system

Abstract

The system according to this embodiment aims to support investments specifically focused on small and medium-sized stocks. [Solution] The system according to the embodiment comprises a collection unit, a recommendation unit, a monitoring unit, an agency unit, and a recording unit. The collection unit collects data. The recommendation unit recommends buying and selling based on the data collected by the collection unit. The monitoring unit monitors the market based on the recommendations made by the recommendation unit. The agency unit acts as an agent for buying and selling based on the market conditions monitored by the monitoring unit. The recording unit records the status and conditions of buying and selling performed by the agency unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the investment targets tend to be biased, and there is a risk that high-quality small and medium-sized stocks may be undervalued.

[0005] The system according to the embodiment aims to support investment specialized in small and medium-sized stocks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a recommendation unit, a monitoring unit, an agency unit, and a recording unit. The data collection unit collects data. The recommendation unit recommends buying and selling based on the data collected by the data collection unit. The monitoring unit monitors the market based on the recommendations made by the recommendation unit. The agency unit acts as an agent for buying and selling based on the market conditions monitored by the monitoring unit. The recording unit records the status and conditions of buying and selling performed by the agency unit. [Effects of the Invention]

[0007] The system according to this embodiment can support investments specifically focused on small and medium-sized stocks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The investment AI agent specializing in small and mid-cap stocks according to an embodiment of the present invention is a system proposed to improve the investment trend that tends to be biased towards US stocks, large-cap Japanese stocks, and high-dividend stocks, in light of the changing perception of investment due to the spread of NISA and iDeCo. This system aims to break the current situation where good quality small and mid-cap Japanese stocks are undervalued and revitalize Japanese companies by promoting the flow of funds. For example, this investment AI agent collects data such as the trends of small and mid-cap stocks over the past 10 years, IR information of small and mid-cap stocks, and global affairs. Next, the AI ​​recommends buying and selling small and mid-cap stocks based on the collected data. Specifically, it displays buy / sell or wait decisions as probabilities and proposes them to the user. It also performs market monitoring and trading on behalf of the user, so that the user does not need to monitor the market around the clock. The advantages of this agent include increased trading activity in small and mid-cap stocks and improved fund circulation. Users do not need to constantly monitor the market and can receive trading commissions. In addition, by recording trading status and conditions, it becomes easier to obtain information from successful traders. Furthermore, because it is limited to small and mid-cap stocks, it can be started with a small amount of money and is expected to be used for practicing stock investing. In this way, an investment AI agent specializing in small and mid-cap stocks can promote investment diversification and contribute to the revitalization of Japanese companies.

[0029] The investment AI agent specializing in small and medium-sized stocks according to this embodiment comprises a data collection unit, a recommendation unit, a monitoring unit, an agency unit, and a recording unit. The data collection unit collects data. The data collection unit can collect data such as the trends of small and medium-sized stocks over the past 10 years, IR information of small and medium-sized stocks, and global affairs. The data collection unit can obtain data from financial data provision services using APIs, for example. The data collection unit can also collect data from websites using scraping technology. Furthermore, the data collection unit can collect economic indicators and political news from news sites and government agency databases. The recommendation unit recommends buying and selling based on the data collected by the data collection unit. The recommendation unit can analyze past data using machine learning algorithms, for example, and display buy / sell or wait / buy decisions probabilistically. The recommendation unit can make suggestions to the user, for example, such as "Now is the time to buy this stock." The recommendation unit can also provide recommendations customized according to the user's investment style. The monitoring unit monitors the market based on recommendations made by the recommendation unit. For example, the monitoring unit acquires market data in real time and monitors specific indicators. For example, the monitoring unit detects anomalies such as sudden fluctuations in stock prices or increases in trading volume. The monitoring unit can also issue alerts based on conditions set by the user. The proxy unit executes trades on behalf of the user based on the market conditions monitored by the monitoring unit. For example, the proxy unit uses an automated trading algorithm to execute trades based on user settings. For example, the proxy unit automatically buys and sells stocks at prices set by the user. The proxy unit can also execute trades at times specified by the user. The recording unit records the status and conditions of trades made by the proxy unit. For example, the recording unit stores information such as the date and time of trades, price, and quantity in a database. For example, the recording unit records the trading history in a log file. The recording unit also allows users to check their trading history. As a result, the investment AI agent specializing in small and medium-sized stocks according to this embodiment can efficiently collect data, recommend buy and sell orders, monitor the market, execute trades on behalf of others, and record trades.

[0030] The data collection unit collects data. For example, it can collect data such as the trends of small and mid-cap stocks over the past 10 years, IR information for small and mid-cap stocks, and global affairs. Specifically, the data collection unit uses APIs from financial data provision services to obtain historical stock price data and corporate financial information. This includes each company's financial statements, earnings forecasts, and dividend information. The data collection unit also uses scraping technology to collect the latest IR information and press releases from companies' official websites and news sites. For example, it collects information that may affect stock prices, such as new product announcements and changes in management. Furthermore, the data collection unit collects economic indicators and political news from news sites and government agency databases. This includes macroeconomic indicators such as GDP growth rate, unemployment rate, and inflation rate, as well as political news such as trade friction and political instability. This data is collected in real time and stored in a central database. The data collection unit integrates information from these diverse data sources to build a comprehensive dataset necessary for investment decisions. This allows the data collection unit to collect data efficiently and accurately, improving the overall performance of the system.

[0031] The recommendation unit recommends buying and selling based on data collected by the data collection unit. For example, the recommendation unit uses machine learning algorithms to analyze historical data and display buy / sell or wait / buy decisions probabilistically. Specifically, the recommendation unit trains a machine learning model using historical stock price data and company financial information as input data. This model learns stock price fluctuation patterns and company performance trends to predict future stock price movements. For example, the recommendation unit analyzes the conditions under which a particular stock has risen in value in the past and recommends "buy" if similar conditions exist in the current market. Furthermore, the recommendation unit can provide customized recommendations according to the user's investment style. For example, it recommends high-risk, high-return stocks to users who prefer risk, and low-risk, stable-return stocks to users seeking stable returns. This allows the recommendation unit to support optimal investment decisions tailored to the user's needs.

[0032] The monitoring unit monitors the market based on recommendations made by the recommendation unit. For example, the monitoring unit acquires market data in real time and monitors specific indicators. Specifically, the monitoring unit monitors market data in real time to detect anomalies such as sudden fluctuations in stock prices or increases in trading volume. For example, the monitoring unit issues alerts when the price of a particular stock exceeds a certain range or when trading volume surges. The monitoring unit can also issue alerts based on conditions set by the user. For example, a user can set it to receive notifications when a certain stock price level is reached. This allows the monitoring unit to support users in responding quickly to market fluctuations. Furthermore, the monitoring unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the monitoring unit to not only monitor the market in real time but also to detect anomalies, improving the reliability and security of the entire system.

[0033] The trading unit acts as an agent for the market, executing trades based on market conditions monitored by the monitoring unit. For example, the trading unit uses automated trading algorithms to execute trades based on user settings. Specifically, the trading unit automatically buys and sells stocks at prices set by the user. For instance, the user can set the unit to automatically buy or sell when a specific stock price level is reached. The trading unit can also execute trades at times specified by the user, such as during specific time periods. This allows the trading unit to efficiently execute trades based on the user's investment strategy. Furthermore, the trading unit can access multiple exchanges and markets to select the most favorable trading conditions. This enables the trading unit to execute trades under the most advantageous conditions for the user.

[0034] The Records Unit records the details and conditions of trades conducted by the Transaction Agent Unit. For example, the Records Unit stores information such as the date and time of trades, price, and quantity in a database. Specifically, the Records Unit records detailed information for each transaction in a log file, allowing users to review it later. For example, users can refer to their past transaction history to see the conditions under which trades were conducted. The Records Unit can also analyze the transaction history and generate reports to evaluate the user's investment performance. This allows the Records Unit to provide users with information to reflect on their investment activities and identify areas for improvement. Furthermore, the Records Unit securely stores transaction data and implements security measures to prevent unauthorized access and data tampering. This ensures that the Records Unit manages user transaction data in a safe and reliable manner.

[0035] The data collection unit can collect data on the trends of small and mid-cap stocks over the past 10 years, IR information for small and mid-cap stocks, and global affairs. For example, the data collection unit can collect daily and monthly data for the past 10 years. The data collection unit can obtain data from sources such as stock exchanges and financial data provision services. The data collection unit can also collect IR information for small and mid-cap stocks from companies' official websites and financial data provision services. For example, the data collection unit can collect financial reports and press releases. The data collection unit can also collect economic indicators and political news from news sites and government agency databases. For example, the data collection unit can collect data on global affairs to aid in investment decisions. This enables the data collection unit to collect more accurate data.

[0036] The recommendation system can display buy / sell or wait / buy decisions based on collected data, presenting them to users as probabilities. For example, the recommendation system uses machine learning models to analyze historical data and make buy / sell or wait / buy decisions. It can, for instance, suggest to users, "This stock is a buy right now." Furthermore, the recommendation system can provide customized recommendations based on the user's investment style. For example, if a user prefers to take risks, it can recommend high-risk, high-return stocks. Conversely, if a user prefers stable investments, it can recommend low-risk, low-return stocks. This allows the recommendation system to help users make appropriate investment decisions.

[0037] The monitoring unit can monitor the market based on recommended information. For example, the monitoring unit can acquire market data in real time and monitor specific indicators. The monitoring unit can detect anomalies, such as sudden fluctuations in stock prices or increases in trading volume. The monitoring unit can also issue alerts based on conditions set by the user. For example, the monitoring unit can issue an alert when a user-defined price is reached. The monitoring unit can also issue an alert when specific economic indicators are released. This makes it easier for the user to understand market trends.

[0038] The brokerage unit can execute trades based on monitored market conditions. For example, the brokerage unit can use automated trading algorithms to execute trades based on user settings. For example, the brokerage unit can automatically buy and sell stocks at prices set by the user. Furthermore, the brokerage unit can execute trades at times specified by the user. For example, the brokerage unit can execute trades based on conditions set by the user. This eliminates the need for the user to monitor the market.

[0039] The recording unit can record the details and conditions of trades. For example, the recording unit stores information such as the date and time of the trade, price, and quantity in a database. For example, the recording unit records the trade history in a log file. The recording unit also allows users to check their trade history. For example, the recording unit can display the trade history as a graph or chart. This makes it easier for users to check their trade history.

[0040] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past data collection history and collect data at that time. For example, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. In addition, the data collection unit can select a collection method focused on a specific market or industry based on past data collection history. This allows the data collection unit to select the optimal collection method.

[0041] The data collection unit can filter data during the collection process, focusing on specific markets or industries. For example, it can collect only data related to a particular market or industry and filter out other data. It can also set keywords focused on a specific market or industry and collect data based on those keywords. Furthermore, the data collection unit can prioritize the collection of news and reports related to a specific market or industry. This allows the data collection unit to collect highly relevant data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of relevant market and industry data based on the user's geographical location. For example, the data collection unit can collect region-specific data by considering the user's geographical location. Furthermore, the data collection unit can also prioritize the collection of data related to local economic conditions and trends based on the user's geographical location. This allows the data collection unit to collect region-specific data.

[0043] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect data on markets and industries of interest. For example, the data collection unit can collect relevant data based on users' social media posts and the accounts they follow. The data collection unit can also monitor users' social media activity in real time and collect relevant data. This allows the data collection unit to collect data on markets and industries of interest.

[0044] The recommendation section can adjust the level of detail in recommendations based on the importance of the stocks. For example, it provides recommendations with detailed information for highly important stocks, and recommendations with concise information for less important stocks. The recommendation section can also adjust the display order of recommendations according to their importance. This allows the recommendation section to provide detailed information about important stocks.

[0045] The recommendation system can apply different recommendation algorithms depending on the stock category when making recommendations. For example, for growth stocks, it applies a recommendation algorithm that emphasizes future growth potential. For high-dividend stocks, it applies a recommendation algorithm that emphasizes dividend yield. Furthermore, for value stocks, it can apply a recommendation algorithm that emphasizes current valuation. This allows the recommendation system to provide more appropriate recommendations.

[0046] The recommendation system can prioritize recommendations based on the filing date of the stocks. For example, it might prioritize recommending stocks with more recent filing dates. For older stocks, it might consider past performance when making recommendations. The recommendation system can also adjust the display order of recommendations according to the filing date. This allows the recommendation system to prioritize providing the latest information.

[0047] The recommendation system can adjust the order of recommendations based on the relevance of the stocks. For example, it can prioritize recommending highly relevant stocks. For less relevant stocks, it can provide recommendations with concise information. The recommendation system can also adjust the display order of recommendations according to their relevance. This allows the recommendation system to prioritize providing highly relevant stocks.

[0048] The monitoring unit can improve the accuracy of its monitoring by considering the interrelationships of stocks during monitoring. For example, the monitoring unit analyzes the interrelationships of stocks and monitors the trends of related stocks. For example, the monitoring unit determines monitoring priorities by considering the interrelationships of stocks. In addition, the monitoring unit can improve the accuracy of its monitoring based on the interrelationships of stocks. This allows the monitoring unit to improve the accuracy of its monitoring.

[0049] The monitoring unit can perform monitoring while considering the attribute information of the stock submitters. For example, the monitoring unit can determine monitoring priorities based on the attribute information of the stock submitters. For example, the monitoring unit can improve the accuracy of monitoring by considering the attribute information of the stock submitters. In addition, the monitoring unit can also monitor the trends of related stocks based on the attribute information of the stock submitters. This improves the accuracy of monitoring.

[0050] The monitoring department can conduct monitoring while considering the geographical distribution of stocks. For example, the monitoring department can monitor trends in relevant markets and industries based on the geographical distribution of stocks. For example, the monitoring department can determine monitoring priorities while considering the geographical distribution of stocks. Furthermore, the monitoring department can improve the accuracy of monitoring based on the geographical distribution of stocks. This allows the monitoring department to monitor region-specific risks.

[0051] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on stocks during monitoring. For example, the monitoring unit improves the accuracy of its monitoring based on relevant literature on stocks. For example, the monitoring unit determines monitoring priorities by referring to relevant literature on stocks. In addition, the monitoring unit can monitor the trends of relevant stocks based on relevant literature on stocks. This improves the accuracy of the monitoring unit's monitoring.

[0052] The agency can analyze the user's past trading history to select the most suitable agency method when acting as an agent. For example, the agency can analyze the user's past trading history and select the agency method with the highest success rate. For example, the agency can select an agency method that minimizes risk based on the user's past trading history. In addition, the agency can select an agency method that suits the user's investment style by referring to the user's past trading history. This allows the agency to select the most suitable agency method.

[0053] The agency unit can customize the agency services based on the user's current living situation. For example, the agency unit can provide agency services that minimize risk by taking into account the user's current living situation. For example, the agency unit can provide agency services that adjust the investment amount based on the user's current living situation. Furthermore, the agency unit can also provide agency services that adjust the timing of investments based on the user's current living situation. This allows the agency unit to provide more appropriate agency services.

[0054] The outsourcing department can select the optimal outsourcing method when performing outsourcing, taking into account the user's geographical location information. For example, the outsourcing department can select an outsourcing method that takes into account the trends in relevant markets and industries based on the user's geographical location information. For example, the outsourcing department can select an outsourcing method that avoids region-specific risks by taking into account the user's geographical location information. Furthermore, the outsourcing department can also select an outsourcing method that takes into account the regional economic conditions and trends based on the user's geographical location information. This allows the outsourcing department to avoid region-specific risks.

[0055] The agency department can analyze the user's social media activity and propose agency methods during the agency process. For example, the agency department can analyze the user's social media activity and propose agency methods based on the user's areas of interest. For example, the agency department can propose relevant agency methods based on the user's social media posts and the accounts they follow. Furthermore, the agency department can monitor the user's social media activity in real time and propose the most suitable agency methods. This allows the agency department to propose agency methods based on the user's areas of interest.

[0056] The recording unit can analyze the user's past trading history and select the optimal recording method at the time of recording. For example, the recording unit can analyze the user's past trading history and select the most effective recording method. For example, the recording unit can select a recording method that minimizes risk based on the user's past trading history. In addition, the recording unit can select a recording method that suits the user's investment style by referring to the user's past trading history. In this way, the recording unit can select the optimal recording method.

[0057] The recording unit can customize the recording method based on the user's current living situation at the time of recording. For example, the recording unit can provide a recording method that minimizes risk by taking into account the user's current living situation. For example, the recording unit can provide a recording method that adjusts the investment amount based on the user's current living situation. Furthermore, the recording unit can also provide a recording method that adjusts the timing of investments based on the user's current living situation. This allows the recording unit to perform more appropriate recording.

[0058] The recording unit can select the optimal recording method at the time of recording, taking into account the user's geographical location information. For example, the recording unit can select a recording method that takes into account the trends of relevant markets and industries based on the user's geographical location information. For example, the recording unit can select a recording method that avoids region-specific risks, taking into account the user's geographical location information. Furthermore, the recording unit can also select a recording method that takes into account the regional economic conditions and trends based on the user's geographical location information. This allows the recording unit to avoid region-specific risks.

[0059] The recording unit can analyze a user's social media activity and suggest recording methods during recording. For example, the recording unit can analyze a user's social media activity and suggest recording methods based on the user's areas of interest or industry. For example, the recording unit can suggest relevant recording methods based on a user's social media posts and the accounts they follow. The recording unit can also monitor a user's social media activity in real time and suggest the most suitable recording method. This allows the recording unit to suggest recording methods based on the user's areas of interest or industry.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze a user's past investment history and prioritize data collection based on their investment trends. For example, if a user has invested heavily in a particular industry in the past, it will prioritize collecting data related to that industry. It can also analyze a user's past successful investment patterns and prioritize collecting data related to similar patterns. Furthermore, it can analyze a user's investment failure patterns and collect relevant data to help mitigate risk. This allows the data collection unit to collect data tailored to the user's investment style.

[0062] The monitoring unit can customize its monitoring criteria based on the user's investment goals. For example, if the user aims for short-term profits, it can set monitoring criteria that are sensitive to short-term market fluctuations. If the user aims for long-term wealth building, it can also set monitoring criteria based on long-term trends. Furthermore, if the user has a specific risk tolerance, it is possible to set monitoring criteria that correspond to that risk tolerance. This allows the monitoring unit to perform monitoring in accordance with the user's investment goals.

[0063] The recording unit can customize the recording method based on the user's investment goals. For example, if the user aims for short-term profits, it can record short-term trading history in detail. If the user aims for long-term wealth building, it can provide a recording method based on long-term trends. Furthermore, if the user has a specific risk tolerance, it can provide a recording method tailored to that risk tolerance. In this way, the recording unit can record information in accordance with the user's investment goals.

[0064] The data collection unit can analyze users' social media activity and prioritize data collection based on their areas of interest, such as markets and industries. For example, if a user shows interest in a particular industry on social media, it will prioritize collecting data related to that industry. It can also analyze the accounts users follow and the content of their posts to collect relevant data. Furthermore, it can monitor users' social media activity in real time and collect data at the optimal time. This allows the data collection unit to collect data that aligns with the user's interests.

[0065] The recommendation system can analyze a user's investment history and provide recommendations based on past success patterns. For example, it can analyze a user's past successful investment patterns and provide recommendations based on similar patterns. It can also analyze a user's past unsuccessful investment patterns and provide relevant recommendations to mitigate risk. Furthermore, it can provide customized recommendations according to the user's investment style. In this way, the recommendation system can provide appropriate recommendations based on the user's investment history.

[0066] The outsourcing department can select the optimal outsourcing method by considering the user's geographical location. For example, it can select an outsourcing method that takes into account the trends in relevant markets and industries based on the user's geographical location. It can also select an outsourcing method that avoids region-specific risks by considering the user's geographical location. Furthermore, it is possible to select an outsourcing method that takes into account the regional economic conditions and trends based on the user's geographical location. This allows the outsourcing department to avoid region-specific risks.

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

[0068] Step 1: The data collection unit collects data. The data collection unit can collect data such as the trends of small and mid-cap stocks over the past 10 years, investor relations information for small and mid-cap stocks, and global affairs. The data collection unit can obtain data from financial data provision services using APIs, for example. The data collection unit can also collect data from websites using scraping techniques. Furthermore, the data collection unit can collect economic indicators and political news from news sites and government agency databases. Step 2: The recommendation unit recommends buying and selling based on the data collected by the data collection unit. The recommendation unit, for example, uses machine learning algorithms to analyze past data and displays buy / sell or wait / buy decisions as probabilities. The recommendation unit may, for example, suggest to the user, "Now is the time to buy this stock." The recommendation unit can also provide recommendations customized to the user's investment style. Step 3: The monitoring unit monitors the market based on the recommendations made by the recommendation unit. For example, the monitoring unit acquires market data in real time and monitors specific indicators. The monitoring unit detects anomalies such as sudden fluctuations in stock prices or increases in trading volume. The monitoring unit can also issue alerts based on conditions set by the user. Step 4: The proxy trading unit executes trades based on market conditions monitored by the monitoring unit. For example, the proxy trading unit uses an automated trading algorithm to execute trades based on the user's settings. For example, the proxy trading unit automatically buys and sells stocks at prices set by the user. The proxy trading unit can also execute trades at times specified by the user. Step 5: The recording unit records the details and conditions of trades conducted by the proxy unit. The recording unit stores information such as the date and time of the trade, price, and quantity in a database. The recording unit also records the trade history in a log file. Furthermore, the recording unit allows users to review the trade history.

[0069] (Example of form 2) The investment AI agent specializing in small and mid-cap stocks according to an embodiment of the present invention is a system proposed to improve the investment trend that tends to be biased towards US stocks, large-cap Japanese stocks, and high-dividend stocks, in light of the changing perception of investment due to the spread of NISA and iDeCo. This system aims to break the current situation where good quality small and mid-cap Japanese stocks are undervalued and revitalize Japanese companies by promoting the flow of funds. For example, this investment AI agent collects data such as the trends of small and mid-cap stocks over the past 10 years, IR information of small and mid-cap stocks, and global affairs. Next, the AI ​​recommends buying and selling small and mid-cap stocks based on the collected data. Specifically, it displays buy / sell or wait decisions as probabilities and proposes them to the user. It also performs market monitoring and trading on behalf of the user, so that the user does not need to monitor the market around the clock. The advantages of this agent include increased trading activity in small and mid-cap stocks and improved fund circulation. Users do not need to constantly monitor the market and can receive trading commissions. In addition, by recording trading status and conditions, it becomes easier to obtain information from successful traders. Furthermore, because it is limited to small and mid-cap stocks, it can be started with a small amount of money and is expected to be used for practicing stock investing. In this way, an investment AI agent specializing in small and mid-cap stocks can promote investment diversification and contribute to the revitalization of Japanese companies.

[0070] The investment AI agent specializing in small and medium-sized stocks according to this embodiment comprises a data collection unit, a recommendation unit, a monitoring unit, an agency unit, and a recording unit. The data collection unit collects data. The data collection unit can collect data such as the trends of small and medium-sized stocks over the past 10 years, IR information of small and medium-sized stocks, and global affairs. The data collection unit can obtain data from financial data provision services using APIs, for example. The data collection unit can also collect data from websites using scraping technology. Furthermore, the data collection unit can collect economic indicators and political news from news sites and government agency databases. The recommendation unit recommends buying and selling based on the data collected by the data collection unit. The recommendation unit can analyze past data using machine learning algorithms, for example, and display buy / sell or wait / buy decisions probabilistically. The recommendation unit can make suggestions to the user, for example, such as "Now is the time to buy this stock." The recommendation unit can also provide recommendations customized according to the user's investment style. The monitoring unit monitors the market based on recommendations made by the recommendation unit. For example, the monitoring unit acquires market data in real time and monitors specific indicators. For example, the monitoring unit detects anomalies such as sudden fluctuations in stock prices or increases in trading volume. The monitoring unit can also issue alerts based on conditions set by the user. The proxy unit executes trades on behalf of the user based on the market conditions monitored by the monitoring unit. For example, the proxy unit uses an automated trading algorithm to execute trades based on user settings. For example, the proxy unit automatically buys and sells stocks at prices set by the user. The proxy unit can also execute trades at times specified by the user. The recording unit records the status and conditions of trades made by the proxy unit. For example, the recording unit stores information such as the date and time of trades, price, and quantity in a database. For example, the recording unit records the trading history in a log file. The recording unit also allows users to check their trading history. As a result, the investment AI agent specializing in small and medium-sized stocks according to this embodiment can efficiently collect data, recommend buy and sell orders, monitor the market, execute trades on behalf of others, and record trades.

[0071] The data collection unit collects data. For example, it can collect data such as the trends of small and mid-cap stocks over the past 10 years, IR information for small and mid-cap stocks, and global affairs. Specifically, the data collection unit uses APIs from financial data provision services to obtain historical stock price data and corporate financial information. This includes each company's financial statements, earnings forecasts, and dividend information. The data collection unit also uses scraping technology to collect the latest IR information and press releases from companies' official websites and news sites. For example, it collects information that may affect stock prices, such as new product announcements and changes in management. Furthermore, the data collection unit collects economic indicators and political news from news sites and government agency databases. This includes macroeconomic indicators such as GDP growth rate, unemployment rate, and inflation rate, as well as political news such as trade friction and political instability. This data is collected in real time and stored in a central database. The data collection unit integrates information from these diverse data sources to build a comprehensive dataset necessary for investment decisions. This allows the data collection unit to collect data efficiently and accurately, improving the overall performance of the system.

[0072] The recommendation unit recommends buying and selling based on data collected by the data collection unit. For example, the recommendation unit uses machine learning algorithms to analyze historical data and display buy / sell or wait / buy decisions probabilistically. Specifically, the recommendation unit trains a machine learning model using historical stock price data and company financial information as input data. This model learns stock price fluctuation patterns and company performance trends to predict future stock price movements. For example, the recommendation unit analyzes the conditions under which a particular stock has risen in value in the past and recommends "buy" if similar conditions exist in the current market. Furthermore, the recommendation unit can provide customized recommendations according to the user's investment style. For example, it recommends high-risk, high-return stocks to users who prefer risk, and low-risk, stable-return stocks to users seeking stable returns. This allows the recommendation unit to support optimal investment decisions tailored to the user's needs.

[0073] The monitoring unit monitors the market based on recommendations made by the recommendation unit. For example, the monitoring unit acquires market data in real time and monitors specific indicators. Specifically, the monitoring unit monitors market data in real time to detect anomalies such as sudden fluctuations in stock prices or increases in trading volume. For example, the monitoring unit issues alerts when the price of a particular stock exceeds a certain range or when trading volume surges. The monitoring unit can also issue alerts based on conditions set by the user. For example, a user can set it to receive notifications when a certain stock price level is reached. This allows the monitoring unit to support users in responding quickly to market fluctuations. Furthermore, the monitoring unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the monitoring unit to not only monitor the market in real time but also to detect anomalies, improving the reliability and security of the entire system.

[0074] The trading unit acts as an agent for the market, executing trades based on market conditions monitored by the monitoring unit. For example, the trading unit uses automated trading algorithms to execute trades based on user settings. Specifically, the trading unit automatically buys and sells stocks at prices set by the user. For instance, the user can set the unit to automatically buy or sell when a specific stock price level is reached. The trading unit can also execute trades at times specified by the user, such as during specific time periods. This allows the trading unit to efficiently execute trades based on the user's investment strategy. Furthermore, the trading unit can access multiple exchanges and markets to select the most favorable trading conditions. This enables the trading unit to execute trades under the most advantageous conditions for the user.

[0075] The Records Unit records the details and conditions of trades conducted by the Transaction Agent Unit. For example, the Records Unit stores information such as the date and time of trades, price, and quantity in a database. Specifically, the Records Unit records detailed information for each transaction in a log file, allowing users to review it later. For example, users can refer to their past transaction history to see the conditions under which trades were conducted. The Records Unit can also analyze the transaction history and generate reports to evaluate the user's investment performance. This allows the Records Unit to provide users with information to reflect on their investment activities and identify areas for improvement. Furthermore, the Records Unit securely stores transaction data and implements security measures to prevent unauthorized access and data tampering. This ensures that the Records Unit manages user transaction data in a safe and reliable manner.

[0076] The data collection unit can collect data on the trends of small and mid-cap stocks over the past 10 years, IR information for small and mid-cap stocks, and global affairs. For example, the data collection unit can collect daily and monthly data for the past 10 years. The data collection unit can obtain data from sources such as stock exchanges and financial data provision services. The data collection unit can also collect IR information for small and mid-cap stocks from companies' official websites and financial data provision services. For example, the data collection unit can collect financial reports and press releases. The data collection unit can also collect economic indicators and political news from news sites and government agency databases. For example, the data collection unit can collect data on global affairs to aid in investment decisions. This enables the data collection unit to collect more accurate data.

[0077] The recommendation system can display buy / sell or wait / buy decisions based on collected data, presenting them to users as probabilities. For example, the recommendation system uses machine learning models to analyze historical data and make buy / sell or wait / buy decisions. It can, for instance, suggest to users, "This stock is a buy right now." Furthermore, the recommendation system can provide customized recommendations based on the user's investment style. For example, if a user prefers to take risks, it can recommend high-risk, high-return stocks. Conversely, if a user prefers stable investments, it can recommend low-risk, low-return stocks. This allows the recommendation system to help users make appropriate investment decisions.

[0078] The monitoring unit can monitor the market based on recommended information. For example, the monitoring unit can acquire market data in real time and monitor specific indicators. The monitoring unit can detect anomalies, such as sudden fluctuations in stock prices or increases in trading volume. The monitoring unit can also issue alerts based on conditions set by the user. For example, the monitoring unit can issue an alert when a user-defined price is reached. The monitoring unit can also issue an alert when specific economic indicators are released. This makes it easier for the user to understand market trends.

[0079] The brokerage unit can execute trades based on monitored market conditions. For example, the brokerage unit can use automated trading algorithms to execute trades based on user settings. For example, the brokerage unit can automatically buy and sell stocks at prices set by the user. Furthermore, the brokerage unit can execute trades at times specified by the user. For example, the brokerage unit can execute trades based on conditions set by the user. This eliminates the need for the user to monitor the market.

[0080] The recording unit can record the details and conditions of trades. For example, the recording unit stores information such as the date and time of the trade, price, and quantity in a database. For example, the recording unit records the trade history in a log file. The recording unit also allows users to check their trade history. For example, the recording unit can display the trade history as a graph or chart. This makes it easier for users to check their trade history.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will reduce the frequency of data collection and collect only important data. For example, if the user is relaxed, the data collection unit will increase the frequency of data collection and collect more detailed data. The data collection unit can also collect data in real time and provide it quickly if the user is in a hurry. This allows the data collection unit to collect data at a more appropriate time. 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.

[0082] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past data collection history and collect data at that time. For example, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. In addition, the data collection unit can select a collection method focused on a specific market or industry based on past data collection history. This allows the data collection unit to select the optimal collection method.

[0083] The data collection unit can filter data during the collection process, focusing on specific markets or industries. For example, it can collect only data related to a particular market or industry and filter out other data. It can also set keywords focused on a specific market or industry and collect data based on those keywords. Furthermore, the data collection unit can prioritize the collection of news and reports related to a specific market or industry. This allows the data collection unit to collect highly relevant data.

[0084] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. The data collection unit can also prioritize collecting data needed in real time if the user is in a hurry. This allows the data collection unit to prioritize the collection of important data. 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.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of relevant market and industry data based on the user's geographical location. For example, the data collection unit can collect region-specific data by considering the user's geographical location. Furthermore, the data collection unit can also prioritize the collection of data related to local economic conditions and trends based on the user's geographical location. This allows the data collection unit to collect region-specific data.

[0086] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect data on markets and industries of interest. For example, the data collection unit can collect relevant data based on users' social media posts and the accounts they follow. The data collection unit can also monitor users' social media activity in real time and collect relevant data. This allows the data collection unit to collect data on markets and industries of interest.

[0087] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is stressed, the recommendation system will provide simple and easy-to-understand recommendations. If the user is relaxed, the recommendation system will provide recommendations that include more detailed information. Furthermore, if the user is in a hurry, the recommendation system can provide concise recommendations. This allows the recommendation system to provide recommendations that are easy for the user to understand. Emotion estimation is achieved using emotion estimation functions, 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.

[0088] The recommendation section can adjust the level of detail in recommendations based on the importance of the stocks. For example, it provides recommendations with detailed information for highly important stocks, and recommendations with concise information for less important stocks. The recommendation section can also adjust the display order of recommendations according to their importance. This allows the recommendation section to provide detailed information about important stocks.

[0089] The recommendation system can apply different recommendation algorithms depending on the stock category when making recommendations. For example, for growth stocks, it applies a recommendation algorithm that emphasizes future growth potential. For high-dividend stocks, it applies a recommendation algorithm that emphasizes dividend yield. Furthermore, for value stocks, it can apply a recommendation algorithm that emphasizes current valuation. This allows the recommendation system to provide more appropriate recommendations.

[0090] The recommendation system can estimate the user's mood and adjust the length of recommendations based on that mood. For example, if the user is in a hurry, the recommendation system will provide short, concise recommendations. If the user is relaxed, the recommendation system will provide longer recommendations with more detailed explanations. Furthermore, if the user is excited, the recommendation system can provide recommendations with visually stimulating effects. This allows the recommendation system to provide recommendations of an appropriate length for the user. Mood estimation is achieved using a mood 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.

[0091] The recommendation system can prioritize recommendations based on the filing date of the stocks. For example, it might prioritize recommending stocks with more recent filing dates. For older stocks, it might consider past performance when making recommendations. The recommendation system can also adjust the display order of recommendations according to the filing date. This allows the recommendation system to prioritize providing the latest information.

[0092] The recommendation system can adjust the order of recommendations based on the relevance of the stocks. For example, it can prioritize recommending highly relevant stocks. For less relevant stocks, it can provide recommendations with concise information. The recommendation system can also adjust the display order of recommendations according to their relevance. This allows the recommendation system to prioritize providing highly relevant stocks.

[0093] The monitoring unit can estimate the user's emotions and adjust monitoring criteria based on the estimated emotions. For example, if the user is tense, the monitoring unit provides simple and easily understandable monitoring criteria. For example, if the user is relaxed, the monitoring unit provides monitoring criteria that include detailed information. The monitoring unit can also provide concise monitoring criteria if the user is in a hurry. This allows the monitoring unit to perform more appropriate monitoring. 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.

[0094] The monitoring unit can improve the accuracy of its monitoring by considering the interrelationships of stocks during monitoring. For example, the monitoring unit analyzes the interrelationships of stocks and monitors the trends of related stocks. For example, the monitoring unit determines monitoring priorities by considering the interrelationships of stocks. In addition, the monitoring unit can improve the accuracy of its monitoring based on the interrelationships of stocks. This allows the monitoring unit to improve the accuracy of its monitoring.

[0095] The monitoring unit can perform monitoring while considering the attribute information of the stock submitters. For example, the monitoring unit can determine monitoring priorities based on the attribute information of the stock submitters. For example, the monitoring unit can improve the accuracy of monitoring by considering the attribute information of the stock submitters. In addition, the monitoring unit can also monitor the trends of related stocks based on the attribute information of the stock submitters. This improves the accuracy of monitoring.

[0096] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize displaying important monitoring results. For example, if the user is relaxed, the monitoring unit will display detailed monitoring results. The monitoring unit can also display concise monitoring results if the user is in a hurry. This allows the monitoring unit to prioritize the display of important information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The monitoring department can conduct monitoring while considering the geographical distribution of stocks. For example, the monitoring department can monitor trends in relevant markets and industries based on the geographical distribution of stocks. For example, the monitoring department can determine monitoring priorities while considering the geographical distribution of stocks. Furthermore, the monitoring department can improve the accuracy of monitoring based on the geographical distribution of stocks. This allows the monitoring department to monitor region-specific risks.

[0098] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on stocks during monitoring. For example, the monitoring unit improves the accuracy of its monitoring based on relevant literature on stocks. For example, the monitoring unit determines monitoring priorities by referring to relevant literature on stocks. In addition, the monitoring unit can monitor the trends of relevant stocks based on relevant literature on stocks. This improves the accuracy of the monitoring unit's monitoring.

[0099] The proxy unit can estimate the user's emotions and adjust its proxy method based on the estimated emotions. For example, if the user is nervous, the proxy unit provides a simple and highly visible proxy method. For example, if the user is relaxed, the proxy unit provides a proxy method that includes detailed information. Furthermore, if the user is in a hurry, the proxy unit can provide a concise proxy method. This allows the proxy unit to perform more appropriate proxy actions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The agency can analyze the user's past trading history to select the most suitable agency method when acting as an agent. For example, the agency can analyze the user's past trading history and select the agency method with the highest success rate. For example, the agency can select an agency method that minimizes risk based on the user's past trading history. In addition, the agency can select an agency method that suits the user's investment style by referring to the user's past trading history. This allows the agency to select the most suitable agency method.

[0101] The agency unit can customize the agency services based on the user's current living situation. For example, the agency unit can provide agency services that minimize risk by taking into account the user's current living situation. For example, the agency unit can provide agency services that adjust the investment amount based on the user's current living situation. Furthermore, the agency unit can also provide agency services that adjust the timing of investments based on the user's current living situation. This allows the agency unit to provide more appropriate agency services.

[0102] The proxy unit can estimate the user's emotions and determine the priority of its proxy actions based on the estimated emotions. For example, if the user is stressed, the proxy unit will prioritize important actions. If the user is relaxed, the proxy unit will perform detailed actions. The proxy unit can also perform actions quickly if the user is in a hurry. This allows the proxy unit to prioritize important actions. 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.

[0103] The outsourcing department can select the optimal outsourcing method when performing outsourcing, taking into account the user's geographical location information. For example, the outsourcing department can select an outsourcing method that takes into account the trends in relevant markets and industries based on the user's geographical location information. For example, the outsourcing department can select an outsourcing method that avoids region-specific risks by taking into account the user's geographical location information. Furthermore, the outsourcing department can also select an outsourcing method that takes into account the regional economic conditions and trends based on the user's geographical location information. This allows the outsourcing department to avoid region-specific risks.

[0104] The agency department can analyze the user's social media activity and propose agency methods during the agency process. For example, the agency department can analyze the user's social media activity and propose agency methods based on the user's areas of interest. For example, the agency department can propose relevant agency methods based on the user's social media posts and the accounts they follow. Furthermore, the agency department can monitor the user's social media activity in real time and propose the most suitable agency methods. This allows the agency department to propose agency methods based on the user's areas of interest.

[0105] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is nervous, the recording unit provides a simple and highly visible recording method. For example, if the user is relaxed, the recording unit provides a recording method that includes detailed information. The recording unit can also provide a concise recording method if the user is in a hurry. This allows the recording unit to perform more appropriate recordings. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The recording unit can analyze the user's past trading history and select the optimal recording method at the time of recording. For example, the recording unit can analyze the user's past trading history and select the most effective recording method. For example, the recording unit can select a recording method that minimizes risk based on the user's past trading history. In addition, the recording unit can select a recording method that suits the user's investment style by referring to the user's past trading history. In this way, the recording unit can select the optimal recording method.

[0107] The recording unit can customize the recording method based on the user's current living situation at the time of recording. For example, the recording unit can provide a recording method that minimizes risk by taking into account the user's current living situation. For example, the recording unit can provide a recording method that adjusts the investment amount based on the user's current living situation. Furthermore, the recording unit can also provide a recording method that adjusts the timing of investments based on the user's current living situation. This allows the recording unit to perform more appropriate recording.

[0108] The recording unit can estimate the user's emotions and determine recording priorities based on the estimated emotions. For example, if the user is nervous, the recording unit will prioritize important recordings. If the user is relaxed, the recording unit will perform detailed recordings. The recording unit can also perform rapid recordings if the user is in a hurry. This allows the recording unit to prioritize important recordings. 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.

[0109] The recording unit can select the optimal recording method at the time of recording, taking into account the user's geographical location information. For example, the recording unit can select a recording method that takes into account the trends of relevant markets and industries based on the user's geographical location information. For example, the recording unit can select a recording method that avoids region-specific risks, taking into account the user's geographical location information. Furthermore, the recording unit can also select a recording method that takes into account the regional economic conditions and trends based on the user's geographical location information. This allows the recording unit to avoid region-specific risks.

[0110] The recording unit can analyze a user's social media activity and suggest recording methods during recording. For example, the recording unit can analyze a user's social media activity and suggest recording methods based on the user's areas of interest or industry. For example, the recording unit can suggest relevant recording methods based on a user's social media posts and the accounts they follow. The recording unit can also monitor a user's social media activity in real time and suggest the most suitable recording method. This allows the recording unit to suggest recording methods based on the user's areas of interest or industry.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data collection unit can analyze a user's past investment history and prioritize data collection based on their investment trends. For example, if a user has invested heavily in a particular industry in the past, it will prioritize collecting data related to that industry. It can also analyze a user's past successful investment patterns and prioritize collecting data related to similar patterns. Furthermore, it can analyze a user's investment failure patterns and collect relevant data to help mitigate risk. This allows the data collection unit to collect data tailored to the user's investment style.

[0113] The recommendation system can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if a user is stressed, the frequency of recommendations can be reduced, and recommendations can only be made at important times. Conversely, if a user is relaxed, the frequency of recommendations can be increased, and more detailed information can be provided. Furthermore, if a user is in a hurry, recommendations can be made quickly, providing concise information. In this way, the recommendation system can make recommendations at the appropriate time according to the user's emotional state.

[0114] The monitoring unit can customize its monitoring criteria based on the user's investment goals. For example, if the user aims for short-term profits, it can set monitoring criteria that are sensitive to short-term market fluctuations. If the user aims for long-term wealth building, it can also set monitoring criteria based on long-term trends. Furthermore, if the user has a specific risk tolerance, it is possible to set monitoring criteria that correspond to that risk tolerance. This allows the monitoring unit to perform monitoring in accordance with the user's investment goals.

[0115] The proxy unit can estimate the user's emotions and adjust the timing of its proxy actions based on those estimates. For example, if the user is stressed, it can reduce the frequency of proxy actions and only perform them at important moments. If the user is relaxed, it can increase the frequency of proxy actions and provide more detailed information. Furthermore, if the user is in a hurry, it can perform proxy actions quickly and provide concise information. In this way, the proxy unit can perform proxy actions at the appropriate time according to the user's emotional state.

[0116] The recording unit can customize the recording method based on the user's investment goals. For example, if the user aims for short-term profits, it can record short-term trading history in detail. If the user aims for long-term wealth building, it can provide a recording method based on long-term trends. Furthermore, if the user has a specific risk tolerance, it can provide a recording method tailored to that risk tolerance. In this way, the recording unit can record information in accordance with the user's investment goals.

[0117] The data collection unit can analyze users' social media activity and prioritize data collection based on their areas of interest, such as markets and industries. For example, if a user shows interest in a particular industry on social media, it will prioritize collecting data related to that industry. It can also analyze the accounts users follow and the content of their posts to collect relevant data. Furthermore, it can monitor users' social media activity in real time and collect data at the optimal time. This allows the data collection unit to collect data that aligns with the user's interests.

[0118] The recommendation system can analyze a user's investment history and provide recommendations based on past success patterns. For example, it can analyze a user's past successful investment patterns and provide recommendations based on similar patterns. It can also analyze a user's past unsuccessful investment patterns and provide relevant recommendations to mitigate risk. Furthermore, it can provide customized recommendations according to the user's investment style. In this way, the recommendation system can provide appropriate recommendations based on the user's investment history.

[0119] The monitoring unit can estimate the user's emotions and adjust how it displays the monitoring results based on those estimated emotions. For example, if the user is stressed, it can provide simple and easy-to-read monitoring results. If the user is relaxed, it can provide monitoring results with more detailed information. Furthermore, if the user is in a hurry, it can provide concise monitoring results. This allows the monitoring unit to display monitoring results in an appropriate manner according to the user's emotional state.

[0120] The outsourcing department can select the optimal outsourcing method by considering the user's geographical location. For example, it can select an outsourcing method that takes into account the trends in relevant markets and industries based on the user's geographical location. It can also select an outsourcing method that avoids region-specific risks by considering the user's geographical location. Furthermore, it is possible to select an outsourcing method that takes into account the regional economic conditions and trends based on the user's geographical location. This allows the outsourcing department to avoid region-specific risks.

[0121] The recording unit can estimate the user's emotions and determine recording priorities based on those estimates. For example, if the user is nervous, important recordings will be prioritized. If the user is relaxed, detailed recordings can be made. Furthermore, if the user is in a hurry, recordings can be made quickly. In this way, the recording unit can record with appropriate priorities according to the user's emotional state.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects data. The data collection unit can collect data such as the trends of small and mid-cap stocks over the past 10 years, investor relations information for small and mid-cap stocks, and global affairs. The data collection unit can obtain data from financial data provision services using APIs, for example. The data collection unit can also collect data from websites using scraping techniques. Furthermore, the data collection unit can collect economic indicators and political news from news sites and government agency databases. Step 2: The recommendation unit recommends buying and selling based on the data collected by the data collection unit. The recommendation unit, for example, uses machine learning algorithms to analyze past data and displays buy / sell or wait / buy decisions as probabilities. The recommendation unit may, for example, suggest to the user, "Now is the time to buy this stock." The recommendation unit can also provide recommendations customized to the user's investment style. Step 3: The monitoring unit monitors the market based on the recommendations made by the recommendation unit. For example, the monitoring unit acquires market data in real time and monitors specific indicators. The monitoring unit detects anomalies such as sudden fluctuations in stock prices or increases in trading volume. The monitoring unit can also issue alerts based on conditions set by the user. Step 4: The proxy trading unit executes trades based on market conditions monitored by the monitoring unit. For example, the proxy trading unit uses an automated trading algorithm to execute trades based on the user's settings. For example, the proxy trading unit automatically buys and sells stocks at prices set by the user. The proxy trading unit can also execute trades at times specified by the user. Step 5: The recording unit records the details and conditions of trades conducted by the proxy unit. The recording unit stores information such as the date and time of the trade, price, and quantity in a database. The recording unit also records the trade history in a log file. Furthermore, the recording unit allows users to review the trade history.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, recommendation unit, monitoring unit, proxy unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data from financial data provision services and websites using APIs and scraping techniques. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends buying and selling using machine learning algorithms. The monitoring unit is implemented by the control unit 46A of the smart device 14 and acquires market data in real time and monitors specific indicators. The proxy unit is implemented by the specific processing unit 290 of the data processing device 12 and performs buying and selling on behalf of others using automated trading algorithms. The recording unit is implemented by the control unit 46A of the smart device 14 and records the status and conditions of buying and selling. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, recommendation unit, monitoring unit, proxy unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from financial data provision services and websites using APIs and scraping techniques. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends buying and selling using machine learning algorithms. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and acquires market data in real time and monitors specific indicators. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs buying and selling on behalf of others using automated trading algorithms. The recording unit is implemented by the control unit 46A of the smart glasses 214 and records the status and conditions of buying and selling. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, recommendation unit, monitoring unit, proxy unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from financial data provision services and websites using APIs and scraping techniques. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends buying and selling using machine learning algorithms. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and acquires market data in real time and monitors specific indicators. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs buying and selling on behalf of the user using an automated trading algorithm. The recording unit is implemented by the control unit 46A of the headset terminal 314 and records the status and conditions of buying and selling. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, recommendation unit, monitoring unit, proxy unit, and recording unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data from financial data provision services and websites using APIs and scraping techniques. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends buying and selling using machine learning algorithms. The monitoring unit is implemented by, for example, the control unit 46A of the robot 414 and acquires market data in real time and monitors specific indicators. The proxy unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs buying and selling on behalf of the robot using automated trading algorithms. The recording unit is implemented by, for example, the control unit 46A of the robot 414 and records the status and conditions of buying and selling. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A data collection unit that collects data, A recommendation unit that recommends buying and selling based on the data collected by the aforementioned collection unit, A monitoring unit monitors the market based on the recommendations made by the aforementioned recommendation unit, An agency unit that acts as an agent for buying and selling based on market conditions monitored by the aforementioned monitoring unit, The system includes a recording unit that records the status and conditions of sales conducted by the aforementioned agency unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on the trends of small and mid-cap stocks over the past 10 years, investor relations information on small and mid-cap stocks, and global affairs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recommendation unit is, Based on the collected data, the system displays buy / sell and wait / reserve decisions as probabilities and suggests them to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, Monitor the market based on recommended content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned agency unit, We act as an intermediary for buying and selling based on monitored market conditions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recording unit is, Record the details and conditions of the transactions that took place. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filter it to focus on specific markets or industries. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the stocks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the stock category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on the filing date of the stocks. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, When making recommendations, adjust the order of recommendations based on the relevance of the stocks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, During monitoring, we improve the accuracy of monitoring by considering the interrelationships between stocks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, the attribute information of the shareholder is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, It estimates the user's sentiment and adjusts the order in which monitoring results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, When monitoring, the geographical distribution of the stocks should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, During monitoring, we improve the accuracy of our monitoring by referring to relevant literature on stocks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned agency unit, It estimates the user's emotions and adjusts the proxy method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned agency unit, When acting as an agent, we analyze the user's past trading history to select the most suitable method of agency. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned agency unit, When providing assistance, the method of assistance is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned agency unit, It estimates the user's emotions and determines the priority of the proxy based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned agency unit, When performing the task on behalf of the user, the optimal method of assistance will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned agency unit, During the proxy service, we analyze the user's social media activity and propose the appropriate methods for handling it. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recording unit is, During recording, the system analyzes the user's past trading history to select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recording unit is, During recording, the recording method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recording unit is, The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recording unit is, During recording, the optimal recording method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The recording unit is, During recording, we analyze the user's social media activity and suggest recording methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, A recommendation unit that recommends buying and selling based on the data collected by the aforementioned collection unit, A monitoring unit monitors the market based on the recommendations made by the aforementioned recommendation unit, An agency unit that acts as an agent for buying and selling based on market conditions monitored by the aforementioned monitoring unit, The system includes a recording unit that records the status and conditions of sales conducted by the aforementioned agency unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on the trends of small and mid-cap stocks over the past 10 years, investor relations information on small and mid-cap stocks, and global affairs. The system according to feature 1.

3. The recommendation unit is, Based on the collected data, the system displays buy / sell and wait / reserve decisions as probabilities and suggests them to the user. The system according to feature 1.

4. The aforementioned monitoring unit, Monitor the market based on recommended content. The system according to feature 1.

5. The aforementioned agency unit, We act as an intermediary for buying and selling based on monitored market conditions. The system according to feature 1.

6. The recording unit is, Record the details and conditions of the transactions that took place. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filter it to focus on specific markets or industries. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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