system

Abstract

The system according to this embodiment aims to enable even novice investors to determine the optimal timing for purchase and to automatically execute trades. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a trading unit, and a recording unit. The reception unit receives user setting information. The analysis unit analyzes transaction data based on the information received by the reception unit and determines the optimal purchase timing. The trading unit automatically buys and sells based on the purchase timing determined by the analysis unit. The recording unit records the transaction results performed by the trading unit and uses them for future transactions.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for novice investors to determine the optimal purchase timing, and there is a risk of incurring large losses.

[0005] The system according to the embodiment aims to enable novice investors to determine the optimal purchase timing and automatically conduct trading.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a trading unit, and a recording unit. The reception unit receives user setting information. The analysis unit analyzes transaction data based on the information received by the reception unit and determines the optimal purchase timing. The trading unit automatically executes trades based on the purchase timing determined by the analysis unit. The recording unit records the transaction results performed by the trading unit and uses them for future transactions. [Effects of the Invention]

[0007] The system according to this embodiment allows even novice investors to determine the optimal timing for purchase and automatically execute trades. [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 controls communication between a plurality of 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 stock investment support system according to an embodiment of the present invention is designed to solve the problem that, with the start of the new NISA program, individual stock investment is booming, and in particular, beginners find it difficult to determine the right timing to buy stocks. This system utilizes the vast amount of transaction information and individual stock information held by the securities system and uses a generating AI to determine the optimal purchase timing with high reproducibility based on past performance, according to each individual's settings. The user simply specifies the stock, amount, and period, and the AI ​​makes the decision and automatically buys and sells. This mechanism has the advantage of eliminating the need for individuals to constantly monitor the situation. First, the user sets the stock they want to buy, the investment amount, and the investment period. This information is input into the generating AI. For example, the user sets, "I want to hold 1 million yen worth of Company A's stock for one year." Based on this information, the generating AI analyzes past transaction data and stock information to determine the optimal purchase timing. Next, the generating AI automatically buys and sells based on the determined purchase timing. For example, if the generating AI determines, "Buy Company A's stock this Friday and sell it on the same day next year," the transaction will be carried out automatically accordingly. As a result, the user does not need to constantly worry about fluctuations in stock prices. Furthermore, the generating AI records the results of trades and uses them for future trades. For example, it can determine the timing of the next purchase with greater accuracy based on past trade results. This enables highly reproducible trading. This mechanism allows even beginners to invest in stocks with peace of mind. Users can reduce risk and invest efficiently by entrusting complex trading decisions to the AI. In addition, because the generating AI determines the optimal purchase timing based on past performance, highly reproducible trading is possible. For example, by making the next trade based on past successful trading patterns, risk can be reduced and profits can be maximized. Thus, this invention is a groundbreaking mechanism to support individual stock investment, and is particularly beneficial for beginners. By utilizing the generating AI, the optimal purchase timing is determined and buying and selling are performed automatically, eliminating the need for individuals to constantly monitor the situation. As a result, the stock investment support system determines the optimal purchase timing based on the user's settings and performs buying and selling automatically, eliminating the need for individuals to constantly monitor the situation.

[0029] The stock investment support system according to this embodiment comprises a reception unit, an analysis unit, a trading unit, and a recording unit. The reception unit receives user setting information. User setting information includes, but is not limited to, examples such as stock names, amounts, and periods. The reception unit receives, for example, information on stock names, amounts, and periods set by the user. The analysis unit analyzes transaction data based on the information received by the reception unit and determines the optimal purchase timing. The analysis unit analyzes, for example, past transaction data and stock information to determine the optimal purchase timing. The analysis unit can also use generating AI to analyze past transaction data and stock information and determine the optimal purchase timing. The trading unit automatically buys and sells based on the purchase timing determined by the analysis unit. The trading unit automatically buys and sells based on the purchase timing determined by the analysis unit. The trading unit can also use AI to automatically buy and sell based on the purchase timing determined by the analysis unit. The recording unit records the results of transactions performed by the trading unit and utilizes them for future transactions. The recording unit records, for example, the results of transactions performed by the trading unit and utilizes them for future transactions. The recording unit can use AI to record the results of transactions conducted by the trading unit and utilize them for future transactions. As a result, the stock investment support system according to this embodiment determines the optimal purchase timing based on the user's settings and automatically executes trades, eliminating the need for individuals to constantly monitor the situation.

[0030] The reception department receives user configuration information. This information includes, but is not limited to, stock names, amounts, and investment periods. For example, the reception department receives information on stock names, amounts, and investment periods set by the user. Specifically, users can log in to the system and input detailed configuration information such as stock names, investment amounts, investment periods, risk tolerance, and target profit margins through a dedicated interface. This information is stored in the system's database and managed so that it can be used by the analysis and trading departments. Furthermore, the reception department also has a function to suggest and automatically complete configuration information based on the user's past trading history and investment style. For example, it can suggest new investment opportunities related to a user who has previously invested in a particular stock. It also has a function to reflect changes in real time when a user changes their configuration information and notify the entire system. This allows the reception department to respond to diverse user needs and receive configuration information flexibly and quickly. In addition, as a security measure, the reception department performs user authentication and data encryption to safely protect users' personal information and configuration information. This allows users to use the system with peace of mind.

[0031] The analysis unit analyzes transaction data based on information received by the reception unit to determine the optimal purchase timing. For example, the analysis unit analyzes past transaction data and stock information to determine the optimal purchase timing. The analysis unit can also use generative AI to analyze past transaction data and stock information to determine the optimal purchase timing. Specifically, the generative AI receives diverse data as input, such as past transaction data, stock price fluctuations, trading volume, economic indicators, and news articles, and analyzes complex patterns and trends based on this data. The generative AI uses advanced algorithms such as deep learning and reinforcement learning to learn from past data and predict future price fluctuations. For example, it analyzes the conditions under which the price of a particular stock rose in the past and suggests a purchase timing when similar conditions occur again. In addition, the generative AI can always perform analysis that reflects the latest information based on market data that is updated in real time. This allows the analysis unit to provide users with highly accurate purchase timing and increase the success rate of their investments. Furthermore, the analysis unit can also perform customized analysis according to the user's risk tolerance and investment goals, providing each user with the optimal investment strategy.

[0032] The trading unit automatically executes trades based on the purchase timing determined by the analysis unit. For example, the trading unit automatically executes trades based on the purchase timing determined by the analysis unit. The trading unit can also use AI to automatically execute trades based on the purchase timing determined by the analysis unit. Specifically, the trading unit receives purchase and sale timing instructions from the analysis unit and automatically executes trades through APIs of stock exchanges and brokers. The trading unit can also use high-frequency trading algorithms to improve the speed and accuracy of trade execution. This enables trading in milliseconds, allowing for rapid responses to market fluctuations. Furthermore, the trading unit is equipped with risk management functions such as stop-loss orders and profit-taking orders to manage trading risks. This minimizes the user's investment risk while maximizing profits. In addition, the trading unit has the ability to monitor trading history and performance in real time and detect abnormal or fraudulent trades. This ensures the security and reliability of the system. The trading unit flexibly and efficiently executes trades based on the trading conditions set by the user, providing support for achieving the user's investment goals.

[0033] The Records Unit records the results of trades made by the Trading Unit and uses them for future trades. For example, the Records Unit can record the results of trades made by the Trading Unit and use them for future trades. The Records Unit can also use AI to record the results of trades made by the Trading Unit and use them for future trades. Specifically, the Records Unit stores detailed trade data such as the date and time of the trade, the stock symbol, the trade price, the trade volume, the commission, and the profit margin in a database. This data is used for analysis and strategy planning in future trades. The Records Unit can use AI to analyze past trade data and identify trading patterns and success factors. For example, it can analyze the conditions under which a particular stock generated high profits and use that information when similar conditions occur again. The Records Unit also has a function to evaluate trading performance and provide feedback to the user. This allows the user to check the effectiveness of their investment strategy and revise it as needed. Furthermore, to ensure the security of trading data, the Records Unit encrypts and backs up the data to prevent data loss or tampering. This allows the recording unit to securely manage transaction data and provide reliable data for future transactions.

[0034] The reception desk can receive information on the securities, amounts, and periods set by the user. For example, the reception desk can receive information on the securities, amounts, and periods set by the user. By receiving information on the securities, amounts, and periods set by the user, the reception desk can respond to individual investment needs. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input information on the securities, amounts, and periods set by the user into the AI, which can then analyze and accept that information.

[0035] The analysis unit can analyze past trading data and stock information to determine the optimal purchase timing. For example, the analysis unit can analyze past trading data and stock information to determine the optimal purchase timing. By analyzing past trading data and stock information, the analysis unit can determine a highly reproducible optimal purchase timing. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input past trading data and stock information into a generating AI, and the generating AI can analyze that data to determine the optimal purchase timing.

[0036] The trading unit can automatically execute trades based on the purchase timing determined by the analysis unit. For example, the trading unit can automatically execute trades based on the purchase timing determined by the analysis unit. By automatically executing trades, the trading unit eliminates the need for the user to constantly monitor the situation. Some or all of the above-described processes in the trading unit may be performed using AI or not. For example, the trading unit can input the purchase timing determined by the analysis unit into the AI, and the AI ​​can automatically execute trades based on that timing.

[0037] The recording unit can record the transaction results and use them for future transactions. For example, the recording unit can record the transaction results and use them for future transactions. By recording the transaction results and using them for future transactions, the recording unit enables highly reproducible trading. Some or all of the above-described processes in the recording unit may be performed using AI or not. For example, the recording unit can input the transaction results into the AI, which can record the results and use them for future transactions.

[0038] The reception desk can analyze the user's past settings history and suggest the optimal settings. For example, the reception desk can automatically display as suggestions the stocks, amounts, and periods that the user has frequently set in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest settings to be used during specific time periods based on the user's past settings history. In this way, by analyzing past settings history, the reception desk can suggest the optimal settings for the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past settings history into an AI, which can then analyze that history and suggest the optimal settings.

[0039] The reception desk can filter the user's investment information based on their investment experience and risk tolerance. For example, if the user is a beginner, the reception desk will prioritize displaying low-risk stocks. If the user is an experienced investor, the reception desk can also suggest high-risk, high-return stocks. The reception desk can also suggest an appropriate investment amount based on the user's risk tolerance. In this way, appropriate investment information can be provided by filtering based on the user's investment experience and risk tolerance. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's investment experience and risk tolerance into the AI, which can then analyze and filter that information.

[0040] The reception unit can prioritize displaying highly relevant stocks when receiving configuration information, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception unit will prioritize displaying stocks related to that region. If the user is traveling, the reception unit can also suggest stocks related to the region the user is visiting. Based on the user's geographical location, the reception unit can also display stocks that reflect region-specific market trends. This allows for the priority display of highly relevant stocks by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into the AI, which can then analyze that information and display highly relevant stocks.

[0041] The reception desk can analyze the user's social media activity when receiving configuration information and suggest relevant stocks. For example, the reception desk can prioritize displaying stocks that the user frequently mentions on social media. The reception desk can also suggest stocks in industries that the user is interested in based on their social media activity. The reception desk can also display stocks recommended by the user's social media followers. In this way, relevant stocks can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into an AI, which can then analyze that activity and suggest relevant stocks.

[0042] The analysis unit can evaluate the reliability of past transaction data during analysis and prioritize the use of highly reliable data. For example, the analysis unit can score the reliability of past transaction data and prioritize the use of data with high scores. The analysis unit can also filter out unreliable data and exclude it from the analysis. The analysis unit can also improve the accuracy of the analysis results based on highly reliable data. This improves the accuracy of the analysis results by prioritizing the use of highly reliable data. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input past transaction data into a generation AI, which can evaluate the reliability of that data and prioritize the use of highly reliable data.

[0043] The analysis unit can incorporate real-time market trends and news information for a given stock during analysis and reflect it in the analysis results. For example, the analysis unit can incorporate real-time market trends into the analysis and perform analysis based on the latest information. The analysis unit can also incorporate real-time news information and reflect it in the evaluation of stocks. The analysis unit can also combine real-time market trends and news information to improve the accuracy of the analysis results. In this way, incorporating real-time information improves the accuracy of the analysis results. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input real-time market trends and news information into a generation AI, which can then analyze that information and reflect it in the analysis results.

[0044] The analysis unit can reflect region-specific market trends by considering the user's geographical location information during analysis. For example, if the user lives in a specific region, the analysis unit can reflect the market trends of that region in its analysis. If the user is traveling, the analysis unit can also reflect the market trends of the travel destination in its analysis. The analysis unit can also reflect region-specific market trends in its analysis based on the user's geographical location information. By reflecting region-specific market trends, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, and the generation AI can analyze that information to reflect region-specific market trends.

[0045] The analysis unit can analyze the user's social media activity during analysis and reflect relevant market trends in the analysis. For example, the analysis unit can reflect market trends that the user frequently mentions on social media. The analysis unit can also reflect market trends that the user is interested in based on their social media activity. The analysis unit can also reflect market trends recommended by the user's social media followers. By reflecting social media activity, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI, which can then analyze that activity and reflect relevant market trends.

[0046] The trading unit can select the optimal trading strategy by referring to past trading history when trading. For example, the trading unit can analyze the user's past trading history and reproduce successful strategies. The trading unit can also avoid failed strategies from the user's past trading history. The trading unit can also select the optimal trading strategy based on the user's past trading history. In this way, the optimal trading strategy can be selected by referring to past trading history. Some or all of the above processes in the trading unit may be performed using AI or not. For example, the trading unit can input the user's past trading history into AI, and the AI ​​can analyze that history to select the optimal trading strategy.

[0047] The trading unit can customize trading methods based on the user's current asset status and risk tolerance at the time of trading. For example, the trading unit can set appropriate trading amounts considering the user's current asset status. The trading unit can also suggest riskier trading methods based on the user's risk tolerance. The trading unit can also customize the optimal trading method based on the user's asset status and risk tolerance. This allows for appropriate trading by customizing trading methods based on the user's asset status and risk tolerance. Some or all of the above processes in the trading unit may be performed using AI or not. For example, the trading unit can input the user's current asset status and risk tolerance into the AI, which can then analyze that information to customize trading methods.

[0048] The trading unit can take into account the user's geographical location information and reflect region-specific market trends when trading. For example, if the user lives in a specific region, the trading unit can perform trades that reflect the market trends of that region. If the user is traveling, the trading unit can also perform trades that reflect the market trends of the travel destination. The trading unit can also perform trades that reflect region-specific market trends based on the user's geographical location information. This allows for appropriate trading by reflecting region-specific market trends. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input the user's geographical location information into AI, and the AI ​​can analyze that information to perform trades that reflect region-specific market trends.

[0049] The trading unit can analyze a user's social media activity during trading and reflect relevant market trends in its trading decisions. For example, the trading unit can reflect market trends that a user frequently mentions on social media. The trading unit can also reflect market trends that a user is interested in based on their social media activity. The trading unit can also reflect market trends recommended by a user's social media followers. This allows for more appropriate trading by reflecting social media activity. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input a user's social media activity into an AI, which can then analyze that activity and reflect relevant market trends in its trading decisions.

[0050] The recording unit can analyze past trading results during recording and select the optimal recording method for use in future trading. For example, the recording unit can analyze past trading results and record successful trading patterns. The recording unit can also exclude unsuccessful trading patterns from past trading results. Based on past trading results, the recording unit can also select the optimal recording method for use in future trading. In this way, by analyzing past trading results, the optimal recording method for use in future trading can be selected. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input past trading results into AI, and the AI ​​can analyze the results and select the optimal recording method.

[0051] The recording unit can customize the content of the recording based on the user's investment goals and risk tolerance at the time of recording. For example, the recording unit can record the degree of achievement based on the user's investment goals. The recording unit can also record risk assessments based on the user's risk tolerance. The recording unit can also customize the optimal recording content based on the user's investment goals and risk tolerance. This allows for appropriate recording by customizing the recording content based on the user's investment goals and risk tolerance. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's investment goals and risk tolerance into the AI, which can then analyze that information and customize the recording content.

[0052] The recording unit can reflect region-specific market trends by considering the user's geographical location information during recording. For example, if the user lives in a specific region, the recording unit can reflect the market trends of that region in the record. If the user is traveling, the recording unit can also reflect the market trends of the travel destination in the record. The recording unit can also reflect region-specific market trends in the record based on the user's geographical location information. This allows for appropriate recording by reflecting region-specific market trends. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location information into the AI, which can then analyze that information and reflect region-specific market trends in the record.

[0053] The recording unit can analyze the user's social media activity during recording and reflect relevant market trends in the record. For example, the recording unit can reflect market trends that the user frequently mentions on social media. The recording unit can also reflect market trends that the user is interested in based on their social media activity. The recording unit can also reflect market trends recommended by the user's social media followers. This allows for accurate recording by reflecting social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity into AI, which can then analyze that activity and reflect relevant market trends in the record.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze a user's past settings history and suggest the optimal settings. For example, it can automatically display as suggestions the stocks, amounts, and periods that the user has frequently used in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Based on the user's past settings history, it can also predict and suggest settings that will be used during specific time periods. In this way, by analyzing past settings history, the system can suggest the most suitable settings for the user.

[0056] The reception desk can filter the user's investment information based on their investment experience and risk tolerance. For example, if the user is a beginner, low-risk stocks will be prioritized. If the user is an experienced investor, high-risk, high-return stocks can be suggested. The system can also suggest an appropriate investment amount based on the user's risk tolerance. In this way, appropriate investment information can be provided by filtering based on the user's investment experience and risk tolerance.

[0057] The analysis unit evaluates the reliability of past transaction data during analysis and prioritizes the use of highly reliable data. For example, it can score the reliability of past transaction data and prioritize the use of data with high scores. It can also filter out unreliable data and exclude it from the analysis. The accuracy of the analysis results can also be improved based on highly reliable data. In this way, the accuracy of the analysis results is improved by prioritizing the use of highly reliable data.

[0058] The trading department can select the optimal trading strategy by referring to past trading history when making trades. For example, it can analyze a user's past trading history and reproduce successful strategies. It can also avoid failed strategies based on a user's past trading history. It can also select the optimal trading strategy based on a user's past trading history. In this way, the optimal trading strategy can be selected by referring to past trading history.

[0059] The recording unit can customize the content of the recording based on the user's investment goals and risk tolerance. For example, it can record the degree of achievement based on the user's investment goals. It can also record risk assessments based on the user's risk tolerance. It can customize the optimal recording content based on the user's investment goals and risk tolerance. This allows for appropriate recording by customizing the recording content based on the user's investment goals and risk tolerance.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives the user's configuration information. This information includes, for example, the stock name, amount, and period. The reception desk receives this information set by the user. Step 2: The analysis unit analyzes the transaction data based on the information received by the reception unit and determines the optimal purchase timing. The analysis unit can also analyze past transaction data and stock information and use generated AI to determine the optimal purchase timing. Step 3: The trading unit automatically executes trades based on the purchase timing determined by the analysis unit. The trading unit can also execute trades automatically using AI. Step 4: The recording unit records the results of trades made by the trading unit and uses them for future trades. The recording unit can also use AI to record trade results and use them for future trades.

[0062] (Example of form 2) The stock investment support system according to an embodiment of the present invention is designed to solve the problem that, with the start of the new NISA program, individual stock investment is booming, and in particular, beginners find it difficult to determine the right timing to buy stocks. This system utilizes the vast amount of transaction information and individual stock information held by the securities system and uses a generating AI to determine the optimal purchase timing with high reproducibility based on past performance, according to each individual's settings. The user simply specifies the stock, amount, and period, and the AI ​​makes the decision and automatically buys and sells. This mechanism has the advantage of eliminating the need for individuals to constantly monitor the situation. First, the user sets the stock they want to buy, the investment amount, and the investment period. This information is input into the generating AI. For example, the user sets, "I want to hold 1 million yen worth of Company A's stock for one year." Based on this information, the generating AI analyzes past transaction data and stock information to determine the optimal purchase timing. Next, the generating AI automatically buys and sells based on the determined purchase timing. For example, if the generating AI determines, "Buy Company A's stock this Friday and sell it on the same day next year," the transaction will be carried out automatically accordingly. As a result, the user does not need to constantly worry about fluctuations in stock prices. Furthermore, the generating AI records the results of trades and uses them for future trades. For example, it can determine the timing of the next purchase with greater accuracy based on past trade results. This enables highly reproducible trading. This mechanism allows even beginners to invest in stocks with peace of mind. Users can reduce risk and invest efficiently by entrusting complex trading decisions to the AI. In addition, because the generating AI determines the optimal purchase timing based on past performance, highly reproducible trading is possible. For example, by making the next trade based on past successful trading patterns, risk can be reduced and profits can be maximized. Thus, this invention is a groundbreaking mechanism to support individual stock investment, and is particularly beneficial for beginners. By utilizing the generating AI, the optimal purchase timing is determined and buying and selling are performed automatically, eliminating the need for individuals to constantly monitor the situation. As a result, the stock investment support system determines the optimal purchase timing based on the user's settings and performs buying and selling automatically, eliminating the need for individuals to constantly monitor the situation.

[0063] The stock investment support system according to this embodiment comprises a reception unit, an analysis unit, a trading unit, and a recording unit. The reception unit receives user setting information. User setting information includes, but is not limited to, examples such as stock names, amounts, and periods. The reception unit receives, for example, information on stock names, amounts, and periods set by the user. The analysis unit analyzes transaction data based on the information received by the reception unit and determines the optimal purchase timing. The analysis unit analyzes, for example, past transaction data and stock information to determine the optimal purchase timing. The analysis unit can also use generating AI to analyze past transaction data and stock information and determine the optimal purchase timing. The trading unit automatically buys and sells based on the purchase timing determined by the analysis unit. The trading unit automatically buys and sells based on the purchase timing determined by the analysis unit. The trading unit can also use AI to automatically buy and sell based on the purchase timing determined by the analysis unit. The recording unit records the results of transactions performed by the trading unit and utilizes them for future transactions. The recording unit records, for example, the results of transactions performed by the trading unit and utilizes them for future transactions. The recording unit can use AI to record the results of transactions conducted by the trading unit and utilize them for future transactions. As a result, the stock investment support system according to this embodiment determines the optimal purchase timing based on the user's settings and automatically executes trades, eliminating the need for individuals to constantly monitor the situation.

[0064] The reception department receives user configuration information. This information includes, but is not limited to, stock names, amounts, and investment periods. For example, the reception department receives information on stock names, amounts, and investment periods set by the user. Specifically, users can log in to the system and input detailed configuration information such as stock names, investment amounts, investment periods, risk tolerance, and target profit margins through a dedicated interface. This information is stored in the system's database and managed so that it can be used by the analysis and trading departments. Furthermore, the reception department also has a function to suggest and automatically complete configuration information based on the user's past trading history and investment style. For example, it can suggest new investment opportunities related to a user who has previously invested in a particular stock. It also has a function to reflect changes in real time when a user changes their configuration information and notify the entire system. This allows the reception department to respond to diverse user needs and receive configuration information flexibly and quickly. In addition, as a security measure, the reception department performs user authentication and data encryption to safely protect users' personal information and configuration information. This allows users to use the system with peace of mind.

[0065] The analysis unit analyzes transaction data based on information received by the reception unit to determine the optimal purchase timing. For example, the analysis unit analyzes past transaction data and stock information to determine the optimal purchase timing. The analysis unit can also use generative AI to analyze past transaction data and stock information to determine the optimal purchase timing. Specifically, the generative AI receives diverse data as input, such as past transaction data, stock price fluctuations, trading volume, economic indicators, and news articles, and analyzes complex patterns and trends based on this data. The generative AI uses advanced algorithms such as deep learning and reinforcement learning to learn from past data and predict future price fluctuations. For example, it analyzes the conditions under which the price of a particular stock rose in the past and suggests a purchase timing when similar conditions occur again. In addition, the generative AI can always perform analysis that reflects the latest information based on market data that is updated in real time. This allows the analysis unit to provide users with highly accurate purchase timing and increase the success rate of their investments. Furthermore, the analysis unit can also perform customized analysis according to the user's risk tolerance and investment goals, providing each user with the optimal investment strategy.

[0066] The trading unit automatically executes trades based on the purchase timing determined by the analysis unit. For example, the trading unit automatically executes trades based on the purchase timing determined by the analysis unit. The trading unit can also use AI to automatically execute trades based on the purchase timing determined by the analysis unit. Specifically, the trading unit receives purchase and sale timing instructions from the analysis unit and automatically executes trades through APIs of stock exchanges and brokers. The trading unit can also use high-frequency trading algorithms to improve the speed and accuracy of trade execution. This enables trading in milliseconds, allowing for rapid responses to market fluctuations. Furthermore, the trading unit is equipped with risk management functions such as stop-loss orders and profit-taking orders to manage trading risks. This minimizes the user's investment risk while maximizing profits. In addition, the trading unit has the ability to monitor trading history and performance in real time and detect abnormal or fraudulent trades. This ensures the security and reliability of the system. The trading unit flexibly and efficiently executes trades based on the trading conditions set by the user, providing support for achieving the user's investment goals.

[0067] The Records Unit records the results of trades made by the Trading Unit and uses them for future trades. For example, the Records Unit can record the results of trades made by the Trading Unit and use them for future trades. The Records Unit can also use AI to record the results of trades made by the Trading Unit and use them for future trades. Specifically, the Records Unit stores detailed trade data such as the date and time of the trade, the stock symbol, the trade price, the trade volume, the commission, and the profit margin in a database. This data is used for analysis and strategy planning in future trades. The Records Unit can use AI to analyze past trade data and identify trading patterns and success factors. For example, it can analyze the conditions under which a particular stock generated high profits and use that information when similar conditions occur again. The Records Unit also has a function to evaluate trading performance and provide feedback to the user. This allows the user to check the effectiveness of their investment strategy and revise it as needed. Furthermore, to ensure the security of trading data, the Records Unit encrypts and backs up the data to prevent data loss or tampering. This allows the recording unit to securely manage transaction data and provide reliable data for future transactions.

[0068] The reception desk can receive information on the securities, amounts, and periods set by the user. For example, the reception desk can receive information on the securities, amounts, and periods set by the user. By receiving information on the securities, amounts, and periods set by the user, the reception desk can respond to individual investment needs. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input information on the securities, amounts, and periods set by the user into the AI, which can then analyze and accept that information.

[0069] The analysis unit can analyze past trading data and stock information to determine the optimal purchase timing. For example, the analysis unit can analyze past trading data and stock information to determine the optimal purchase timing. By analyzing past trading data and stock information, the analysis unit can determine a highly reproducible optimal purchase timing. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input past trading data and stock information into a generating AI, and the generating AI can analyze that data to determine the optimal purchase timing.

[0070] The trading unit can automatically execute trades based on the purchase timing determined by the analysis unit. For example, the trading unit can automatically execute trades based on the purchase timing determined by the analysis unit. By automatically executing trades, the trading unit eliminates the need for the user to constantly monitor the situation. Some or all of the above-described processes in the trading unit may be performed using AI or not. For example, the trading unit can input the purchase timing determined by the analysis unit into the AI, and the AI ​​can automatically execute trades based on that timing.

[0071] The recording unit can record the transaction results and use them for future transactions. For example, the recording unit can record the transaction results and use them for future transactions. By recording the transaction results and using them for future transactions, the recording unit enables highly reproducible trading. Some or all of the above-described processes in the recording unit may be performed using AI or not. For example, the recording unit can input the transaction results into the AI, which can record the results and use them for future transactions.

[0072] The reception desk can estimate the user's emotions and customize the input interface for configuration information based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of configuration information. This improves user convenience by customizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can analyze the data to estimate emotions and customize the interface.

[0073] The reception desk can analyze the user's past settings history and suggest the optimal settings. For example, the reception desk can automatically display as suggestions the stocks, amounts, and periods that the user has frequently set in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest settings to be used during specific time periods based on the user's past settings history. In this way, by analyzing past settings history, the reception desk can suggest the optimal settings for the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past settings history into an AI, which can then analyze that history and suggest the optimal settings.

[0074] The reception desk can filter the user's investment information based on their investment experience and risk tolerance. For example, if the user is a beginner, the reception desk will prioritize displaying low-risk stocks. If the user is an experienced investor, the reception desk can also suggest high-risk, high-return stocks. The reception desk can also suggest an appropriate investment amount based on the user's risk tolerance. In this way, appropriate investment information can be provided by filtering based on the user's investment experience and risk tolerance. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's investment experience and risk tolerance into the AI, which can then analyze and filter that information.

[0075] The reception desk can estimate the user's emotions and adjust the input order of the configuration information based on the estimated emotions. For example, if the user is nervous, the reception desk may prompt them to input the simplest items first. If the user is relaxed, the reception desk may prompt them to input the detailed items first. If the user is in a hurry, the reception desk may prioritize the input of important items. This improves user convenience by adjusting the input order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which can analyze the data to estimate the emotion and adjust the input order.

[0076] The reception unit can prioritize displaying highly relevant stocks when receiving configuration information, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception unit will prioritize displaying stocks related to that region. If the user is traveling, the reception unit can also suggest stocks related to the region the user is visiting. Based on the user's geographical location, the reception unit can also display stocks that reflect region-specific market trends. This allows for the priority display of highly relevant stocks by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location information into the AI, which can then analyze that information and display highly relevant stocks.

[0077] The reception desk can analyze the user's social media activity when receiving configuration information and suggest relevant stocks. For example, the reception desk can prioritize displaying stocks that the user frequently mentions on social media. The reception desk can also suggest stocks in industries that the user is interested in based on their social media activity. The reception desk can also display stocks recommended by the user's social media followers. In this way, relevant stocks can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into an AI, which can then analyze that activity and suggest relevant stocks.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit may use a risky analysis algorithm. If the user is tense, the analysis unit may also use a conservative analysis algorithm. If the user is excited, the analysis unit may also use an aggressive analysis algorithm. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis results is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, the generative AI can analyze the data to estimate emotions, and then adjust the analysis algorithm.

[0079] The analysis unit can evaluate the reliability of past transaction data during analysis and prioritize the use of highly reliable data. For example, the analysis unit can score the reliability of past transaction data and prioritize the use of data with high scores. The analysis unit can also filter out unreliable data and exclude it from the analysis. The analysis unit can also improve the accuracy of the analysis results based on highly reliable data. This improves the accuracy of the analysis results by prioritizing the use of highly reliable data. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input past transaction data into a generation AI, which can evaluate the reliability of that data and prioritize the use of highly reliable data.

[0080] The analysis unit can incorporate real-time market trends and news information for a given stock during analysis and reflect it in the analysis results. For example, the analysis unit can incorporate real-time market trends into the analysis and perform analysis based on the latest information. The analysis unit can also incorporate real-time news information and reflect it in the evaluation of stocks. The analysis unit can also combine real-time market trends and news information to improve the accuracy of the analysis results. In this way, incorporating real-time information improves the accuracy of the analysis results. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input real-time market trends and news information into a generation AI, which can then analyze that information and reflect it in the analysis results.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, the generative AI can analyze the data to estimate emotions, and adjust the display method.

[0082] The analysis unit can reflect region-specific market trends by considering the user's geographical location information during analysis. For example, if the user lives in a specific region, the analysis unit can reflect the market trends of that region in its analysis. If the user is traveling, the analysis unit can also reflect the market trends of the travel destination in its analysis. The analysis unit can also reflect region-specific market trends in its analysis based on the user's geographical location information. By reflecting region-specific market trends, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, and the generation AI can analyze that information to reflect region-specific market trends.

[0083] The analysis unit can analyze the user's social media activity during analysis and reflect relevant market trends in the analysis. For example, the analysis unit can reflect market trends that the user frequently mentions on social media. The analysis unit can also reflect market trends that the user is interested in based on their social media activity. The analysis unit can also reflect market trends recommended by the user's social media followers. By reflecting social media activity, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI, which can then analyze that activity and reflect relevant market trends.

[0084] The trading unit can estimate the user's emotions and fine-tune the timing of trades based on those emotions. For example, if the user is tense, the trading unit can carefully adjust the timing of trades to reduce risk. If the user is relaxed, the trading unit can also set aggressive trading timings. If the user is excited, the trading unit can also set risky trading timings. This allows for risk reduction by adjusting the timing of trades according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input user emotion data into a generative AI, which can analyze that data to estimate emotions and fine-tune the timing of trades.

[0085] The trading unit can select the optimal trading strategy by referring to past trading history when trading. For example, the trading unit can analyze the user's past trading history and reproduce successful strategies. The trading unit can also avoid failed strategies from the user's past trading history. The trading unit can also select the optimal trading strategy based on the user's past trading history. In this way, the optimal trading strategy can be selected by referring to past trading history. Some or all of the above processes in the trading unit may be performed using AI or not. For example, the trading unit can input the user's past trading history into AI, and the AI ​​can analyze that history to select the optimal trading strategy.

[0086] The trading unit can customize trading methods based on the user's current asset status and risk tolerance at the time of trading. For example, the trading unit can set appropriate trading amounts considering the user's current asset status. The trading unit can also suggest riskier trading methods based on the user's risk tolerance. The trading unit can also customize the optimal trading method based on the user's asset status and risk tolerance. This allows for appropriate trading by customizing trading methods based on the user's asset status and risk tolerance. Some or all of the above processes in the trading unit may be performed using AI or not. For example, the trading unit can input the user's current asset status and risk tolerance into the AI, which can then analyze that information to customize trading methods.

[0087] The trading unit can estimate the user's emotions and determine trading priorities based on those estimated emotions. For example, if the user is stressed, the trading unit will prioritize trading low-risk stocks. If the user is relaxed, the trading unit may also prioritize trading high-risk, high-return stocks. If the user is excited, the trading unit may also prioritize aggressive trading. This reduces risk by determining trading priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and determine trading priorities.

[0088] The trading unit can take into account the user's geographical location information and reflect region-specific market trends when trading. For example, if the user lives in a specific region, the trading unit can perform trades that reflect the market trends of that region. If the user is traveling, the trading unit can also perform trades that reflect the market trends of the travel destination. The trading unit can also perform trades that reflect region-specific market trends based on the user's geographical location information. This allows for appropriate trading by reflecting region-specific market trends. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input the user's geographical location information into AI, and the AI ​​can analyze that information to perform trades that reflect region-specific market trends.

[0089] The trading unit can analyze a user's social media activity during trading and reflect relevant market trends in its trading decisions. For example, the trading unit can reflect market trends that a user frequently mentions on social media. The trading unit can also reflect market trends that a user is interested in based on their social media activity. The trading unit can also reflect market trends recommended by a user's social media followers. This allows for more appropriate trading by reflecting social media activity. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input a user's social media activity into an AI, which can then analyze that activity and reflect relevant market trends in its trading decisions.

[0090] The recording unit can estimate the user's emotions and adjust the display method of the recording based on the estimated emotions. For example, if the user is nervous, the recording unit can provide a simple and highly visible display method. If the user is relaxed, the recording unit can also provide a display method that includes detailed information. If the user is in a hurry, the recording unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the display method.

[0091] The recording unit can analyze past trading results during recording and select the optimal recording method for use in future trading. For example, the recording unit can analyze past trading results and record successful trading patterns. The recording unit can also exclude unsuccessful trading patterns from past trading results. Based on past trading results, the recording unit can also select the optimal recording method for use in future trading. In this way, by analyzing past trading results, the optimal recording method for use in future trading can be selected. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input past trading results into AI, and the AI ​​can analyze the results and select the optimal recording method.

[0092] The recording unit can customize the content of the recording based on the user's investment goals and risk tolerance at the time of recording. For example, the recording unit can record the degree of achievement based on the user's investment goals. The recording unit can also record risk assessments based on the user's risk tolerance. The recording unit can also customize the optimal recording content based on the user's investment goals and risk tolerance. This allows for appropriate recording by customizing the recording content based on the user's investment goals and risk tolerance. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's investment goals and risk tolerance into the AI, which can then analyze that information and customize the recording content.

[0093] The recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated emotions. For example, if the user is tense, the recording unit may prioritize recording important transaction results. If the user is relaxed, the recording unit may also prioritize recording detailed transaction results. If the user is in a hurry, the recording unit may also prioritize recording concise transaction results. This allows important information to be recorded preferentially by determining the priority of recordings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit may input user emotion data into a generative AI, which may analyze the data to estimate emotions and determine the priority of recordings.

[0094] The recording unit can reflect region-specific market trends by considering the user's geographical location information during recording. For example, if the user lives in a specific region, the recording unit can reflect the market trends of that region in the record. If the user is traveling, the recording unit can also reflect the market trends of the travel destination in the record. The recording unit can also reflect region-specific market trends in the record based on the user's geographical location information. This allows for appropriate recording by reflecting region-specific market trends. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location information into the AI, which can then analyze that information and reflect region-specific market trends in the record.

[0095] The recording unit can analyze the user's social media activity during recording and reflect relevant market trends in the record. For example, the recording unit can reflect market trends that the user frequently mentions on social media. The recording unit can also reflect market trends that the user is interested in based on their social media activity. The recording unit can also reflect market trends recommended by the user's social media followers. This allows for accurate recording by reflecting social media activity. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity into AI, which can then analyze that activity and reflect relevant market trends in the record.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can estimate the user's emotions and customize the input interface for setting information based on those emotions. For example, if the user is nervous, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick input of setting information. This improves user convenience by customizing the input interface according to the user's emotions.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, a risky analysis algorithm can be used. If the user is tense, a conservative analysis algorithm can be used. If the user is excited, an aggressive analysis algorithm can be used. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis results is improved.

[0099] The trading unit can estimate the user's emotions and fine-tune the timing of trades based on those emotions. For example, if the user is stressed, the trading timing can be carefully adjusted to reduce risk. If the user is relaxed, aggressive trading timings can be set. If the user is excited, riskier trading timings can be set. In this way, risk can be reduced by adjusting the trading timing according to the user's emotions.

[0100] The recording unit can estimate the user's emotions and adjust the way the records are displayed based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. If the user is in a hurry, it can provide a display that gets straight to the point. By adjusting the display method according to the user's emotions, user convenience is improved.

[0101] The recording unit can estimate the user's emotions and determine the priority of recordings based on those emotions. For example, if the user is stressed, important transaction results will be prioritized for recording. If the user is relaxed, detailed transaction results may also be recorded. If the user is in a hurry, concise transaction results may also be prioritized for recording. In this way, by prioritizing recordings according to the user's emotions, important information can be recorded first.

[0102] The reception desk can analyze a user's past settings history and suggest the optimal settings. For example, it can automatically display as suggestions the stocks, amounts, and periods that the user has frequently used in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Based on the user's past settings history, it can also predict and suggest settings that will be used during specific time periods. In this way, by analyzing past settings history, the system can suggest the most suitable settings for the user.

[0103] The reception desk can filter the user's investment information based on their investment experience and risk tolerance. For example, if the user is a beginner, low-risk stocks will be prioritized. If the user is an experienced investor, high-risk, high-return stocks can be suggested. The system can also suggest an appropriate investment amount based on the user's risk tolerance. In this way, appropriate investment information can be provided by filtering based on the user's investment experience and risk tolerance.

[0104] The analysis unit evaluates the reliability of past transaction data during analysis and prioritizes the use of highly reliable data. For example, it can score the reliability of past transaction data and prioritize the use of data with high scores. It can also filter out unreliable data and exclude it from the analysis. The accuracy of the analysis results can also be improved based on highly reliable data. In this way, the accuracy of the analysis results is improved by prioritizing the use of highly reliable data.

[0105] The trading department can select the optimal trading strategy by referring to past trading history when making trades. For example, it can analyze a user's past trading history and reproduce successful strategies. It can also avoid failed strategies based on a user's past trading history. It can also select the optimal trading strategy based on a user's past trading history. In this way, the optimal trading strategy can be selected by referring to past trading history.

[0106] The recording unit can customize the content of the recording based on the user's investment goals and risk tolerance. For example, it can record the degree of achievement based on the user's investment goals. It can also record risk assessments based on the user's risk tolerance. It can customize the optimal recording content based on the user's investment goals and risk tolerance. This allows for appropriate recording by customizing the recording content based on the user's investment goals and risk tolerance.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives the user's configuration information. This information includes, for example, the stock name, amount, and period. The reception desk receives this information that the user has set. Step 2: The analysis unit analyzes the transaction data based on the information received by the reception unit and determines the optimal purchase timing. The analysis unit can also analyze past transaction data and stock information and use generated AI to determine the optimal purchase timing. Step 3: The trading unit automatically executes trades based on the purchase timing determined by the analysis unit. The trading unit can also execute trades automatically using AI. Step 4: The recording unit records the results of trades made by the trading unit and uses them for future trades. The recording unit can also use AI to record trade results and use them for future trades.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, trading unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user setting information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past trading data and stock information to determine the optimal purchase timing. The trading unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically buys and sells based on the purchase timing determined by the analysis unit. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12 and records the trading results performed by the trading unit and uses them for the next trade. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, trading unit, and recording unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user setting information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes past trading data and stock information to determine the optimal purchase timing. The trading unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically buys and sells based on the purchase timing determined by the analysis unit. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and records the trading results performed by the trading unit and uses them for the next trade. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, trading unit, and recording unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user setting information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past trading data and stock information to determine the optimal purchase timing. The trading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically buys and sells based on the purchase timing determined by the analysis unit. The recording unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the trading results performed by the trading unit and uses them for the next trade. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, trading 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 reception unit is implemented by the microphone 238 of the robot 414 and receives user setting information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes past trading data and stock information to determine the optimal purchase timing. The trading unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically buys and sells based on the purchase timing determined by the analysis unit. The recording unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and records the trading results performed by the trading unit and uses them for the next trade. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception unit that receives user configuration information, An analysis unit analyzes transaction data based on the information received by the reception unit and determines the optimal purchase timing. A trading unit that automatically buys and sells based on the purchase timing determined by the aforementioned analysis unit, The system includes a recording unit that records the transaction results performed by the aforementioned trading unit and uses them for future transactions. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system accepts information from users regarding the stock, amount, and period they specify. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze past trading data and stock information to determine the optimal purchase timing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned sales department, Buying and selling are performed automatically based on the purchase timing determined by the aforementioned analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recording unit is Record the transaction results and use them for future transactions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for setting information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past settings history and suggests the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving configuration information, filtering is performed based on the user's investment experience and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the input order of setting information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving configuration information, the system prioritizes displaying highly relevant stocks based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving user configuration information, the system analyzes the user's social media activity and suggests relevant stocks. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the reliability of historical transaction data is evaluated, and reliable data is used preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, market trends and news information for each stock are incorporated in real time and reflected in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration to reflect region-specific market trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, we analyze users' social media activity and reflect relevant market trends in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned sales department, It estimates user sentiment and fine-tunes the timing of buy and sell orders based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned sales department, When buying or selling, the optimal trading strategy is selected by referring to past trading history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned sales department, When buying or selling, the trading method is customized based on the user's current asset status and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned sales department, It estimates user sentiment and determines buying and selling priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned sales department, When buying or selling, the system takes into account the user's geographical location to reflect region-specific market trends. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned sales department, When buying or selling, the system analyzes users' social media activity and reflects relevant market trends in the trading decisions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recording unit is It estimates the user's emotions and adjusts how the records are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recording unit is During recording, past transaction results are analyzed, and the optimal recording method is selected for use in future transactions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recording unit is When recording data, the content of the recording is customized based on the user's investment goals and risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned 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 28) The aforementioned recording unit is During recording, the system takes into account the user's geographical location to reflect region-specific market trends. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recording unit is During recording, the system analyzes users' social media activity and reflects relevant market trends in the records. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception unit that receives user configuration information, An analysis unit analyzes transaction data based on the information received by the reception unit and determines the optimal purchase timing. A trading unit that automatically buys and sells based on the purchase timing determined by the aforementioned analysis unit, The system includes a recording unit that records the transaction results performed by the aforementioned trading unit and uses them for future transactions. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts information from users regarding the stock, amount, and period they specify. The system according to feature 1.

3. The aforementioned analysis unit, We analyze past trading data and stock information to determine the optimal purchase timing. The system according to feature 1.

4. The aforementioned sales department, Buying and selling are performed automatically based on the purchase timing determined by the aforementioned analysis unit. The system according to feature 1.

5. The aforementioned recording unit is Record the transaction results and use them for future transactions. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for setting information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past settings history and suggests the optimal settings. The system according to feature 1.

8. The aforementioned reception unit is When receiving configuration information, filtering is performed based on the user's investment experience and risk tolerance. The system according to feature 1.

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