system

Abstract

We provide the system. [Solution] A means of collecting financial data obtained from users in real time, Means for preprocessing and analyzing collected financial data, A means of generating an investment strategy based on a user's investment profile using a generative AI, A means of notifying the user terminal of the generated investment strategy, A method for automatically optimizing a portfolio based on fluctuations in market data, A system that includes means for sending risk-based warning messages to users.

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, the method 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 modern investment environment, market fluctuations are intense, making it difficult for individual investors to make appropriate investment decisions. Also, it requires high expertise and time to provide an investment strategy in real time that takes into account an individual's risk tolerance while processing a vast amount of information. Therefore, there is a need for a support system that enables individual investors to effectively manage their investments and make optimal investment decisions.

Means for Solving the Problems

[0005] This invention provides a system that collects financial data from users in real time and generates investment strategies based on the user's investment profile using a generating AI. Furthermore, by incorporating a function to notify the user of the investment strategy and automatically optimize the portfolio based on market data fluctuations, it provides advice tailored to individual risk levels and real-time risk warnings. In addition, it adds a means for users to perform investment simulations, enabling individual investors with little experience or investment knowledge to invest with confidence. As a result, individual investors can make optimal investment decisions in response to the market in real time.

[0006] "Financial data" refers to information related to a user's transaction history and asset status, and is fundamental data necessary for investment decisions.

[0007] "Generative AI" refers to artificial intelligence technology that uses machine learning algorithms to analyze data and automatically generate user-specific investment strategies.

[0008] An "investment profile" is information that shows an individual's characteristics related to investment, such as the user's financial situation, investment experience, and risk tolerance.

[0009] An "investment strategy" refers to the specific investment actions and policies that a user should take, and it is a plan individually formulated by a generating AI.

[0010] A "portfolio" refers to a combination of financial products and assets held by a user, and is an important concept for managing risk and optimizing returns.

[0011] "Real-time" means that data acquisition and processing occur almost simultaneously with real-world time, enabling the provision of information with an emphasis on immediacy.

[0012] A "risk warning" is a cautionary message issued to users when there is a possibility of potential losses due to fluctuations in asset values.

[0013] An "investment simulation" is a function that allows users to conduct investment activities in a virtual environment without actually investing real money, and it is a tool for novice investors to gain experience. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is implemented as a system for collecting and analyzing user financial data, generating and notifying investment strategies, managing risk, and performing simulations. Specific embodiments of each component are described below.

[0036] Data acquisition and preprocessing

[0037] The server collects cashless payment information and asset status in real time via the user's device. This data includes user identification, past transaction history, and details of assets held. The server preprocesses the collected data, formatting it and performing cleansing to remove inconsistent or duplicate data.

[0038] Analysis using generative AI models

[0039] The server runs a generative AI model using pre-processed data. This AI model considers the user's individual investment profile and generates an optimal investment strategy tailored to their risk tolerance and market conditions. The generated strategy includes recommendations for buying and selling specific assets.

[0040] User notifications

[0041] The server sends the generated investment strategy to the user's terminal. The terminal displays the strategy information in a way that is intuitively understandable to the user. The user receives information through the terminal to make decisions about the necessary investment actions.

[0042] Risk management and portfolio optimization

[0043] The server monitors market data fluctuations in real time and performs risk assessments. If the risk exceeds a certain threshold, the server automatically optimizes the portfolio and issues a risk alert to the user. This allows the user to respond to risks quickly.

[0044] Investment simulation

[0045] The device provides a function that allows users to perform investment simulations in a virtual environment. By trying out various investment scenarios and checking the results without using actual funds, users can gain a deeper understanding and experience of investing.

[0046] As a concrete example, consider the case of a user in their 20s who is starting asset management for the first time. This user is risk-averse and aims for long-term wealth building. Based on this user's profile, the server recommends investing in bonds that provide stable dividends and notifies the user via their device of the appropriate timing and proportion of purchases. In this way, even beginners can invest with peace of mind.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server retrieves cashless payment information and asset status from the user's device. It uses an API to retrieve this data, associates it with each individual user, and stores it in a database.

[0050] Step 2:

[0051] The server performs data cleansing on the acquired financial data. Specifically, it standardizes the data format, eliminates inconsistencies, and fills in missing data. This generates clean data suitable for analysis.

[0052] Step 3:

[0053] The server inputs clean data into an AI model. The AI ​​model utilizes machine learning algorithms to analyze the user's risk tolerance and past investment history, and generates a user-specific investment strategy.

[0054] Step 4:

[0055] The server sends the generated investment strategy to the user's terminal. The user's terminal displays this strategy in an easy-to-understand visual format and provides trading recommendations and risk assessments.

[0056] Step 5:

[0057] The server monitors market trends in real time and analyzes changes in market data. If risk exceeds a threshold, it automatically re-evaluates the portfolio and implements necessary optimizations.

[0058] Step 6:

[0059] The server sends a risk alert to the user's terminal. The terminal immediately notifies the user of this alert and prompts them to take action to mitigate the risk.

[0060] Step 7:

[0061] Users make investment decisions based on the provided investment strategies and risk warnings. Furthermore, users can use the device's investment simulation function to virtually try out different investment scenarios.

[0062] Step 8:

[0063] User investment behavior and feedback are sent from the device to the server. The server analyzes this data and uses it to improve the generated AI model, enabling it to provide more accurate investment advice.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] For users facing the complexity of financial markets and the difficulty of making investment decisions based on a large amount of data, generating and notifying them of appropriate investment strategies in real time is challenging. Furthermore, there is a need to respond quickly to market fluctuations and provide effective portfolio management that minimizes risk. Additionally, providing investment simulations for beginners and offering a low-risk investment experience is another challenge.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for collecting financial information obtained from users in real time, means for preprocessing and analyzing the collected financial information, and means for generating investment strategies based on individual investment profiles using a generating AI. This enables the provision of optimal investment strategies to users in real time, allowing for effective investment decisions and portfolio management.

[0069] A "user" is an individual or organization that accesses the system and makes information or investment decisions.

[0070] "Financial information" refers to a collection of information related to a user's financial activities, such as cashless payment information, asset status, and transaction history.

[0071] "Generative AI" is an artificial intelligence system that uses machine learning algorithms to automatically generate investment strategies for users.

[0072] An "investment profile" is a collection of information used to develop a customized investment strategy based on the user's risk tolerance, investment goals, and trading history.

[0073] An "investment strategy" is a plan of investment activities designed to achieve a specific objective, and includes recommendations for buying and selling assets.

[0074] A "portfolio" is a combination of various assets held by a user, and its purpose is risk management and return optimization.

[0075] "Real-time" refers to a system where information processing and data updates are performed instantly, providing users with the latest information at all times.

[0076] "Preprocessing" refers to the process of converting raw data into a format that can be analyzed, and includes standardizing data formats and removing invalid data.

[0077] This invention is a system that collects financial data in real time and provides users with optimal investment strategies. The system requires interaction between a server, terminals, and users. The server is primarily responsible for information collection, analysis, and strategy generation using AI models. The terminals, on the other hand, function as an interface with the user, providing strategy notifications and simulation functions.

[0078] The server uses a database management system to retrieve users' financial information from cashless payment platforms and asset management software. This information requires real-time data, and the database is always kept up-to-date. The data is preprocessed and formatted through cleansing.

[0079] Next, the server runs a generative AI model using the pre-processed data. This AI model, based on machine learning algorithms, generates optimal investment strategies tailored to each user's investment profile. Specific technologies used include advanced statistical analysis software and AI tools.

[0080] The generated investment strategy is communicated to the user via their device. The device uses data visualization tools to display the information in intuitive graphs and charts. Based on this information, the user can then make investment decisions.

[0081] In the simulation function, the terminal provides a virtual environment, allowing users to hone their investment skills. This environment assumes a risk-free situation, enabling the verification of various investment scenarios.

[0082] As a concrete example, consider a user in their 30s who is a new investor. This user wants to increase their assets over the long term with controlled risk. Based on this user's profile, the server recommends long-term investment in low-risk bonds and notifies the user of this strategy via their terminal. An example of a prompt message would be, "Please suggest the optimal long-term investment strategy considering the user's current asset and financial situation."

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects users' financial information in real time. Specifically, it obtains cashless payment information and asset status via APIs. This input data includes transaction history and asset breakdowns. The server stores this in a database, preparing it for processing in the next step.

[0086] Step 2:

[0087] The server preprocesses the collected financial information. It standardizes the data format, detects and corrects or deletes missing or inconsistent data. The input is raw, unformatted data, and the output is a clean dataset suitable for analysis. Specifically, it performs tasks such as deduplication using data cleaning tools.

[0088] Step 3:

[0089] The server runs a generative AI model based on pre-processed data. Input data includes the user's past behavioral tendencies and risk tolerance. The AI ​​model then generates an optimal investment strategy, taking into account risk tolerance and market conditions, and outputs it as a recommendation. This requires data analysis using machine learning algorithms.

[0090] Step 4:

[0091] The server notifies the user's device of the generated investment strategy. The output strategy includes a specific list of actions regarding the purchase and sale of recommended assets. The device receives this and displays it in a user-friendly UI. Specifically, push notifications and chart display functions on the dashboard are used.

[0092] Step 5:

[0093] The server monitors market data in real time and performs risk assessments. It takes the latest market information as input data and generates risk warning messages and portfolio adjustment suggestions as output. This allows users to respond quickly to unforeseen circumstances.

[0094] Step 6:

[0095] The terminal provides a virtual environment where users can perform investment simulations. Users set investment scenarios they wish to try as input, and the output displays predicted returns and risks. By utilizing simulation tools, it reproduces realistic market fluctuations, contributing to the user's investment skills.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] Traditional asset management systems offered a uniform investment strategy to each user, making it difficult to tailor appropriate responses to individual risk profiles and market fluctuations. Furthermore, users often struggled to adjust strategies based on their own experience, leading to anxiety about asset management. Additionally, the lack of robust simulation capabilities limited opportunities for beginners to learn about investing.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for collecting transaction information obtained from users in real time, means for preprocessing and analyzing the collected transaction information, means for generating a strategy based on the user's asset management profile using a generative AI, means for notifying the user device of the generated strategy, means for automatically optimizing the investment portfolio based on fluctuations in market information, means for sending risk-based warning messages to the user, means for executing investment simulations on the user device, means for the generative AI to generate an optimized strategy for the user based on the transaction information and notifying the user of the strategy in real time using data communication means, means for enabling the user to experimentally perform asset management simulations, and means for receiving feedback on strategy generation through a provided user interface and for the generative AI model to continuously improve the strategy. This enables the proposal of an optimal investment strategy tailored to individual profiles and flexible, real-time asset management, allowing even beginners to gain investment experience with peace of mind.

[0101] A "user" is an individual or organization that utilizes asset management services and provides financial information.

[0102] "Transaction information" refers to data related to cashless payments made by users and their asset status.

[0103] "Generative AI" is an artificial intelligence technology that generates investment strategies based on the user's profile.

[0104] A "strategy" is a specific investment plan tailored to the user's risk profile and market conditions.

[0105] A "user device" is an electronic terminal used by a user to receive information.

[0106] "Market information" refers to data related to trends and fluctuations in financial markets.

[0107] An "investment portfolio" is a collection of the types and allocations of assets owned by a user.

[0108] A "simulation" is a method of evaluation performed in a virtual investment environment without using actual funds.

[0109] "Data communication means" refers to communication technology used to transmit generated information to a user's device.

[0110] "Feedback" refers to the opinions and evaluations that users provide after a strategy has been implemented.

[0111] The system for implementing this invention collects user transaction information in real time, generates individual investment strategies based on that information, and optimizes them. The server receives cashless payment information and asset status from users via terminals and stores them in a financial database. The collected transaction information is preprocessed using a Python program, and data cleansing is performed. At this stage, the information is formatted and inconsistent data is removed.

[0112] Subsequently, the pre-processed data is analyzed by a generative AI model using TENSORFLOW®. This model considers the user's investment profile and current market information to generate an optimal asset management strategy. The generated strategy is then notified to the user's device via the Firebase Cloud Messaging service. In this way, the user can immediately obtain concrete investment proposals.

[0113] Furthermore, the server monitors market fluctuations in real time and quickly and automatically optimizes investment portfolios. When risk increases, it sends a warning message to the user, prompting appropriate action. In addition, a simulation environment using React Native is provided on the user's device, allowing users to try out virtual investment scenarios and gain experience without using actual funds.

[0114] As a concrete example, consider the case of a 30-year-old individual user investing in technology stocks for the first time. This user is seeking moderate risk and moderate return, and the AI ​​model generates a strategy recommending diversified investment in technology stocks based on this profile and notifies the user's device. An example of a prompt message to the AI ​​model would be: "Based on the user's past financial data and current market conditions, please suggest an appropriate investment strategy for technology stocks."

[0115] This allows even novice individual users to manage their assets safely and efficiently.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server collects transaction information in real time via the user's terminal. Inputs include the user's cashless payment records and asset status. The server stores this data in a financial database, preparing it for subsequent processing.

[0119] Step 2:

[0120] The server preprocesses the collected transaction information using Python. The input is the raw data collected in step 1. The server scientifically formats and cleanses this data, removing inconsistent data and duplicate information. The output is data formatted in a way that is easy for the generative AI model to handle.

[0121] Step 3:

[0122] The server analyzes pre-processed data using a generative AI model based on TensorFlow. The input requires the clean data obtained in step 2 and the user's investment profile. Based on this information, the generative AI model generates an optimal investment strategy tailored to the user's risk profile and market information. The output is a strategy that includes specific investment policies and buy / sell recommendations.

[0123] Step 4:

[0124] The server uses Firebase Cloud Messaging to notify the user's device of the generated strategy. The input is the investment strategy generated in step 3. The user receives this information on their device and gains guidance to intuitively decide on investment actions. The output is the notification sent to the user.

[0125] Step 5:

[0126] The server monitors market data fluctuations and performs risk assessments. Inputs are current market data and the user's portfolio information. If risk exceeds a certain threshold, the server optimizes the portfolio and sends a warning message to the user. Outputs are the optimized portfolio and risk warnings.

[0127] Step 6:

[0128] The application uses React Native to provide users with an investment simulation function. Inputs include a virtual initial investment amount and a selected scenario. Through this simulation, users can try out various asset management strategies and see the results virtually. Outputs are the investment profits and losses as a result of the simulation.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is implemented as a system that combines an emotion engine with a system for generating and notifying users of their unique investment strategies. The detailed configuration of the system and its embodiments are shown below.

[0131] Data collection and emotion recognition

[0132] The server analyzes the user's emotional state by collecting not only financial data but also data such as the user's facial expressions and voice through the user's terminal. The emotion engine analyzes this data and recognizes the user's current emotional state, such as whether they are currently positive or negative, in real time.

[0133] Analysis and strategic adjustment using generative AI models

[0134] The server inputs both financial and emotional data into a generating AI model. Based on the collected data, the AI ​​model generates flexible investment strategies that take into account the user's investment profile and current emotions. For example, if the user is feeling stressed, it can suggest an investment strategy that reduces risk.

[0135] User notifications

[0136] The server sends investment strategies and risk warnings tailored to the generated emotions to the user's terminal. The terminal visually displays the strategies in an emotionally sensitive manner and adjusts the tone of risk advice as needed.

[0137] Risk management and portfolio optimization

[0138] The server uses emotional data obtained through the emotion engine to manage risk. By capturing changes in users' emotions along with market trends, it can instantly review and optimize the portfolio.

[0139] Investment simulation

[0140] Users can use their devices to perform investment simulations that take emotional data into account. The emotional engine tracks emotional changes during the simulation and accumulates data that can be used to improve future emotional responses.

[0141] As a concrete example, consider a scenario where a user is experiencing intense pressure during a period of high market tension and volatility. The server, through its emotion engine, recognizes this state and notifies the user's device of an investment strategy suggesting a shift to lower-risk bonds or stocks. In this way, dynamic responses based on the user's emotional state are possible.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The server acquires facial expressions and voice information along with financial data from the user's device. This information is necessary to infer the user's emotional state and is therefore captured using high-precision sensors and cameras.

[0145] Step 2:

[0146] The server analyzes the acquired emotion-related data using an emotion engine. The emotion engine utilizes machine learning models to determine the user's emotions in real time from their facial expressions and tone of voice. The determined emotion data is then used to adjust investment strategies.

[0147] Step 3:

[0148] The server integrates financial and emotional data and inputs it into a generative AI. The generative AI considers the user's investment profile and emotions to generate an investment strategy with enhanced risk management. For example, if the user is feeling anxious, the AI ​​will prioritize suggesting stable investment options.

[0149] Step 4:

[0150] The server sends the generated investment strategy to the user's terminal. The terminal displays this strategy in a user-friendly format and presents an emotionally sensitive risk assessment and investment recommendations.

[0151] Step 5:

[0152] The server monitors market trends and user sentiment data in real time. If the market is highly volatile or users are experiencing stress, it quickly adjusts the portfolio.

[0153] Step 6:

[0154] The device instantly notifies users of risk warnings and information on readjusted portfolios. The tone and content of the notifications are carefully considered to ensure users can continue investing with confidence.

[0155] Step 7:

[0156] Users use their devices to conduct investment simulations that reflect their emotions. The simulations predict investment outcomes under different emotional states, providing users with a new perspective.

[0157] Step 8:

[0158] User feedback and changes in emotional state are sent from the device to the server. The server uses this data to improve the accuracy of the emotion engine and generative AI. This allows for more personalized strategies to be provided to the user in subsequent interactions.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] Conventional investment strategy generation systems often consider only financial data, making it difficult to propose flexible investment strategies that take into account the user's emotional state. Furthermore, they struggle to immediately optimize portfolios in response to rapid market fluctuations or changes in individual users' emotions.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for collecting emotional and financial data acquired from users in real time, means for preprocessing and analyzing the collected data, and means for generating investment strategies based on the user's investment profile and emotional state using a generative AI model. This enables the proposal of flexible and dynamic investment strategies that take into account the user's emotional state, and real-time portfolio optimization.

[0164] A "user" is an individual or group that uses the system to conduct investment activities.

[0165] "Emotional data" refers to information about a user's emotional state obtained by analyzing their facial expressions, voice, and other data.

[0166] "Financial data" refers to numerical information about market trends and individual assets, and is the data necessary for formulating investment strategies.

[0167] A "generative AI model" is a program that uses machine learning algorithms to generate the optimal investment strategy from a user's investment profile and emotional state.

[0168] An "investment profile" is a collection of distinctive investment information that integrates a user's asset situation, risk tolerance, investment goals, and other relevant factors.

[0169] A "portfolio" refers to a combination of multiple financial assets owned by a user.

[0170] "Real-time" refers to processing that instantly reflects the latest state with virtually no delay.

[0171] This invention is a system that generates investment strategies that take into account the user's emotional state and proposes them to the user in real time. The system includes the user's terminal, a server, and a generating AI model, and they all work together in coordination.

[0172] The server collects emotional and financial data from the user's device in real time. This data collection utilizes the device's hardware, such as the camera and microphone, to analyze the user's emotional state from their facial expressions and tone of voice. The emotion engine processes this data to determine whether the user is currently positive or negative.

[0173] Next, the server inputs emotional and financial data into the generating AI model. The AI ​​model considers the user's investment profile and emotional state to create an optimal investment strategy. The generated investment strategy may include shifting to government bonds if the user desires lower-risk options. An example of a specific prompt might be, "The user is feeling stressed and wants a safe investment."

[0174] The generated investment strategy is notified from the server to the user's terminal. The terminal displays the strategy through a visual interface, assisting the user in making emotionally informed decisions. The terminal also adjusts the tone of risk warnings as needed, ensuring the user can make investment decisions with confidence.

[0175] This invention provides users with a dynamic investment strategy that takes emotions into account, enabling appropriate portfolio management in response to fluctuating markets and individual emotional states. Users can explore future investment strategies by running investment simulations that take emotional data into account.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server collects emotional and financial data from the user's device in real time. Specifically, it uses the camera and microphone provided by the device to capture the user's facial expressions and voice. The inputs include image data and audio data. This data is analyzed on the server and output in a numerical format representing the user's emotional state.

[0179] Step 2:

[0180] The server uses an emotion engine to analyze the collected emotion data. The input is the emotion information that was quantified earlier. In the data analysis, an image recognition algorithm is used to analyze facial features and a voice analysis algorithm is used to detect the tone of voice. The output is a determination result indicating whether the user's emotional state is positive, negative, or neutral.

[0181] Step 3:

[0182] The server inputs emotional and financial data into a generating AI model. This input includes the user's investment profile, emotional assessment results, and the latest market data. The AI ​​model uses this data to generate an optimal investment strategy. Specifically, a machine learning algorithm processes the data and outputs investment selections that match the user's risk tolerance.

[0183] Step 4:

[0184] The server constructs the generated investment strategy in the form of a prompt message and sends it to the user's terminal. For example, it might output text such as, "The user is experiencing stress, so we recommend a safe investment choice." The prompt message is displayed on the terminal in a visual format.

[0185] Step 5:

[0186] The terminal displays the received investment strategy to the user through a visual interface. Input consists of prompt messages from the server. This information is presented in a user-friendly format using a GUI. If necessary, the tone of risk warnings is also adjusted.

[0187] Step 6:

[0188] Users can use their devices to perform investment simulations while receiving feedback on emotional data. Inputs include past emotional data and investment results. This feedback is used to inform future investment decisions based on the simulation results. The output is an optimized strategy suggestion for the next investment.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Conventional investment strategy generation systems formulate investment strategies based solely on the user's financial information, making it difficult to flexibly consider the user's emotional state and biometric information. Furthermore, they cannot offer relaxation suggestions tailored to the user's emotional state, such as stress and anxiety, and thus fail to alleviate the psychological burden of investment activities.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for collecting financial and biometric information obtained from the user in real time, means for preprocessing and analyzing the collected financial and biometric information, and means for generating investment strategies based on the user's investment profile and emotional state using a generating AI. This makes it possible to propose flexible investment strategies that take the user's emotions into consideration and to provide relaxation menus.

[0194] "Financial information" refers to data including the value of assets related to a user's investments, transaction history, and market trends.

[0195] "Biometric information" refers to information such as facial expression data, voice data, and other physiological data acquired in order to infer the user's emotional state.

[0196] "Generative AI" is an artificial intelligence technology that automatically generates investment strategies tailored to a user's investment profile and emotional state, based on their financial and biometric information.

[0197] An "investment profile" is basic information used to formulate individual strategies for users, including their investment objectives and risk tolerance.

[0198] A "relaxation menu" refers to services such as music and guided meditation that are suggested to reduce stress and anxiety based on the user's emotional state.

[0199] In this embodiment, a system including a server and a user terminal provides investment support to the user. The server collects the user's financial information and biometric information in real time and analyzes the user's emotional state based on this information. The financial information includes the user's investment-related asset information and market trends, while the biometric information is data obtained from the user's facial expressions and voice.

[0200] The server preprocesses the collected information and performs analysis using a generated AI model. Specifically, it uses Google Cloud's Speech-to-Text API and Microsoft Azure's Sentiment Recognition API to convert data from speech to text and recognize emotional states. The analysis results are input into an AI model generated using TensorFlow or PyTorch frameworks, which then generates a flexible investment strategy tailored to the user's investment profile and emotional state.

[0201] The generated investment strategy and relaxation menu are notified to the user's device. Notifications are delivered via the device's display and audio output. If the device is deemed high-risk, it displays a warning message and recommends relaxation options such as music or guided meditation, tailored to the user's emotional state. This allows the user to make investment decisions while managing their emotions.

[0202] As a concrete example, consider a situation where a user is experiencing stress due to market fluctuations. The server, based on this biometric information, determines that the user is experiencing negative emotions and generates and notifies them of a risk-reducing investment strategy. At the same time, it suggests relaxing music to help the user regain their composure.

[0203] An example of a prompt might be, "Given the current market conditions, what flexible investment strategy should be proposed if the user appears anxious?" Based on this prompt, the AI ​​model generates suggestions tailored to each user.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server collects the user's financial and biometric information. Specifically, it receives financial information such as asset information and transaction history from the user's terminal via the internet, and simultaneously acquires facial expression data and voice data using the terminal's camera and microphone. The input for this step is the user's financial and biometric information, and the output is a dataset containing this information.

[0207] Step 2:

[0208] The server preprocesses the acquired data. At this stage, the obtained audio data is converted to text using Google Cloud's Speech-to-Text API, and the facial expression data is converted to emotion labels using Microsoft Azure's emotion recognition API. Financial information is normalized and cleansed. The input is the collected raw data, and the output is data in a parseable format.

[0209] Step 3:

[0210] The server inputs pre-processed data into a generating AI model, which then generates an investment strategy based on the user's investment profile and emotional state. The AI ​​model is built using TensorFlow and PyTorch, and the prompts are in the form of pre-configured questions. The input is pre-processed data, and the output is the generated investment strategy.

[0211] Step 4:

[0212] The server notifies the user's terminal of the generated investment strategy. The notification is in visual or audio format, using the terminal's display or speaker. The input is the generated investment strategy, and the output is the notification to the user.

[0213] Step 5:

[0214] The server provides relaxation options based on the user's emotional state. If the server determines that the user is stressed, specific music or guided meditation will be suggested for relaxation. The input for this step is the result of the emotional analysis, and the output is the recommended relaxation menu.

[0215] Step 6:

[0216] The user selects on the device whether to accept or reject the proposed investment strategy and relaxation menu. The input is the notified strategy and menu, and the output is the user's selection. This selection is used to generate subsequent strategies.

[0217] 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.

[0218] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0224] 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.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0226] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] 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.

[0228] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The 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.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is implemented as a system for collecting and analyzing user financial data, generating and notifying investment strategies, managing risk, and performing simulations. Specific embodiments of each component are described below.

[0234] Data acquisition and preprocessing

[0235] The server collects cashless payment information and asset status in real time via the user's device. This data includes user identification, past transaction history, and details of assets held. The server preprocesses the collected data, formatting it and performing cleansing to remove inconsistent or duplicate data.

[0236] Analysis using generative AI models

[0237] The server runs a generative AI model using pre-processed data. This AI model considers the user's individual investment profile and generates an optimal investment strategy tailored to their risk tolerance and market conditions. The generated strategy includes recommendations for buying and selling specific assets.

[0238] User notifications

[0239] The server sends the generated investment strategy to the user's terminal. The terminal displays the strategy information in a way that is intuitively understandable to the user. The user receives information through the terminal to make decisions about the necessary investment actions.

[0240] Risk management and portfolio optimization

[0241] The server monitors market data fluctuations in real time and performs risk assessments. If the risk exceeds a certain threshold, the server automatically optimizes the portfolio and issues a risk alert to the user. This allows the user to respond to risks quickly.

[0242] Investment simulation

[0243] The device provides a function that allows users to perform investment simulations in a virtual environment. By trying out various investment scenarios and checking the results without using actual funds, users can gain a deeper understanding and experience of investing.

[0244] As a concrete example, consider the case of a user in their 20s who is starting asset management for the first time. This user is risk-averse and aims for long-term wealth building. Based on this user's profile, the server recommends investing in bonds that provide stable dividends and notifies the user via their device of the appropriate timing and proportion of purchases. In this way, even beginners can invest with peace of mind.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The server retrieves cashless payment information and asset status from the user's device. It uses an API to retrieve this data, associates it with each individual user, and stores it in a database.

[0248] Step 2:

[0249] The server performs data cleansing on the acquired financial data. Specifically, it standardizes the data format, eliminates inconsistencies, and fills in missing data. This generates clean data suitable for analysis.

[0250] Step 3:

[0251] The server inputs clean data into an AI model. The AI ​​model utilizes machine learning algorithms to analyze the user's risk tolerance and past investment history, and generates a user-specific investment strategy.

[0252] Step 4:

[0253] The server sends the generated investment strategy to the user's terminal. The user's terminal displays this strategy in an easy-to-understand visual format and provides trading recommendations and risk assessments.

[0254] Step 5:

[0255] The server monitors market trends in real time and analyzes changes in market data. If risk exceeds a threshold, it automatically re-evaluates the portfolio and implements necessary optimizations.

[0256] Step 6:

[0257] The server sends a risk alert to the user's terminal. The terminal immediately notifies the user of this alert and prompts them to take action to mitigate the risk.

[0258] Step 7:

[0259] Users make investment decisions based on the provided investment strategies and risk warnings. Furthermore, users can use the device's investment simulation function to virtually try out different investment scenarios.

[0260] Step 8:

[0261] User investment behavior and feedback are sent from the device to the server. The server analyzes this data and uses it to improve the generated AI model, enabling it to provide more accurate investment advice.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] For users facing the complexity of financial markets and the difficulty of making investment decisions based on a large amount of data, generating and notifying them of appropriate investment strategies in real time is challenging. Furthermore, there is a need to respond quickly to market fluctuations and provide effective portfolio management that minimizes risk. Additionally, providing investment simulations for beginners and offering a low-risk investment experience is another challenge.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for collecting financial information obtained from users in real time, means for preprocessing and analyzing the collected financial information, and means for generating investment strategies based on individual investment profiles using a generating AI. This enables the provision of optimal investment strategies to users in real time, allowing for effective investment decisions and portfolio management.

[0267] A "user" is an individual or organization that accesses the system and makes information or investment decisions.

[0268] "Financial information" refers to a collection of information related to a user's financial activities, such as cashless payment information, asset status, and transaction history.

[0269] "Generative AI" is an artificial intelligence system that uses machine learning algorithms to automatically generate investment strategies for users.

[0270] An "investment profile" is a collection of information used to develop a customized investment strategy based on the user's risk tolerance, investment goals, and trading history.

[0271] An "investment strategy" is a plan of investment activities designed to achieve a specific objective, and includes recommendations for buying and selling assets.

[0272] A "portfolio" is a combination of various assets held by a user, and its purpose is risk management and return optimization.

[0273] "Real-time" refers to a system where information processing and data updates are performed instantly, providing users with the latest information at all times.

[0274] "Preprocessing" refers to the process of converting raw data into a format that can be analyzed, and includes standardizing data formats and removing invalid data.

[0275] This invention is a system that collects financial data in real time and provides users with optimal investment strategies. The system requires interaction between a server, terminals, and users. The server is primarily responsible for information collection, analysis, and strategy generation using AI models. The terminals, on the other hand, function as an interface with the user, providing strategy notifications and simulation functions.

[0276] The server uses a database management system to retrieve users' financial information from cashless payment platforms and asset management software. This information requires real-time data, and the database is always kept up-to-date. The data is preprocessed and formatted through cleansing.

[0277] Next, the server runs a generative AI model using the pre-processed data. This AI model, based on machine learning algorithms, generates optimal investment strategies tailored to each user's investment profile. Specific technologies used include advanced statistical analysis software and AI tools.

[0278] The generated investment strategy is communicated to the user via their device. The device uses data visualization tools to display the information in intuitive graphs and charts. Based on this information, the user can then make investment decisions.

[0279] In the simulation function, the terminal provides a virtual environment, allowing users to hone their investment skills. This environment assumes a risk-free situation, enabling the verification of various investment scenarios.

[0280] As a specific example, consider a 30-year-old user who is a new investor. This user wants to increase their assets while minimizing risk in the long term. Based on this user's profile, the server recommends a long-term investment in low-risk bonds and notifies the user of this strategy through the terminal. As an example of a prompt sentence, an input such as "Please propose an optimal long-term investment strategy considering the user's current asset situation and economic situation." is provided.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The server collects the user's financial information in real time. Specifically, it obtains cashless payment information and asset status through an API. This input data includes transaction history and the breakdown of assets. The server stores this in a database in preparation for the next step of processing.

[0284] Step 2:

[0285] The server preprocesses the collected financial information. It unifies the data format, detects and corrects or deletes missing values and conflicting data. As input, there is raw data that is not formatted, and as output, a clean dataset suitable for analysis is obtained. As specific operations, duplicate elimination using data cleaning tools is performed.

[0286] Step 3:

[0287] The server runs a generated AI model based on the preprocessed data. The input data includes the user's past behavior trends and risk preferences. The AI model then generates an optimal investment strategy considering the risk tolerance and market conditions and outputs it as a recommendation. This requires data analysis using machine learning algorithms.

[0288] Step 4:

[0289] The server notifies the user's device of the generated investment strategy. The output strategy includes a specific list of actions regarding the purchase and sale of recommended assets. The device receives this and displays it in a user-friendly UI. Specifically, push notifications and chart display functions on the dashboard are used.

[0290] Step 5:

[0291] The server monitors market data in real time and performs risk assessments. It takes the latest market information as input data and generates risk warning messages and portfolio adjustment suggestions as output. This allows users to respond quickly to unforeseen circumstances.

[0292] Step 6:

[0293] The terminal provides a virtual environment where users can perform investment simulations. Users set investment scenarios they wish to try as input, and the output displays predicted returns and risks. By utilizing simulation tools, it reproduces realistic market fluctuations, contributing to the user's investment skills.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] Traditional asset management systems offered a uniform investment strategy to each user, making it difficult to tailor appropriate responses to individual risk profiles and market fluctuations. Furthermore, users often struggled to adjust strategies based on their own experience, leading to anxiety about asset management. Additionally, the lack of robust simulation capabilities limited opportunities for beginners to learn about investing.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for collecting transaction information obtained from users in real time, means for preprocessing and analyzing the collected transaction information, means for generating a strategy based on the user's asset management profile using a generative AI, means for notifying the user device of the generated strategy, means for automatically optimizing the investment portfolio based on fluctuations in market information, means for sending risk-based warning messages to the user, means for executing investment simulations on the user device, means for the generative AI to generate an optimized strategy for the user based on the transaction information and notifying the user of the strategy in real time using data communication means, means for enabling the user to experimentally perform asset management simulations, and means for receiving feedback on strategy generation through a provided user interface and for the generative AI model to continuously improve the strategy. This enables the proposal of an optimal investment strategy tailored to individual profiles and flexible, real-time asset management, allowing even beginners to gain investment experience with peace of mind.

[0299] A "user" is an individual or organization that utilizes asset management services and provides financial information.

[0300] "Transaction information" refers to data related to cashless payments made by users and their asset status.

[0301] "Generative AI" is an artificial intelligence technology that generates investment strategies based on the user's profile.

[0302] A "strategy" is a specific investment plan tailored to the user's risk profile and market conditions.

[0303] A "user device" is an electronic terminal used by a user to receive information.

[0304] "Market information" refers to data related to the trends and fluctuations of the financial market.

[0305] "Investment portfolio" refers to the collection of the types and allocations of assets owned by the user.

[0306] "Simulation" is a method of evaluation in a virtual investment environment without using actual funds.

[0307] "Data communication means" is a communication technology for transmitting the generated information to the user's device.

[0308] "Feedback" refers to the opinions and evaluations provided by the user after applying the strategy.

[0309] The system for implementing this invention is for collecting the user's transaction information in real time and generating and optimizing individual investment strategies based on it. The server receives cashless payment information and asset status from the user through the terminal and stores them in the financial database. The collected transaction information is preprocessed by a program using Python, and data cleansing is performed. At this stage, the formatting of information and the deletion of conflicting data are carried out.

[0310] After that, the preprocessed data is analyzed by a generative AI model using TensorFlow. This model takes into account the user's investment profile and current market information and generates an optimal asset management strategy. The generated strategy is notified to the user's device via the Firebase Cloud Messaging service. In this way, the user can immediately obtain a specific investment plan.

[0311] Furthermore, the server monitors market fluctuations in real time and quickly and automatically optimizes investment portfolios. When risk increases, it sends a warning message to the user, prompting appropriate action. In addition, a simulation environment using React Native is provided on the user's device, allowing users to try out virtual investment scenarios and gain experience without using actual funds.

[0312] As a concrete example, consider the case of a 30-year-old individual user investing in technology stocks for the first time. This user is seeking moderate risk and moderate return, and the AI ​​model generates a strategy recommending diversified investment in technology stocks based on this profile and notifies the user's device. An example of a prompt message to the AI ​​model would be: "Based on the user's past financial data and current market conditions, please suggest an appropriate investment strategy for technology stocks."

[0313] This allows even novice individual users to manage their assets safely and efficiently.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The server collects transaction information in real time via the user's terminal. Inputs include the user's cashless payment records and asset status. The server stores this data in a financial database, preparing it for subsequent processing.

[0317] Step 2:

[0318] The server preprocesses the collected transaction information using Python. The input is the raw data collected in step 1. The server scientifically formats and cleanses this data, removing inconsistent data and duplicate information. The output is data formatted in a way that is easy for the generative AI model to handle.

[0319] Step 3:

[0320] The server analyzes pre-processed data using a generative AI model based on TensorFlow. The input requires the clean data obtained in step 2 and the user's investment profile. Based on this information, the generative AI model generates an optimal investment strategy tailored to the user's risk profile and market information. The output is a strategy that includes specific investment policies and buy / sell recommendations.

[0321] Step 4:

[0322] The server uses Firebase Cloud Messaging to notify the user's device of the generated strategy. The input is the investment strategy generated in step 3. The user receives this information on their device and gains guidance to intuitively decide on investment actions. The output is the notification sent to the user.

[0323] Step 5:

[0324] The server monitors market data fluctuations and performs risk assessments. Inputs are current market data and the user's portfolio information. If risk exceeds a certain threshold, the server optimizes the portfolio and sends a warning message to the user. Outputs are the optimized portfolio and risk warnings.

[0325] Step 6:

[0326] The application uses React Native to provide users with an investment simulation function. Inputs include a virtual initial investment amount and a selected scenario. Through this simulation, users can try out various asset management strategies and see the results virtually. Outputs are the investment profits and losses as a result of the simulation.

[0327] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0328] This invention is implemented as a system that combines an emotion engine with a system for generating and notifying users of their unique investment strategies. The detailed configuration of the system and its embodiments are shown below.

[0329] Data collection and emotion recognition

[0330] The server analyzes the user's emotional state by collecting not only financial data but also data such as the user's facial expressions and voice through the user's terminal. The emotion engine analyzes this data and recognizes the user's current emotional state, such as whether they are currently positive or negative, in real time.

[0331] Analysis and strategic adjustment using generative AI models

[0332] The server inputs both financial and emotional data into a generating AI model. Based on the collected data, the AI ​​model generates flexible investment strategies that take into account the user's investment profile and current emotions. For example, if the user is feeling stressed, it can suggest an investment strategy that reduces risk.

[0333] User notifications

[0334] The server sends investment strategies and risk warnings tailored to the generated emotions to the user's terminal. The terminal visually displays the strategies in an emotionally sensitive manner and adjusts the tone of risk advice as needed.

[0335] Risk management and portfolio optimization

[0336] The server uses emotional data obtained through the emotion engine to manage risk. By capturing changes in users' emotions along with market trends, it can instantly review and optimize the portfolio.

[0337] Investment simulation

[0338] Users can use their devices to perform investment simulations that take emotional data into account. The emotional engine tracks emotional changes during the simulation and accumulates data that can be used to improve future emotional responses.

[0339] As a concrete example, consider a scenario where a user is experiencing intense pressure during a period of high market tension and volatility. The server, through its emotion engine, recognizes this state and notifies the user's device of an investment strategy suggesting a shift to lower-risk bonds or stocks. In this way, dynamic responses based on the user's emotional state are possible.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The server acquires facial expressions and voice information along with financial data from the user's device. This information is necessary to infer the user's emotional state and is therefore captured using high-precision sensors and cameras.

[0343] Step 2:

[0344] The server analyzes the acquired emotion-related data using an emotion engine. The emotion engine utilizes machine learning models to determine the user's emotions in real time from their facial expressions and tone of voice. The determined emotion data is then used to adjust investment strategies.

[0345] Step 3:

[0346] The server integrates financial and emotional data and inputs it into a generative AI. The generative AI considers the user's investment profile and emotions to generate an investment strategy with enhanced risk management. For example, if the user is feeling anxious, the AI ​​will prioritize suggesting stable investment options.

[0347] Step 4:

[0348] The server sends the generated investment strategy to the user's terminal. The terminal displays this strategy in a user-friendly format and presents an emotionally sensitive risk assessment and investment recommendations.

[0349] Step 5:

[0350] The server monitors market trends and user sentiment data in real time. If the market is highly volatile or users are experiencing stress, it quickly adjusts the portfolio.

[0351] Step 6:

[0352] The device instantly notifies users of risk warnings and information on readjusted portfolios. The tone and content of the notifications are carefully considered to ensure users can continue investing with confidence.

[0353] Step 7:

[0354] Users use their devices to conduct investment simulations that reflect their emotions. The simulations predict investment outcomes under different emotional states, providing users with a new perspective.

[0355] Step 8:

[0356] User feedback and changes in emotional state are sent from the device to the server. The server uses this data to improve the accuracy of the emotion engine and generative AI. This allows for more personalized strategies to be provided to the user in subsequent interactions.

[0357] (Example 2)

[0358] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0359] Conventional investment strategy generation systems often consider only financial data, making it difficult to propose flexible investment strategies that take into account the user's emotional state. Furthermore, they struggle to immediately optimize portfolios in response to rapid market fluctuations or changes in individual users' emotions.

[0360] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0361] In this invention, the server includes means for collecting emotional and financial data acquired from users in real time, means for preprocessing and analyzing the collected data, and means for generating investment strategies based on the user's investment profile and emotional state using a generative AI model. This enables the proposal of flexible and dynamic investment strategies that take into account the user's emotional state, and real-time portfolio optimization.

[0362] A "user" is an individual or group that uses the system to conduct investment activities.

[0363] "Emotional data" refers to information about a user's emotional state obtained by analyzing their facial expressions, voice, and other data.

[0364] "Financial data" refers to numerical information about market trends and individual assets, and is the data necessary for formulating investment strategies.

[0365] A "generative AI model" is a program that uses machine learning algorithms to generate the optimal investment strategy from a user's investment profile and emotional state.

[0366] An "investment profile" is a collection of distinctive investment information that integrates a user's asset situation, risk tolerance, investment goals, and other relevant factors.

[0367] A "portfolio" refers to a combination of multiple financial assets owned by a user.

[0368] "Real-time" refers to processing that instantly reflects the latest state with virtually no delay.

[0369] This invention is a system that generates investment strategies that take into account the user's emotional state and proposes them to the user in real time. The system includes the user's terminal, a server, and a generating AI model, and they all work together in coordination.

[0370] The server collects emotional and financial data from the user's device in real time. This data collection utilizes the device's hardware, such as the camera and microphone, to analyze the user's emotional state from their facial expressions and tone of voice. The emotion engine processes this data to determine whether the user is currently positive or negative.

[0371] Next, the server inputs emotional and financial data into the generating AI model. The AI ​​model considers the user's investment profile and emotional state to create an optimal investment strategy. The generated investment strategy may include shifting to government bonds if the user desires lower-risk options. An example of a specific prompt might be, "The user is feeling stressed and wants a safe investment."

[0372] The generated investment strategy is notified from the server to the user's terminal. The terminal displays the strategy through a visual interface, assisting the user in making emotionally informed decisions. The terminal also adjusts the tone of risk warnings as needed, ensuring the user can make investment decisions with confidence.

[0373] This invention provides users with a dynamic investment strategy that takes emotions into account, enabling appropriate portfolio management in response to fluctuating markets and individual emotional states. Users can explore future investment strategies by running investment simulations that take emotional data into account.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] The server collects emotional and financial data from the user's device in real time. Specifically, it uses the camera and microphone provided by the device to capture the user's facial expressions and voice. The inputs include image data and audio data. This data is analyzed on the server and output in a numerical format representing the user's emotional state.

[0377] Step 2:

[0378] The server uses an emotion engine to analyze the collected emotion data. The input is the emotion information that was quantified earlier. In the data analysis, an image recognition algorithm is used to analyze facial features and a voice analysis algorithm is used to detect the tone of voice. The output is a determination result indicating whether the user's emotional state is positive, negative, or neutral.

[0379] Step 3:

[0380] The server inputs emotional and financial data into a generating AI model. This input includes the user's investment profile, emotional assessment results, and the latest market data. The AI ​​model uses this data to generate an optimal investment strategy. Specifically, a machine learning algorithm processes the data and outputs investment selections that match the user's risk tolerance.

[0381] Step 4:

[0382] The server constructs the generated investment strategy in the form of a prompt message and sends it to the user's terminal. For example, it might output text such as, "The user is experiencing stress, so we recommend a safe investment choice." The prompt message is displayed on the terminal in a visual format.

[0383] Step 5:

[0384] The terminal displays the received investment strategy to the user through a visual interface. Input consists of prompt messages from the server. This information is presented in a user-friendly format using a GUI. If necessary, the tone of risk warnings is also adjusted.

[0385] Step 6:

[0386] Users can use their devices to perform investment simulations while receiving feedback on emotional data. Inputs include past emotional data and investment results. This feedback is used to inform future investment decisions based on the simulation results. The output is an optimized strategy suggestion for the next investment.

[0387] (Application Example 2)

[0388] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0389] Conventional investment strategy generation systems formulate investment strategies based solely on the user's financial information, making it difficult to flexibly consider the user's emotional state and biometric information. Furthermore, they cannot offer relaxation suggestions tailored to the user's emotional state, such as stress and anxiety, and thus fail to alleviate the psychological burden of investment activities.

[0390] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0391] In this invention, the server includes means for collecting financial and biometric information obtained from the user in real time, means for preprocessing and analyzing the collected financial and biometric information, and means for generating investment strategies based on the user's investment profile and emotional state using a generating AI. This makes it possible to propose flexible investment strategies that take the user's emotions into consideration and to provide relaxation menus.

[0392] "Financial information" refers to data including the value of assets related to a user's investments, transaction history, and market trends.

[0393] "Biometric information" refers to information such as facial expression data, voice data, and other physiological data acquired in order to infer the user's emotional state.

[0394] "Generative AI" is an artificial intelligence technology that automatically generates investment strategies tailored to a user's investment profile and emotional state, based on their financial and biometric information.

[0395] An "investment profile" is basic information used to formulate individual strategies for users, including their investment objectives and risk tolerance.

[0396] A "relaxation menu" refers to services such as music and guided meditation that are suggested to reduce stress and anxiety based on the user's emotional state.

[0397] In this embodiment, a system including a server and a user terminal provides investment support to the user. The server collects the user's financial information and biometric information in real time and analyzes the user's emotional state based on this information. The financial information includes the user's investment-related asset information and market trends, while the biometric information is data obtained from the user's facial expressions and voice.

[0398] The server preprocesses the collected information and performs analysis using a generated AI model. Specifically, it uses Google Cloud's Speech-to-Text API and Microsoft Azure's emotion recognition API to convert the data from speech to text and recognize the emotional state. The analysis results are input into an AI model generated using TensorFlow or PyTorch frameworks, which generates a flexible investment strategy tailored to the user's investment profile and emotional state.

[0399] The generated investment strategy and relaxation menu are notified to the user's device. Notifications are delivered via the device's display and audio output. If the device is deemed high-risk, it displays a warning message and recommends relaxation options such as music or guided meditation, tailored to the user's emotional state. This allows the user to make investment decisions while managing their emotions.

[0400] As a concrete example, consider a situation where a user is experiencing stress due to market fluctuations. The server, based on this biometric information, determines that the user is experiencing negative emotions and generates and notifies them of a risk-reducing investment strategy. At the same time, it suggests relaxing music to help the user regain their composure.

[0401] An example of a prompt might be, "Given the current market conditions, what flexible investment strategy should be proposed if the user appears anxious?" Based on this prompt, the AI ​​model generates suggestions tailored to each user.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server collects the user's financial and biometric information. Specifically, it receives financial information such as asset information and transaction history from the user's terminal via the internet, and simultaneously acquires facial expression data and voice data using the terminal's camera and microphone. The input for this step is the user's financial and biometric information, and the output is a dataset containing this information.

[0405] Step 2:

[0406] The server preprocesses the acquired data. At this stage, the obtained audio data is converted to text using Google Cloud's Speech-to-Text API, and the facial expression data is converted to emotion labels using Microsoft Azure's emotion recognition API. Financial information is normalized and cleansed. The input is the collected raw data, and the output is data in a parseable format.

[0407] Step 3:

[0408] The server inputs pre-processed data into a generating AI model, which then generates an investment strategy based on the user's investment profile and emotional state. The AI ​​model is built using TensorFlow and PyTorch, and the prompts are in the form of pre-configured questions. The input is pre-processed data, and the output is the generated investment strategy.

[0409] Step 4:

[0410] The server notifies the user's terminal of the generated investment strategy. The notification is in visual or audio format, using the terminal's display or speaker. The input is the generated investment strategy, and the output is the notification to the user.

[0411] Step 5:

[0412] The server provides relaxation options based on the user's emotional state. If the server determines that the user is stressed, specific music or guided meditation will be suggested for relaxation. The input for this step is the result of the emotional analysis, and the output is the recommended relaxation menu.

[0413] Step 6:

[0414] The user selects on the device whether to accept or reject the proposed investment strategy and relaxation menu. The input is the notified strategy and menu, and the output is the user's selection. This selection is used to generate subsequent strategies.

[0415] 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.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0422] 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.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0424] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] 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.

[0426] 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.

[0427] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The 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.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention is implemented as a system for collecting and analyzing user financial data, generating and notifying investment strategies, managing risk, and performing simulations. Specific embodiments of each component are described below.

[0432] Data acquisition and preprocessing

[0433] The server collects cashless payment information and asset status in real time via the user's device. This data includes user identification, past transaction history, and details of assets held. The server preprocesses the collected data, formatting it and performing cleansing to remove inconsistent or duplicate data.

[0434] Analysis using generative AI models

[0435] The server runs a generative AI model using pre-processed data. This AI model considers the user's individual investment profile and generates an optimal investment strategy tailored to their risk tolerance and market conditions. The generated strategy includes recommendations for buying and selling specific assets.

[0436] User notifications

[0437] The server sends the generated investment strategy to the user's terminal. The terminal displays the strategy information in a way that is intuitively understandable to the user. The user receives information through the terminal to make decisions about the necessary investment actions.

[0438] Risk management and portfolio optimization

[0439] The server monitors market data fluctuations in real time and performs risk assessments. If the risk exceeds a certain threshold, the server automatically optimizes the portfolio and issues a risk alert to the user. This allows the user to respond to risks quickly.

[0440] Investment simulation

[0441] The device provides a function that allows users to perform investment simulations in a virtual environment. By trying out various investment scenarios and checking the results without using actual funds, users can gain a deeper understanding and experience of investing.

[0442] As a concrete example, consider the case of a user in their 20s who is starting asset management for the first time. This user is risk-averse and aims for long-term wealth building. Based on this user's profile, the server recommends investing in bonds that provide stable dividends and notifies the user via their device of the appropriate timing and proportion of purchases. In this way, even beginners can invest with peace of mind.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The server retrieves cashless payment information and asset status from the user's device. It uses an API to retrieve this data, associates it with each individual user, and stores it in a database.

[0446] Step 2:

[0447] The server performs data cleansing on the acquired financial data. Specifically, it standardizes the data format, eliminates inconsistencies, and fills in missing data. This generates clean data suitable for analysis.

[0448] Step 3:

[0449] The server inputs clean data into an AI model. The AI ​​model utilizes machine learning algorithms to analyze the user's risk tolerance and past investment history, and generates a user-specific investment strategy.

[0450] Step 4:

[0451] The server sends the generated investment strategy to the user's terminal. The user's terminal displays this strategy in an easy-to-understand visual format and provides trading recommendations and risk assessments.

[0452] Step 5:

[0453] The server monitors market trends in real time and analyzes changes in market data. If risk exceeds a threshold, it automatically re-evaluates the portfolio and implements necessary optimizations.

[0454] Step 6:

[0455] The server sends a risk alert to the user's terminal. The terminal immediately notifies the user of this alert and prompts them to take action to mitigate the risk.

[0456] Step 7:

[0457] Users make investment decisions based on the provided investment strategies and risk warnings. Furthermore, users can use the device's investment simulation function to virtually try out different investment scenarios.

[0458] Step 8:

[0459] User investment behavior and feedback are sent from the device to the server. The server analyzes this data and uses it to improve the generated AI model, enabling it to provide more accurate investment advice.

[0460] (Example 1)

[0461] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] For users facing the complexity of financial markets and the difficulty of making investment decisions based on a large amount of data, generating and notifying them of appropriate investment strategies in real time is challenging. Furthermore, there is a need to respond quickly to market fluctuations and provide effective portfolio management that minimizes risk. Additionally, providing investment simulations for beginners and offering a low-risk investment experience is another challenge.

[0463] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0464] In this invention, the server includes means for collecting financial information obtained from users in real time, means for preprocessing and analyzing the collected financial information, and means for generating investment strategies based on individual investment profiles using a generating AI. This enables the provision of optimal investment strategies to users in real time, allowing for effective investment decisions and portfolio management.

[0465] A "user" is an individual or organization that accesses the system and makes information or investment decisions.

[0466] "Financial information" refers to a collection of information related to a user's financial activities, such as cashless payment information, asset status, and transaction history.

[0467] "Generative AI" is an artificial intelligence system that uses machine learning algorithms to automatically generate investment strategies for users.

[0468] An "investment profile" is a collection of information used to develop a customized investment strategy based on the user's risk tolerance, investment goals, and trading history.

[0469] An "investment strategy" is a plan of investment activities designed to achieve a specific objective, and includes recommendations for buying and selling assets.

[0470] A "portfolio" is a combination of various assets held by a user, and its purpose is risk management and return optimization.

[0471] "Real-time" refers to a system where information processing and data updates are performed instantly, providing users with the latest information at all times.

[0472] "Preprocessing" refers to the process of converting raw data into a format that can be analyzed, and includes standardizing data formats and removing invalid data.

[0473] This invention is a system that collects financial data in real time and provides users with optimal investment strategies. The system requires interaction between a server, terminals, and users. The server is primarily responsible for information collection, analysis, and strategy generation using AI models. The terminals, on the other hand, function as an interface with the user, providing strategy notifications and simulation functions.

[0474] The server uses a database management system to retrieve users' financial information from cashless payment platforms and asset management software. This information requires real-time data, and the database is always kept up-to-date. The data is preprocessed and formatted through cleansing.

[0475] Next, the server runs a generative AI model using the pre-processed data. This AI model, based on machine learning algorithms, generates optimal investment strategies tailored to each user's investment profile. Specific technologies used include advanced statistical analysis software and AI tools.

[0476] The generated investment strategy is communicated to the user via their device. The device uses data visualization tools to display the information in intuitive graphs and charts. Based on this information, the user can then make investment decisions.

[0477] In the simulation function, the terminal provides a virtual environment, allowing users to hone their investment skills. This environment assumes a risk-free situation, enabling the verification of various investment scenarios.

[0478] As a concrete example, consider a user in their 30s who is a new investor. This user wants to increase their assets over the long term with controlled risk. Based on this user's profile, the server recommends long-term investment in low-risk bonds and notifies the user of this strategy via their terminal. An example of a prompt message would be, "Please suggest the optimal long-term investment strategy considering the user's current asset and financial situation."

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The server collects users' financial information in real time. Specifically, it obtains cashless payment information and asset status via APIs. This input data includes transaction history and asset breakdowns. The server stores this in a database, preparing it for processing in the next step.

[0482] Step 2:

[0483] The server preprocesses the collected financial information. It standardizes the data format, detects and corrects or deletes missing or inconsistent data. The input is raw, unformatted data, and the output is a clean dataset suitable for analysis. Specifically, it performs tasks such as deduplication using data cleaning tools.

[0484] Step 3:

[0485] The server runs a generative AI model based on pre-processed data. Input data includes the user's past behavioral tendencies and risk tolerance. The AI ​​model then generates an optimal investment strategy, taking into account risk tolerance and market conditions, and outputs it as a recommendation. This requires data analysis using machine learning algorithms.

[0486] Step 4:

[0487] The server notifies the user's device of the generated investment strategy. The output strategy includes a specific list of actions regarding the purchase and sale of recommended assets. The device receives this and displays it in a user-friendly UI. Specifically, push notifications and chart display functions on the dashboard are used.

[0488] Step 5:

[0489] The server monitors market data in real time and performs risk assessments. It takes the latest market information as input data and generates risk warning messages and portfolio adjustment suggestions as output. This allows users to respond quickly to unforeseen circumstances.

[0490] Step 6:

[0491] The terminal provides a virtual environment where users can perform investment simulations. Users set investment scenarios they wish to try as input, and the output displays predicted returns and risks. By utilizing simulation tools, it reproduces realistic market fluctuations, contributing to the user's investment skills.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] Traditional asset management systems offered a uniform investment strategy to each user, making it difficult to tailor appropriate responses to individual risk profiles and market fluctuations. Furthermore, users often struggled to adjust strategies based on their own experience, leading to anxiety about asset management. Additionally, the lack of robust simulation capabilities limited opportunities for beginners to learn about investing.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] In this invention, the server includes means for collecting transaction information obtained from users in real time, means for preprocessing and analyzing the collected transaction information, means for generating a strategy based on the user's asset management profile using a generative AI, means for notifying the user device of the generated strategy, means for automatically optimizing the investment portfolio based on fluctuations in market information, means for sending risk-based warning messages to the user, means for executing investment simulations on the user device, means for the generative AI to generate an optimized strategy for the user based on the transaction information and notifying the user of the strategy in real time using data communication means, means for enabling the user to experimentally perform asset management simulations, and means for receiving feedback on strategy generation through a provided user interface and for the generative AI model to continuously improve the strategy. This enables the proposal of an optimal investment strategy tailored to individual profiles and flexible, real-time asset management, allowing even beginners to gain investment experience with peace of mind.

[0497] A "user" is an individual or organization that utilizes asset management services and provides financial information.

[0498] "Transaction information" refers to data related to cashless payments made by users and their asset status.

[0499] "Generative AI" is an artificial intelligence technology that generates investment strategies based on the user's profile.

[0500] A "strategy" is a specific investment plan tailored to the user's risk profile and market conditions.

[0501] A "user device" is an electronic terminal used by a user to receive information.

[0502] "Market information" refers to data related to trends and fluctuations in financial markets.

[0503] An "investment portfolio" is a collection of the types and allocations of assets owned by a user.

[0504] A "simulation" is a method of evaluation performed in a virtual investment environment without using actual funds.

[0505] "Data communication means" refers to communication technology used to transmit generated information to a user's device.

[0506] "Feedback" refers to the opinions and evaluations that users provide after a strategy has been implemented.

[0507] The system for implementing this invention collects user transaction information in real time, generates individual investment strategies based on that information, and optimizes them. The server receives cashless payment information and asset status from users via terminals and stores them in a financial database. The collected transaction information is preprocessed using a Python program, and data cleansing is performed. At this stage, the information is formatted and inconsistent data is removed.

[0508] Subsequently, the pre-processed data is analyzed by a generative AI model using TensorFlow. This model considers the user's investment profile and current market information to generate an optimal asset management strategy. The generated strategy is then notified to the user's device via the Firebase Cloud Messaging service. In this way, the user can immediately obtain concrete investment proposals.

[0509] Furthermore, the server monitors market fluctuations in real time and quickly and automatically optimizes investment portfolios. When risk increases, it sends a warning message to the user, prompting appropriate action. In addition, a simulation environment using React Native is provided on the user's device, allowing users to try out virtual investment scenarios and gain experience without using actual funds.

[0510] As a concrete example, consider the case of a 30-year-old individual user investing in technology stocks for the first time. This user is seeking moderate risk and moderate return, and the AI ​​model generates a strategy recommending diversified investment in technology stocks based on this profile and notifies the user's device. An example of a prompt message to the AI ​​model would be: "Based on the user's past financial data and current market conditions, please suggest an appropriate investment strategy for technology stocks."

[0511] This allows even novice individual users to manage their assets safely and efficiently.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The server collects transaction information in real time via the user's terminal. Inputs include the user's cashless payment records and asset status. The server stores this data in a financial database, preparing it for subsequent processing.

[0515] Step 2:

[0516] The server preprocesses the collected transaction information using Python. The input is the raw data collected in step 1. The server scientifically formats and cleanses this data, removing inconsistent data and duplicate information. The output is data formatted in a way that is easy for the generative AI model to handle.

[0517] Step 3:

[0518] The server analyzes pre-processed data using a generative AI model based on TensorFlow. The input requires the clean data obtained in step 2 and the user's investment profile. Based on this information, the generative AI model generates an optimal investment strategy tailored to the user's risk profile and market information. The output is a strategy that includes specific investment policies and buy / sell recommendations.

[0519] Step 4:

[0520] The server uses Firebase Cloud Messaging to notify the user's device of the generated strategy. The input is the investment strategy generated in step 3. The user receives this information on their device and gains guidance to intuitively decide on investment actions. The output is the notification sent to the user.

[0521] Step 5:

[0522] The server monitors market data fluctuations and performs risk assessments. Inputs are current market data and the user's portfolio information. If risk exceeds a certain threshold, the server optimizes the portfolio and sends a warning message to the user. Outputs are the optimized portfolio and risk warnings.

[0523] Step 6:

[0524] The application uses React Native to provide users with an investment simulation function. Inputs include a virtual initial investment amount and a selected scenario. Through this simulation, users can try out various asset management strategies and see the results virtually. Outputs are the investment profits and losses as a result of the simulation.

[0525] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0526] This invention is implemented as a system that combines an emotion engine with a system for generating and notifying users of their unique investment strategies. The detailed configuration of the system and its embodiments are shown below.

[0527] Data collection and emotion recognition

[0528] The server analyzes the user's emotional state by collecting not only financial data but also data such as the user's facial expressions and voice through the user's terminal. The emotion engine analyzes this data and recognizes the user's current emotional state, such as whether they are currently positive or negative, in real time.

[0529] Analysis and strategic adjustment using generative AI models

[0530] The server inputs both financial and emotional data into a generating AI model. Based on the collected data, the AI ​​model generates flexible investment strategies that take into account the user's investment profile and current emotions. For example, if the user is feeling stressed, it can suggest an investment strategy that reduces risk.

[0531] User notifications

[0532] The server sends investment strategies and risk warnings tailored to the generated emotions to the user's terminal. The terminal visually displays the strategies in an emotionally sensitive manner and adjusts the tone of risk advice as needed.

[0533] Risk management and portfolio optimization

[0534] The server uses emotional data obtained through the emotion engine to manage risk. By capturing changes in users' emotions along with market trends, it can instantly review and optimize the portfolio.

[0535] Investment simulation

[0536] Users can use their devices to perform investment simulations that take emotional data into account. The emotional engine tracks emotional changes during the simulation and accumulates data that can be used to improve future emotional responses.

[0537] As a concrete example, consider a scenario where a user is experiencing intense pressure during a period of high market tension and volatility. The server, through its emotion engine, recognizes this state and notifies the user's device of an investment strategy suggesting a shift to lower-risk bonds or stocks. In this way, dynamic responses based on the user's emotional state are possible.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The server acquires facial expressions and voice information along with financial data from the user's device. This information is necessary to infer the user's emotional state and is therefore captured using high-precision sensors and cameras.

[0541] Step 2:

[0542] The server analyzes the acquired emotion-related data using an emotion engine. The emotion engine utilizes machine learning models to determine the user's emotions in real time from their facial expressions and tone of voice. The determined emotion data is then used to adjust investment strategies.

[0543] Step 3:

[0544] The server integrates financial and emotional data and inputs it into a generative AI. The generative AI considers the user's investment profile and emotions to generate an investment strategy with enhanced risk management. For example, if the user is feeling anxious, the AI ​​will prioritize suggesting stable investment options.

[0545] Step 4:

[0546] The server sends the generated investment strategy to the user's terminal. The terminal displays this strategy in a user-friendly format and presents an emotionally sensitive risk assessment and investment recommendations.

[0547] Step 5:

[0548] The server monitors market trends and user sentiment data in real time. If the market is highly volatile or users are experiencing stress, it quickly adjusts the portfolio.

[0549] Step 6:

[0550] The device instantly notifies users of risk warnings and information on readjusted portfolios. The tone and content of the notifications are carefully considered to ensure users can continue investing with confidence.

[0551] Step 7:

[0552] Users use their devices to conduct investment simulations that reflect their emotions. The simulations predict investment outcomes under different emotional states, providing users with a new perspective.

[0553] Step 8:

[0554] User feedback and changes in emotional state are sent from the device to the server. The server uses this data to improve the accuracy of the emotion engine and generative AI. This allows for more personalized strategies to be provided to the user in subsequent interactions.

[0555] (Example 2)

[0556] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] Conventional investment strategy generation systems often consider only financial data, making it difficult to propose flexible investment strategies that take into account the user's emotional state. Furthermore, they struggle to immediately optimize portfolios in response to rapid market fluctuations or changes in individual users' emotions.

[0558] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0559] In this invention, the server includes means for collecting emotional and financial data acquired from users in real time, means for preprocessing and analyzing the collected data, and means for generating investment strategies based on the user's investment profile and emotional state using a generative AI model. This enables the proposal of flexible and dynamic investment strategies that take into account the user's emotional state, and real-time portfolio optimization.

[0560] A "user" is an individual or group that uses the system to conduct investment activities.

[0561] "Emotional data" refers to information about a user's emotional state obtained by analyzing their facial expressions, voice, and other data.

[0562] "Financial data" refers to numerical information about market trends and individual assets, and is the data necessary for formulating investment strategies.

[0563] A "generative AI model" is a program that uses machine learning algorithms to generate the optimal investment strategy from a user's investment profile and emotional state.

[0564] An "investment profile" is a collection of distinctive investment information that integrates a user's asset situation, risk tolerance, investment goals, and other relevant factors.

[0565] A "portfolio" refers to a combination of multiple financial assets owned by a user.

[0566] "Real-time" refers to processing that instantly reflects the latest state with virtually no delay.

[0567] This invention is a system that generates investment strategies that take into account the user's emotional state and proposes them to the user in real time. The system includes the user's terminal, a server, and a generating AI model, and they all work together in coordination.

[0568] The server collects emotional and financial data from the user's device in real time. This data collection utilizes the device's hardware, such as the camera and microphone, to analyze the user's emotional state from their facial expressions and tone of voice. The emotion engine processes this data to determine whether the user is currently positive or negative.

[0569] Next, the server inputs emotional and financial data into the generating AI model. The AI ​​model considers the user's investment profile and emotional state to create an optimal investment strategy. The generated investment strategy may include shifting to government bonds if the user desires lower-risk options. An example of a specific prompt might be, "The user is feeling stressed and wants a safe investment."

[0570] The generated investment strategy is notified from the server to the user's terminal. The terminal displays the strategy through a visual interface, assisting the user in making emotionally informed decisions. The terminal also adjusts the tone of risk warnings as needed, ensuring the user can make investment decisions with confidence.

[0571] This invention provides users with a dynamic investment strategy that takes emotions into account, enabling appropriate portfolio management in response to fluctuating markets and individual emotional states. Users can explore future investment strategies by running investment simulations that take emotional data into account.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The server collects emotional and financial data from the user's device in real time. Specifically, it uses the camera and microphone provided by the device to capture the user's facial expressions and voice. The inputs include image data and audio data. This data is analyzed on the server and output in a numerical format representing the user's emotional state.

[0575] Step 2:

[0576] The server uses an emotion engine to analyze the collected emotion data. The input is the emotion information that was quantified earlier. In the data analysis, an image recognition algorithm is used to analyze facial features and a voice analysis algorithm is used to detect the tone of voice. The output is a determination result indicating whether the user's emotional state is positive, negative, or neutral.

[0577] Step 3:

[0578] The server inputs emotional and financial data into a generating AI model. This input includes the user's investment profile, emotional assessment results, and the latest market data. The AI ​​model uses this data to generate an optimal investment strategy. Specifically, a machine learning algorithm processes the data and outputs investment selections that match the user's risk tolerance.

[0579] Step 4:

[0580] The server constructs the generated investment strategy in the form of a prompt message and sends it to the user's terminal. For example, it might output text such as, "The user is experiencing stress, so we recommend a safe investment choice." The prompt message is displayed on the terminal in a visual format.

[0581] Step 5:

[0582] The terminal displays the received investment strategy to the user through a visual interface. Input consists of prompt messages from the server. This information is presented in a user-friendly format using a GUI. If necessary, the tone of risk warnings is also adjusted.

[0583] Step 6:

[0584] Users can use their devices to perform investment simulations while receiving feedback on emotional data. Inputs include past emotional data and investment results. This feedback is used to inform future investment decisions based on the simulation results. The output is an optimized strategy suggestion for the next investment.

[0585] (Application Example 2)

[0586] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] Conventional investment strategy generation systems formulate investment strategies based solely on the user's financial information, making it difficult to flexibly consider the user's emotional state and biometric information. Furthermore, they cannot offer relaxation suggestions tailored to the user's emotional state, such as stress and anxiety, and thus fail to alleviate the psychological burden of investment activities.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes means for collecting financial and biometric information obtained from the user in real time, means for preprocessing and analyzing the collected financial and biometric information, and means for generating investment strategies based on the user's investment profile and emotional state using a generating AI. This makes it possible to propose flexible investment strategies that take the user's emotions into consideration and to provide relaxation menus.

[0590] "Financial information" refers to data including the value of assets related to a user's investments, transaction history, and market trends.

[0591] "Biometric information" refers to information such as facial expression data, voice data, and other physiological data acquired in order to infer the user's emotional state.

[0592] "Generative AI" is an artificial intelligence technology that automatically generates investment strategies tailored to a user's investment profile and emotional state, based on their financial and biometric information.

[0593] An "investment profile" is basic information used to formulate individual strategies for users, including their investment objectives and risk tolerance.

[0594] A "relaxation menu" refers to services such as music and guided meditation that are suggested to reduce stress and anxiety based on the user's emotional state.

[0595] In this embodiment, a system including a server and a user terminal provides investment support to the user. The server collects the user's financial information and biometric information in real time and analyzes the user's emotional state based on this information. The financial information includes the user's investment-related asset information and market trends, while the biometric information is data obtained from the user's facial expressions and voice.

[0596] The server preprocesses the collected information and performs analysis using a generated AI model. Specifically, it uses Google Cloud's Speech-to-Text API and Microsoft Azure's emotion recognition API to convert the data from speech to text and recognize the emotional state. The analysis results are input into an AI model generated using TensorFlow or PyTorch frameworks, which generates a flexible investment strategy tailored to the user's investment profile and emotional state.

[0597] The generated investment strategy and relaxation menu are notified to the user's device. Notifications are delivered via the device's display and audio output. If the device is deemed high-risk, it displays a warning message and recommends relaxation options such as music or guided meditation, tailored to the user's emotional state. This allows the user to make investment decisions while managing their emotions.

[0598] As a concrete example, consider a situation where a user is experiencing stress due to market fluctuations. The server, based on this biometric information, determines that the user is experiencing negative emotions and generates and notifies them of a risk-reducing investment strategy. At the same time, it suggests relaxing music to help the user regain their composure.

[0599] An example of a prompt might be, "Given the current market conditions, what flexible investment strategy should be proposed if the user appears anxious?" Based on this prompt, the AI ​​model generates suggestions tailored to each user.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The server collects the user's financial and biometric information. Specifically, it receives financial information such as asset information and transaction history from the user's terminal via the internet, and simultaneously acquires facial expression data and voice data using the terminal's camera and microphone. The input for this step is the user's financial and biometric information, and the output is a dataset containing this information.

[0603] Step 2:

[0604] The server preprocesses the acquired data. At this stage, the obtained audio data is converted to text using Google Cloud's Speech-to-Text API, and the facial expression data is converted to emotion labels using Microsoft Azure's emotion recognition API. Financial information is normalized and cleansed. The input is the collected raw data, and the output is data in a parseable format.

[0605] Step 3:

[0606] The server inputs pre-processed data into a generating AI model, which then generates an investment strategy based on the user's investment profile and emotional state. The AI ​​model is built using TensorFlow and PyTorch, and the prompts are in the form of pre-configured questions. The input is pre-processed data, and the output is the generated investment strategy.

[0607] Step 4:

[0608] The server notifies the user's terminal of the generated investment strategy. The notification is in visual or audio format, using the terminal's display or speaker. The input is the generated investment strategy, and the output is the notification to the user.

[0609] Step 5:

[0610] The server provides relaxation options based on the user's emotional state. If the server determines that the user is stressed, specific music or guided meditation will be suggested for relaxation. The input for this step is the result of the emotional analysis, and the output is the recommended relaxation menu.

[0611] Step 6:

[0612] The user selects on the device whether to accept or reject the proposed investment strategy and relaxation menu. The input is the notified strategy and menu, and the output is the user's selection. This selection is used to generate subsequent strategies.

[0613] 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.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0620] 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.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0622] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] 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.

[0624] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] 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.

[0626] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0627] The 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.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] This invention is implemented as a system for collecting and analyzing user financial data, generating and notifying investment strategies, managing risk, and performing simulations. Specific embodiments of each component are described below.

[0631] Data acquisition and preprocessing

[0632] The server collects cashless payment information and asset status in real time via the user's device. This data includes user identification, past transaction history, and details of assets held. The server preprocesses the collected data, formatting it and performing cleansing to remove inconsistent or duplicate data.

[0633] Analysis using generative AI models

[0634] The server runs a generative AI model using pre-processed data. This AI model considers the user's individual investment profile and generates an optimal investment strategy tailored to their risk tolerance and market conditions. The generated strategy includes recommendations for buying and selling specific assets.

[0635] User notifications

[0636] The server sends the generated investment strategy to the user's terminal. The terminal displays the strategy information in a way that is intuitively understandable to the user. The user receives information through the terminal to make decisions about the necessary investment actions.

[0637] Risk management and portfolio optimization

[0638] The server monitors market data fluctuations in real time and performs risk assessments. If the risk exceeds a certain threshold, the server automatically optimizes the portfolio and issues a risk alert to the user. This allows the user to respond to risks quickly.

[0639] Investment simulation

[0640] The device provides a function that allows users to perform investment simulations in a virtual environment. By trying out various investment scenarios and checking the results without using actual funds, users can gain a deeper understanding and experience of investing.

[0641] As a concrete example, consider the case of a user in their 20s who is starting asset management for the first time. This user is risk-averse and aims for long-term wealth building. Based on this user's profile, the server recommends investing in bonds that provide stable dividends and notifies the user via their device of the appropriate timing and proportion of purchases. In this way, even beginners can invest with peace of mind.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The server retrieves cashless payment information and asset status from the user's device. It uses an API to retrieve this data, associates it with each individual user, and stores it in a database.

[0645] Step 2:

[0646] The server performs data cleansing on the acquired financial data. Specifically, it standardizes the data format, eliminates inconsistencies, and fills in missing data. This generates clean data suitable for analysis.

[0647] Step 3:

[0648] The server inputs clean data into an AI model. The AI ​​model utilizes machine learning algorithms to analyze the user's risk tolerance and past investment history, and generates a user-specific investment strategy.

[0649] Step 4:

[0650] The server sends the generated investment strategy to the user's terminal. The user's terminal displays this strategy in an easy-to-understand visual format and provides trading recommendations and risk assessments.

[0651] Step 5:

[0652] The server monitors market trends in real time and analyzes changes in market data. If risk exceeds a threshold, it automatically re-evaluates the portfolio and implements necessary optimizations.

[0653] Step 6:

[0654] The server sends a risk alert to the user's terminal. The terminal immediately notifies the user of this alert and prompts them to take action to mitigate the risk.

[0655] Step 7:

[0656] Users make investment decisions based on the provided investment strategies and risk warnings. Furthermore, users can use the device's investment simulation function to virtually try out different investment scenarios.

[0657] Step 8:

[0658] User investment behavior and feedback are sent from the device to the server. The server analyzes this data and uses it to improve the generated AI model, enabling it to provide more accurate investment advice.

[0659] (Example 1)

[0660] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] For users facing the complexity of financial markets and the difficulty of making investment decisions based on a large amount of data, generating and notifying them of appropriate investment strategies in real time is challenging. Furthermore, there is a need to respond quickly to market fluctuations and provide effective portfolio management that minimizes risk. Additionally, providing investment simulations for beginners and offering a low-risk investment experience is another challenge.

[0662] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0663] In this invention, the server includes means for collecting financial information obtained from users in real time, means for preprocessing and analyzing the collected financial information, and means for generating investment strategies based on individual investment profiles using a generating AI. This enables the provision of optimal investment strategies to users in real time, allowing for effective investment decisions and portfolio management.

[0664] A "user" is an individual or organization that accesses the system and makes information or investment decisions.

[0665] "Financial information" refers to a collection of information related to a user's financial activities, such as cashless payment information, asset status, and transaction history.

[0666] "Generative AI" is an artificial intelligence system that uses machine learning algorithms to automatically generate investment strategies for users.

[0667] An "investment profile" is a collection of information used to develop a customized investment strategy based on the user's risk tolerance, investment goals, and trading history.

[0668] An "investment strategy" is a plan of investment activities designed to achieve a specific objective, and includes recommendations for buying and selling assets.

[0669] A "portfolio" is a combination of various assets held by a user, and its purpose is risk management and return optimization.

[0670] "Real-time" refers to a system where information processing and data updates are performed instantly, providing users with the latest information at all times.

[0671] "Preprocessing" refers to the process of converting raw data into a format that can be analyzed, and includes standardizing data formats and removing invalid data.

[0672] This invention is a system that collects financial data in real time and provides users with optimal investment strategies. The system requires interaction between a server, terminals, and users. The server is primarily responsible for information collection, analysis, and strategy generation using AI models. The terminals, on the other hand, function as an interface with the user, providing strategy notifications and simulation functions.

[0673] The server uses a database management system to retrieve users' financial information from cashless payment platforms and asset management software. This information requires real-time data, and the database is always kept up-to-date. The data is preprocessed and formatted through cleansing.

[0674] Next, the server runs a generative AI model using the pre-processed data. This AI model, based on machine learning algorithms, generates optimal investment strategies tailored to each user's investment profile. Specific technologies used include advanced statistical analysis software and AI tools.

[0675] The generated investment strategy is communicated to the user via their device. The device uses data visualization tools to display the information in intuitive graphs and charts. Based on this information, the user can then make investment decisions.

[0676] In the simulation function, the terminal provides a virtual environment, allowing users to hone their investment skills. This environment assumes a risk-free situation, enabling the verification of various investment scenarios.

[0677] As a concrete example, consider a user in their 30s who is a new investor. This user wants to increase their assets over the long term with controlled risk. Based on this user's profile, the server recommends long-term investment in low-risk bonds and notifies the user of this strategy via their terminal. An example of a prompt message would be, "Please suggest the optimal long-term investment strategy considering the user's current asset and financial situation."

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The server collects users' financial information in real time. Specifically, it obtains cashless payment information and asset status via APIs. This input data includes transaction history and asset breakdowns. The server stores this in a database, preparing it for processing in the next step.

[0681] Step 2:

[0682] The server preprocesses the collected financial information. It standardizes the data format, detects and corrects or deletes missing or inconsistent data. The input is raw, unformatted data, and the output is a clean dataset suitable for analysis. Specifically, it performs tasks such as deduplication using data cleaning tools.

[0683] Step 3:

[0684] The server runs a generative AI model based on pre-processed data. Input data includes the user's past behavioral tendencies and risk tolerance. The AI ​​model then generates an optimal investment strategy, taking into account risk tolerance and market conditions, and outputs it as a recommendation. This requires data analysis using machine learning algorithms.

[0685] Step 4:

[0686] The server notifies the user's device of the generated investment strategy. The output strategy includes a specific list of actions regarding the purchase and sale of recommended assets. The device receives this and displays it in a user-friendly UI. Specifically, push notifications and chart display functions on the dashboard are used.

[0687] Step 5:

[0688] The server monitors market data in real time and performs risk assessments. It takes the latest market information as input data and generates risk warning messages and portfolio adjustment suggestions as output. This allows users to respond quickly to unforeseen circumstances.

[0689] Step 6:

[0690] The terminal provides a virtual environment where users can perform investment simulations. Users set investment scenarios they wish to try as input, and the output displays predicted returns and risks. By utilizing simulation tools, it reproduces realistic market fluctuations, contributing to the user's investment skills.

[0691] (Application Example 1)

[0692] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] Traditional asset management systems offered a uniform investment strategy to each user, making it difficult to tailor appropriate responses to individual risk profiles and market fluctuations. Furthermore, users often struggled to adjust strategies based on their own experience, leading to anxiety about asset management. Additionally, the lack of robust simulation capabilities limited opportunities for beginners to learn about investing.

[0694] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0695] In this invention, the server includes means for collecting transaction information obtained from users in real time, means for preprocessing and analyzing the collected transaction information, means for generating a strategy based on the user's asset management profile using a generative AI, means for notifying the user device of the generated strategy, means for automatically optimizing the investment portfolio based on fluctuations in market information, means for sending risk-based warning messages to the user, means for executing investment simulations on the user device, means for the generative AI to generate an optimized strategy for the user based on the transaction information and notifying the user of the strategy in real time using data communication means, means for enabling the user to experimentally perform asset management simulations, and means for receiving feedback on strategy generation through a provided user interface and for the generative AI model to continuously improve the strategy. This enables the proposal of an optimal investment strategy tailored to individual profiles and flexible, real-time asset management, allowing even beginners to gain investment experience with peace of mind.

[0696] A "user" is an individual or organization that utilizes asset management services and provides financial information.

[0697] "Transaction information" refers to data related to cashless payments made by users and their asset status.

[0698] "Generative AI" is an artificial intelligence technology that generates investment strategies based on the user's profile.

[0699] A "strategy" is a specific investment plan tailored to the user's risk profile and market conditions.

[0700] A "user device" is an electronic terminal used by a user to receive information.

[0701] "Market information" refers to data related to trends and fluctuations in financial markets.

[0702] An "investment portfolio" is a collection of the types and allocations of assets owned by a user.

[0703] A "simulation" is a method of evaluation performed in a virtual investment environment without using actual funds.

[0704] "Data communication means" refers to communication technology used to transmit generated information to a user's device.

[0705] "Feedback" refers to the opinions and evaluations that users provide after a strategy has been implemented.

[0706] The system for implementing this invention collects user transaction information in real time, generates individual investment strategies based on that information, and optimizes them. The server receives cashless payment information and asset status from users via terminals and stores them in a financial database. The collected transaction information is preprocessed using a Python program, and data cleansing is performed. At this stage, the information is formatted and inconsistent data is removed.

[0707] Subsequently, the pre-processed data is analyzed by a generative AI model using TensorFlow. This model considers the user's investment profile and current market information to generate an optimal asset management strategy. The generated strategy is then notified to the user's device via the Firebase Cloud Messaging service. In this way, the user can immediately obtain concrete investment proposals.

[0708] Furthermore, the server monitors market fluctuations in real time and quickly and automatically optimizes investment portfolios. When risk increases, it sends a warning message to the user, prompting appropriate action. In addition, a simulation environment using React Native is provided on the user's device, allowing users to try out virtual investment scenarios and gain experience without using actual funds.

[0709] As a concrete example, consider the case of a 30-year-old individual user investing in technology stocks for the first time. This user is seeking moderate risk and moderate return, and the AI ​​model generates a strategy recommending diversified investment in technology stocks based on this profile and notifies the user's device. An example of a prompt message to the AI ​​model would be: "Based on the user's past financial data and current market conditions, please suggest an appropriate investment strategy for technology stocks."

[0710] This allows even novice individual users to manage their assets safely and efficiently.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The server collects transaction information in real time via the user's terminal. Inputs include the user's cashless payment records and asset status. The server stores this data in a financial database, preparing it for subsequent processing.

[0714] Step 2:

[0715] The server preprocesses the collected transaction information using Python. The input is the raw data collected in step 1. The server scientifically formats and cleanses this data, removing inconsistent data and duplicate information. The output is data formatted in a way that is easy for the generative AI model to handle.

[0716] Step 3:

[0717] The server analyzes pre-processed data using a generative AI model based on TensorFlow. The input requires the clean data obtained in step 2 and the user's investment profile. Based on this information, the generative AI model generates an optimal investment strategy tailored to the user's risk profile and market information. The output is a strategy that includes specific investment policies and buy / sell recommendations.

[0718] Step 4:

[0719] The server uses Firebase Cloud Messaging to notify the user's device of the generated strategy. The input is the investment strategy generated in step 3. The user receives this information on their device and gains guidance to intuitively decide on investment actions. The output is the notification sent to the user.

[0720] Step 5:

[0721] The server monitors market data fluctuations and performs risk assessments. Inputs are current market data and the user's portfolio information. If risk exceeds a certain threshold, the server optimizes the portfolio and sends a warning message to the user. Outputs are the optimized portfolio and risk warnings.

[0722] Step 6:

[0723] The application uses React Native to provide users with an investment simulation function. Inputs include a virtual initial investment amount and a selected scenario. Through this simulation, users can try out various asset management strategies and see the results virtually. Outputs are the investment profits and losses as a result of the simulation.

[0724] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0725] This invention is implemented as a system that combines an emotion engine with a system for generating and notifying users of their unique investment strategies. The detailed configuration of the system and its embodiments are shown below.

[0726] Data collection and emotion recognition

[0727] The server analyzes the user's emotional state by collecting not only financial data but also data such as the user's facial expressions and voice through the user's terminal. The emotion engine analyzes this data and recognizes the user's current emotional state, such as whether they are currently positive or negative, in real time.

[0728] Analysis and strategic adjustment using generative AI models

[0729] The server inputs both financial and emotional data into a generating AI model. Based on the collected data, the AI ​​model generates flexible investment strategies that take into account the user's investment profile and current emotions. For example, if the user is feeling stressed, it can suggest an investment strategy that reduces risk.

[0730] User notifications

[0731] The server sends investment strategies and risk warnings tailored to the generated emotions to the user's terminal. The terminal visually displays the strategies in an emotionally sensitive manner and adjusts the tone of risk advice as needed.

[0732] Risk management and portfolio optimization

[0733] The server uses emotional data obtained through the emotion engine to manage risk. By capturing changes in users' emotions along with market trends, it can instantly review and optimize the portfolio.

[0734] Investment simulation

[0735] Users can use their devices to perform investment simulations that take emotional data into account. The emotional engine tracks emotional changes during the simulation and accumulates data that can be used to improve future emotional responses.

[0736] As a concrete example, consider a scenario where a user is experiencing intense pressure during a period of high market tension and volatility. The server, through its emotion engine, recognizes this state and notifies the user's device of an investment strategy suggesting a shift to lower-risk bonds or stocks. In this way, dynamic responses based on the user's emotional state are possible.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The server acquires facial expressions and voice information along with financial data from the user's device. This information is necessary to infer the user's emotional state and is therefore captured using high-precision sensors and cameras.

[0740] Step 2:

[0741] The server analyzes the acquired emotion-related data using an emotion engine. The emotion engine utilizes machine learning models to determine the user's emotions in real time from their facial expressions and tone of voice. The determined emotion data is then used to adjust investment strategies.

[0742] Step 3:

[0743] The server integrates financial and emotional data and inputs it into a generative AI. The generative AI considers the user's investment profile and emotions to generate an investment strategy with enhanced risk management. For example, if the user is feeling anxious, the AI ​​will prioritize suggesting stable investment options.

[0744] Step 4:

[0745] The server sends the generated investment strategy to the user's terminal. The terminal displays this strategy in a user-friendly format and presents an emotionally sensitive risk assessment and investment recommendations.

[0746] Step 5:

[0747] The server monitors market trends and user sentiment data in real time. If the market is highly volatile or users are experiencing stress, it quickly adjusts the portfolio.

[0748] Step 6:

[0749] The device instantly notifies users of risk warnings and information on readjusted portfolios. The tone and content of the notifications are carefully considered to ensure users can continue investing with confidence.

[0750] Step 7:

[0751] Users use their devices to conduct investment simulations that reflect their emotions. The simulations predict investment outcomes under different emotional states, providing users with a new perspective.

[0752] Step 8:

[0753] User feedback and changes in emotional state are sent from the device to the server. The server uses this data to improve the accuracy of the emotion engine and generative AI. This allows for more personalized strategies to be provided to the user in subsequent interactions.

[0754] (Example 2)

[0755] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] Conventional investment strategy generation systems often consider only financial data, making it difficult to propose flexible investment strategies that take into account the user's emotional state. Furthermore, they struggle to immediately optimize portfolios in response to rapid market fluctuations or changes in individual users' emotions.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0758] In this invention, the server includes means for collecting emotional and financial data acquired from users in real time, means for preprocessing and analyzing the collected data, and means for generating investment strategies based on the user's investment profile and emotional state using a generative AI model. This enables the proposal of flexible and dynamic investment strategies that take into account the user's emotional state, and real-time portfolio optimization.

[0759] A "user" is an individual or group that uses the system to conduct investment activities.

[0760] "Emotional data" refers to information about a user's emotional state obtained by analyzing their facial expressions, voice, and other data.

[0761] "Financial data" refers to numerical information about market trends and individual assets, and is the data necessary for formulating investment strategies.

[0762] A "generative AI model" is a program that uses machine learning algorithms to generate the optimal investment strategy from a user's investment profile and emotional state.

[0763] An "investment profile" is a collection of distinctive investment information that integrates a user's asset situation, risk tolerance, investment goals, and other relevant factors.

[0764] A "portfolio" refers to a combination of multiple financial assets owned by a user.

[0765] "Real-time" refers to processing that instantly reflects the latest state with virtually no delay.

[0766] This invention is a system that generates investment strategies that take into account the user's emotional state and proposes them to the user in real time. The system includes the user's terminal, a server, and a generating AI model, and they all work together in coordination.

[0767] The server collects emotional and financial data from the user's device in real time. This data collection utilizes the device's hardware, such as the camera and microphone, to analyze the user's emotional state from their facial expressions and tone of voice. The emotion engine processes this data to determine whether the user is currently positive or negative.

[0768] Next, the server inputs emotional and financial data into the generating AI model. The AI ​​model considers the user's investment profile and emotional state to create an optimal investment strategy. The generated investment strategy may include shifting to government bonds if the user desires lower-risk options. An example of a specific prompt might be, "The user is feeling stressed and wants a safe investment."

[0769] The generated investment strategy is notified from the server to the user's terminal. The terminal displays the strategy through a visual interface, assisting the user in making emotionally informed decisions. The terminal also adjusts the tone of risk warnings as needed, ensuring the user can make investment decisions with confidence.

[0770] This invention provides users with a dynamic investment strategy that takes emotions into account, enabling appropriate portfolio management in response to fluctuating markets and individual emotional states. Users can explore future investment strategies by running investment simulations that take emotional data into account.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The server collects emotional and financial data from the user's device in real time. Specifically, it uses the camera and microphone provided by the device to capture the user's facial expressions and voice. The inputs include image data and audio data. This data is analyzed on the server and output in a numerical format representing the user's emotional state.

[0774] Step 2:

[0775] The server uses an emotion engine to analyze the collected emotion data. The input is the emotion information that was quantified earlier. In the data analysis, an image recognition algorithm is used to analyze facial features and a voice analysis algorithm is used to detect the tone of voice. The output is a determination result indicating whether the user's emotional state is positive, negative, or neutral.

[0776] Step 3:

[0777] The server inputs emotional and financial data into a generating AI model. This input includes the user's investment profile, emotional assessment results, and the latest market data. The AI ​​model uses this data to generate an optimal investment strategy. Specifically, a machine learning algorithm processes the data and outputs investment selections that match the user's risk tolerance.

[0778] Step 4:

[0779] The server constructs the generated investment strategy in the form of a prompt message and sends it to the user's terminal. For example, it might output text such as, "The user is experiencing stress, so we recommend a safe investment choice." The prompt message is displayed on the terminal in a visual format.

[0780] Step 5:

[0781] The terminal displays the received investment strategy to the user through a visual interface. Input consists of prompt messages from the server. This information is presented in a user-friendly format using a GUI. If necessary, the tone of risk warnings is also adjusted.

[0782] Step 6:

[0783] Users can use their devices to perform investment simulations while receiving feedback on emotional data. Inputs include past emotional data and investment results. This feedback is used to inform future investment decisions based on the simulation results. The output is an optimized strategy suggestion for the next investment.

[0784] (Application Example 2)

[0785] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0786] Conventional investment strategy generation systems formulate investment strategies based solely on the user's financial information, making it difficult to flexibly consider the user's emotional state and biometric information. Furthermore, they cannot offer relaxation suggestions tailored to the user's emotional state, such as stress and anxiety, and thus fail to alleviate the psychological burden of investment activities.

[0787] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0788] In this invention, the server includes means for collecting financial and biometric information obtained from the user in real time, means for preprocessing and analyzing the collected financial and biometric information, and means for generating investment strategies based on the user's investment profile and emotional state using a generating AI. This makes it possible to propose flexible investment strategies that take the user's emotions into consideration and to provide relaxation menus.

[0789] "Financial information" refers to data including the value of assets related to a user's investments, transaction history, and market trends.

[0790] "Biometric information" refers to information such as facial expression data, voice data, and other physiological data acquired in order to infer the user's emotional state.

[0791] "Generative AI" is an artificial intelligence technology that automatically generates investment strategies tailored to a user's investment profile and emotional state, based on their financial and biometric information.

[0792] An "investment profile" is basic information used to formulate individual strategies for users, including their investment objectives and risk tolerance.

[0793] A "relaxation menu" refers to services such as music and guided meditation that are suggested to reduce stress and anxiety based on the user's emotional state.

[0794] In this embodiment, a system including a server and a user terminal provides investment support to the user. The server collects the user's financial information and biometric information in real time and analyzes the user's emotional state based on this information. The financial information includes the user's investment-related asset information and market trends, while the biometric information is data obtained from the user's facial expressions and voice.

[0795] The server preprocesses the collected information and performs analysis using a generated AI model. Specifically, it uses Google Cloud's Speech-to-Text API and Microsoft Azure's emotion recognition API to convert the data from speech to text and recognize the emotional state. The analysis results are input into an AI model generated using TensorFlow or PyTorch frameworks, which generates a flexible investment strategy tailored to the user's investment profile and emotional state.

[0796] The generated investment strategy and relaxation menu are notified to the user's device. Notifications are delivered via the device's display and audio output. If the device is deemed high-risk, it displays a warning message and recommends relaxation options such as music or guided meditation, tailored to the user's emotional state. This allows the user to make investment decisions while managing their emotions.

[0797] As a concrete example, consider a situation where a user is experiencing stress due to market fluctuations. The server, based on this biometric information, determines that the user is experiencing negative emotions and generates and notifies them of a risk-reducing investment strategy. At the same time, it suggests relaxing music to help the user regain their composure.

[0798] An example of a prompt might be, "Given the current market conditions, what flexible investment strategy should be proposed if the user appears anxious?" Based on this prompt, the AI ​​model generates suggestions tailored to each user.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The server collects the user's financial and biometric information. Specifically, it receives financial information such as asset information and transaction history from the user's terminal via the internet, and simultaneously acquires facial expression data and voice data using the terminal's camera and microphone. The input for this step is the user's financial and biometric information, and the output is a dataset containing this information.

[0802] Step 2:

[0803] The server preprocesses the acquired data. At this stage, the obtained audio data is converted to text using Google Cloud's Speech-to-Text API, and the facial expression data is converted to emotion labels using Microsoft Azure's emotion recognition API. Financial information is normalized and cleansed. The input is the collected raw data, and the output is data in a parseable format.

[0804] Step 3:

[0805] The server inputs pre-processed data into a generating AI model, which then generates an investment strategy based on the user's investment profile and emotional state. The AI ​​model is built using TensorFlow and PyTorch, and the prompts are in the form of pre-configured questions. The input is pre-processed data, and the output is the generated investment strategy.

[0806] Step 4:

[0807] The server notifies the user's terminal of the generated investment strategy. The notification is in visual or audio format, using the terminal's display or speaker. The input is the generated investment strategy, and the output is the notification to the user.

[0808] Step 5:

[0809] The server provides relaxation options based on the user's emotional state. If the server determines that the user is stressed, specific music or guided meditation will be suggested for relaxation. The input for this step is the result of the emotional analysis, and the output is the recommended relaxation menu.

[0810] Step 6:

[0811] The user selects on the device whether to accept or reject the proposed investment strategy and relaxation menu. The input is the notified strategy and menu, and the output is the user's selection. This selection is used to generate subsequent strategies.

[0812] 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.

[0813] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0814] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0815] 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.

[0816] Figure 9 shows an 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.

[0817] 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.

[0818] 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.

[0819] 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, motorcycles, etc., 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, for example, based 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.

[0820] 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."

[0821] 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.

[0822] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0823] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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 the like 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.

[0832] 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.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means of collecting financial data obtained from users in real time,

[0836] Means for preprocessing and analyzing collected financial data,

[0837] A means of generating an investment strategy based on a user's investment profile using a generative AI,

[0838] A means of notifying the user terminal of the generated investment strategy,

[0839] A method for automatically optimizing a portfolio based on fluctuations in market data,

[0840] A system that includes means for sending risk-based warning messages to users.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for enabling a user to perform an investment simulation.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the generating AI includes means for continuously improving the investment strategy using user feedback.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A means of collecting financial information obtained from users in real time,

[0848] Means for preprocessing and analyzing collected financial information,

[0849] A means for generating investment strategies based on individual investment profiles using generative AI,

[0850] A means of notifying the user's terminal of the generated investment strategy,

[0851] A method for automatically optimizing investment portfolios based on fluctuations in market information,

[0852] A means of sending risk-based warning information to users,

[0853] A means of providing customized financial advice based on user identification information and transaction history,

[0854] A system that includes means for generating intuitive graphs and charts to visually display collected financial information.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising means that enable a user to perform a simulation of an investment in a virtual environment.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the generating AI includes means for continuously improving the investment strategy using feedback from the user and setting realistic and achievable investment goals.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] A means of collecting transaction information obtained from users in real time,

[0862] Means for preprocessing and analyzing collected transaction information,

[0863] A means of generating strategies based on a user's asset management profile using generative AI,

[0864] A means of notifying the user's device of the generated strategy,

[0865] A method for automatically optimizing investment portfolios based on fluctuations in market information,

[0866] A means of sending risk-based warning messages to users,

[0867] A means of running an investment simulation on a user's device,

[0868] A generating AI generates a strategy optimized for the user based on trading information, and a means of notifying the user of the strategy in real time using data communication means.

[0869] A means to enable users to conduct experimental asset management simulations,

[0870] Through the provided user interface, feedback on strategy generation is accepted, providing a means for the generating AI model to continuously improve the strategy.

[0871] ...a system that includes

[0872] (Claim 2)

[0873] The system according to claim 1, comprising means for enabling a user to perform an experimental asset management simulation.

[0874] (Claim 3)

[0875] The system according to claim 1, comprising means for receiving feedback on strategy generation through a provided user interface, and for the generating AI model to continuously improve the strategy.

[0876] "Example 2 of combining an emotion engine"

[0877] (Claim 1)

[0878] A means of collecting emotional and financial data obtained from users in real time,

[0879] Means for preprocessing and analyzing the collected data,

[0880] A means for generating an investment strategy based on a user's investment profile and emotional state using a generative AI model,

[0881] A means of notifying the user terminal of the generated investment strategy,

[0882] A means to automatically optimize portfolios based on market data fluctuations and changes in user sentiment,

[0883] A system that includes means for sending risk-based warning messages to users, adjusting the tone according to their emotions.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising means that enable a user to perform an investment simulation that takes emotional data into account.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the generating AI model includes means for continuously improving the investment strategy using user feedback and sentiment data.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] A means of collecting financial and biometric information obtained from users in real time,

[0891] means for preprocessing and analyzing collected financial and biometric information,

[0892] A means for generating investment strategies based on a user's investment profile and emotional state using generative AI,

[0893] A means of notifying the user terminal of the generated investment strategy and emotionally sensitive relaxation menu,

[0894] A means of automatically optimizing portfolios based on market information fluctuations and changes in user sentiment,

[0895] A system that includes means for sending risk-based warning messages to users.

[0896] (Claim 2)

[0897] The system according to claim 1, which enables a user to perform an investment simulation that takes biometric information into account.

[0898] (Claim 3)

[0899] The system according to claim 1, wherein the generating AI includes means for continuously improving the investment strategy using user feedback and biometric information. [Explanation of Symbols]

[0900] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting financial data obtained from users in real time, Means for preprocessing and analyzing collected financial data, A means of generating an investment strategy based on a user's investment profile using a generative AI, A means of notifying the user terminal of the generated investment strategy, A method for automatically optimizing a portfolio based on fluctuations in market data, A system that includes means for sending risk-based warning messages to users.

2. The system according to claim 1, comprising means for enabling a user to perform an investment simulation.

3. The system according to claim 1, wherein the generating AI includes means for continuously improving the investment strategy using user feedback.

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