system

Abstract

The system according to the embodiment aims to provide easy-to-understand guidance on current prices and outlooks when individuals make their initial investments. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The reception unit receives investment information from users. The collection unit collects market data based on the information received by the reception unit. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes investment targets based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of investments. The adjustment unit makes adjustments as necessary.

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 technologies do not adequately guide individuals making initial investments in a way that makes it easy for them to understand current prices and outlooks, and there is room for improvement.

[0005] The system according to the embodiment aims to provide easy-to-understand guidance on current prices and outlooks when individuals make their initial investments. [Means for solving the problem]

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The reception unit receives investment information from users. The collection unit collects market data based on the information received by the reception unit. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes investment targets based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of investments. The adjustment unit makes adjustments as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can provide easy-to-understand guidance on current prices and outlooks when individuals make their initial investments. [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) An investment support system according to an embodiment of the present invention provides a function that provides easy-to-understand, smooth guidance to individuals making initial investments, including information on current prices and published forecasts. This investment support system allows users to input investment information, and AI analyzes the information to provide current prices and forecasts. Furthermore, the AI ​​provides assistance for beginners in change investment, microinvestment, and FX, promoting investment activity. For example, if a user inputs "I want to start investing in stocks," the information is input into the AI. The AI ​​then analyzes the input information and provides current prices and forecasts. The AI ​​then suggests optimal investments for the user based on the latest market data. For example, if a user wishes to invest in stocks, the AI ​​provides current stock prices and future forecasts. Furthermore, the AI ​​provides assistance for beginners in change investment, microinvestment, and FX. For example, the system suggests "change investment," which automatically invests change from everyday shopping. The system also provides assistance for microinvestment, which can be started with a small amount, and for beginners in FX. This allows users to easily start investing. This system promotes investment activity. Users can invest with the assistance of AI without having to understand complex investment information. For example, if a user is investing in stocks for the first time, AI can provide current prices and forecasts and suggest optimal investment options, allowing the user to begin investing with confidence. AI also monitors investment progress in real time and makes adjustments as necessary. For example, if stock prices suddenly fall, the AI ​​automatically reviews investment options and suggests ways to minimize risk. This allows users to continue investing with peace of mind. In this way, utilizing AI can provide support for individuals making basic investments and promote investment activities. This allows the investment support system to efficiently support users' investment activities and improve the success rate of their investments.

[0029] The investment support system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The reception unit receives investment information from a user. The investment information from the user includes, but is not limited to, stock information, bond information, and real estate investment information. For example, the reception unit can receive the investment information when the user inputs, "I want to start investing in stocks." The collection unit collects market data based on the information received by the reception unit. The market data includes, but is not limited to, stock price data, economic indicators, and news articles. For example, the collection unit can collect the latest stock price data from the Internet. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm. For example, the analysis unit can predict future stock price prospects based on the collected stock price data. The proposal unit proposes investment targets based on the analysis results obtained by the analysis unit. The proposal is performed based on, for example, but is not limited to, selection criteria and risk assessment for the investment targets. The suggestion unit can, for example, suggest optimal stock investment destinations to the user. The monitoring unit monitors the progress of investments. Monitoring can, for example, be performed in real time or on a regular basis, but is not limited to these examples. The monitoring unit can, for example, monitor the user's investment portfolio in real time and issue an alert if an abnormality occurs. The adjustment unit makes adjustments as necessary. Adjustments can, for example, be performed by restructuring the investment portfolio or managing risk, but are not limited to these examples. For example, if stock prices suddenly fall, the adjustment unit can review investment destinations and make suggestions to minimize risk. This enables the investment support system according to the embodiment to efficiently accept, analyze, suggest, monitor, and adjust users' investment information.

[0030] The investment support system includes an assisting unit that suggests change investment or micro-investment. The assisting unit, for example, suggests "change investment," in which the user automatically invests the change generated from everyday shopping. For example, if a user makes a purchase of 100 yen, the 1 yen change can be automatically invested. The assisting unit can also suggest micro-investments that can be started with small amounts. For example, it can suggest micro-investments that the user can start with as little as 500 yen. This allows even beginners to easily start investing. Some or all of the above-mentioned processing in the assisting unit may be performed, for example, using AI, or may be performed without using AI. For example, the assisting unit can input the user's shopping data into a generating AI and have the generating AI execute a change investment suggestion.

[0031] The investment support system includes an evaluation unit that performs risk evaluation. The evaluation unit, for example, evaluates the risk of a user's investment. The risk evaluation is performed, for example, based on a risk score calculation method and identification of risk factors, but is not limited to such examples. The evaluation unit, for example, can calculate a risk score of a user's investment and identify high-risk investments. This makes it easier for the user to understand the risks. Some or all of the above-described processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the user's investment data into a generation AI and have the generation AI perform a risk evaluation.

[0032] The investment support system includes a review unit that reviews investments. The review unit, for example, periodically reviews the user's investments. The review is performed, for example, based on periodic review or review under specific conditions, but is not limited to these examples. The review unit, for example, can review the user's investments monthly and exclude high-risk investments. This can minimize risk. Some or all of the above-described processing in the review unit may be performed using, for example, AI, or may be performed without using AI. For example, the review unit can input the user's investment data into a generation AI and have the generation AI review the investments.

[0033] The collection unit can collect market data. Market data includes, but is not limited to, stock price data, economic indicators, and news articles, for example. The collection unit can collect, for example, the latest stock price data from the Internet. The collection unit can also collect economic indicators from official government websites. Furthermore, the collection unit can collect news articles from news feeds. This allows the user to be provided with the latest information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input stock price data collected from the Internet into the generation AI and have the generation AI analyze the data.

[0034] The analysis unit can analyze the collected data and provide current prices or forecasts. The analysis can be performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. The analysis unit can, for example, predict future stock price forecasts based on collected stock price data. The analysis unit can also predict economic trends based on economic indicators. Furthermore, the analysis unit can analyze news articles to grasp market trends, making it easier for users to make investment decisions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0035] The reception unit can analyze the user's past investment history and select the optimal reception method. For example, the reception unit can prioritize suggesting investment methods that the user has frequently used in the past. For example, if the user has frequently invested in stocks in the past, the reception unit can prioritize providing information about stock investments. The reception unit can also select an investment method with a high success rate based on the user's past investment history. For example, it can suggest a similar investment method based on an investment method that the user has been successful in the past. Furthermore, the reception unit can also suggest an investment method that minimizes risk based on the user's past investment history. For example, if the user has failed in high-risk investments in the past, it can suggest a low-risk investment method. This allows the user to be provided with the optimal reception method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's investment history data into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving investment information, the reception unit can filter the investment information based on the user's current investment status and areas of interest. For example, the reception unit can prioritize receiving related investment information based on the user's current investment portfolio. For example, if the user is currently investing in stocks, stock-related investment information can be prioritized. The reception unit can also filter investment information for specific sectors based on the user's areas of interest. For example, if the user is interested in the technology sector, technology-related investment information can be prioritized. Furthermore, the reception unit can select appropriate investment information according to the user's investment goals. For example, if the user is aiming for long-term asset formation, information suitable for long-term investments can be provided. This allows for the provision of highly relevant information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's investment status data into the generation AI and have the generation AI perform filtering.

[0037] When receiving investment information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize receiving investment information related to a region based on the user's current location. For example, if the user lives in a specific region, real estate investment information related to that region can be provided preferentially. The reception unit can also filter investment information for specific markets or sectors based on the user's geographical location information. For example, if the user lives in a specific country, information related to that country's stock market can be provided preferentially. Furthermore, the reception unit can select investment information related to the economic situation of the region taking into account the user's geographical location information. For example, appropriate investment information can be provided based on the economic situation of the region in which the user lives. This allows for the provision of investment information related to the region. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant information.

[0038] The reception unit may analyze the user's social media activity and receive related information when receiving investment information. For example, the reception unit may prioritize receiving related investment information based on the user's social media interests. For example, if the user frequently posts technology-related information on social media, technology-related investment information may be provided preferentially. The reception unit may also analyze the user's social media activity history and select investment information that is likely to interest the user. For example, related investment information may be provided based on the content of posts from accounts the user follows on social media. Furthermore, the reception unit may receive related information based on the investment activities of the user's followers and friends on social media. For example, similar investment information may be provided based on the investment activities of the user's friends. This may provide investment information that is likely to interest the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's social media data into a generation AI and cause the generation AI to select related information.

[0039] The collection unit can analyze past market data and select an appropriate collection method. For example, the collection unit can optimize the collection frequency based on the past market data. For example, the collection unit can analyze the past market data and select a collection method for a specific time period. The collection unit can also prioritize collection of data related to a specific market or sector from the past market data. For example, data on a specific sector can be collected based on the past market data. The collection unit can also analyze the past market data and narrow down the collection targets. For example, data on a specific market can be prioritized based on the past market data. This makes it possible to provide an optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past market data to a generation AI and have the generation AI select a collection method.

[0040] When collecting market data, the collection unit can filter the market data based on a specific market or sector. For example, the collection unit can prioritize collecting data related to a specific market. For example, the collection unit can prioritize collecting data related to the stock market of a specific country. The collection unit can also filter relevant market data based on a specific sector. For example, the collection unit can prioritize collecting data related to the technology sector. Furthermore, the collection unit can select data to be collected taking into account market or sector trends. For example, data on a specific sector can be collected based on current market trends. This allows for the provision of highly relevant data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on a specific market or sector into the generation AI and have the generation AI perform the filtering.

[0041] When collecting market data, the collection unit can prioritize collecting highly relevant data by taking geographical market information into consideration. The collection unit, for example, prioritizes collecting geographically relevant market data. For example, it can prioritize collecting real estate market data related to a specific region. The collection unit can also filter data for a specific region based on geographical market information. For example, it can prioritize collecting data related to the stock market of a specific country. Furthermore, the collection unit can select collection targets by taking geographical market information into consideration. For example, it can collect data for a specific region based on the current regional economic situation. This makes it possible to provide market data related to the region. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical market information to the generation AI and cause the generation AI to select highly relevant data.

[0042] When collecting market data, the collection unit can analyze related news and reports and select data to collect. The collection unit, for example, collects related market data based on the latest news and reports. For example, it can collect related stock price data based on the latest economic news. The collection unit can also analyze the content of the news and reports and narrow down the data to be collected. For example, it can collect related market data based on a specific news article. Furthermore, the collection unit can select data to collect taking into account trends in news and reports. For example, it can collect specific market data based on current news trends. This makes it possible to provide the latest market data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input news and report data into a generation AI and have the generation AI select related data.

[0043] During analysis, the analysis unit can adjust the analysis algorithm by referring to past investment performance. The analysis unit, for example, adjusts the analysis algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal analysis algorithm. The analysis unit can also select an optimal analysis method by referring to past investment performance. For example, it can select a specific analysis method based on past investment performance. Furthermore, the analysis unit can analyze past investment performance and optimize the analysis algorithm. For example, it can optimize the analysis algorithm based on past investment performance. This can optimize the analysis algorithm and improve accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past investment performance data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0044] During analysis, the analysis unit can apply different analysis methods depending on the specific investment category. For example, the analysis unit applies a specific analysis method to stock investments. For example, time series analysis of stock price data can be applied to stock investments. The analysis unit can also apply a different analysis method to FX investments. For example, statistical analysis of exchange rates can be applied to FX investments. Furthermore, the analysis unit can apply a dedicated analysis method to microinvestments. For example, risk assessment of small investments can be applied to microinvestments. This allows for applying the optimal analysis method according to the specific investment category, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data of a specific investment category into the generation AI and have the generation AI apply the analysis method.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the investment information. For example, the analysis unit prioritizes analysis of investment information submitted earlier. For example, if investment information is submitted early, the analysis unit can prioritize analysis of that information. The analysis unit can also determine the order of analysis based on the submission date of the investment information. For example, the analysis unit can analyze information submitted earlier. Furthermore, the analysis unit can adjust the priority of analysis taking into account the submission date of the investment information. For example, if the submission date is an important factor, the analysis priority can be determined based on that factor. This enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the investment information to the generation AI and have the generation AI determine the analysis priority.

[0046] The analysis unit can improve the accuracy of the analysis by referring to related market trends during analysis. The analysis unit can improve the accuracy of the analysis, for example, based on market trends. For example, the analysis unit can analyze current market trends and perform analysis based on the results. The analysis unit can also adjust the analysis method by referring to related market trends. For example, the analysis unit can select an appropriate analysis method depending on the market trend. Furthermore, the analysis unit can optimize the accuracy of the analysis by taking market trends into consideration. For example, trend analysis can be used to predict future market trends and perform analysis based on the results. This can improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market trend data to a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] When making a proposal, the proposal unit can adjust the specificity of the proposal based on the importance of the investment. For example, the proposal unit can provide a detailed proposal for a highly important investment. For example, the proposal unit can provide a detailed analysis report for a highly important stock investment. The proposal unit can also provide a concise proposal for a less important investment. For example, the proposal unit can provide a concise summary for a less important investment. Furthermore, the proposal unit can adjust the level of detail of the proposal taking into account the importance of the investment. For example, the level of detail of the proposal can be adjusted depending on the importance of the investment. This enables a more appropriate proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input importance data of the investment to the generation AI and cause the generation AI to adjust the specificity of the proposal.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the investment. For example, the proposal unit can apply a specific proposal algorithm to stock investments. For example, a proposal algorithm based on time series analysis of stock price data can be applied to stock investments. The proposal unit can also apply a different proposal algorithm to FX investments. For example, a proposal algorithm based on statistical analysis of exchange rates can be applied to FX investments. Furthermore, the proposal unit can apply a dedicated proposal algorithm to microinvestments. For example, a proposal algorithm based on risk assessment of small investments can be applied to microinvestments. This allows for applying the optimal proposal algorithm depending on the category of the investment, thereby improving the accuracy of the proposal. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or without AI. For example, the proposal unit can input investment category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0049] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission dates of the investment targets. For example, the proposal unit prioritizes proposals based on the earliest submission dates of the investment targets. For example, if information about an investment target is submitted early, the proposal unit can prioritize that information. The proposal unit can also determine the order of proposals based on the submission dates of the investment targets. For example, proposals can be made in order of closest submission dates. Furthermore, the proposal unit can adjust the priority of proposals taking into account the submission dates of the investment targets. For example, if submission dates are an important factor, the proposal priority can be determined based on that factor. This enables efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input submission date data of investment targets into the generation AI and have the generation AI determine the priority of proposals.

[0050] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the investments. For example, the proposal unit can prioritize proposing investments with high relevance. For example, the proposal unit can prioritize proposing investments with high relevance to the user's investment portfolio. The proposal unit can also determine the order of proposals based on the relevance of the investments. For example, the proposal unit can propose investments in order of relevance. Furthermore, the proposal unit can adjust the order of proposals taking into account the relevance of the investments. For example, the proposal unit can prioritize proposing investments with high relevance and postpone proposing investments with low relevance. This enables more appropriate proposals. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input relevance data of the investments to the generation AI and cause the generation AI to adjust the order of proposals.

[0051] During monitoring, the monitoring unit can adjust the monitoring algorithm by referring to past investment performance. The monitoring unit, for example, adjusts the monitoring algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal monitoring algorithm. The monitoring unit can also select an optimal monitoring method by referring to past investment performance. For example, it can select a specific monitoring method based on past investment performance. Furthermore, the monitoring unit can analyze past investment performance and optimize the monitoring algorithm. For example, it can optimize the monitoring algorithm based on past investment performance. This can optimize the monitoring algorithm and improve its accuracy. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past investment performance data into the generation AI and cause the generation AI to adjust the monitoring algorithm.

[0052] During monitoring, the monitoring unit can apply different monitoring methods depending on the specific investment category. For example, the monitoring unit applies a specific monitoring method to stock investments. For example, real-time monitoring of stock price data can be applied to stock investments. The monitoring unit can also apply a different monitoring method to FX investments. For example, real-time monitoring of exchange rates can be applied to FX investments. Furthermore, the monitoring unit can apply a dedicated monitoring method to microinvestments. For example, risk monitoring of small investments can be applied to microinvestments. This allows for applying the optimal monitoring method according to the specific investment category, thereby improving the accuracy of monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data of a specific investment category into the generation AI and have the generation AI apply the monitoring method.

[0053] During monitoring, the monitoring unit can determine the monitoring priority based on the submission time of the investment information. For example, the monitoring unit prioritizes monitoring of investment information that was submitted earlier. For example, if investment information is submitted early, the monitoring unit can prioritize monitoring of that information. The monitoring unit can also determine the monitoring order based on the submission time of the investment information. For example, it can monitor information that is submitted closest to the original date. Furthermore, the monitoring unit can adjust the monitoring priority taking into account the submission time of the investment information. For example, if the submission time is an important factor, the monitoring priority can be determined based on that factor. This enables efficient monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the submission time of the investment information to the generation AI and have the generation AI determine the monitoring priority.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to related market trends during monitoring. The monitoring unit can improve the accuracy of monitoring, for example, based on market trends. For example, the monitoring unit can analyze current market trends and perform monitoring based on the results. The monitoring unit can also adjust the monitoring method by referring to related market trends. For example, the monitoring unit can select an appropriate monitoring method depending on the market trend. Furthermore, the monitoring unit can optimize the accuracy of monitoring by taking market trends into consideration. For example, trend analysis can be used to predict future market trends and monitoring can be performed based on the results. This can improve the accuracy of monitoring. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input market trend data to a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0055] During adjustment, the adjustment unit can adjust the adjustment algorithm by referring to past investment performance. The adjustment unit, for example, adjusts the adjustment algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal adjustment algorithm. The adjustment unit can also select an optimal adjustment method by referring to past investment performance. For example, it can select a specific adjustment method based on past investment performance. Furthermore, the adjustment unit can analyze past investment performance and optimize the adjustment algorithm. For example, it can optimize the adjustment algorithm based on past investment performance. This can optimize the adjustment algorithm and improve its accuracy. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past investment performance data into the generation AI and cause the generation AI to adjust the adjustment algorithm.

[0056] During adjustment, the adjustment unit can apply different adjustment methods depending on the specific investment category. For example, the adjustment unit applies a specific adjustment method to stock investments. For example, a stock price data risk management method can be applied to stock investments. The adjustment unit can also apply a different adjustment method to FX investments. For example, an exchange rate risk management method can be applied to FX investments. Furthermore, the adjustment unit can also apply a dedicated adjustment method to microinvestments. For example, a small investment risk management method can be applied to microinvestments. This allows for applying the optimal adjustment method according to the specific investment category, thereby improving the accuracy of the adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data of a specific investment category into the generation AI and cause the generation AI to apply the adjustment method.

[0057] During adjustment, the adjustment unit can determine the priority of adjustment based on the submission date of the investment information. For example, the adjustment unit prioritizes adjustment of investment information submitted earlier. For example, if investment information is submitted early, the adjustment unit can prioritize that information. The adjustment unit can also determine the order of adjustment based on the submission date of the investment information. For example, adjustment can be performed in order of closest submission date. Furthermore, the adjustment unit can also adjust the priority of adjustment taking into account the submission date of the investment information. For example, if the submission date is an important factor, the adjustment priority can be determined based on that factor. This enables efficient adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the submission date of investment information to the generation AI and cause the generation AI to determine the priority of adjustment.

[0058] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to related market trends. The adjustment unit can improve the accuracy of the adjustment based on, for example, market trends. For example, the adjustment unit can analyze current market trends and make adjustments based on the results. The adjustment unit can also adjust the adjustment method by referring to related market trends. For example, the adjustment unit can select an appropriate adjustment method depending on the market trend. Furthermore, the adjustment unit can optimize the accuracy of the adjustment by taking market trends into consideration. For example, trend analysis can be used to predict future market trends and make adjustments based on the results. This can improve the accuracy of the adjustment. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input market trend data to the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0059] During assistance, the assisting unit can adjust the assist algorithm by referring to past investment performance. The assisting unit, for example, adjusts the assist algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal assist algorithm. The assisting unit can also select an optimal assist method by referring to past investment performance. For example, it can select a specific assist method based on past investment performance. Furthermore, the assisting unit can analyze past investment performance and optimize the assist algorithm. For example, it can optimize the assist algorithm based on past investment performance. This can optimize the assist algorithm and improve its accuracy. Some or all of the above-described processing in the assisting unit may be performed using, for example, AI, or may be performed without using AI. For example, the assisting unit can input past investment performance data into the generating AI and have the generating AI adjust the assist algorithm.

[0060] During assistance, the assisting unit can adjust the content of the assistance based on the timing of submission of the investment information. For example, the assisting unit prioritizes assistance for investment information that is submitted earlier. For example, if investment information is submitted early, the assisting unit can prioritize assistance for that information. The assisting unit can also determine the order of assistance based on the timing of submission of the investment information. For example, assistance can be provided in order of closest submission. Furthermore, the assisting unit can adjust the priority of assistance taking into account the timing of submission of the investment information. For example, if the timing of submission is an important factor, the priority of assistance can be determined based on that factor. This enables efficient assistance. Some or all of the above-described processing in the assisting unit may be performed using, for example, AI, or may be performed without using AI. For example, the assisting unit can input data on the timing of submission of investment information to the generating AI and have the generating AI determine the priority of assistance.

[0061] During evaluation, the evaluation unit can adjust the evaluation algorithm by referring to past investment performance. The evaluation unit can adjust the evaluation algorithm based on, for example, past investment performance. For example, it can analyze past investment performance and select an optimal evaluation algorithm. The evaluation unit can also select an optimal evaluation method by referring to past investment performance. For example, it can select a specific evaluation method based on past investment performance. Furthermore, the evaluation unit can analyze past investment performance and optimize the evaluation algorithm. For example, it can optimize the evaluation algorithm based on past investment performance. This can optimize the evaluation algorithm and improve accuracy. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input past investment performance data into the generation AI and cause the generation AI to adjust the evaluation algorithm.

[0062] The evaluation unit can adjust the evaluation content based on the timing of investment information submission during the evaluation process. For example, the evaluation unit can prioritize evaluation of investment information submitted earlier. For instance, if investment information is submitted early, it can be given priority in the evaluation. The evaluation unit can also determine the order of evaluation based on the timing of investment information submission. For example, evaluations can be performed in order from those with the closest submission dates. Furthermore, the evaluation unit can adjust the priority of evaluation by considering the timing of investment information submission. For example, if the submission date is an important factor, the evaluation priority can be determined based on that factor. This enables efficient evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input investment information submission date data into a generating AI and have the generating AI determine the evaluation priority.

[0063] The review unit can adjust the review algorithm by referring to past investment performance during the review process. For example, the review unit can adjust the review algorithm based on past investment performance. For example, it can analyze past investment performance and select the optimal review algorithm. The review unit can also select the optimal review method by referring to past investment performance. For example, it can select a specific review method based on past investment performance. Furthermore, the review unit can analyze past investment performance and optimize the review algorithm. For example, it can optimize the review algorithm based on past investment performance. This allows for the optimization of the review algorithm and improvement of its accuracy. Some or all of the above processes in the review unit may be performed using AI, for example, or without AI. For example, the review unit can input past investment performance data into a generating AI and have the generating AI perform the adjustment of the review algorithm.

[0064] During review, the review unit can adjust the content of the review based on the timing of submission of the investment information. For example, the review unit prioritizes review of investment information that was submitted earlier. For example, if investment information is submitted early, the review unit can prioritize review of that information. The review unit can also determine the order of review based on the timing of submission of the investment information. For example, the review unit can review information that is submitted most recently. Furthermore, the review unit can adjust the priority of review taking into account the timing of submission of the investment information. For example, if the timing of submission is an important factor, the priority of review can be determined based on that factor. This enables efficient review. Some or all of the above-mentioned processing in the review unit may be performed using, for example, AI, or may be performed without using AI. For example, the review unit can input data on the timing of submission of investment information into the generation AI and have the generation AI determine the priority of review.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The proposal section can customize its proposals based on the user's investment goals. For example, if the user is aiming for short-term profits, the proposal section can suggest high-risk, high-return investments. Alternatively, if the user is aiming for long-term asset formation, the proposal section can suggest investments that are expected to achieve stable growth. Furthermore, if the user is interested in a specific sector, the proposal section can prioritize investments related to that sector. This allows for optimal proposals tailored to the user's investment goals.

[0067] The monitoring department can evaluate the diversity of a user's investment portfolio and make diversification investment suggestions as necessary. For example, if a user's portfolio is biased toward a particular sector, the monitoring department can suggest investments in other sectors. Also, if a user's portfolio is concentrated in a particular region, the monitoring department can suggest geographically diversified investments. Furthermore, if a user's portfolio is biased toward a particular asset class, the monitoring department can suggest investments in different asset classes. This allows for risk diversification and stable investment results.

[0068] The adjustment unit can adjust the investment portfolio based on the user's life events. For example, when the user approaches a life event such as marriage or childbirth, the adjustment unit can suggest restructuring the portfolio to reduce risk. Also, when the user approaches retirement, the adjustment unit can suggest investments to ensure a stable income. Furthermore, when the user plans to make a large expenditure, the adjustment unit can suggest securing funds to prepare for that expenditure. This makes it possible to provide a flexible investment strategy that corresponds to the user's life events.

[0069] The evaluation unit can adjust the level of detail of the risk evaluation based on the user's investment experience. For example, it can provide a simple and easy-to-understand risk evaluation for beginner investors and a detailed risk evaluation report for experienced investors. It can also adjust the frequency of risk evaluation based on the user's investment experience. For example, it can perform regular risk evaluations for beginners and provide risk evaluations as needed for experienced investors. This makes it possible to perform appropriate risk evaluations based on the user's investment experience.

[0070] The proposal unit can adjust its proposal approach based on the user's investment style. For example, it can propose high-risk, high-return investments to a user with an aggressive investment style, and low-risk, stable-return investments to a user with a conservative investment style. It can also adjust the frequency of proposals based on the user's investment style. For example, it can make frequent proposals to aggressive investors, and make proposals as needed to conservative investors. This allows it to make optimal proposals based on the user's investment style.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception desk receives investment information from users. This information includes, for example, stock information, bond information, and real estate investment information. The reception desk can receive investment information, for example, when a user enters "I want to start investing in stocks." Step 2: The collection unit collects market data based on the information received by the reception unit. Market data includes, for example, stock price data, economic indicators, and news articles. The collection unit can, for example, collect the latest stock price data from the internet. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can predict future stock price forecasts based on the collected stock price data. Step 4: The proposal unit proposes investment targets based on the analysis results obtained by the analysis unit. The proposal is made based, for example, on investment selection criteria and risk assessment. For example, the proposal unit can propose the optimal stock investment targets to the user. Step 5: The monitoring unit monitors the progress of the investment. Monitoring can include, for example, real-time monitoring and periodic checks. The monitoring unit can, for example, monitor the user's investment portfolio in real time and issue an alert if an anomaly occurs. Step 6: The adjustment department makes adjustments as needed. These adjustments may include, for example, restructuring the investment portfolio or managing risk. For instance, if stock prices plummet, the adjustment department may review investment targets and propose ways to minimize risk.

[0073] (Example 2) An investment support system according to an embodiment of the present invention provides a function that provides easy-to-understand, smooth guidance to individuals making initial investments, including information on current prices and published forecasts. This investment support system allows users to input investment information, and AI analyzes the information to provide current prices and forecasts. Furthermore, the AI ​​provides assistance for beginners in change investment, microinvestment, and FX, promoting investment activity. For example, if a user inputs "I want to start investing in stocks," the information is input into the AI. The AI ​​then analyzes the input information and provides current prices and forecasts. The AI ​​then suggests optimal investments for the user based on the latest market data. For example, if a user wishes to invest in stocks, the AI ​​provides current stock prices and future forecasts. Furthermore, the AI ​​provides assistance for beginners in change investment, microinvestment, and FX. For example, the system suggests "change investment," which automatically invests change from everyday shopping. The system also provides assistance for microinvestment, which can be started with a small amount, and for beginners in FX. This allows users to easily start investing. This system promotes investment activity. Users can invest with the assistance of AI without having to understand complex investment information. For example, if a user is investing in stocks for the first time, AI can provide current prices and forecasts and suggest optimal investment options, allowing the user to begin investing with confidence. AI also monitors investment progress in real time and makes adjustments as necessary. For example, if stock prices suddenly fall, the AI ​​automatically reviews investment options and suggests ways to minimize risk. This allows users to continue investing with peace of mind. In this way, utilizing AI can provide support for individuals making basic investments and promote investment activities. This allows the investment support system to efficiently support users' investment activities and improve the success rate of their investments.

[0074] The investment support system according to the embodiment includes a reception unit, a collection unit, an analysis unit, a proposal unit, a monitoring unit, and an adjustment unit. The reception unit receives investment information from a user. The investment information from the user includes, but is not limited to, stock information, bond information, and real estate investment information. For example, the reception unit can receive the investment information when the user inputs, "I want to start investing in stocks." The collection unit collects market data based on the information received by the reception unit. The market data includes, but is not limited to, stock price data, economic indicators, and news articles. For example, the collection unit can collect the latest stock price data from the Internet. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm. For example, the analysis unit can predict future stock price prospects based on the collected stock price data. The proposal unit proposes investment targets based on the analysis results obtained by the analysis unit. The proposal is performed based on, for example, but is not limited to, selection criteria and risk assessment for the investment targets. The suggestion unit can, for example, suggest optimal stock investment destinations to the user. The monitoring unit monitors the progress of investments. Monitoring can, for example, be performed in real time or on a regular basis, but is not limited to these examples. The monitoring unit can, for example, monitor the user's investment portfolio in real time and issue an alert if an abnormality occurs. The adjustment unit makes adjustments as necessary. Adjustments can, for example, be performed by restructuring the investment portfolio or managing risk, but are not limited to these examples. For example, if stock prices suddenly fall, the adjustment unit can review investment destinations and make suggestions to minimize risk. This enables the investment support system according to the embodiment to efficiently accept, analyze, suggest, monitor, and adjust users' investment information.

[0075] The investment support system includes an assisting unit that suggests change investment or micro-investment. The assisting unit, for example, suggests "change investment," in which the user automatically invests the change generated from everyday shopping. For example, if a user makes a purchase of 100 yen, the 1 yen change can be automatically invested. The assisting unit can also suggest micro-investments that can be started with small amounts. For example, it can suggest micro-investments that the user can start with as little as 500 yen. This allows even beginners to easily start investing. Some or all of the above-mentioned processing in the assisting unit may be performed, for example, using AI, or may be performed without using AI. For example, the assisting unit can input the user's shopping data into a generating AI and have the generating AI execute a change investment suggestion.

[0076] The investment support system includes an evaluation unit that performs risk evaluation. The evaluation unit, for example, evaluates the risk of a user's investment. The risk evaluation is performed, for example, based on a risk score calculation method and identification of risk factors, but is not limited to such examples. The evaluation unit, for example, can calculate a risk score of a user's investment and identify high-risk investments. This makes it easier for the user to understand the risks. Some or all of the above-described processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the user's investment data into a generation AI and have the generation AI perform a risk evaluation.

[0077] The investment support system includes a review unit that reviews investments. The review unit, for example, periodically reviews the user's investments. The review is performed, for example, based on periodic review or review under specific conditions, but is not limited to these examples. The review unit, for example, can review the user's investments monthly and exclude high-risk investments. This can minimize risk. Some or all of the above-described processing in the review unit may be performed using, for example, AI, or may be performed without using AI. For example, the review unit can input the user's investment data into a generation AI and have the generation AI review the investments.

[0078] The collection unit can collect market data. Market data includes, but is not limited to, stock price data, economic indicators, and news articles, for example. The collection unit can collect, for example, the latest stock price data from the Internet. The collection unit can also collect economic indicators from official government websites. Furthermore, the collection unit can collect news articles from news feeds. This allows the user to be provided with the latest information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input stock price data collected from the Internet into the generation AI and have the generation AI analyze the data.

[0079] The analysis unit can analyze the collected data and provide current prices or forecasts. The analysis can be performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. The analysis unit can, for example, predict future stock price forecasts based on collected stock price data. The analysis unit can also predict economic trends based on economic indicators. Furthermore, the analysis unit can analyze news articles to grasp market trends, making it easier for users to make investment decisions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0080] The reception desk can estimate the user's emotions and adjust the timing of receiving investment information based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can receive investment information during a time when the user can relax. For example, it can receive investment information during the user's relaxation time after returning home from work. The reception desk can also provide a quick response by immediately receiving investment information if the user is excited. For example, it can immediately receive investment information when the user is interested in investing. Furthermore, if the user is tired, the reception desk can receive investment information after the user has rested. For example, it can receive investment information after the user has had sufficient rest. This allows for receiving information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception unit can analyze the user's past investment history and select the optimal reception method. For example, the reception unit can prioritize suggesting investment methods that the user has frequently used in the past. For example, if the user has frequently invested in stocks in the past, the reception unit can prioritize providing information about stock investments. The reception unit can also select an investment method with a high success rate based on the user's past investment history. For example, it can suggest a similar investment method based on an investment method that the user has been successful in the past. Furthermore, the reception unit can also suggest an investment method that minimizes risk based on the user's past investment history. For example, if the user has failed in high-risk investments in the past, it can suggest a low-risk investment method. This allows the user to be provided with the optimal reception method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's investment history data into the generation AI and have the generation AI select the optimal reception method.

[0082] When receiving investment information, the reception unit can filter the investment information based on the user's current investment status and areas of interest. For example, the reception unit can prioritize receiving related investment information based on the user's current investment portfolio. For example, if the user is currently investing in stocks, stock-related investment information can be prioritized. The reception unit can also filter investment information for specific sectors based on the user's areas of interest. For example, if the user is interested in the technology sector, technology-related investment information can be prioritized. Furthermore, the reception unit can select appropriate investment information according to the user's investment goals. For example, if the user is aiming for long-term asset formation, information suitable for long-term investments can be provided. This allows for the provision of highly relevant information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's investment status data into the generation AI and have the generation AI perform filtering.

[0083] The reception unit can estimate the user's emotions and determine the priority of investment information to be received based on the estimated user emotions. For example, when the user is nervous, the reception unit can prioritize low-risk investment information. For example, when the user is nervous, the reception unit can prioritize providing safe bond investment information. Furthermore, when the user is relaxed, the reception unit can also receive high-risk investment information. For example, when the user is relaxed, the reception unit can provide high-risk investment information such as stocks and FX. Furthermore, when the user is excited, the reception unit can immediately receive investment information and provide a prompt response. For example, when the user shows a strong interest in investment, the reception unit can immediately provide investment information and provide a prompt response. This allows for the provision of more appropriate information. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0084] When receiving investment information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize receiving investment information related to a region based on the user's current location. For example, if the user lives in a specific region, real estate investment information related to that region can be provided preferentially. The reception unit can also filter investment information for specific markets or sectors based on the user's geographical location information. For example, if the user lives in a specific country, information related to that country's stock market can be provided preferentially. Furthermore, the reception unit can select investment information related to the economic situation of the region taking into account the user's geographical location information. For example, appropriate investment information can be provided based on the economic situation of the region in which the user lives. This allows for the provision of investment information related to the region. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant information.

[0085] The reception unit may analyze the user's social media activity and receive related information when receiving investment information. For example, the reception unit may prioritize receiving related investment information based on the user's social media interests. For example, if the user frequently posts technology-related information on social media, technology-related investment information may be provided preferentially. The reception unit may also analyze the user's social media activity history and select investment information that is likely to interest the user. For example, related investment information may be provided based on the content of posts from accounts the user follows on social media. Furthermore, the reception unit may receive related information based on the investment activities of the user's followers and friends on social media. For example, similar investment information may be provided based on the investment activities of the user's friends. This may provide investment information that is likely to interest the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's social media data into a generation AI and cause the generation AI to select related information.

[0086] The collection unit can estimate the user's emotions and adjust the timing of market data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects market data during a time when the user is able to relax. For example, the collection unit can collect market data during the user's relaxation time after returning home from work. Furthermore, if the user is excited, the collection unit can immediately collect market data and provide a prompt response. For example, if the user shows a strong interest in investing, the collection unit can immediately collect market data. Furthermore, if the user is tired, the collection unit can collect market data after the user has rested. For example, the collection unit can collect market data after the user has had a sufficient rest. This allows data to be collected at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The collection unit can analyze past market data and select an appropriate collection method. For example, the collection unit can optimize the collection frequency based on the past market data. For example, the collection unit can analyze the past market data and select a collection method for a specific time period. The collection unit can also prioritize collection of data related to a specific market or sector from the past market data. For example, data on a specific sector can be collected based on the past market data. The collection unit can also analyze the past market data and narrow down the collection targets. For example, data on a specific market can be prioritized based on the past market data. This makes it possible to provide an optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past market data to a generation AI and have the generation AI select a collection method.

[0088] When collecting market data, the collection unit can filter the market data based on a specific market or sector. For example, the collection unit can prioritize collecting data related to a specific market. For example, the collection unit can prioritize collecting data related to the stock market of a specific country. The collection unit can also filter relevant market data based on a specific sector. For example, the collection unit can prioritize collecting data related to the technology sector. Furthermore, the collection unit can select data to be collected taking into account market or sector trends. For example, data on a specific sector can be collected based on current market trends. This allows for the provision of highly relevant data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on a specific market or sector into the generation AI and have the generation AI perform the filtering.

[0089] The collection unit can estimate the user's emotions and prioritize the market data to be collected based on the estimated user emotions. For example, when the user is nervous, the collection unit prioritizes collecting low-risk market data. For example, when the user is nervous, the collection unit can prioritize providing safe bond market data. Furthermore, when the user is relaxed, the collection unit can also collect high-risk market data. For example, when the user is relaxed, high-risk market data such as stocks and FX can be provided. Furthermore, when the user is excited, the collection unit can instantly collect market data and provide a prompt response. For example, when the user shows a strong interest in investing, market data can be instantly provided and a prompt response can be made. This allows for more appropriate data to be provided. Emotion estimation is realized using an emotion estimation function, for example, using 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0090] When collecting market data, the collection unit can prioritize collecting highly relevant data by taking geographical market information into consideration. The collection unit, for example, prioritizes collecting geographically relevant market data. For example, it can prioritize collecting real estate market data related to a specific region. The collection unit can also filter data for a specific region based on geographical market information. For example, it can prioritize collecting data related to the stock market of a specific country. Furthermore, the collection unit can select collection targets by taking geographical market information into consideration. For example, it can collect data for a specific region based on the current regional economic situation. This makes it possible to provide market data related to the region. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical market information to the generation AI and cause the generation AI to select highly relevant data.

[0091] When collecting market data, the collection unit can analyze related news and reports and select data to collect. The collection unit, for example, collects related market data based on the latest news and reports. For example, it can collect related stock price data based on the latest economic news. The collection unit can also analyze the content of the news and reports and narrow down the data to be collected. For example, it can collect related market data based on a specific news article. Furthermore, the collection unit can select data to collect taking into account trends in news and reports. For example, it can collect specific market data based on current news trends. This makes it possible to provide the latest market data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input news and report data into a generation AI and have the generation AI select related data.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis method. For example, when the user is stressed, it can provide a simple statistical analysis. The analysis unit can also provide a detailed analysis method if the user is relaxed. For example, when the user is relaxed, it can provide a detailed analysis using a machine learning algorithm. Furthermore, the analysis unit can provide a rapid analysis method if the user is excited. For example, when the user shows a strong interest in an investment, it can provide a rapid analysis. This allows for the provision of more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] During analysis, the analysis unit can adjust the analysis algorithm by referring to past investment performance. The analysis unit, for example, adjusts the analysis algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal analysis algorithm. The analysis unit can also select an optimal analysis method by referring to past investment performance. For example, it can select a specific analysis method based on past investment performance. Furthermore, the analysis unit can analyze past investment performance and optimize the analysis algorithm. For example, it can optimize the analysis algorithm based on past investment performance. This can optimize the analysis algorithm and improve accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past investment performance data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0094] During analysis, the analysis unit can apply different analysis methods depending on the specific investment category. For example, the analysis unit applies a specific analysis method to stock investments. For example, time series analysis of stock price data can be applied to stock investments. The analysis unit can also apply a different analysis method to FX investments. For example, statistical analysis of exchange rates can be applied to FX investments. Furthermore, the analysis unit can apply a dedicated analysis method to microinvestments. For example, risk assessment of small investments can be applied to microinvestments. This allows for applying the optimal analysis method according to the specific investment category, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data of a specific investment category into the generation AI and have the generation AI apply the analysis method.

[0095] 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, when the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, when the user is nervous, the analysis unit can provide a display method using concise graphs or charts. Furthermore, when the user is relaxed, the analysis unit can provide a display method including detailed information. For example, when the user is relaxed, the analysis unit can provide a display method using a detailed text report. Furthermore, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, when the user is in a hurry, the analysis unit can provide a display method that highlights only the important points. This enables a more appropriate display. 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 AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0096] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the investment information. For example, the analysis unit prioritizes analysis of investment information submitted earlier. For example, if investment information is submitted early, the analysis unit can prioritize analysis of that information. The analysis unit can also determine the order of analysis based on the submission date of the investment information. For example, the analysis unit can analyze information submitted earlier. Furthermore, the analysis unit can adjust the priority of analysis taking into account the submission date of the investment information. For example, if the submission date is an important factor, the analysis priority can be determined based on that factor. This enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the investment information to the generation AI and have the generation AI determine the analysis priority.

[0097] The analysis unit can improve the accuracy of the analysis by referring to related market trends during analysis. The analysis unit can improve the accuracy of the analysis, for example, based on market trends. For example, the analysis unit can analyze current market trends and perform analysis based on the results. The analysis unit can also adjust the analysis method by referring to related market trends. For example, the analysis unit can select an appropriate analysis method depending on the market trend. Furthermore, the analysis unit can optimize the accuracy of the analysis by taking market trends into consideration. For example, trend analysis can be used to predict future market trends and perform analysis based on the results. This can improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market trend data to a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, when the user is nervous, the suggestion unit can provide simple, highly visible suggestions. For example, when the user is nervous, the suggestion unit can provide suggestions using concise graphs or charts. Furthermore, when the user is relaxed, the suggestion unit can provide suggestions including detailed information. For example, when the user is relaxed, the suggestion unit can provide suggestions using detailed text reports. Furthermore, when the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. For example, when the user is in a hurry, the suggestion unit can provide suggestions that emphasize only the important points. This enables more appropriate suggestions. 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 suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] When making a proposal, the proposal unit can adjust the specificity of the proposal based on the importance of the investment. For example, the proposal unit can provide a detailed proposal for a highly important investment. For example, the proposal unit can provide a detailed analysis report for a highly important stock investment. The proposal unit can also provide a concise proposal for a less important investment. For example, the proposal unit can provide a concise summary for a less important investment. Furthermore, the proposal unit can adjust the level of detail of the proposal taking into account the importance of the investment. For example, the level of detail of the proposal can be adjusted depending on the importance of the investment. This enables a more appropriate proposal. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input importance data of the investment to the generation AI and cause the generation AI to adjust the specificity of the proposal.

[0100] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the investment. For example, the proposal unit can apply a specific proposal algorithm to stock investments. For example, a proposal algorithm based on time series analysis of stock price data can be applied to stock investments. The proposal unit can also apply a different proposal algorithm to FX investments. For example, a proposal algorithm based on statistical analysis of exchange rates can be applied to FX investments. Furthermore, the proposal unit can apply a dedicated proposal algorithm to microinvestments. For example, a proposal algorithm based on risk assessment of small investments can be applied to microinvestments. This allows for applying the optimal proposal algorithm depending on the category of the investment, thereby improving the accuracy of the proposal. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or without AI. For example, the proposal unit can input investment category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, when the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. For example, when the user is in a hurry, the suggestion unit can provide short suggestions that highlight only the important points. Furthermore, when the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, when the user is relaxed, the suggestion unit can provide long suggestions using detailed text reports. Furthermore, when the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, when the user is excited, the suggestion unit can provide suggestions using visually appealing graphs and charts. This enables more appropriate suggestions. 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 suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0102] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission dates of the investment targets. For example, the proposal unit prioritizes proposals based on the earliest submission dates of the investment targets. For example, if information about an investment target is submitted early, the proposal unit can prioritize that information. The proposal unit can also determine the order of proposals based on the submission dates of the investment targets. For example, proposals can be made in order of closest submission dates. Furthermore, the proposal unit can adjust the priority of proposals taking into account the submission dates of the investment targets. For example, if submission dates are an important factor, the proposal priority can be determined based on that factor. This enables efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input submission date data of investment targets into the generation AI and have the generation AI determine the priority of proposals.

[0103] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the investments. For example, the proposal unit can prioritize proposing investments with high relevance. For example, the proposal unit can prioritize proposing investments with high relevance to the user's investment portfolio. The proposal unit can also determine the order of proposals based on the relevance of the investments. For example, the proposal unit can propose investments in order of relevance. Furthermore, the proposal unit can adjust the order of proposals taking into account the relevance of the investments. For example, the proposal unit can prioritize proposing investments with high relevance and postpone proposing investments with low relevance. This enables more appropriate proposals. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input relevance data of the investments to the generation AI and cause the generation AI to adjust the order of proposals.

[0104] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit can provide a simple monitoring method when the user is stressed. For example, a simple alert system can be provided when the user is stressed. The monitoring unit can also provide a detailed monitoring method when the user is relaxed. For example, a detailed monitoring report can be provided when the user is relaxed. The monitoring unit can also provide a quick monitoring method when the user is excited. For example, a quick alert system can be provided when the user shows strong interest in investing. This enables more appropriate monitoring. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0105] During monitoring, the monitoring unit can adjust the monitoring algorithm by referring to past investment performance. The monitoring unit, for example, adjusts the monitoring algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal monitoring algorithm. The monitoring unit can also select an optimal monitoring method by referring to past investment performance. For example, it can select a specific monitoring method based on past investment performance. Furthermore, the monitoring unit can analyze past investment performance and optimize the monitoring algorithm. For example, it can optimize the monitoring algorithm based on past investment performance. This can optimize the monitoring algorithm and improve its accuracy. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past investment performance data into the generation AI and cause the generation AI to adjust the monitoring algorithm.

[0106] During monitoring, the monitoring unit can apply different monitoring methods depending on the specific investment category. For example, the monitoring unit applies a specific monitoring method to stock investments. For example, real-time monitoring of stock price data can be applied to stock investments. The monitoring unit can also apply a different monitoring method to FX investments. For example, real-time monitoring of exchange rates can be applied to FX investments. Furthermore, the monitoring unit can apply a dedicated monitoring method to microinvestments. For example, risk monitoring of small investments can be applied to microinvestments. This allows for applying the optimal monitoring method according to the specific investment category, thereby improving the accuracy of monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data of a specific investment category into the generation AI and have the generation AI apply the monitoring method.

[0107] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, when the user is nervous, the monitoring unit can provide a simple, highly visible display method. For example, when the user is nervous, the monitoring unit can provide a display method using concise graphs or charts. Furthermore, when the user is relaxed, the monitoring unit can provide a display method including detailed information. For example, when the user is relaxed, the monitoring unit can provide a display method using a detailed text report. Furthermore, when the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. For example, when the user is in a hurry, the monitoring unit can provide a display method that highlights only the important points. This enables a more appropriate display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0108] During monitoring, the monitoring unit can determine the monitoring priority based on the submission time of the investment information. For example, the monitoring unit prioritizes monitoring of investment information that was submitted earlier. For example, if investment information is submitted early, the monitoring unit can prioritize monitoring of that information. The monitoring unit can also determine the monitoring order based on the submission time of the investment information. For example, it can monitor information that is submitted closest to the original date. Furthermore, the monitoring unit can adjust the monitoring priority taking into account the submission time of the investment information. For example, if the submission time is an important factor, the monitoring priority can be determined based on that factor. This enables efficient monitoring. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the submission time of the investment information to the generation AI and have the generation AI determine the monitoring priority.

[0109] The monitoring unit can improve the accuracy of monitoring by referring to related market trends during monitoring. The monitoring unit can improve the accuracy of monitoring, for example, based on market trends. For example, the monitoring unit can analyze current market trends and perform monitoring based on the results. The monitoring unit can also adjust the monitoring method by referring to related market trends. For example, the monitoring unit can select an appropriate monitoring method depending on the market trend. Furthermore, the monitoring unit can optimize the accuracy of monitoring by taking market trends into consideration. For example, trend analysis can be used to predict future market trends and monitoring can be performed based on the results. This can improve the accuracy of monitoring. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input market trend data to a generation AI and cause the generation AI to improve the accuracy of monitoring.

[0110] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, when the user is stressed, the adjustment unit provides a simple adjustment method. For example, when the user is stressed, the adjustment unit can provide a simple risk management method. Furthermore, when the user is relaxed, the adjustment unit can provide a detailed adjustment method. For example, when the user is relaxed, the adjustment unit can provide a detailed portfolio restructuring method. Furthermore, when the user is excited, the adjustment unit can provide a quick adjustment method. For example, when the user shows strong interest in investing, the adjustment unit can provide a quick risk management method. This enables more appropriate adjustment. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using an AI, for example, or without an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0111] During adjustment, the adjustment unit can adjust the adjustment algorithm by referring to past investment performance. The adjustment unit, for example, adjusts the adjustment algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal adjustment algorithm. The adjustment unit can also select an optimal adjustment method by referring to past investment performance. For example, it can select a specific adjustment method based on past investment performance. Furthermore, the adjustment unit can analyze past investment performance and optimize the adjustment algorithm. For example, it can optimize the adjustment algorithm based on past investment performance. This can optimize the adjustment algorithm and improve its accuracy. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past investment performance data into the generation AI and cause the generation AI to adjust the adjustment algorithm.

[0112] During adjustment, the adjustment unit can apply different adjustment methods depending on the specific investment category. For example, the adjustment unit applies a specific adjustment method to stock investments. For example, a stock price data risk management method can be applied to stock investments. The adjustment unit can also apply a different adjustment method to FX investments. For example, an exchange rate risk management method can be applied to FX investments. Furthermore, the adjustment unit can also apply a dedicated adjustment method to microinvestments. For example, a small investment risk management method can be applied to microinvestments. This allows for applying the optimal adjustment method according to the specific investment category, thereby improving the accuracy of the adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data of a specific investment category into the generation AI and cause the generation AI to apply the adjustment method.

[0113] The adjustment unit can estimate the user's emotion and adjust the display method of the adjustment result based on the estimated user's emotion. For example, when the user is nervous, the adjustment unit can provide a simple, highly visible display method. For example, when the user is nervous, the adjustment unit can provide a display method using a concise graph or chart. Furthermore, when the user is relaxed, the adjustment unit can provide a display method including detailed information. For example, when the user is relaxed, the adjustment unit can provide a display method using a detailed text report. Furthermore, when the user is in a hurry, the adjustment unit can provide a display method that focuses on the main points. For example, when the user is in a hurry, the adjustment unit can provide a display method that highlights only the important points. This enables a more appropriate display. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using an AI, for example, or without an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0114] During adjustment, the adjustment unit can determine the priority of adjustment based on the submission date of the investment information. For example, the adjustment unit prioritizes adjustment of investment information submitted earlier. For example, if investment information is submitted early, the adjustment unit can prioritize that information. The adjustment unit can also determine the order of adjustment based on the submission date of the investment information. For example, adjustment can be performed in order of closest submission date. Furthermore, the adjustment unit can also adjust the priority of adjustment taking into account the submission date of the investment information. For example, if the submission date is an important factor, the adjustment priority can be determined based on that factor. This enables efficient adjustment. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the submission date of investment information to the generation AI and cause the generation AI to determine the priority of adjustment.

[0115] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to related market trends. The adjustment unit can improve the accuracy of the adjustment based on, for example, market trends. For example, the adjustment unit can analyze current market trends and make adjustments based on the results. The adjustment unit can also adjust the adjustment method by referring to related market trends. For example, the adjustment unit can select an appropriate adjustment method depending on the market trend. Furthermore, the adjustment unit can optimize the accuracy of the adjustment by taking market trends into consideration. For example, trend analysis can be used to predict future market trends and make adjustments based on the results. This can improve the accuracy of the adjustment. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input market trend data to the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0116] The assistance unit can estimate the user's emotions and adjust its assistance methods based on the estimated emotions. For example, if the user is stressed, the assistance unit can provide a simple assistance method. For instance, when the user is stressed, it can provide simple investment advice. The assistance unit can also provide a detailed assistance method if the user is relaxed. For instance, when the user is relaxed, it can provide a detailed investment strategy. Furthermore, if the user is excited, the assistance unit can provide a rapid assistance method. For example, when the user shows a strong interest in investing, it can provide rapid investment advice. This enables more appropriate assistance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the assistance unit may be performed using AI, or not using AI. For example, the assistance unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] During assistance, the assisting unit can adjust the assist algorithm by referring to past investment performance. The assisting unit, for example, adjusts the assist algorithm based on past investment performance. For example, it can analyze past investment performance and select an optimal assist algorithm. The assisting unit can also select an optimal assist method by referring to past investment performance. For example, it can select a specific assist method based on past investment performance. Furthermore, the assisting unit can analyze past investment performance and optimize the assist algorithm. For example, it can optimize the assist algorithm based on past investment performance. This can optimize the assist algorithm and improve its accuracy. Some or all of the above-described processing in the assisting unit may be performed using, for example, AI, or may be performed without using AI. For example, the assisting unit can input past investment performance data into the generating AI and have the generating AI adjust the assist algorithm.

[0118] The assistance unit can estimate the user's emotions and determine the priority of assistance based on the estimated emotions. For example, if the user is nervous, the assistance unit will prioritize providing low-risk assistance methods. For example, when the user is nervous, it can prioritize providing safe investment advice. The assistance unit can also provide high-risk assistance methods if the user is relaxed. For example, when the user is relaxed, it can provide high-risk investment strategies. Furthermore, if the user is excited, the assistance unit can provide immediate assistance and a quick response. For example, when the user shows a strong interest in investing, it can immediately provide investment advice and a quick response. This enables more appropriate assistance. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assistance unit may be performed using AI or not using AI. For example, the assistance unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] During assistance, the assisting unit can adjust the content of the assistance based on the timing of submission of the investment information. For example, the assisting unit prioritizes assistance for investment information that is submitted earlier. For example, if investment information is submitted early, the assisting unit can prioritize assistance for that information. The assisting unit can also determine the order of assistance based on the timing of submission of the investment information. For example, assistance can be provided in order of closest submission. Furthermore, the assisting unit can adjust the priority of assistance taking into account the timing of submission of the investment information. For example, if the timing of submission is an important factor, the priority of assistance can be determined based on that factor. This enables efficient assistance. Some or all of the above-described processing in the assisting unit may be performed using, for example, AI, or may be performed without using AI. For example, the assisting unit can input data on the timing of submission of investment information to the generating AI and have the generating AI determine the priority of assistance.

[0120] The evaluation unit can estimate the user's emotions and adjust the risk assessment method based on the estimated user emotions. For example, the evaluation unit can provide a simple risk assessment method when the user is stressed. For example, when the user is stressed, a simple risk scoring can be provided. The evaluation unit can also provide a detailed risk assessment method when the user is relaxed. For example, when the user is relaxed, a detailed risk assessment report can be provided. Furthermore, the evaluation unit can also provide a quick risk assessment method when the user is excited. For example, when the user shows a strong interest in investing, a quick risk assessment can be provided. This enables more appropriate risk assessment. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0121] During evaluation, the evaluation unit can adjust the evaluation algorithm by referring to past investment performance. The evaluation unit can adjust the evaluation algorithm based on, for example, past investment performance. For example, it can analyze past investment performance and select an optimal evaluation algorithm. The evaluation unit can also select an optimal evaluation method by referring to past investment performance. For example, it can select a specific evaluation method based on past investment performance. Furthermore, the evaluation unit can analyze past investment performance and optimize the evaluation algorithm. For example, it can optimize the evaluation algorithm based on past investment performance. This can optimize the evaluation algorithm and improve accuracy. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input past investment performance data into the generation AI and cause the generation AI to adjust the evaluation algorithm.

[0122] 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 nervous, the evaluation unit can prioritize low-risk evaluation methods. For example, when the user is nervous, the evaluation unit can prioritize risk assessments of safe investments. Furthermore, when the user is relaxed, the evaluation unit can also provide high-risk evaluation methods. For example, when the user is relaxed, the evaluation unit can provide risk assessments of high-risk investments. Furthermore, when the user is excited, the evaluation unit can immediately provide risk assessments and provide a quick response. For example, when the user shows strong interest in an investment, the evaluation unit can immediately provide risk assessments and provide a quick response. This enables more appropriate risk assessments. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0123] The evaluation unit can adjust the evaluation content based on the timing of investment information submission during the evaluation process. For example, the evaluation unit can prioritize evaluation of investment information submitted earlier. For instance, if investment information is submitted early, it can be given priority in the evaluation. The evaluation unit can also determine the order of evaluation based on the timing of investment information submission. For example, evaluations can be performed in order from those with the closest submission dates. Furthermore, the evaluation unit can adjust the priority of evaluation by considering the timing of investment information submission. For example, if the submission date is an important factor, the evaluation priority can be determined based on that factor. This enables efficient evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input investment information submission date data into a generating AI and have the generating AI determine the evaluation priority.

[0124] The review unit can estimate the user's emotions and adjust the review method based on the estimated user emotions. For example, the review unit can provide a simple review method when the user is stressed. For example, when the user is stressed, the review unit can provide a simple risk management method. The review unit can also provide a detailed review method when the user is relaxed. For example, when the user is relaxed, the review unit can provide a detailed portfolio restructuring method. Furthermore, the review unit can provide a quick review method when the user is excited. For example, when the user shows strong interest in investing, the review unit can provide a quick risk management method. This enables more appropriate review. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the review unit can be performed using an AI, for example, or without an AI. For example, the review unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0125] The review unit can adjust the review algorithm by referring to past investment performance during the review process. For example, the review unit can adjust the review algorithm based on past investment performance. For example, it can analyze past investment performance and select the optimal review algorithm. The review unit can also select the optimal review method by referring to past investment performance. For example, it can select a specific review method based on past investment performance. Furthermore, the review unit can analyze past investment performance and optimize the review algorithm. For example, it can optimize the review algorithm based on past investment performance. This allows for the optimization of the review algorithm and improvement of its accuracy. Some or all of the above processes in the review unit may be performed using AI, for example, or without AI. For example, the review unit can input past investment performance data into a generating AI and have the generating AI perform the adjustment of the review algorithm.

[0126] The review unit can estimate the user's emotions and determine the priority of the review based on the estimated emotions. For example, if the user is stressed, the review unit can prioritize providing low-risk review methods. For example, when the user is stressed, it can prioritize providing reviews of high-safety investments. The review unit can also provide reviews of high-risk methods if the user is relaxed. For example, when the user is relaxed, it can provide reviews of high-risk investments. Furthermore, if the user is excited, the review unit can provide immediate reviews and a quick response. For example, when the user shows strong interest in an investment, it can provide immediate reviews and a quick response. This enables more appropriate reviews. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the review unit may be performed using AI or not using AI. For example, the review unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0127] During review, the review unit can adjust the content of the review based on the timing of submission of the investment information. For example, the review unit prioritizes review of investment information that was submitted earlier. For example, if investment information is submitted early, the review unit can prioritize review of that information. The review unit can also determine the order of review based on the timing of submission of the investment information. For example, the review unit can review information that is submitted most recently. Furthermore, the review unit can adjust the priority of review taking into account the timing of submission of the investment information. For example, if the timing of submission is an important factor, the priority of review can be determined based on that factor. This enables efficient review. Some or all of the above-mentioned processing in the review unit may be performed using, for example, AI, or may be performed without using AI. For example, the review unit can input data on the timing of submission of investment information into the generation AI and have the generation AI determine the priority of review. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, monitoring unit, adjustment unit, assist unit, evaluation unit, and review unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives investment information from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects market data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes investment targets based on the analysis results. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the progress of the investment. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes adjustments as needed. The assist unit is implemented by the control unit 46A of the smart device 14 and proposes change investments and micro-investments. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the risk of the investment destination. The review unit is realized by the specific processing unit 290 of the data processing device 12, and reviews the investment destination. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, monitoring unit, adjustment unit, assist unit, evaluation unit, and review unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives investment information from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects market data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes investment targets based on the analysis results. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress of the investment. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes adjustments as needed. The assist unit is implemented by the control unit 46A of the smart glasses 214 and proposes change investments and micro-investments. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the risk of the investment destination. The review unit is realized by the specific processing unit 290 of the data processing device 12, and reviews the investment destination. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, monitoring unit, adjustment unit, assist unit, evaluation unit, and review unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives investment information from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects market data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes investment targets based on the analysis results. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of investments. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes adjustments as needed. The assist unit is implemented by the control unit 46A of the headset terminal 314 and proposes change investments and micro-investments. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the risk of the investment destination. The review unit is realized by the specific processing unit 290 of the data processing device 12, and reviews the investment destination. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, monitoring unit, adjustment unit, assist unit, evaluation unit, and review unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives investment information from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects market data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes investment targets based on the analysis results. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the progress of the investment. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes adjustments as needed. The assist unit is implemented by the control unit 46A of the robot 414 and proposes change investments and micro-investments. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the risk of the investment destination. The review unit is realized by the specific processing unit 290 of the data processing device 12, and reviews the investment destination.

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] The proposal section can customize its proposals based on the user's investment goals. For example, if the user is aiming for short-term profits, the proposal section can suggest high-risk, high-return investments. Alternatively, if the user is aiming for long-term asset formation, the proposal section can suggest investments that are expected to achieve stable growth. Furthermore, if the user is interested in a specific sector, the proposal section can prioritize investments related to that sector. This allows for optimal proposals tailored to the user's investment goals.

[0130] The monitoring department can evaluate the diversity of a user's investment portfolio and make diversification investment suggestions as necessary. For example, if a user's portfolio is biased toward a particular sector, the monitoring department can suggest investments in other sectors. Also, if a user's portfolio is concentrated in a particular region, the monitoring department can suggest geographically diversified investments. Furthermore, if a user's portfolio is biased toward a particular asset class, the monitoring department can suggest investments in different asset classes. This allows for risk diversification and stable investment results.

[0131] The adjustment unit can adjust the investment portfolio based on the user's life events. For example, when the user approaches a life event such as marriage or childbirth, the adjustment unit can suggest restructuring the portfolio to reduce risk. Also, when the user approaches retirement, the adjustment unit can suggest investments to ensure a stable income. Furthermore, when the user plans to make a large expenditure, the adjustment unit can suggest securing funds to prepare for that expenditure. This makes it possible to provide a flexible investment strategy that corresponds to the user's life events.

[0132] The evaluation unit can adjust the level of detail of the risk evaluation based on the user's investment experience. For example, it can provide a simple and easy-to-understand risk evaluation for beginner investors and a detailed risk evaluation report for experienced investors. It can also adjust the frequency of risk evaluation based on the user's investment experience. For example, it can perform regular risk evaluations for beginners and provide risk evaluations as needed for experienced investors. This makes it possible to perform appropriate risk evaluations based on the user's investment experience.

[0133] The proposal unit can adjust its proposal approach based on the user's investment style. For example, it can propose high-risk, high-return investments to a user with an aggressive investment style, and low-risk, stable-return investments to a user with a conservative investment style. It can also adjust the frequency of proposals based on the user's investment style. For example, it can make frequent proposals to aggressive investors, and make proposals as needed to conservative investors. This allows it to make optimal proposals based on the user's investment style.

[0134] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. For instance, when the user is nervous, it can provide a display method using concise graphs or charts. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, when the user is relaxed, it can provide a display method using a detailed text report. Furthermore, the analysis unit can provide a display method that focuses on the essentials if the user is in a hurry. For example, when the user is in a hurry, it can provide a display method that highlights only the important points. This allows for a more appropriate display.

[0135] The suggestion function can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is stressed, it can provide simple and highly visual suggestions. For instance, when the user is stressed, it can provide suggestions using concise graphs and charts. Furthermore, if the user is relaxed, the suggestion function can provide suggestions with more detailed information. For example, when the user is relaxed, it can provide suggestions using detailed text reports. Additionally, if the user is in a hurry, the suggestion function can provide concise suggestions. For example, when the user is in a hurry, it can provide suggestions that highlight only the most important points. This allows for more appropriate suggestions.

[0136] The monitoring unit can estimate the user's emotions and adjust its monitoring methods based on those emotions. For example, if the user is stressed, it can provide a simple monitoring method. For instance, it can provide a simple alert system when the user is stressed. The monitoring unit can also provide a more detailed monitoring method when the user is relaxed. For example, it can provide a detailed monitoring report when the user is relaxed. Furthermore, the monitoring unit can provide a rapid monitoring method when the user is excited. For example, it can provide a rapid alert system when the user shows strong interest in an investment. This allows for more appropriate monitoring.

[0137] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple adjustment method can be provided. For example, if the user is feeling stressed, a simple risk management method can be provided. Furthermore, the adjustment unit can also provide a detailed adjustment method if the user is relaxed. For example, if the user is relaxed, a detailed portfolio reconfiguration method can be provided. Furthermore, the adjustment unit can also provide a quick adjustment method if the user is excited. For example, if the user is showing a strong interest in investing, a quick risk management method can be provided. This enables more appropriate adjustment.

[0138] The evaluation unit can estimate the user's emotions and adjust the risk evaluation method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple risk evaluation method can be provided. For example, when the user is feeling stressed, a simple risk scoring can be provided. The evaluation unit can also provide a detailed risk evaluation method if the user is relaxed. For example, when the user is relaxed, a detailed risk evaluation report can be provided. Furthermore, the evaluation unit can also provide a quick risk evaluation method if the user is excited. For example, when the user shows a strong interest in investing, a quick risk evaluation can be provided. This enables more appropriate risk evaluation.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The reception desk receives investment information from users. This information includes, for example, stock information, bond information, and real estate investment information. The reception desk can receive investment information, for example, when a user enters "I want to start investing in stocks." Step 2: The collection unit collects market data based on the information received by the reception unit. Market data includes, for example, stock price data, economic indicators, and news articles. The collection unit can, for example, collect the latest stock price data from the internet. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can predict future stock price forecasts based on the collected stock price data. Step 4: The proposal unit proposes investment targets based on the analysis results obtained by the analysis unit. The proposal is made based, for example, on investment selection criteria and risk assessment. For example, the proposal unit can propose the optimal stock investment targets to the user. Step 5: The monitoring unit monitors the progress of the investment. Monitoring can include, for example, real-time monitoring and periodic checks. The monitoring unit can, for example, monitor the user's investment portfolio in real time and issue an alert if an anomaly occurs. Step 6: The adjustment department makes adjustments as needed. These adjustments may include, for example, restructuring the investment portfolio or managing risk. For instance, if stock prices plummet, the adjustment department may review investment targets and propose ways to minimize risk.

[0141] 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.

[0142] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 7, a 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Explanation of symbols]

[0213] 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 reception desk that receives investment information from users, A collection unit collects market data based on the information received by the aforementioned reception unit, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes investment targets, The monitoring department, which monitors the progress of the investment, It includes an adjustment unit for making adjustments as needed. A system characterized by:

2. It features an assist section that suggests investing spare change or micro-investments.

2. The system of claim 1.

3. Equipped with an evaluation department that performs risk assessment 2. The system of claim 1.

4. It has a review department that reviews investment targets.

2. The system of claim 1.

5. The collecting unit Collect market data 2. The system of claim 1.

6. The analysis unit The collected data is analyzed to provide current prices or forecasts.

2. The system of claim 1.

7. The reception unit The system estimates user sentiment and adjusts the timing of investment information submissions based on the estimated user sentiment.

2. The system of claim 1.

8. The reception unit Analyze the user's past investment history and select the appropriate application method.

2. The system of claim 1.

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