. By adjusting the display method of the prediction results according to the user's emotions, a display that is easy for the user to view can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the prediction results.

[0092] During prediction, the prediction unit can optimize the prediction algorithm by comparing past prediction results with actual results. For example, the prediction unit can compare past prediction results with actual real estate prices and optimize the algorithm. The prediction unit can also compare past prediction results with actual transaction volumes and optimize the algorithm. Furthermore, the prediction unit can compare past prediction results with actual market trends and optimize the algorithm. In this way, by comparing past prediction results with actual results, the prediction algorithm can be optimized and the accuracy of prediction can be improved. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the prediction unit can have the generation AI compare past prediction results with actual results and cause the generation AI to optimize the prediction algorithm.

[0093] The prediction unit can improve the accuracy of prediction by combining different prediction models during prediction. The prediction unit can improve the accuracy of prediction by, for example, combining a machine learning model and a statistical model. The prediction unit can also improve the accuracy of prediction by combining a deep learning model and a regression model. Furthermore, the prediction unit can improve the accuracy of prediction by combining a clustering model and a time series model. In this way, the accuracy of prediction can be improved by combining different prediction models. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can cause the generation AI to combine different prediction models and improve the accuracy of prediction.

[0094] The prediction unit can make predictions taking into account market characteristics for each region. The prediction unit can make predictions taking into account, for example, different market characteristics between urban and rural areas. The prediction unit can also make predictions taking into account the economic situation of the region. Furthermore, the prediction unit can make predictions taking into account the characteristics of the real estate market in the region. By taking into account the market characteristics for each region, more accurate prediction results can be provided. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input market characteristic data for each region into the generation AI and have the generation AI execute the prediction.

[0095] The prediction unit can estimate the user's emotions and prioritize the prediction results based on the estimated user emotions. For example, when the user is stressed, the prediction unit can prioritize displaying only important prediction results. Furthermore, when the user is relaxed, the prediction unit can prioritize displaying detailed prediction results. Furthermore, when the user is in a hurry, the prediction unit can quickly display prediction results. This allows for prioritized provision of important information to the user by prioritizing the prediction results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the prediction results.

[0096] The prediction unit can improve the accuracy of the prediction by referring to related economic indicators and political situations when making predictions. The prediction unit can improve the accuracy of the prediction by referring to economic indicators such as economic growth rate and unemployment rate, for example. The prediction unit can also improve the accuracy of the prediction by referring to political situations such as political risks and policy changes. Furthermore, the prediction unit can improve the accuracy of the prediction by referring to international economic trends. This allows the accuracy of the prediction to be improved by referring to related economic indicators and political situations. Some or all of the above-mentioned processing in the prediction unit can be performed using, or without, the generation AI, for example. For example, the prediction unit can input data on economic indicators and political situations into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0097] The prediction unit can customize the prediction result based on the user's investment strategy when making a prediction. The prediction unit customizes the prediction result based on, for example, the user's risk tolerance. The prediction unit can also customize the prediction result based on the user's investment goals. Furthermore, the prediction unit can customize the prediction result based on the user's investment period. This allows the prediction result to be customized based on the user's investment strategy, thereby providing optimal information for the user. Some or all of the above-described processing in the prediction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the user's investment strategy data into the generation AI and have the generation AI customize the prediction result.

[0098] The prediction unit can improve the accuracy of the prediction by integrating information from different data sources during prediction. For example, the prediction unit can improve the accuracy of the prediction by integrating real estate transaction data and economic indicator data. The prediction unit can also improve the accuracy of the prediction by integrating social media data and news article data. Furthermore, the prediction unit can improve the accuracy of the prediction by integrating regional market data and international economic data. In this way, by integrating information from different data sources, the accuracy of the prediction can be improved. Some or all of the above-described processing in the prediction unit can be performed using, or without, the generation AI. For example, the prediction unit can have the generation AI integrate information from different data sources and cause the generation AI to improve the accuracy of the prediction.

[0099] The evaluation unit can estimate the user's emotions and adjust the display method of the risk assessment based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the evaluation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows the display method of the risk assessment to be adjusted according to the user's emotions, thereby providing a display that is easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the risk assessment.

[0100] During risk assessment, the assessment unit can optimize the assessment algorithm by comparing past risk assessment results with actual results. The assessment unit, for example, compares past risk assessment results with actual real estate prices and optimizes the algorithm. The assessment unit can also compare past risk assessment results with actual transaction volumes and optimize the algorithm. The assessment unit can also compare past risk assessment results with actual market trends and optimize the algorithm. In this way, by comparing past risk assessment results with actual results, the assessment algorithm can be optimized and the accuracy of risk assessment can be improved. Some or all of the above-mentioned processing in the assessment unit may be performed, for example, using or without the generation AI. For example, the assessment unit can have the generation AI compare past risk assessment results with actual results and cause the generation AI to optimize the assessment algorithm.

[0101] During risk assessment, the assessment unit can perform a comprehensive risk assessment by combining different risk factors. The assessment unit can, for example, perform a comprehensive risk assessment by combining economic risk and political risk. The assessment unit can also perform a comprehensive risk assessment by combining natural disaster risk and market risk. Furthermore, the assessment unit can perform a comprehensive risk assessment by combining risk factors for each region. This allows a comprehensive risk assessment to be performed by combining different risk factors. Some or all of the above-described processing in the assessment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the assessment unit can input data on different risk factors into the generation AI and have the generation AI perform a comprehensive risk assessment.

[0102] The evaluation unit can perform risk assessment by taking into account the risk characteristics of each region. For example, the evaluation unit performs the assessment by taking into account different risk characteristics between urban and rural areas. The evaluation unit can also perform risk assessment by taking into account the economic situation of the region. Furthermore, the evaluation unit can perform risk assessment by taking into account the risk of natural disasters in the region. This allows for a more accurate risk assessment to be provided by taking into account the risk characteristics of each region. Some or all of the above-described processing in the evaluation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input risk characteristic data for each region into the generation AI and have the generation AI perform the risk assessment.

[0103] The evaluation unit can estimate the user's emotions and determine the priority of risk assessments based on the estimated user emotions. For example, when the user is stressed, the evaluation unit can prioritize displaying only important risk assessments. Furthermore, when the user is relaxed, the evaluation unit can prioritize displaying detailed risk assessments. Furthermore, when the user is in a hurry, the evaluation unit can quickly display risk assessments. This allows for prioritized provision of important information to the user by determining the priority of risk assessments according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of risk assessments.

[0104] The evaluation unit can improve the accuracy of the assessment by referring to related economic indicators and political situations during risk assessment. The evaluation unit can improve the accuracy of the assessment by referring to economic indicators such as economic growth rate and unemployment rate, for example. The evaluation unit can also improve the accuracy of the assessment by referring to political situations such as political risks and policy changes. Furthermore, the evaluation unit can improve the accuracy of the assessment by referring to international economic trends. In this way, the accuracy of the assessment can be improved by referring to related economic indicators and political situations. Some or all of the above-mentioned processing in the evaluation unit can be performed using, or without, the generation AI, for example. For example, the evaluation unit can input data on economic indicators and political situations into the generation AI and cause the generation AI to improve the accuracy of the assessment.

[0105] The evaluation unit can customize the evaluation results based on the user's investment strategy during risk evaluation. The evaluation unit customizes the evaluation results based on, for example, the user's risk tolerance. The evaluation unit can also customize the evaluation results based on the user's investment goals. Furthermore, the evaluation unit can customize the evaluation results based on the user's investment period. This makes it possible to provide optimal information for the user by customizing the evaluation results based on the user's investment strategy. Some or all of the above-described processing in the evaluation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input the user's investment strategy data into the generation AI and have the generation AI customize the evaluation results.

[0106] The evaluation unit can improve the accuracy of the evaluation by integrating information from different data sources during risk evaluation. For example, the evaluation unit can improve the accuracy of the evaluation by integrating real estate transaction data and economic indicator data. The evaluation unit can also improve the accuracy of the evaluation by integrating social media data and news article data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by integrating regional market data and international economic data. In this way, by integrating information from different data sources, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using or without the generation AI. For example, the evaluation unit can cause the generation AI to integrate information from different data sources and improve the accuracy of the evaluation. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and evaluation unit, described above, may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future market trends based on the analysis results. The evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and performs risk assessment based on the prediction results. The collection unit may be realized, for example, by the control unit 46A of the smart device 14, and the analysis unit, prediction unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future market trends based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs risk assessment based on the prediction results. The collection unit may be realized, for example, by the control unit 46A of the smart glasses 214, and the analysis unit, prediction unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and evaluation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future market trends based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs risk assessment based on the prediction results. The collection unit may be realized, for example, by the control unit 46A of the headset-type terminal 314, and the analysis unit, prediction unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and evaluation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and predicts future market trends based on the analysis results. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs risk assessment based on the prediction results. The collection unit may be realized, for example, by the control unit 46A of the robot 414, and the analysis unit, prediction unit, and evaluation unit may be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can perform a simple analysis to avoid burdening the user. If the user is relaxed, the analysis unit can perform a detailed analysis to provide deeper insights. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of only important data to provide results quickly. This allows the analysis unit to adjust the depth of the analysis according to the user's emotions and provide the user with optimal information.

[0109] When collecting data, the collection unit can prioritize collecting highly relevant data taking into consideration the user's investment goals. For example, if the user is aiming for short-term profits, the collection unit can prioritize collecting data related to short-term market trends. Also, if the user is aiming for long-term asset formation, the collection unit can prioritize collecting data related to long-term market trends. Furthermore, if the user is interested in a specific region, detailed data related to that region can be prioritized. This allows for more appropriate investment decisions to be supported by collecting highly relevant data according to the user's investment goals.

[0110] The prediction unit can estimate the user's emotions and adjust the accuracy of the prediction based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit can make a simple prediction to avoid burdening the user. If the user is relaxed, the prediction unit can make a detailed prediction to provide deeper insights. Furthermore, if the user is in a hurry, the prediction unit can prioritize prediction of only important data to provide results quickly. In this way, by adjusting the accuracy of the prediction according to the user's emotions, it is possible to provide the user with the most appropriate information.

[0111] When assessing risk, the assessment unit can customize the assessment results by taking into account the user's investment history. For example, the assessment unit can perform risk assessment based on the user's successful investment patterns in the past. The assessment unit can also provide advice to help the user avoid investment patterns that have failed in the past. Furthermore, the assessment unit can perform risk assessment based on the user's investment strategy and propose the optimal risk management method for the user. In this way, by taking into account the user's investment history, a more personalized risk assessment can be provided.

[0112] During data collection, the collection unit can collect real-time user behavior data and adjust the timing of data collection based on the user's behavior patterns. For example, the frequency of data collection can be increased during times when the user frequently trades, or reduced during times when the user does not trade, thereby reducing the load on the system. Furthermore, important data can be collected preferentially based on the user's behavior patterns. This allows for more efficient data collection by adjusting the timing of data collection according to the user's behavior patterns.

[0113] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a notification that briefly summarizes the analysis results can be sent. If the user is relaxed, a notification that includes detailed analysis results can be sent. Furthermore, if the user is in a hurry, a notification that covers the main points can be sent quickly. In this way, by adjusting the notification method of the analysis results according to the user's emotions, it is possible to provide the user with the most appropriate information.

[0114] The forecasting unit can improve the accuracy of the forecast by integrating information from different data sources during the forecasting process. For example, real estate transaction data and economic indicator data can be integrated to improve the accuracy of the forecast. Also, social media data and news article data can be integrated to improve the accuracy of the forecast. Furthermore, regional market data and international economic data can be integrated to improve the accuracy of the forecast. In this way, by integrating information from different data sources, the accuracy of the forecast can be improved.

[0115] The evaluation unit can estimate the user's emotions and adjust the method of notifying the risk assessment based on the estimated user's emotions. For example, if the user is feeling stressed, a notification that briefly summarizes the risk assessment results can be sent. If the user is relaxed, a notification that includes detailed risk assessment results can be sent. Furthermore, if the user is in a hurry, a notification that covers the main points can be sent quickly. In this way, by adjusting the method of notifying the risk assessment according to the user's emotions, it is possible to provide the user with optimal information.

[0116] The collection unit can customize the collection method by reflecting user feedback when collecting data. For example, the type of data to be collected can be adjusted based on user feedback. The frequency of data collection can also be adjusted based on user feedback. Furthermore, the data collection method can be improved based on user feedback. In this way, by reflecting user feedback, the collection method can be customized and more appropriate data can be collected.

[0117] During analysis, the analysis unit can combine different analysis algorithms to evaluate the analysis results from multiple angles. For example, the analysis results can be evaluated by combining a machine learning algorithm and statistical analysis. The analysis results can also be evaluated by combining deep learning and regression analysis. Furthermore, the analysis results can be evaluated by combining clustering and time series analysis. In this way, by combining different analysis algorithms, the analysis results can be evaluated from multiple angles.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection department collects data. This data includes real estate data, economic indicator data, transaction data, etc. The collection department collects data such as past real estate price fluctuations, transaction volume, and economic indicators. The collection department can also collect data taking into consideration the data source, collection frequency, collection method, etc. For example, the collection department collects data on real estate price fluctuations over the past five years, monthly transaction volume, annual transaction volume, and economic indicator data such as GDP, unemployment rate, and inflation rate. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis is performed based on the analysis algorithm used and the purpose of the analysis. For example, the analysis unit analyzes market trends based on the collected data, and analyzes trends of rising prices and increasing transaction volumes, as well as correlations between data. For example, it analyzes the correlation between real estate prices and transaction volumes. Step 3: The prediction unit uses the generative AI to make predictions based on the analysis results obtained by the analysis unit. Predictions are made based on the prediction model and the forecast period. For example, based on the analysis results, future real estate prices and transaction volumes, increases and decreases in real estate prices in specific areas, and future market trends are predicted. The prediction algorithm can also be optimized by comparing past prediction results with actual results. Step 4: The evaluation unit uses the generation AI to perform risk assessment based on the prediction results obtained by the prediction unit. Risk assessment is performed based on the evaluation criteria and type of risk factor. For example, investment risk is assessed based on the prediction results and advice is provided to investors to reduce the risk. Risk assessment can also take into account factors such as economic conditions, political risk, and natural disaster risk. The assessment algorithm can also be optimized by comparing past risk assessment results with actual results.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that performs prediction based on the analysis result obtained by the analysis unit; an evaluation unit that performs risk evaluation based on the prediction result obtained by the prediction unit; Equipped with A system characterized by:

2. The collecting unit Collect data on past real estate price fluctuations, transaction volumes, and economic indicators 2. The system of claim 1.

3. The analysis unit Analyzing market trends based on collected data 2. The system of claim 1.

4. The prediction unit Predict future real estate prices and transaction volumes based on analysis results 2. The system of claim 1.

5. The evaluation unit Evaluate investment risks based on the prediction results and provide advice to investors to reduce those risks 2. The system of claim 1.

6. The evaluation unit Conduct risk assessments taking into account economic conditions, political risks, and natural disaster risks.

2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Evaluate the reliability of past data and prioritize collection of reliable data 2. The system of claim 1.

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