system

Abstract

The system according to this embodiment aims to allow users to obtain asset management advice based on the latest financial news. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs the user's asset information. The collection unit obtains the latest financial news from news sites and financial information sites. The analysis unit analyzes the news obtained by the collection unit. The provision unit provides asset management advice based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult for a user to obtain advice on asset management based on the latest financial news, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user to obtain advice on asset management based on the latest financial news.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives the user's asset information. The collection unit obtains the latest financial news from news sites and financial information sites. The analysis unit analyzes the news obtained by the collection unit. The provision unit provides asset management advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to receive asset management advice based on the latest financial news. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The asset management support system according to an embodiment of the present invention is a system using an AI agent to support asset management in Japan, against the backdrop of the start of the new NISA system in 2024 and the Financial Services Agency's estimate of the 20 million yen retirement problem. This system allows the user to input asset information, and the AI ​​agent obtains the latest financial news from news sites and financial information sites, analyzes it, and provides advice on the user's asset management. The advice is individually customized based on the user's assets and life plan. For example, the user inputs asset information. For example, detailed information such as the type and quantity of assets held, such as stocks, bonds, and real estate, and purchase price are input. This information is input to the AI ​​agent. Next, the AI ​​agent obtains the latest financial news from news sites and financial information sites. For example, it obtains news such as important domestic and international economic indicators, political and banking monetary policies, and bookkeeping and accounting information. This allows the user to grasp the latest market trends. The AI ​​agent analyzes the obtained news. The AI ​​agent analyzes the obtained news and extracts important information related to the user's assets. For example, it extracts financial information for specific stocks or news regarding changes in monetary policy. Based on the analysis results, the system provides users with advice on asset management. For example, it can advise on the timing of buying and selling specific stocks or reviewing portfolios. This advice is individually customized based on the user's assets and life plan. This system allows users to manage their assets efficiently even without specialized knowledge. In addition, by having the AI ​​agent analyze daily financial news, users can grasp the latest market trends and receive appropriate advice. This enables efficient savings for retirement. For example, if financial results information for stocks held by the user is released, the AI ​​agent will analyze that information and advise on the timing of buying and selling. Also, if news regarding changes in monetary policy is released, the AI ​​agent will analyze the impact and suggest a portfolio review. This allows users to manage their assets appropriately based on the latest market trends.This allows the asset management support system to efficiently assist users with their asset management.

[0029] The asset management support system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs the user's asset information. The user's asset information includes, but is not limited to, cash, stocks, and real estate. For example, the reception unit can input detailed information such as the type and quantity of stocks held by the user and their purchase price. The reception unit can also input information on bonds and real estate held by the user. The collection unit obtains the latest financial news from news sites and financial information sites. For example, the collection unit can obtain news such as important domestic and international economic indicators, political and banking monetary policies, and bookkeeping and accounting information. For example, the collection unit obtains domestic economic indicators such as GDP, unemployment rate, and inflation rate. The collection unit can also obtain news such as interest rate policies, quantitative easing, and regulatory changes as political and banking monetary policies. Furthermore, the collection unit can obtain news such as corporate financial statements, revenue reports, and cash flow as bookkeeping and accounting information. The analysis unit analyzes the news obtained by the collection unit. The analysis unit can analyze news using methods such as text mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can use text mining to analyze the content of news and extract important information. The analysis unit can also use statistical analysis to evaluate the impact and relevance of news. Furthermore, the analysis unit can use machine learning algorithms to evaluate the reliability and importance of news. The provision unit provides asset management advice based on the analysis results obtained by the analysis unit. The provision unit can provide advice such as investment recommendations, risk management suggestions, and portfolio optimization. For example, the provision unit can advise on the timing of buying and selling specific stocks. The provision unit can also suggest portfolio revisions. Furthermore, the provision unit can also make risk management suggestions. Thus, the asset management support system according to this embodiment can input the user's asset holdings information, acquire and analyze the latest financial news, and provide asset management advice.

[0030] The reception desk inputs the user's asset information. This information includes, but is not limited to, cash, stocks, and real estate. The reception desk can input detailed information such as the type, quantity, and purchase price of stocks held by the user. It can also input information about bonds and real estate held by the user. Specifically, the user inputs detailed information such as the type, quantity, purchase date, purchase price, and current valuation of their assets through a dedicated interface. For example, for stocks, the user can input the company name, security code, number of shares held, purchase price per share, and current market price. For real estate, the user can input information such as the property's location, purchase price, current valuation, and rental income. Furthermore, the reception desk can customize input fields according to the type of asset the user holds. For example, for bonds, the user can input information such as the issuer, interest rate, maturity date, and purchase price. The reception desk centrally manages the information entered by the user and stores it in a database. This allows users to easily input and manage their asset information, improving the overall efficiency and accuracy of the system.

[0031] The data collection unit obtains the latest financial news from news sites and financial information sites. For example, it can obtain news on important domestic and international economic indicators, political and banking monetary policies, and accounting and financial statements. For example, it can obtain domestic economic indicators such as GDP, unemployment rate, and inflation rate. Furthermore, it can obtain news on political and banking monetary policies such as interest rate policies, quantitative easing, and regulatory changes. In addition, it can obtain news on accounting and financial statements such as corporate financial statements, revenue reports, and cash flow. Specifically, the data collection unit collects the latest financial news in real time from multiple reliable news sources on the internet using APIs and web scraping technologies. The data collection unit categorizes the collected news and stores it in a database. For example, news on economic indicators is categorized under the economy category, news on monetary policy under the policy category, and corporate financial statements under the corporate category. The data collection unit can set the frequency of news collection and can also be set to prioritize collection when important news occurs. This allows the data collection unit to always obtain the latest financial news, maintaining the freshness and reliability of information throughout the entire system.

[0032] The analysis department analyzes the news collected by the data collection department. The analysis department can analyze news using methods such as text mining, statistical analysis, and machine learning algorithms. For example, the analysis department uses text mining to analyze the content of news and extract important information. Furthermore, the analysis department can use statistical analysis to evaluate the impact and relevance of news. In addition, the analysis department can use machine learning algorithms to evaluate the reliability and importance of news. Specifically, using text mining technology, the content of news articles is analyzed using natural language processing (NLP) technology to extract keywords and perform sentiment analysis. This allows for the evaluation of the positive and negative impact of news. Statistical analysis quantitatively evaluates the impact of current news on the market by comparing it with past data. For example, it predicts the impact of current news based on the market impact of similar past news. Machine learning algorithms build models that automatically evaluate the reliability and importance of news by training them with past news data. This allows the analysis department to analyze collected news from multiple perspectives and improve the accuracy and reliability of the information provided to users.

[0033] The service provider provides asset management advice based on the analysis results obtained by the analysis department. The service provider can offer advice such as investment recommendations, risk management suggestions, and portfolio optimization. For example, it may advise on the timing of buying and selling specific stocks. It can also suggest portfolio revisions. Furthermore, it can offer risk management suggestions. Specifically, the service provider generates specific advice for the user's assets based on the impact and reliability assessments of news provided by the analysis department. For example, if a particular stock is deemed likely to rise in value in the short term, it will recommend purchasing that stock. Also, if a particular economic indicator is deemed to have a high risk of deterioration, it will recommend selling that asset to avoid risk. Portfolio optimization will propose the optimal asset allocation according to the user's risk tolerance and investment goals. Risk management suggestions will evaluate the risk of the user's portfolio and propose specific measures to reduce risk. Examples include recommendations for diversification and suggestions for hedging measures. The service provider presents this advice to the user in an easy-to-understand manner, supporting the user in making appropriate asset management decisions. This allows the service provider to improve the efficiency and security of users' asset management.

[0034] The service provider includes a customization unit that tailors advice based on the user's life plan. The customization unit can customize advice based on, for example, the user's retirement plan, education fund plan, or home purchase plan. For instance, the customization unit can provide long-term asset management advice based on the user's retirement plan. It can also propose methods for saving for education based on the user's education fund plan. Furthermore, it can propose a mortgage repayment plan based on the user's home purchase plan. This allows for the provision of individually customized advice based on the user's life plan.

[0035] The service includes a timing unit that advises on the timing of buying and selling specific stocks. The timing unit can advise on buying and selling timing using methods such as technical analysis, fundamental analysis, and market trends. For example, the timing unit can use technical analysis to analyze stock price trends and propose buying and selling timings. It can also use fundamental analysis to evaluate a company's performance and financial condition and propose buying and selling timings. Furthermore, the timing unit can analyze market trends and propose buying and selling timings based on market movements. This allows the service to support users' asset management by advising on the timing of buying and selling specific stocks.

[0036] The service provider includes a review unit that proposes portfolio revisions. The review unit can propose portfolio revisions using methods such as risk assessment, asset allocation changes, and performance assessments. For example, the review unit can use risk assessment to evaluate the portfolio's risk and propose revisions to minimize that risk. It can also propose asset allocation changes to optimize the portfolio's balance. Furthermore, the review unit can use performance assessment to evaluate the portfolio's performance and propose areas for improvement. In this way, by proposing portfolio revisions, it optimizes the user's asset management.

[0037] The analysis department analyzes acquired news and extracts important information related to the user's assets. For example, the analysis department can extract important information based on criteria such as news impact, relevance, and reliability. For instance, it can evaluate news impact and extract news that significantly affects the user's assets. It can also evaluate news relevance and extract news relevant to the user's assets. Furthermore, it can evaluate news reliability and extract highly reliable news. This process improves the accuracy of investment advice by extracting important information related to the user's assets.

[0038] The data collection unit acquires news such as important domestic and international economic indicators, political and banking monetary policies, and accounting and financial information. For example, it can acquire key economic indicators such as GDP, unemployment rate, and inflation rate. It can also acquire monetary policies such as interest rate policies, quantitative easing, and regulatory changes. Furthermore, it can acquire accounting and financial information such as corporate financial statements, earnings reports, and cash flow statements. This allows for the acquisition of important domestic and international financial news, enabling a grasp of the latest market trends.

[0039] The reception desk analyzes the user's past asset information input history and selects the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). Furthermore, the reception desk can predict and suggest input methods that the user will use at specific times of day based on their past input history. In addition, the reception desk can analyze the type and format of asset information the user has entered in the past and suggest the optimal input method. This allows the reception desk to suggest the most suitable input method for the user by analyzing their past input history.

[0040] The reception desk filters asset information input based on the user's current asset status and areas of interest. For example, the reception desk can filter the information that needs to be entered based on the user's current asset status. It can also prioritize the asset information to be entered based on the user's areas of interest. Furthermore, the reception desk can adjust the level of detail of the information to be entered based on the user's asset status and areas of interest. This allows for efficient information input by filtering information based on the user's asset status and areas of interest.

[0041] The reception desk prioritizes inputting highly relevant information when users enter asset information, taking into account their geographical location. For example, the reception desk can prioritize inputting asset information related to a specific region based on the user's geographical location. It can also prioritize inputting information related to the local economic situation based on the user's geographical location. Furthermore, it can prioritize inputting information related to local financial policy based on the user's geographical location. This allows for efficient information entry by prioritizing highly relevant information based on the user's geographical location.

[0042] The reception desk analyzes the user's social media activity when entering asset information and inputs relevant information. For example, the reception desk can prioritize inputting asset information of interest based on the user's social media activity. It can also input relevant news and information based on the user's social media activity. Furthermore, the reception desk can input information related to asset management based on the user's social media activity. This allows for efficient information input by inputting relevant information based on the user's social media activity.

[0043] The data collection unit analyzes past news collection history and selects the optimal collection method. For example, the data collection unit can prioritize collecting the types of news that the user has frequently collected in the past. Furthermore, the data collection unit can predict the types of news to collect at specific time periods based on the user's past collection history. In addition, the data collection unit can analyze the content of news collected by the user in the past and suggest the optimal collection method. This allows the system to suggest the most suitable collection method to the user by analyzing their past collection history.

[0044] The data collection unit filters news based on the user's areas of interest. For example, it can prioritize collecting relevant news based on the user's areas of interest. It can also filter and collect important news based on the user's areas of interest. Furthermore, it can collect detailed news based on the user's areas of interest. This allows for efficient news collection by filtering news based on the user's areas of interest.

[0045] The news collection unit prioritizes collecting highly relevant news by considering the user's geographical location. For example, the collection unit can prioritize collecting news related to a specific region based on the user's geographical location. It can also prioritize collecting news related to the local economic situation based on the user's geographical location. Furthermore, it can prioritize collecting news related to local financial policy based on the user's geographical location. This allows for efficient news collection by prioritizing highly relevant news based on the user's geographical location.

[0046] The data collection unit analyzes users' social media activity when collecting news and gathers relevant news. For example, the data collection unit can prioritize collecting news of interest based on users' social media activity. It can also collect relevant news based on users' social media activity. Furthermore, the data collection unit can collect news related to asset management based on users' social media activity. This allows for efficient news collection by gathering relevant news based on users' social media activity.

[0047] The analysis department prioritizes extracting important information related to the user's assets when analyzing news. For example, the analysis department can prioritize analyzing news related to the user's assets. It can also extract important information related to the user's assets. Furthermore, the analysis department can conduct detailed analyses of news related to the user's assets. By prioritizing the extraction of important information related to the user's assets, the accuracy of investment advice is improved.

[0048] The analysis department improves the accuracy of its analysis by referring to the user's past investment history when analyzing news. For example, the analysis department can improve the accuracy of its analysis by referring to the user's past investment history. Furthermore, the analysis department can select the optimal analysis method based on the user's past investment history. In addition, the analysis department can analyze the user's past investment history and extract important information. This improves the accuracy of the analysis by referring to the user's past investment history.

[0049] The analysis department prioritizes analyzing highly relevant information by considering the user's geographical location when analyzing news. For example, the analysis department can prioritize analyzing news related to a user's region based on their geographical location. It can also prioritize analyzing news related to the local economic situation based on the user's geographical location. Furthermore, the analysis department can prioritize analyzing news related to local financial policy based on the user's geographical location. This allows for efficient information analysis by prioritizing highly relevant information based on the user's geographical location.

[0050] The analytics department analyzes users' social media activity when analyzing news and extracts relevant information. For example, the analytics department can prioritize analyzing news that users are interested in based on their social media activity. It can also analyze news that is relevant to users' social media activity. Furthermore, the analytics department can analyze news related to asset management based on users' social media activity. This allows for efficient information analysis by extracting relevant information based on users' social media activity.

[0051] The service provider customizes the advice given based on the user's assets and life plan. For example, the service provider can provide optimal advice based on the user's assets. Furthermore, the service provider can provide long-term asset management advice based on the user's life plan. In addition, the service provider can provide individually customized advice considering the user's assets and life plan. This allows for the provision of individually optimal advice by customizing the advice based on the user's assets and life plan.

[0052] The service provider will provide optimal advice by referring to the user's past investment history. For example, the service provider can provide optimal advice by referring to the user's past investment history. Furthermore, the service provider can also provide advice that minimizes risk based on the user's past investment history. In addition, the service provider can analyze the user's past investment history and provide advice based on successful investment methods. This allows the service provider to provide optimal advice by referring to the user's past investment history.

[0053] The service provider will provide optimal advice by considering the user's geographical location. For example, the service provider can provide region-specific advice based on the user's geographical location. It can also provide advice related to the local economic situation based on the user's geographical location. Furthermore, it can provide advice related to local monetary policy based on the user's geographical location. This allows for efficient advice delivery by providing optimal advice based on the user's geographical location.

[0054] The service provider analyzes the user's social media activity when providing advice and offers relevant advice. For example, the service provider can offer advice that is of interest to the user based on their social media activity. Furthermore, the service provider can offer advice related to asset management based on the user's social media activity. This allows for efficient advice delivery by providing relevant advice based on the user's social media activity.

[0055] The customization department customizes advice based on the user's life plan to provide optimal solutions. For example, it can provide long-term asset management advice based on the user's life plan. It can also provide advice that minimizes risk based on the user's life plan. Furthermore, it can provide individually customized advice based on the user's life plan. This allows for the provision of individually optimal advice by customizing it based on the user's life plan.

[0056] The customization unit considers the user's geographical location when customizing advice to ensure optimal results. For example, the customization unit can perform region-related customizations based on the user's geographical location. It can also perform customizations related to the local economic situation based on the user's geographical location. Furthermore, it can perform customizations related to local monetary policy based on the user's geographical location. This allows for efficient customization of advice by providing optimal results based on the user's geographical location.

[0057] The timing unit proposes the optimal timing for buying and selling based on the user's assets when providing advice on buying and selling timing. For example, the timing unit can propose the optimal buying and selling timing based on the user's assets. Furthermore, the timing unit can propose buying and selling timing that minimizes risk based on the user's assets. In addition, the timing unit can propose individually customized buying and selling timing based on the user's assets. This allows the system to provide advice that minimizes risk by proposing the optimal buying and selling timing based on the user's assets.

[0058] The timing unit proposes optimal trading timings by considering the user's geographical location when providing advice on buying and selling timings. For example, the timing unit can propose trading timings related to a region based on the user's geographical location. It can also propose trading timings related to the regional economic situation based on the user's geographical location. Furthermore, it can propose trading timings related to regional monetary policy based on the user's geographical location. This allows for efficient advice provision by proposing optimal trading timings based on the user's geographical location.

[0059] The portfolio review department, when providing portfolio review advice, proposes the optimal review based on the user's assets. For example, the review department can propose an optimal portfolio review based on the user's assets. Furthermore, the review department can propose a portfolio review that minimizes risk based on the user's assets. In addition, the review department can propose an individually customized portfolio review based on the user's assets. This allows the department to provide advice that minimizes risk by proposing the optimal portfolio review based on the user's assets.

[0060] The portfolio review department, when providing portfolio review advice, considers the user's geographical location to propose the most suitable review. For example, the review department can propose portfolio reviews relevant to the user's region based on their geographical location. It can also propose portfolio reviews related to the local economic situation based on the user's geographical location. Furthermore, it can propose portfolio reviews related to local monetary policy based on the user's geographical location. This allows for efficient advice provision by proposing the most suitable portfolio review based on the user's geographical location.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can improve input efficiency by referring to the user's past input history when they input their asset information. For example, it can analyze the type and format of asset information the user has entered in the past and suggest the most suitable input method. The reception desk can also predict and suggest the input method the user will use at a particular time of day based on their input history. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. In this way, by analyzing past input history, it can suggest the most suitable input method for the user.

[0063] The analysis department can improve the accuracy of news analysis by referring to the user's past investment history. For example, it can improve the accuracy of the analysis by referring to the user's past investment history. It can also select the optimal analysis method based on the user's past investment history. Furthermore, it can analyze the user's past investment history and extract important information. In this way, the accuracy of the analysis is improved by referring to the user's past investment history.

[0064] The customization function can optimize advice based on the user's life plan by considering the user's geographical location. For example, it can perform region-related customization based on the user's geographical location. It can also perform customization related to the local economic situation based on the user's geographical location. Furthermore, it can perform customization related to local financial policies based on the user's geographical location. This allows for efficient advice customization by optimizing the advice based on the user's geographical location.

[0065] The portfolio review department can propose the optimal portfolio review based on the user's assets when providing advice on portfolio review. For example, it can propose the optimal portfolio review based on the user's assets. It can also propose a portfolio review that minimizes risk based on the user's assets. Furthermore, it can propose a portfolio review that is individually customized based on the user's assets. This allows the department to provide advice that minimizes risk by proposing the optimal portfolio review based on the user's assets.

[0066] The news collection unit can analyze users' social media activity and collect relevant news when gathering news. For example, it can prioritize collecting news of interest based on users' social media activity. It can also collect relevant news based on users' social media activity. Furthermore, it can collect news related to asset management based on users' social media activity. This allows for efficient news collection by gathering relevant news based on users' social media activity.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk enters the user's asset information. This information includes cash, stocks, real estate, etc. The reception desk can enter detailed information such as the type and quantity of stocks the user owns and the purchase price. It can also enter information about bonds and real estate the user owns. Step 2: The data collection unit obtains the latest financial news from news sites and financial information sites. The data collection unit can obtain news on important domestic and international economic indicators, political and banking monetary policies, and accounting and financial statement information. For example, it can obtain domestic economic indicators such as GDP, unemployment rate, and inflation rate, and news on political and banking monetary policies such as interest rate policies, quantitative easing, and regulatory changes. Furthermore, it can also obtain news on corporate financial statements, earnings reports, and cash flow. Step 3: The analysis department analyzes the news collected by the data collection department. The analysis department can analyze the news using methods such as text mining, statistical analysis, and machine learning algorithms. For example, text mining can be used to analyze the content of the news and extract important information. Statistical analysis can be used to evaluate the impact and relevance of the news, and machine learning algorithms can be used to evaluate the reliability and importance of the news. Step 4: The service department provides asset management advice based on the analysis results obtained by the analysis department. The service department can provide advice such as investment recommendations, risk management suggestions, and portfolio optimization. For example, they can advise on the timing of buying and selling specific stocks, suggest portfolio revisions, and offer risk management suggestions.

[0069] (Example of form 2) The asset management support system according to an embodiment of the present invention is a system using an AI agent to support asset management in Japan, against the backdrop of the start of the new NISA system in 2024 and the Financial Services Agency's estimate of the 20 million yen retirement problem. This system allows the user to input asset information, and the AI ​​agent obtains the latest financial news from news sites and financial information sites, analyzes it, and provides advice on the user's asset management. The advice is individually customized based on the user's assets and life plan. For example, the user inputs asset information. For example, detailed information such as the type and quantity of assets held, such as stocks, bonds, and real estate, and purchase price are input. This information is input to the AI ​​agent. Next, the AI ​​agent obtains the latest financial news from news sites and financial information sites. For example, it obtains news such as important domestic and international economic indicators, political and banking monetary policies, and bookkeeping and accounting information. This allows the user to grasp the latest market trends. The AI ​​agent analyzes the obtained news. The AI ​​agent analyzes the obtained news and extracts important information related to the user's assets. For example, it extracts financial information for specific stocks or news regarding changes in monetary policy. Based on the analysis results, the system provides users with advice on asset management. For example, it can advise on the timing of buying and selling specific stocks or reviewing portfolios. This advice is individually customized based on the user's assets and life plan. This system allows users to manage their assets efficiently even without specialized knowledge. In addition, by having the AI ​​agent analyze daily financial news, users can grasp the latest market trends and receive appropriate advice. This enables efficient savings for retirement. For example, if financial results information for stocks held by the user is released, the AI ​​agent will analyze that information and advise on the timing of buying and selling. Also, if news regarding changes in monetary policy is released, the AI ​​agent will analyze the impact and suggest a portfolio review. This allows users to manage their assets appropriately based on the latest market trends.This allows the asset management support system to efficiently assist users with their asset management.

[0070] The asset management support system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit inputs the user's asset information. The user's asset information includes, but is not limited to, cash, stocks, and real estate. For example, the reception unit can input detailed information such as the type and quantity of stocks held by the user and their purchase price. The reception unit can also input information on bonds and real estate held by the user. The collection unit obtains the latest financial news from news sites and financial information sites. For example, the collection unit can obtain news such as important domestic and international economic indicators, political and banking monetary policies, and bookkeeping and accounting information. For example, the collection unit obtains domestic economic indicators such as GDP, unemployment rate, and inflation rate. The collection unit can also obtain news such as interest rate policies, quantitative easing, and regulatory changes as political and banking monetary policies. Furthermore, the collection unit can obtain news such as corporate financial statements, revenue reports, and cash flow as bookkeeping and accounting information. The analysis unit analyzes the news obtained by the collection unit. The analysis unit can analyze news using methods such as text mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can use text mining to analyze the content of news and extract important information. The analysis unit can also use statistical analysis to evaluate the impact and relevance of news. Furthermore, the analysis unit can use machine learning algorithms to evaluate the reliability and importance of news. The provision unit provides asset management advice based on the analysis results obtained by the analysis unit. The provision unit can provide advice such as investment recommendations, risk management suggestions, and portfolio optimization. For example, the provision unit can advise on the timing of buying and selling specific stocks. The provision unit can also suggest portfolio revisions. Furthermore, the provision unit can also make risk management suggestions. Thus, the asset management support system according to this embodiment can input the user's asset holdings information, acquire and analyze the latest financial news, and provide asset management advice.

[0071] The reception desk inputs the user's asset information. This information includes, but is not limited to, cash, stocks, and real estate. The reception desk can input detailed information such as the type, quantity, and purchase price of stocks held by the user. It can also input information about bonds and real estate held by the user. Specifically, the user inputs detailed information such as the type, quantity, purchase date, purchase price, and current valuation of their assets through a dedicated interface. For example, for stocks, the user can input the company name, security code, number of shares held, purchase price per share, and current market price. For real estate, the user can input information such as the property's location, purchase price, current valuation, and rental income. Furthermore, the reception desk can customize input fields according to the type of asset the user holds. For example, for bonds, the user can input information such as the issuer, interest rate, maturity date, and purchase price. The reception desk centrally manages the information entered by the user and stores it in a database. This allows users to easily input and manage their asset information, improving the overall efficiency and accuracy of the system.

[0072] The data collection unit obtains the latest financial news from news sites and financial information sites. For example, it can obtain news on important domestic and international economic indicators, political and banking monetary policies, and accounting and financial statements. For example, it can obtain domestic economic indicators such as GDP, unemployment rate, and inflation rate. Furthermore, it can obtain news on political and banking monetary policies such as interest rate policies, quantitative easing, and regulatory changes. In addition, it can obtain news on accounting and financial statements such as corporate financial statements, revenue reports, and cash flow. Specifically, the data collection unit collects the latest financial news in real time from multiple reliable news sources on the internet using APIs and web scraping technologies. The data collection unit categorizes the collected news and stores it in a database. For example, news on economic indicators is categorized under the economy category, news on monetary policy under the policy category, and corporate financial statements under the corporate category. The data collection unit can set the frequency of news collection and can also be set to prioritize collection when important news occurs. This allows the data collection unit to always obtain the latest financial news, maintaining the freshness and reliability of information throughout the entire system.

[0073] The analysis department analyzes the news collected by the data collection department. The analysis department can analyze news using methods such as text mining, statistical analysis, and machine learning algorithms. For example, the analysis department uses text mining to analyze the content of news and extract important information. Furthermore, the analysis department can use statistical analysis to evaluate the impact and relevance of news. In addition, the analysis department can use machine learning algorithms to evaluate the reliability and importance of news. Specifically, using text mining technology, the content of news articles is analyzed using natural language processing (NLP) technology to extract keywords and perform sentiment analysis. This allows for the evaluation of the positive and negative impact of news. Statistical analysis quantitatively evaluates the impact of current news on the market by comparing it with past data. For example, it predicts the impact of current news based on the market impact of similar past news. Machine learning algorithms build models that automatically evaluate the reliability and importance of news by training them with past news data. This allows the analysis department to analyze collected news from multiple perspectives and improve the accuracy and reliability of the information provided to users.

[0074] The service provider provides asset management advice based on the analysis results obtained by the analysis department. The service provider can offer advice such as investment recommendations, risk management suggestions, and portfolio optimization. For example, it may advise on the timing of buying and selling specific stocks. It can also suggest portfolio revisions. Furthermore, it can offer risk management suggestions. Specifically, the service provider generates specific advice for the user's assets based on the impact and reliability assessments of news provided by the analysis department. For example, if a particular stock is deemed likely to rise in value in the short term, it will recommend purchasing that stock. Also, if a particular economic indicator is deemed to have a high risk of deterioration, it will recommend selling that asset to avoid risk. Portfolio optimization will propose the optimal asset allocation according to the user's risk tolerance and investment goals. Risk management suggestions will evaluate the risk of the user's portfolio and propose specific measures to reduce risk. Examples include recommendations for diversification and suggestions for hedging measures. The service provider presents this advice to the user in an easy-to-understand manner, supporting the user in making appropriate asset management decisions. This allows the service provider to improve the efficiency and security of users' asset management.

[0075] The service provider includes a customization unit that tailors advice based on the user's life plan. The customization unit can customize advice based on, for example, the user's retirement plan, education fund plan, or home purchase plan. For instance, the customization unit can provide long-term asset management advice based on the user's retirement plan. It can also propose methods for saving for education based on the user's education fund plan. Furthermore, it can propose a mortgage repayment plan based on the user's home purchase plan. This allows for the provision of individually customized advice based on the user's life plan.

[0076] The service includes a timing unit that advises on the timing of buying and selling specific stocks. The timing unit can advise on buying and selling timing using methods such as technical analysis, fundamental analysis, and market trends. For example, the timing unit can use technical analysis to analyze stock price trends and propose buying and selling timings. It can also use fundamental analysis to evaluate a company's performance and financial condition and propose buying and selling timings. Furthermore, the timing unit can analyze market trends and propose buying and selling timings based on market movements. This allows the service to support users' asset management by advising on the timing of buying and selling specific stocks.

[0077] The service provider includes a review unit that proposes portfolio revisions. The review unit can propose portfolio revisions using methods such as risk assessment, asset allocation changes, and performance assessments. For example, the review unit can use risk assessment to evaluate the portfolio's risk and propose revisions to minimize that risk. It can also propose asset allocation changes to optimize the portfolio's balance. Furthermore, the review unit can use performance assessment to evaluate the portfolio's performance and propose areas for improvement. In this way, by proposing portfolio revisions, it optimizes the user's asset management.

[0078] The analysis department analyzes acquired news and extracts important information related to the user's assets. For example, the analysis department can extract important information based on criteria such as news impact, relevance, and reliability. For instance, it can evaluate news impact and extract news that significantly affects the user's assets. It can also evaluate news relevance and extract news relevant to the user's assets. Furthermore, it can evaluate news reliability and extract highly reliable news. This process improves the accuracy of investment advice by extracting important information related to the user's assets.

[0079] The data collection unit acquires news such as important domestic and international economic indicators, political and banking monetary policies, and accounting and financial information. For example, it can acquire key economic indicators such as GDP, unemployment rate, and inflation rate. It can also acquire monetary policies such as interest rate policies, quantitative easing, and regulatory changes. Furthermore, it can acquire accounting and financial information such as corporate financial statements, earnings reports, and cash flow statements. This allows for the acquisition of important domestic and international financial news, enabling a grasp of the latest market trends.

[0080] The reception desk estimates the user's emotions and adjusts the timing of asset information input based on the estimated emotions. The reception desk can estimate the user's emotions using methods such as facial recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception desk can delay the input timing to allow them to input information in a relaxed state. Conversely, if the user is relaxed, the reception desk can speed up the input timing to allow for efficient information input. Furthermore, if the user is in a hurry, the reception desk can adjust the input timing to allow for quick information input. In this way, by adjusting the timing of asset information input according to the user's emotions, information can be entered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk analyzes the user's past asset information input history and selects the optimal input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). Furthermore, the reception desk can predict and suggest input methods that the user will use at specific times of day based on their past input history. In addition, the reception desk can analyze the type and format of asset information the user has entered in the past and suggest the optimal input method. This allows the reception desk to suggest the most suitable input method for the user by analyzing their past input history.

[0082] The reception desk filters asset information input based on the user's current asset status and areas of interest. For example, the reception desk can filter the information that needs to be entered based on the user's current asset status. It can also prioritize the asset information to be entered based on the user's areas of interest. Furthermore, the reception desk can adjust the level of detail of the information to be entered based on the user's asset status and areas of interest. This allows for efficient information input by filtering information based on the user's asset status and areas of interest.

[0083] The reception desk estimates the user's emotions and determines the priority of the asset information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of important asset information. It may also prioritize the input of detailed asset information if the user is relaxed. Furthermore, if the user is in a hurry, the reception desk may prioritize the input of the most important asset information. This allows for the priority of important information to be entered by determining the priority of asset information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk prioritizes inputting highly relevant information when users enter asset information, taking into account their geographical location. For example, the reception desk can prioritize inputting asset information related to a specific region based on the user's geographical location. It can also prioritize inputting information related to the local economic situation based on the user's geographical location. Furthermore, it can prioritize inputting information related to local financial policy based on the user's geographical location. This allows for efficient information entry by prioritizing highly relevant information based on the user's geographical location.

[0085] The reception desk analyzes the user's social media activity when entering asset information and inputs relevant information. For example, the reception desk can prioritize inputting asset information of interest based on the user's social media activity. It can also input relevant news and information based on the user's social media activity. Furthermore, the reception desk can input information related to asset management based on the user's social media activity. This allows for efficient information input by inputting relevant information based on the user's social media activity.

[0086] The data collection unit estimates the user's emotions and adjusts the timing of news collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of news collection to allow for more relaxed collection. Conversely, if the user is relaxed, the data collection unit can speed up the timing of news collection for more efficient collection. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing of news collection to allow for faster collection. By adjusting the timing of news collection according to the user's emotions, news can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The data collection unit analyzes past news collection history and selects the optimal collection method. For example, the data collection unit can prioritize collecting the types of news that the user has frequently collected in the past. Furthermore, the data collection unit can predict the types of news to collect at specific time periods based on the user's past collection history. In addition, the data collection unit can analyze the content of news collected by the user in the past and suggest the optimal collection method. This allows the system to suggest the most suitable collection method to the user by analyzing their past collection history.

[0088] The data collection unit filters news based on the user's areas of interest. For example, it can prioritize collecting relevant news based on the user's areas of interest. It can also filter and collect important news based on the user's areas of interest. Furthermore, it can collect detailed news based on the user's areas of interest. This allows for efficient news collection by filtering news based on the user's areas of interest.

[0089] The data collection unit estimates the user's emotions and determines the priority of news to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting important news. If the user is relaxed, the data collection unit can prioritize collecting detailed news. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting the most important news. In this way, by prioritizing news according to the user's emotions, important news can be collected preferentially. 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.

[0090] The news collection unit prioritizes collecting highly relevant news by considering the user's geographical location. For example, the collection unit can prioritize collecting news related to a specific region based on the user's geographical location. It can also prioritize collecting news related to the local economic situation based on the user's geographical location. Furthermore, it can prioritize collecting news related to local financial policy based on the user's geographical location. This allows for efficient news collection by prioritizing highly relevant news based on the user's geographical location.

[0091] The data collection unit analyzes users' social media activity when collecting news and gathers relevant news. For example, the data collection unit can prioritize collecting news of interest based on users' social media activity. It can also collect relevant news based on users' social media activity. Furthermore, the data collection unit can collect news related to asset management based on users' social media activity. This allows for efficient news collection by gathering relevant news based on users' social media activity.

[0092] The analysis unit estimates the user's emotions and adjusts the news analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing important information. If the user is relaxed, the analysis unit can also analyze detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing the most important information. This allows for a more appropriate analysis of the news by adjusting the news analysis method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The analysis department prioritizes extracting important information related to the user's assets when analyzing news. For example, the analysis department can prioritize analyzing news related to the user's assets. It can also extract important information related to the user's assets. Furthermore, the analysis department can conduct detailed analyses of news related to the user's assets. By prioritizing the extraction of important information related to the user's assets, the accuracy of investment advice is improved.

[0094] The analysis department improves the accuracy of its analysis by referring to the user's past investment history when analyzing news. For example, the analysis department can improve the accuracy of its analysis by referring to the user's past investment history. Furthermore, the analysis department can select the optimal analysis method based on the user's past investment history. In addition, the analysis department can analyze the user's past investment history and extract important information. This improves the accuracy of the analysis by referring to the user's past investment history.

[0095] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results according to the user's emotions, the results can be displayed in a more appropriate manner. 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.

[0096] The analysis department prioritizes analyzing highly relevant information by considering the user's geographical location when analyzing news. For example, the analysis department can prioritize analyzing news related to a user's region based on their geographical location. It can also prioritize analyzing news related to the local economic situation based on the user's geographical location. Furthermore, the analysis department can prioritize analyzing news related to local financial policy based on the user's geographical location. This allows for efficient information analysis by prioritizing highly relevant information based on the user's geographical location.

[0097] The analytics department analyzes users' social media activity when analyzing news and extracts relevant information. For example, the analytics department can prioritize analyzing news that users are interested in based on their social media activity. It can also analyze news that is relevant to users' social media activity. Furthermore, the analytics department can analyze news related to asset management based on users' social media activity. This allows for efficient information analysis by extracting relevant information based on users' social media activity.

[0098] The service provider estimates the user's emotions and adjusts the way advice is delivered based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple, easy-to-understand advice. If the user is relaxed, the service provider can provide advice with more detailed information. Furthermore, if the user is in a hurry, the service provider can provide concise advice. By adjusting the way advice is delivered according to the user's emotions, advice can be delivered in a more appropriate manner. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The service provider customizes the advice given based on the user's assets and life plan. For example, the service provider can provide optimal advice based on the user's assets. Furthermore, the service provider can provide long-term asset management advice based on the user's life plan. In addition, the service provider can provide individually customized advice considering the user's assets and life plan. This allows for the provision of individually optimal advice by customizing the advice based on the user's assets and life plan.

[0100] The service provider will provide optimal advice by referring to the user's past investment history. For example, the service provider can provide optimal advice by referring to the user's past investment history. Furthermore, the service provider can also provide advice that minimizes risk based on the user's past investment history. In addition, the service provider can analyze the user's past investment history and provide advice based on successful investment methods. This allows the service provider to provide optimal advice by referring to the user's past investment history.

[0101] The service provider estimates the user's emotions and prioritizes advice based on those emotions. For example, if the user is stressed, the service provider can prioritize important advice. If the user is relaxed, the service provider can prioritize detailed advice. Furthermore, if the user is in a hurry, the service provider can prioritize the most important advice. This ensures that important advice is prioritized by determining the priority of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The service provider will provide optimal advice by considering the user's geographical location. For example, the service provider can provide region-specific advice based on the user's geographical location. It can also provide advice related to the local economic situation based on the user's geographical location. Furthermore, it can provide advice related to local monetary policy based on the user's geographical location. This allows for efficient advice delivery by providing optimal advice based on the user's geographical location.

[0103] The service provider analyzes the user's social media activity when providing advice and offers relevant advice. For example, the service provider can offer advice that is of interest to the user based on their social media activity. Furthermore, the service provider can offer advice related to asset management based on the user's social media activity. This allows for efficient advice delivery by providing relevant advice based on the user's social media activity.

[0104] The customization unit estimates the user's emotions and adjusts how advice is customized based on those emotions. For example, if the user is stressed, the customization unit can provide simple and easy-to-understand advice. If the user is relaxed, it can provide advice with more detailed information. Furthermore, if the user is in a hurry, it can provide concise advice. This allows for more appropriate advice customization by adjusting how advice is customized according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The customization department customizes advice based on the user's life plan to provide optimal solutions. For example, it can provide long-term asset management advice based on the user's life plan. It can also provide advice that minimizes risk based on the user's life plan. Furthermore, it can provide individually customized advice based on the user's life plan. This allows for the provision of individually optimal advice by customizing it based on the user's life plan.

[0106] The customization unit estimates the user's emotions and determines the priority of customizations based on the estimated emotions. For example, if the user is stressed, the customization unit can prioritize important customizations. It can also prioritize detailed customizations if the user is relaxed. Furthermore, if the user is in a hurry, the customization unit can prioritize the most important customizations. This allows for prioritizing important customizations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The customization unit considers the user's geographical location when customizing advice to ensure optimal results. For example, the customization unit can perform region-related customizations based on the user's geographical location. It can also perform customizations related to the local economic situation based on the user's geographical location. Furthermore, it can perform customizations related to local monetary policy based on the user's geographical location. This allows for efficient customization of advice by providing optimal results based on the user's geographical location.

[0108] The timing unit estimates the user's emotions and adjusts the trading timing advice based on the estimated emotions. For example, if the user is stressed, the timing unit can provide simple and easy-to-understand advice. If the user is relaxed, the timing unit can provide advice with more detailed information. Furthermore, if the user is in a hurry, the timing unit can provide concise advice. By adjusting the trading timing advice according to the user's emotions, advice can be provided in a more appropriate manner. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The timing unit proposes the optimal timing for buying and selling based on the user's assets when providing advice on buying and selling timing. For example, the timing unit can propose the optimal buying and selling timing based on the user's assets. Furthermore, the timing unit can propose buying and selling timing that minimizes risk based on the user's assets. In addition, the timing unit can propose individually customized buying and selling timing based on the user's assets. This allows the system to provide advice that minimizes risk by proposing the optimal buying and selling timing based on the user's assets.

[0110] The timing unit estimates the user's emotions and determines the priority of buy and sell timings based on the estimated emotions. For example, if the user is feeling stressed, the timing unit can prioritize suggesting important buy and sell timings. If the user is relaxed, the timing unit can prioritize suggesting detailed buy and sell timings. Furthermore, if the user is in a hurry, the timing unit can prioritize suggesting the most important buy and sell timings. In this way, by prioritizing buy and sell timings according to the user's emotions, important buy and sell timings can be suggested preferentially. 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.

[0111] The timing unit proposes optimal trading timings by considering the user's geographical location when providing advice on buying and selling timings. For example, the timing unit can propose trading timings related to a region based on the user's geographical location. It can also propose trading timings related to the regional economic situation based on the user's geographical location. Furthermore, it can propose trading timings related to regional monetary policy based on the user's geographical location. This allows for efficient advice provision by proposing optimal trading timings based on the user's geographical location.

[0112] The review unit estimates the user's emotions and adjusts the portfolio review advice based on those emotions. For example, if the user is stressed, the review unit can provide simple and easy-to-understand advice. If the user is relaxed, it can provide advice with more detailed information. Furthermore, if the user is in a hurry, it can provide concise advice. By adjusting the portfolio review advice according to the user's emotions, advice can be provided in a more appropriate way. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The portfolio review department, when providing portfolio review advice, proposes the optimal review based on the user's assets. For example, the review department can propose an optimal portfolio review based on the user's assets. Furthermore, the review department can propose a portfolio review that minimizes risk based on the user's assets. In addition, the review department can propose an individually customized portfolio review based on the user's assets. This allows the department to provide advice that minimizes risk by proposing the optimal portfolio review based on the user's assets.

[0114] The review unit estimates the user's emotions and determines the priority of portfolio reviews based on the estimated emotions. For example, if the user is feeling stressed, the review unit can prioritize suggesting important portfolio reviews. If the user is relaxed, the review unit can prioritize suggesting detailed portfolio reviews. Furthermore, if the user is in a hurry, the review unit can prioritize suggesting the most important portfolio reviews. This allows for the prioritization of important portfolio reviews based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The portfolio review department, when providing portfolio review advice, considers the user's geographical location to propose the most suitable review. For example, the review department can propose portfolio reviews relevant to the user's region based on their geographical location. It can also propose portfolio reviews related to the local economic situation based on the user's geographical location. Furthermore, it can propose portfolio reviews related to local monetary policy based on the user's geographical location. This allows for efficient advice provision by proposing the most suitable portfolio review based on the user's geographical location.

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

[0117] The reception desk can improve input efficiency by referring to the user's past input history when they input their asset information. For example, it can analyze the type and format of asset information the user has entered in the past and suggest the most suitable input method. The reception desk can also predict and suggest the input method the user will use at a particular time of day based on their input history. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. In this way, by analyzing past input history, it can suggest the most suitable input method for the user.

[0118] The data collection unit can estimate the user's emotions and adjust the timing of news collection based on those emotions. For example, if the user is stressed, the timing of news collection can be delayed to allow for more relaxed collection. Conversely, if the user is relaxed, the timing of news collection can be accelerated for more efficient collection. Furthermore, if the user is in a hurry, the timing of news collection can be adjusted to allow for faster collection. In this way, by adjusting the timing of news collection according to the user's emotions, news can be collected at a more appropriate time.

[0119] The analysis department can improve the accuracy of news analysis by referring to the user's past investment history. For example, it can improve the accuracy of the analysis by referring to the user's past investment history. It can also select the optimal analysis method based on the user's past investment history. Furthermore, it can analyze the user's past investment history and extract important information. In this way, the accuracy of the analysis is improved by referring to the user's past investment history.

[0120] The service provider can estimate the user's emotions and adjust the way advice is delivered based on those emotions. For example, if the user is stressed, it can provide simple, easy-to-understand advice. If the user is relaxed, it can provide advice that includes more detailed information. Furthermore, if the user is in a hurry, it can provide concise advice. By adjusting the way advice is delivered according to the user's emotions, it can provide advice in a more appropriate way.

[0121] The customization function can optimize advice based on the user's life plan by considering the user's geographical location. For example, it can perform region-related customization based on the user's geographical location. It can also perform customization related to the local economic situation based on the user's geographical location. Furthermore, it can perform customization related to local financial policies based on the user's geographical location. This allows for efficient advice customization by optimizing the advice based on the user's geographical location.

[0122] The timing unit can estimate the user's emotions and adjust the trading timing advice based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand advice. If the user is relaxed, it can provide advice with more detailed information. Furthermore, if the user is in a hurry, it can provide concise advice. By adjusting the trading timing advice according to the user's emotions, it can provide advice in a more appropriate way.

[0123] The portfolio review department can propose the optimal portfolio review based on the user's assets when providing advice on portfolio review. For example, it can propose the optimal portfolio review based on the user's assets. It can also propose a portfolio review that minimizes risk based on the user's assets. Furthermore, it can propose a portfolio review that is individually customized based on the user's assets. This allows the department to provide advice that minimizes risk by proposing the optimal portfolio review based on the user's assets.

[0124] The reception desk can estimate the user's emotions and prioritize the asset information to be entered based on those emotions. For example, if the user is stressed, it can prioritize the entry of important asset information. If the user is relaxed, it can prioritize the entry of detailed asset information. Furthermore, if the user is in a hurry, it can prioritize the entry of the most important asset information. In this way, by prioritizing asset information according to the user's emotions, important information can be entered preferentially.

[0125] The news collection unit can analyze users' social media activity and collect relevant news when gathering news. For example, it can prioritize collecting news of interest based on users' social media activity. It can also collect relevant news based on users' social media activity. Furthermore, it can collect news related to asset management based on users' social media activity. This allows for efficient news collection by gathering relevant news based on users' social media activity.

[0126] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how the analysis results are displayed according to the user's emotions, the results can be presented in a more appropriate way.

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

[0128] Step 1: The reception desk enters the user's asset information. This information includes cash, stocks, real estate, etc. The reception desk can enter detailed information such as the type and quantity of stocks the user owns and the purchase price. It can also enter information about bonds and real estate the user owns. Step 2: The data collection unit obtains the latest financial news from news sites and financial information sites. The data collection unit can obtain news on important domestic and international economic indicators, political and banking monetary policies, and accounting and financial statement information. For example, it can obtain domestic economic indicators such as GDP, unemployment rate, and inflation rate, and news on political and banking monetary policies such as interest rate policies, quantitative easing, and regulatory changes. Furthermore, it can also obtain news on corporate financial statements, earnings reports, and cash flow. Step 3: The analysis department analyzes the news collected by the data collection department. The analysis department can analyze the news using methods such as text mining, statistical analysis, and machine learning algorithms. For example, text mining can be used to analyze the content of the news and extract important information. Statistical analysis can be used to evaluate the impact and relevance of the news, and machine learning algorithms can be used to evaluate the reliability and importance of the news. Step 4: The service department provides asset management advice based on the analysis results obtained by the analysis department. The service department can provide advice such as investment recommendations, risk management suggestions, and portfolio optimization. For example, they can advise on the timing of buying and selling specific stocks, suggest portfolio revisions, and offer risk management suggestions.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision 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 inputs the user's asset information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains the latest financial news from news sites and financial information sites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the obtained news. The provision unit is implemented by the control unit 46A of the smart device 14 and provides asset management advice based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision unit, is implemented, for example, 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 inputs the user's asset information. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and obtains the latest financial news from news sites and financial information sites. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the obtained news. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides asset management advice based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision 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 inputs the user's asset information. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and acquires the latest financial news from news sites and financial information sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the acquired news. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides asset management advice based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision 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 inputs the user's asset information. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and obtains the latest financial news from news sites and financial information sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the obtained news. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides asset management advice based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A reception area where users input their asset information, The collection department acquires the latest financial news from news sites and financial information sites, An analysis unit analyzes the news acquired by the aforementioned collection unit, The system includes a provisioning unit that provides asset management advice based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It includes a customization section that tailors advice based on the user's life plan. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It includes a timing section that advises on the optimal timing for buying and selling specific stocks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We propose a portfolio review and establish a review department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is The system analyzes the acquired news and extracts important information related to the user's assets. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Obtain news on important domestic and international economic indicators, political and banking monetary policies, and accounting and financial information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of asset information input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past asset information input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering asset information, filtering is performed based on the user's current asset status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the asset information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering asset information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering asset information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of news collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze past news gathering history and select the optimal gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting news, filter it based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates user sentiment and determines the priority of news to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting news, the system prioritizes collecting highly relevant news by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When collecting news, the system analyzes users' social media activity and gathers relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate user sentiment and adjust news analysis methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing news, prioritize extracting important information related to the user's assets. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When analyzing news, we improve the accuracy of the analysis by referring to the user's past asset management history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When analyzing news, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When analyzing news, we analyze users' social media activity and extract relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how advice is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, it is customized based on the user's assets and life plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, we refer to the user's past investment history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is It estimates the user's emotions and adjusts how advice is customized based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned customization unit is When customizing advice, the system optimizes the customization based on the user's life plan. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customization unit is When customizing advice, the system takes the user's geographical location into consideration to optimize the customization. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned timing unit, The system estimates the user's emotions and adjusts the trading timing advice based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned timing unit, When providing advice on buying and selling timing, we propose the optimal timing based on the user's asset holdings. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned timing unit, The system estimates user sentiment and prioritizes buy / sell timing based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned timing unit, When providing advice on buying and selling timing, we take the user's geographical location into consideration to suggest the optimal timing. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned review unit is, We estimate the user's emotions and adjust the portfolio review advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned review unit is, When advising on portfolio review, we propose the optimal review based on the user's assets. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned review unit is, The system estimates user sentiment and determines portfolio review priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned review unit is, When advising on portfolio review, we propose the optimal review by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their asset information, The collection department acquires the latest financial news from news sites and financial information sites, An analysis unit analyzes the news acquired by the aforementioned collection unit, The system includes a provisioning unit that provides asset management advice based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, It includes a customization section that tailors advice based on the user's life plan. The system according to feature 1.

3. The aforementioned supply unit is, It includes a timing section that advises on the optimal timing for buying and selling specific stocks. The system according to feature 1.

4. The aforementioned supply unit is, We propose a portfolio review and establish a review department. The system according to feature 1.

5. The aforementioned analysis unit is The system analyzes the acquired news and extracts important information related to the user's assets. The system according to feature 1.

6. The aforementioned collection unit is Obtain news on important domestic and international economic indicators, political and banking monetary policies, and accounting and financial information. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of asset information input based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past asset information input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering asset information, filtering is performed based on the user's current asset status and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the asset information to be entered based on the estimated user emotions. The system according to feature 1.

11. The aforementioned reception unit is When entering asset information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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