system

Abstract

We provide the system. [Solution] A communication means for receiving attribute information obtained from the user, Processing means for applying a generative model to determine the optimal asset allocation based on the attribute information, A display means for presenting the asset allocation determined by the aforementioned generation model to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many defined contribution pension participants lack the knowledge and judgment materials for effectively managing their assets, and as a result, they tend to select inefficient asset allocations. This causes unnecessary risks in asset formation and economic instability in old age.

Means for Solving the Problems

[0005] This invention provides a system that utilizes attribute information acquired from users and automatically determines the optimal asset allocation using a generative model. Furthermore, this system receives attribute information using communication means and presents the asset allocation results to the user through display means, enabling the user to easily make investment decisions. In this way, it provides an environment in which even users without specialized knowledge can perform rational and efficient asset management.

[0006] "User" refers to an individual or legal entity that uses the system to manage their own assets.

[0007] "Attribute information" refers to information related to individual asset management, such as the user's age, asset status, and investment goals.

[0008] "Communication means" refers to the internet and other communication technologies used to receive information from users and to exchange data between the system and users.

[0009] A "generative model" refers to a machine learning algorithm or AI model that takes user attribute information and market-related data as input and calculates the optimal asset allocation.

[0010] "Processing means" refers to the computer functions used to process attribute information using a generative model and to determine investment allocation.

[0011] "Display means" refers to devices or interfaces that visually present the results of the optimal asset allocation to the user.

[0012] "Market-related data" refers to information used as a basis for investment decisions, such as trends in financial markets, economic indicators, and past investment performance. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [[ID=二十三]] [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage 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.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a system for optimizing asset management. Specifically, it optimizes a user's asset allocation using a generative AI model and presents the results to the user. This system consists of communication means, processing means, and display means.

[0035] First, the user enters their personal information using a terminal. This information includes the user's age, current asset status, and investment objectives. The terminal formats this information and sends it to the server via a communication method.

[0036] Next, the server processes the received attribute information and prepares it for input into the generative model. At this time, it also retrieves the latest market-related data via API. The generative model utilizes machine learning techniques to calculate the optimal asset allocation for the user. The calculated results are adjusted based on the user's risk tolerance and current market conditions.

[0037] This generated asset allocation information is transmitted from the server to the terminal and presented to the user through a display device. The presented information includes a detailed explanation of the optimal investment ratio suggested by the generation model, the reasoning behind it, and the anticipated risks. Based on this information, the user can make specific investment decisions through the 401K management site.

[0038] For example, if a 30-year-old user starts investing with the goal of saving for their child's education, the generated model might suggest a high-risk, high-return allocation based on their age and asset situation. In this case, the advice would likely recommend a high allocation to domestic stocks and a certain percentage of investment in bonds. The user, having received this information, can then adjust their actual investment settings by adjusting the proportions to specific asset classes.

[0039] In this way, the system provides an environment where even users without specialized knowledge can easily start using it, supporting effective asset building.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users input attribute information related to asset management (age, asset status, investment objectives, etc.) into a dedicated interface using their own devices.

[0043] Step 2:

[0044] The terminal converts the entered attribute information into a predetermined format and verifies the data's integrity. If there are no integrity issues, it sends the information to the server using a communication method.

[0045] Step 3:

[0046] The server receives attribute information sent from the terminal and securely stores it in the database. After confirming that the information has been successfully received, it communicates with external systems such as APIs to obtain market-related data necessary for processing.

[0047] Step 4:

[0048] The server preprocesses the received attribute information and acquired market-related data, preparing them for input into the generative model. Preprocessing includes data normalization and imputation of missing values.

[0049] Step 5:

[0050] The server uses a generative model to calculate the optimal asset allocation for the user. At this stage, the model generates an optimized investment plan based on the user's risk tolerance and objectives.

[0051] Step 6:

[0052] The server converts the asset allocation information obtained from the generative model into an advice format that is easy for the user to understand. This advice includes explanations of risk assessments and investment strategies.

[0053] Step 7:

[0054] The server sends asset allocation information, including advice, to the terminal. Security measures, such as data encryption, are applied during transmission.

[0055] Step 8:

[0056] The terminal displays the received advice to the user. Based on the optimization information presented, the user decides to take specific operational procedures.

[0057] (Example 1)

[0058] 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."

[0059] Optimizing asset management requires specialized knowledge and an understanding of market trends, making it difficult for many users to make sufficiently appropriate decisions. As a result, the effectiveness of asset management may decrease, and the expected results may not be achieved. Furthermore, it is necessary to quickly present asset allocations that reflect the latest market information while utilizing user-specific attribute information.

[0060] 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.

[0061] In this invention, the server includes information communication means for receiving attribute information acquired from the user, information processing means for applying a generative model that combines market-related data to determine the optimal asset allocation, and information display means for visually presenting the determined asset allocation. This makes it possible for users to effectively manage their assets even without specialized knowledge.

[0062] "Information and communication means" refers to a means for receiving attribute information from users, communicating with external data providers as needed, and exchanging information bidirectionally.

[0063] "Information processing means" refers to a means for determining the optimal asset allocation by combining received attribute information and market-related data and applying a generative model.

[0064] An "information display means" is a means of visually presenting the asset allocation results determined by a generative model to the user, and providing detailed information including the reasons and risks.

[0065] A "generative model" is an algorithm that uses machine learning techniques to calculate the optimal asset allocation for a user.

[0066] "Market-related data" refers to data obtained from external data providers, such as stock indices, exchange rates, and interest rate information, that pertain to external factors influencing asset management.

[0067] "Attribute information" refers to personal information provided by users, such as age, asset status, and investment objectives, which is necessary to determine individual asset allocation.

[0068] This invention provides a system that uses user attribute information and market-related data to generate an AI model and present an optimal asset allocation. Its specific form is described below.

[0069] Users input their personal information using a terminal equipped with an interface. This terminal is equipped with application software that provides a format to facilitate data entry. The inputted personal information includes the user's age, asset status, and investment purpose.

[0070] The terminal converts the entered attribute information into a standardized data format and sends it to the server via the internet. This transmission is performed using a secure and reliable protocol (e.g., HTTPS).

[0071] The server analyzes the received attribute information and also acquires market-related data via an external data provider. This market-related data includes financial data necessary for calculating asset allocation, such as stock price indices, exchange rates, and interest rate information. After collecting this data, the server passes it to the generating AI model.

[0072] The generative AI model utilizes machine learning techniques (e.g., neural networks) to calculate the optimal asset allocation for the user. The generated asset allocation results are further adjusted based on the user's risk tolerance and market trends.

[0073] The adjusted results are sent back to the device and presented to the user visually. The device displays the results in an easy-to-understand format, using graphs and charts to explain the rationale and risks of the asset allocation. This allows users to intuitively understand the information and make decisions, even without specialized knowledge.

[0074] For example, if a 30-year-old user starts investing for educational fund purposes, the AI ​​model might suggest a high-risk, high-return allocation based on attribute information and market data. In this case, a high allocation to domestic stocks and a certain percentage of investment in bonds might be recommended. The user then uses this information to execute their specific investments through the 401K investment platform.

[0075] An example of a prompt would be: "If a 30-year-old user is starting to invest for the purpose of saving for their education, please suggest the optimal asset allocation. Please consider the current market conditions and the user's risk tolerance." Based on this prompt, the generating AI model calculates the asset allocation.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user enters their personal information into the terminal's input screen. This personal information includes age, asset status, and investment objectives. The entered data is converted into digital data according to the terminal's formatting rules. The output is the formatted personal information.

[0079] Step 2:

[0080] The terminal receives formatted attribute information and transmits this information to the server using an information communication method. Secure internet protocols such as HTTPS are used for transmission. The output is a data packet containing the attribute information.

[0081] Step 3:

[0082] The server receives attribute information and prepares it for analysis. Here, data validation and normalization are performed, and missing or inaccurate data are corrected or imputed. The output is validated and normalized attribute information.

[0083] Step 4:

[0084] The server retrieves market-related data from an external data provider. This process involves API calls to obtain the latest financial data (stock indices, exchange rates, interest rates, etc.). The output is the most up-to-date market-related data.

[0085] Step 5:

[0086] The server combines attribute information and market-related data and inputs it into a generative AI model. The generative AI model uses machine learning techniques to calculate the optimal asset allocation based on the input data. As a result of this calculation, an investment strategy suitable for the user is generated. The output is the generated asset allocation result.

[0087] Step 6:

[0088] The server reviews the generated asset allocation results and makes further adjustments based on the user's risk tolerance and market conditions. This establishes the most appropriate investment strategy for the user. The output is the adjusted asset allocation.

[0089] Step 7:

[0090] The server sends the adjusted asset allocation to the terminal. A secure protocol is used for this communication, ensuring user privacy. The output is the asset allocation information sent to the user's terminal.

[0091] Step 8:

[0092] The terminal visually displays the received asset allocation information, visualizing the data as charts and graphs. Based on this, users can check detailed information (risk assessment and underlying reasons). The output is the visualized asset allocation information presented to the user.

[0093] Step 9:

[0094] Based on the asset allocation information presented on the device, users make specific investment decisions. This includes adjusting different investment ratios and making investment decisions through the 401K management platform. The output is the user's final investment decision.

[0095] (Application Example 1)

[0096] 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."

[0097] Modern consumers face the challenge of difficulty in obtaining optimal investment strategies in real time by leveraging their personal attribute information and electronic transaction history. Traditional asset management systems cannot provide real-time advice tailored to each user's individual transaction situation, making it difficult to optimize asset management. Therefore, there is a need for a system that provides personalized investment advice in real time.

[0098] 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.

[0099] In this invention, the server includes communication means for receiving attribute information and electronic transaction history acquired from the user, processing means for applying a generative model for determining the optimal asset allocation based on the attribute information and market-related information, and display means for presenting the asset allocation determined by the generative model to the user and providing real-time advice. As a result, consumers can receive optimized asset management and real-time advice based on attribute information and transaction history, enabling them to execute more effective investment strategies.

[0100] "Attribute information" refers to information related to an individual user, such as data on age, asset status, and investment objectives.

[0101] "Electronic transaction history" refers to a record of an electronic economic activity conducted by a user, including information such as purchase history and details of expenditures.

[0102] "Communication means" refers to a system for sending and receiving information bidirectionally via a network such as the internet.

[0103] "Market-related information" refers to various data concerning financial markets, including information such as stock prices, exchange rates, and economic indicators.

[0104] A "generative model" is an algorithm built using machine learning techniques to calculate the optimal asset allocation based on specific input data.

[0105] The "processing means" refers to the system component for analyzing data based on received information, and has the function of performing calculations, including the application of generative models.

[0106] "Display means" refers to an interface that visually presents calculated results or suggested information to the user.

[0107] "Real-time advice" refers to immediate investment suggestions tailored to the user's current financial situation and market data.

[0108] The system that realizes this invention utilizes user attribute information and electronic transaction history to propose an optimal asset allocation. The system is equipped with a communication means that receives information from users via smartphones and communication terminals and transmits it to a server. The server uses a high-performance processing unit and applies a generated AI model utilizing machine learning libraries such as Python and TENSORFLOW® to analyze the collected data.

[0109] First, users enter attribute information such as their age, asset status, and investment objectives through a smartphone application. This information is transmitted to the server via the internet. Next, the server retrieves the latest market-related information via an API and prepares to integrate this data and input it into a generative model.

[0110] The data is processed by a generative AI model, and real-time asset allocation is determined based on the user's individual investment situation. This asset allocation is sent back to the smartphone in JSON format and displayed. Real-time advice is updated instantly based on electronic trading history using data streaming technologies such as Firebase.

[0111] As a concrete example, a generative AI model might provide advice such as, "I recommend reinvesting a certain percentage of your total purchases at convenience stores this week into stocks." Another example of a prompt to the generative AI model is, "Given the user's attribute information (age: 30, asset status: 5 million yen, investment purpose: education funds), please propose the optimal asset allocation taking into account the latest market data."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users input attribute information such as age, asset status, and investment objectives using a smartphone application. This input information is transmitted to a server via communication means. This input information serves as the basic data necessary for optimizing asset allocation.

[0115] Step 2:

[0116] The server retrieves the latest market-related data via an API. This data includes financial market information such as stock prices and exchange rates. This market-related data is combined with user attribute information and used to create input datasets for generative models.

[0117] Step 3:

[0118] The server uses Python and machine learning libraries such as TensorFlow to input user attribute information and market-related data into an AI model. Based on this input data, the model calculates the optimal asset allocation. This calculation result is adjusted to account for the user's individual risk tolerance and market conditions.

[0119] Step 4:

[0120] The server formats the optimal asset allocation results obtained by the generated AI model into JSON format and sends them to the smartphone. These results are displayed visually in the application on the smartphone. The display includes details of the investment proportions, recommended investment strategies, and explanations of the anticipated risks.

[0121] Step 5:

[0122] Users can view asset allocation information displayed on their smartphones and monitor their electronic transaction history in real time. Using Firebase or similar data streaming technologies, they receive real-time investment advice based on their history. Based on this advice, users can then take specific investment actions.

[0123] 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.

[0124] This invention provides a system for more precisely optimizing asset management, and in particular, a system that can make investment decisions while taking into account the user's emotional state. The system is composed of communication means, processing means, display means, and an emotion engine.

[0125] First, the user operates the terminal to input attribute information related to their asset management. This includes age, asset status, and investment goals. Once the input is complete, the terminal formats the information and sends it to the server via a communication method.

[0126] Next, the server receives the transmitted attribute information and stores it in the database. Simultaneously, the emotion engine analyzes the user's past input data and history to infer their current emotional state. This analysis result is provided to the processing unit as an emotional state parameter.

[0127] Based on this emotional state and attribute information, the server uses a generative model to calculate the optimal asset allocation. The emotional state contributes to adjusting the risk tolerance of the investment; for example, a more conservative allocation is set when the user is unwilling to take risks. By combining this with market-related data, an even more accurate asset allocation is proposed.

[0128] The server then processes the calculation results into an advice format and sends it to the terminal. The advice includes details of the generated asset allocation and the results of the associated sentiment analysis, helping the user understand how they should be configured.

[0129] As a concrete example, consider a 40-year-old user who wants to invest their assets with the aim of short-term returns. If the emotion engine recognizes anxiety about the current market, in addition to the usual information, the generative model will suggest a conservative allocation with reduced risk. In this case, advice such as increasing the proportion of bonds may be provided. In this way, the user can avoid emotional misjudgments and achieve logical yet emotionally balanced asset management.

[0130] This system supports scientific and rational operation while incorporating human factors such as emotional fluctuations.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses their device to input attribute information related to asset management (age, asset status, investment goals, etc.) into the interface. They also input a short questionnaire and feedback to assess their emotions.

[0134] Step 2:

[0135] The terminal formats the entered information and verifies data integrity. After verification, it sends the information to the server using a communication method.

[0136] Step 3:

[0137] The server stores attribute information and sentiment data received from the terminal in a database. After receiving the data, it communicates with relevant APIs to retrieve the latest market-related data.

[0138] Step 4:

[0139] The server runs an emotion engine that analyzes the user's current emotional state using received information and historical data. The analysis results are used to adjust risk tolerance.

[0140] Step 5:

[0141] The server inputs attribute information, emotional states, and market-related data into a generative model to determine the optimal asset allocation. Here, emotional analysis is incorporated to dynamically adjust the user's risk tolerance and generate an individually tailored investment strategy.

[0142] Step 6:

[0143] The server converts the generated asset allocation results into text-based advice, creating content that is easy for the user to understand. The advice also includes explanations related to the estimated emotional state.

[0144] Step 7:

[0145] The server sends an advice message to the terminal. Communication is conducted quickly while ensuring security.

[0146] Step 8:

[0147] The terminal displays the received advice and asset allocation information to the user. Based on this, the user can more carefully visit the 401K management site and proceed with the switching procedure.

[0148] (Example 2)

[0149] 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".

[0150] In optimizing asset management, there is a need for a system that can provide more precise and personalized asset allocation proposals while preventing misjudgments based on the user's emotions. Existing methods do not take into account the user's emotional state and are insufficient in adjusting risk tolerance.

[0151] 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.

[0152] In this invention, the server includes an analysis means for inferring the user's emotional state, a processing means for combining the inferred emotional state with attribute information and applying a generative model to determine the optimal asset allocation, and a display means for presenting the asset allocation determined by the generative model to the user in a visual display format. This makes it possible to propose a highly optimized asset allocation that takes into account the individual emotional state of each user.

[0153] "Analysis means" refers to a device or method that has the function of inferring the emotional state of a user from past data and behavioral history.

[0154] "Attribute information" refers to data that shows details about a user's asset management, including age, asset status, and investment goals.

[0155] A "generative model" is an algorithm or program that calculates the optimal asset allocation based on the user's attribute information and emotional state.

[0156] A "visual display format" is a format that uses visual elements such as graphs and charts to display the results of the generated asset allocation proposal in a user-friendly way.

[0157] "Communication means" refers to a device or method that utilizes network technologies such as the Internet for the bidirectional exchange of data.

[0158] "Market-related information" refers to data on fluctuations and trends in the stock market and financial markets, and is used to optimize asset allocation.

[0159] The embodiment for carrying out this invention is configured as follows.

[0160] First, the user operates a device, such as a smartphone or computer, to input attribute information related to their asset management. This information includes age, asset status, and investment goals. This initial information is entered through a user-friendly interface provided by the application on the device.

[0161] Next, the terminal receives attribute information obtained from the user and formats it into an appropriate data format, such as JSON. At this stage, the terminal utilizes its communication capabilities to send the organized information to the server via the internet. Standard internet protocols are used for communication.

[0162] The server stores the received attribute information in a database. Relational database management systems such as MySQL® or PostgreSQL are used as the database. Simultaneously, the server activates an emotion engine to analyze and predict the current emotional state based on past data and user history. The emotion engine utilizes text analysis libraries and machine learning frameworks, such as NLTK and Scikit-learn.

[0163] Subsequently, the server uses a generative AI model to calculate the optimal asset allocation, taking the inferred emotional state and user attribute information as input. The generative AI model is built using deep learning frameworks such as TensorFlow or PyTorch. Emotional state plays a particularly important role in this process; for example, if the user is determined to be in an emotional state of risk aversion, a more conservative asset allocation will be concluded.

[0164] The calculated asset allocation results are processed by the server into advice and sent to the terminal for visual display. The terminal displays this advice using graphs and charts, providing the user with reference information to make investment decisions. Tools such as Python's Matplotlib and JavaScript's D3.js are used for specific visualizations.

[0165] As a concrete example, suppose a 40-year-old user is considering asset management aimed at short-term returns. If this user's emotional engine detects market anxiety, the generative model may suggest a low-risk asset allocation and advise increasing the proportion of safe assets such as bonds. In this way, the user can engage in rational asset management that takes their emotions into consideration.

[0166] An example of a prompt message to use with a generative AI model might be, "Calculate the optimal asset allocation considering the user's emotional state."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user enters attribute information related to asset management using a terminal. This information includes the user's age, asset status, and investment goals. This attribute information is collected through an input form on the terminal and formatted in JSON format. Once the input is complete, the terminal sends the data to the server via the internet.

[0170] Step 2:

[0171] The server analyzes attribute information received from the terminal and stores it in a database. This database creates user-specific records based on a data structure generated using SQL queries. Simultaneously, the server prepares input data to infer emotional states by comparing past data and logs to run the emotion engine.

[0172] Step 3:

[0173] The server uses an emotion engine to infer the user's current emotional state based on collected data. This inference of emotional state employs machine learning algorithms and analyzes text and historical data using natural language processing libraries. The output of this calculation generates emotional state parameters, such as risk tolerance.

[0174] Step 4:

[0175] The server passes emotional state and attribute information as input data to a generating AI model to calculate the optimal asset allocation. This model is implemented using a deep learning framework and adjusts the balance of risk and return according to the emotional state. As a result of the calculation, a proposed portfolio is output, showing its composition ratios and details.

[0176] Step 5:

[0177] The server processes the generated asset allocation results into a visual advice format. This process uses data visualization tools, such as Matplotlib, to generate graphs. The generated advice is then sent to the user's terminal in an easy-to-understand format.

[0178] Step 6:

[0179] The terminal displays advice sent from the server on the user interface. The information displayed on the terminal includes details of the proposed asset allocation and sentiment analysis results. Based on this, the user can consider an asset management strategy and make decisions.

[0180] (Application Example 2)

[0181] 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".

[0182] In today's consumer environment, consumers are often influenced by their emotions at the time of purchase, making them prone to irrational decisions. Therefore, there is a need for systems that support purchasing decisions based on the consumer's emotional state. In particular, there is a need to evaluate the impact of emotions on purchasing decisions and provide more appropriate purchasing advice.

[0183] 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.

[0184] In this invention, the server includes means equipped with an emotion engine for evaluating and analyzing emotional states, communication means for receiving attribute information obtained from a user, and processing means for applying a generative model to determine the optimal asset allocation based on the attribute information. This makes it possible to provide appropriate advice regarding purchase decisions while taking the user's emotions into consideration.

[0185] "Communication means" refers to devices or methods for receiving attribute information from users and exchanging information bidirectionally with a server.

[0186] "Processing means" refers to a method or apparatus that calculates the optimal asset allocation by applying a generative model based on received attribute information.

[0187] "Display means" refers to devices or methods for presenting the asset allocation determined by the generative model to the user.

[0188] An "emotional engine" is a device or method designed to evaluate and analyze a user's emotional state.

[0189] A "user" is an entity that provides attribute information and receives advice from the system.

[0190] A "generative model" is a mathematical or computational method for calculating the optimal asset allocation using attribute information and market-related data.

[0191] "Attribute information" refers to personal data about the user, including information such as age, asset status, and investment goals.

[0192] "Market-related data" refers to information about current market conditions and economic indicators that are considered when calculating asset allocation.

[0193] One embodiment of the present invention requires a server. This server comprises several program modules, including an emotion engine. The emotion engine is designed to infer the emotional state of a user and uses an existing dataset in combination with a machine learning algorithm (e.g., TensorFlow).

[0194] Users operate devices such as smartphones and computers to input attribute information. This information includes age, financial status, and investment goals. This information is transmitted to a server via communication means. The server stores the received attribute information in a database and uses an emotion engine to evaluate the user's emotional state.

[0195] Furthermore, the server uses a generative model to calculate the optimal asset allocation by combining attribute information, emotional states, and market-related data. This process uses AI technology to simulate multiple scenarios and generate the most effective asset strategy.

[0196] Finally, the calculation results are presented to the user in the form of advice. Through a display mechanism, suggestions including specific asset allocation and sentiment analysis results are sent to the user's device. This advice helps users avoid emotion-based misjudgments and make more logical decisions.

[0197] For example, suppose a user is planning to buy a new home appliance, and the emotion engine detects an optimistic emotional state. In this case, the server can then provide advice to reconsider the purchase, taking into account general market prices and past purchase data.

[0198] Example prompt: "Based on the user's recent emotional state and past purchase history, please suggest the best advice regarding the purchase of the following items."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] Users enter attribute information using their smartphones or computers. This information includes age, financial status, and investment goals. The entered data is transmitted to the server via communication means. The attribute information is formatted and prepared for the next processing step.

[0202] Step 2:

[0203] The server stores the received attribute information in its own database. Processing this database enables centralized management of user data. This ensures that the base data necessary for analysis by the emotion engine is secured.

[0204] Step 3:

[0205] The server uses an emotion engine to infer the user's emotional state from the received attribute information. During this process, an AI algorithm operates based on past user input data and history to evaluate the current emotional state. The output is a parameter representing the user's emotional state, which is used in the subsequent asset allocation calculation.

[0206] Step 4:

[0207] Based on emotional state parameters and attribute information, the server uses a generative AI model to calculate the optimal asset allocation. Market-related data is also incorporated, and multiple scenarios are simulated to calculate an allocation strategy that takes risk into account. As a result of the calculation, a proposed asset allocation plan is generated.

[0208] Step 5:

[0209] The server processes the generated asset allocation plan into an advice format and sends it to the user's terminal. The user is then presented with specific asset allocations and sentiment analysis results via a display device. The outputted advice assists the user in decision-making and helps them avoid irrational judgments driven by emotions.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention provides a system for optimizing asset management. Specifically, it optimizes a user's asset allocation using a generative AI model and presents the results to the user. This system consists of communication means, processing means, and display means.

[0227] First, the user enters their personal information using a terminal. This information includes the user's age, current asset status, and investment objectives. The terminal formats this information and sends it to the server via a communication method.

[0228] Next, the server processes the received attribute information and prepares it for input into the generative model. At this time, it also retrieves the latest market-related data via API. The generative model utilizes machine learning techniques to calculate the optimal asset allocation for the user. The calculated results are adjusted based on the user's risk tolerance and current market conditions.

[0229] This generated asset allocation information is transmitted from the server to the terminal and presented to the user through a display device. The presented information includes a detailed explanation of the optimal investment ratio suggested by the generation model, the reasoning behind it, and the anticipated risks. Based on this information, the user can make specific investment decisions through the 401K management site.

[0230] For example, if a 30-year-old user starts investing with the goal of saving for their child's education, the generated model might suggest a high-risk, high-return allocation based on their age and asset situation. In this case, the advice would likely recommend a high allocation to domestic stocks and a certain percentage of investment in bonds. The user, having received this information, can then adjust their actual investment settings by adjusting the proportions to specific asset classes.

[0231] In this way, the system provides an environment where even users without specialized knowledge can easily start using it, supporting effective asset building.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] Users input attribute information related to asset management (age, asset status, investment objectives, etc.) into a dedicated interface using their own devices.

[0235] Step 2:

[0236] The terminal converts the entered attribute information into a predetermined format and verifies the data's integrity. If there are no integrity issues, it sends the information to the server using a communication method.

[0237] Step 3:

[0238] The server receives attribute information sent from the terminal and securely stores it in the database. After confirming that the information has been successfully received, it communicates with external systems such as APIs to obtain market-related data necessary for processing.

[0239] Step 4:

[0240] The server preprocesses the received attribute information and acquired market-related data, preparing them for input into the generative model. Preprocessing includes data normalization and imputation of missing values.

[0241] Step 5:

[0242] The server uses a generative model to calculate the optimal asset allocation for the user. At this stage, the model generates an optimized investment plan based on the user's risk tolerance and objectives.

[0243] Step 6:

[0244] The server converts the asset allocation information obtained from the generative model into an advice format that is easy for the user to understand. This advice includes explanations of risk assessments and investment strategies.

[0245] Step 7:

[0246] The server sends asset allocation information, including advice, to the terminal. Security measures, such as data encryption, are applied during transmission.

[0247] Step 8:

[0248] The terminal displays the received advice to the user. Based on the optimization information presented, the user decides to take specific operational procedures.

[0249] (Example 1)

[0250] 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."

[0251] Optimizing asset management requires specialized knowledge and an understanding of market trends, making it difficult for many users to make sufficiently appropriate decisions. As a result, the effectiveness of asset management may decrease, and the expected results may not be achieved. Furthermore, it is necessary to quickly present asset allocations that reflect the latest market information while utilizing user-specific attribute information.

[0252] 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.

[0253] In this invention, the server includes information communication means for receiving attribute information acquired from the user, information processing means for applying a generative model that combines market-related data to determine the optimal asset allocation, and information display means for visually presenting the determined asset allocation. This makes it possible for users to effectively manage their assets even without specialized knowledge.

[0254] "Information and communication means" refers to a means for receiving attribute information from users, communicating with external data providers as needed, and exchanging information bidirectionally.

[0255] "Information processing means" refers to a means for determining the optimal asset allocation by combining received attribute information and market-related data and applying a generative model.

[0256] An "information display means" is a means of visually presenting the asset allocation results determined by a generative model to the user, and providing detailed information including the reasons and risks.

[0257] A "generative model" is an algorithm that uses machine learning techniques to calculate the optimal asset allocation for a user.

[0258] "Market-related data" refers to data obtained from external data providers, such as stock indices, exchange rates, and interest rate information, that pertain to external factors influencing asset management.

[0259] "Attribute information" refers to personal information provided by users, such as age, asset status, and investment objectives, which is necessary to determine individual asset allocation.

[0260] This invention provides a system that uses user attribute information and market-related data to generate an AI model and present an optimal asset allocation. Its specific form is described below.

[0261] Users input their personal information using a terminal equipped with an interface. This terminal is equipped with application software that provides a format to facilitate data entry. The inputted personal information includes the user's age, asset status, and investment purpose.

[0262] The terminal converts the entered attribute information into a standardized data format and sends it to the server via the internet. This transmission is performed using a secure and reliable protocol (e.g., HTTPS).

[0263] The server analyzes the received attribute information and also acquires market-related data via an external data provider. This market-related data includes financial data necessary for calculating asset allocation, such as stock price indices, exchange rates, and interest rate information. After collecting this data, the server passes it to the generating AI model.

[0264] The generative AI model utilizes machine learning techniques (e.g., neural networks) to calculate the optimal asset allocation for the user. The generated asset allocation results are further adjusted based on the user's risk tolerance and market trends.

[0265] The adjusted results are sent back to the device and presented to the user visually. The device displays the results in an easy-to-understand format, using graphs and charts to explain the rationale and risks of the asset allocation. This allows users to intuitively understand the information and make decisions, even without specialized knowledge.

[0266] For example, if a 30-year-old user starts investing for educational fund purposes, the AI ​​model might suggest a high-risk, high-return allocation based on attribute information and market data. In this case, a high allocation to domestic stocks and a certain percentage of investment in bonds might be recommended. The user then uses this information to execute their specific investments through the 401K investment platform.

[0267] An example of a prompt would be: "If a 30-year-old user is starting to invest for the purpose of saving for their education, please suggest the optimal asset allocation. Please consider the current market conditions and the user's risk tolerance." Based on this prompt, the generating AI model calculates the asset allocation.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user enters their personal information into the terminal's input screen. This personal information includes age, asset status, and investment objectives. The entered data is converted into digital data according to the terminal's formatting rules. The output is the formatted personal information.

[0271] Step 2:

[0272] The terminal receives formatted attribute information and transmits this information to the server using an information communication method. Secure internet protocols such as HTTPS are used for transmission. The output is a data packet containing the attribute information.

[0273] Step 3:

[0274] The server receives attribute information and prepares it for analysis. Here, data validation and normalization are performed, and missing or inaccurate data are corrected or imputed. The output is validated and normalized attribute information.

[0275] Step 4:

[0276] The server retrieves market-related data from an external data provider. This process involves API calls to obtain the latest financial data (stock indices, exchange rates, interest rates, etc.). The output is the most up-to-date market-related data.

[0277] Step 5:

[0278] The server combines attribute information and market-related data and inputs it into a generative AI model. The generative AI model uses machine learning techniques to calculate the optimal asset allocation based on the input data. As a result of this calculation, an investment strategy suitable for the user is generated. The output is the generated asset allocation result.

[0279] Step 6:

[0280] The server reviews the generated asset allocation results and makes further adjustments based on the user's risk tolerance and market conditions. This establishes the most appropriate investment strategy for the user. The output is the adjusted asset allocation.

[0281] Step 7:

[0282] The server sends the adjusted asset allocation to the terminal. A secure protocol is also used for this communication to ensure the user's privacy. The output is the asset allocation information sent to the user's terminal.

[0283] Step 8:

[0284] The terminal visualizes the data as charts or graphs to visually display the received asset allocation information. The user can check the detailed information (risk assessment and background reasons) based on this. The output is the visualized asset allocation information presented to the user.

[0285] Step 9:

[0286] Based on the asset allocation information presented from the terminal, the user makes a specific asset management decision. This includes adjusting different investment ratios and investment procedures through the 401K management platform. The output is the user's final investment decision.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] Modern consumers have the problem that it is difficult to obtain an optimal investment strategy in real time by utilizing their own attribute information and electronic transaction history. In conventional asset management systems, it is impossible to provide real-time advice according to the individual transaction situations of users, and it is difficult to optimize asset management. Therefore, a system that provides real-time personalized investment advice is required.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes communication means for receiving attribute information and electronic transaction history acquired from the user, processing means for applying a generative model for determining the optimal asset allocation based on the attribute information and market-related information, and display means for presenting the asset allocation determined by the generative model to the user and providing real-time advice. As a result, consumers can receive optimized asset management and real-time advice based on attribute information and transaction history, enabling them to execute more effective investment strategies.

[0292] "Attribute information" refers to information related to an individual user, such as data on age, asset status, and investment objectives.

[0293] "Electronic transaction history" refers to a record of an electronic economic activity conducted by a user, including information such as purchase history and details of expenditures.

[0294] "Communication means" refers to a system for sending and receiving information bidirectionally via a network such as the internet.

[0295] "Market-related information" refers to various data concerning financial markets, including information such as stock prices, exchange rates, and economic indicators.

[0296] A "generative model" is an algorithm built using machine learning techniques to calculate the optimal asset allocation based on specific input data.

[0297] The "processing means" refers to the system component for analyzing data based on received information, and has the function of performing calculations, including the application of generative models.

[0298] "Display means" refers to an interface that visually presents calculated results or suggested information to the user.

[0299] "Real-time advice" refers to immediate investment suggestions tailored to the user's current financial situation and market data.

[0300] The system that realizes this invention utilizes user attribute information and electronic transaction history to propose an optimal asset allocation. The system is equipped with a communication means that receives information from users via smartphones and communication terminals and transmits it to a server. The server uses a high-performance processing unit and applies a generated AI model utilizing machine learning libraries such as Python and TensorFlow to analyze the collected data.

[0301] First, users enter attribute information such as their age, asset status, and investment objectives through a smartphone application. This information is transmitted to the server via the internet. Next, the server retrieves the latest market-related information via an API and prepares to integrate this data and input it into a generative model.

[0302] The data is processed by a generative AI model, and real-time asset allocation is determined based on the user's individual investment situation. This asset allocation is sent back to the smartphone in JSON format and displayed. Real-time advice is updated instantly based on electronic trading history using data streaming technologies such as Firebase.

[0303] As a concrete example, a generative AI model might provide advice such as, "I recommend reinvesting a certain percentage of your total purchases at convenience stores this week into stocks." Another example of a prompt to the generative AI model is, "Given the user's attribute information (age: 30, asset status: 5 million yen, investment purpose: education funds), please propose the optimal asset allocation taking into account the latest market data."

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The user uses the application on the smartphone to input attribute information such as age, asset status, and investment purpose. The input information is sent to the server via the communication means. This input information serves as the basic data necessary for optimizing asset allocation.

[0307] Step 2:

[0308] The server obtains the latest market-related data through the API. This data includes financial market information such as stock prices and exchange rates. The market-related data is combined with the user attribute information and used to create an input dataset for the generation model.

[0309] Step 3:

[0310] The server uses machine learning libraries such as Python and TensorFlow to input the user's attribute information and market-related data into the generation AI model. Based on these input data, the model calculates the optimal asset allocation. This calculation result is adjusted considering the user's individual risk tolerance and market conditions.

[0311] Step 4:

[0312] The server formats the optimal asset allocation result obtained by the generation AI model into JSON format and sends it to the smartphone. This result is visually displayed on the application on the smartphone. The display includes details of the investment ratio, recommended investment strategies, and explanations of the assumed risks.

[0313] Step 5:

[0314] The user checks the asset allocation information displayed on the smartphone and monitors the electronic trading history in real time. Using Firebase or similar data streaming technologies, the user receives real-time investment advice based on the history. The user can take specific investment actions based on this advice.

[0315] 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.

[0316] This invention provides a system for more precisely optimizing asset management, and in particular, a system that can make investment decisions while taking into account the user's emotional state. The system is composed of communication means, processing means, display means, and an emotion engine.

[0317] First, the user operates the terminal to input attribute information related to their asset management. This includes age, asset status, and investment goals. Once the input is complete, the terminal formats the information and sends it to the server via a communication method.

[0318] Next, the server receives the transmitted attribute information and stores it in the database. Simultaneously, the emotion engine analyzes the user's past input data and history to infer their current emotional state. This analysis result is provided to the processing unit as an emotional state parameter.

[0319] Based on this emotional state and attribute information, the server uses a generative model to calculate the optimal asset allocation. The emotional state contributes to adjusting the risk tolerance of the investment; for example, a more conservative allocation is set when the user is unwilling to take risks. By combining this with market-related data, an even more accurate asset allocation is proposed.

[0320] The server then processes the calculation results into an advice format and sends it to the terminal. The advice includes details of the generated asset allocation and the results of the associated sentiment analysis, helping the user understand how they should be configured.

[0321] As a concrete example, consider a 40-year-old user who wants to invest their assets with the aim of short-term returns. If the emotion engine recognizes anxiety about the current market, in addition to the usual information, the generative model will suggest a conservative allocation with reduced risk. In this case, advice such as increasing the proportion of bonds may be provided. In this way, the user can avoid emotional misjudgments and achieve logical yet emotionally balanced asset management.

[0322] This system supports scientific and rational operation while incorporating human factors such as emotional fluctuations.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The user uses their device to input attribute information related to asset management (age, asset status, investment goals, etc.) into the interface. They also input a short questionnaire and feedback to assess their emotions.

[0326] Step 2:

[0327] The terminal formats the entered information and verifies data integrity. After verification, it sends the information to the server using a communication method.

[0328] Step 3:

[0329] The server stores attribute information and sentiment data received from the terminal in a database. After receiving the data, it communicates with relevant APIs to retrieve the latest market-related data.

[0330] Step 4:

[0331] The server runs an emotion engine that analyzes the user's current emotional state using received information and historical data. The analysis results are used to adjust risk tolerance.

[0332] Step 5:

[0333] The server inputs attribute information, emotional states, and market-related data into a generative model to determine the optimal asset allocation. Here, emotional analysis is incorporated to dynamically adjust the user's risk tolerance and generate an individually tailored investment strategy.

[0334] Step 6:

[0335] The server converts the generated asset allocation results into text-based advice, creating content that is easy for the user to understand. The advice also includes explanations related to the estimated emotional state.

[0336] Step 7:

[0337] The server sends an advice message to the terminal. Communication is conducted quickly while ensuring security.

[0338] Step 8:

[0339] The terminal displays the received advice and asset allocation information to the user. Based on this, the user can more carefully visit the 401K management site and proceed with the switching procedure.

[0340] (Example 2)

[0341] 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".

[0342] In optimizing asset management, there is a need for a system that can provide more precise and personalized asset allocation proposals while preventing misjudgments based on the user's emotions. Existing methods do not take into account the user's emotional state and are insufficient in adjusting risk tolerance.

[0343] 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.

[0344] In this invention, the server includes an analysis means for inferring the user's emotional state, a processing means for combining the inferred emotional state with attribute information and applying a generative model to determine the optimal asset allocation, and a display means for presenting the asset allocation determined by the generative model to the user in a visual display format. This makes it possible to propose a highly optimized asset allocation that takes into account the individual emotional state of each user.

[0345] "Analysis means" refers to a device or method that has the function of inferring the emotional state of a user from past data and behavioral history.

[0346] "Attribute information" refers to data that shows details about a user's asset management, including age, asset status, and investment goals.

[0347] A "generative model" is an algorithm or program that calculates the optimal asset allocation based on the user's attribute information and emotional state.

[0348] A "visual display format" is a format that uses visual elements such as graphs and charts to display the results of the generated asset allocation proposal in a user-friendly way.

[0349] "Communication means" refers to a device or method that utilizes network technologies such as the Internet for the bidirectional exchange of data.

[0350] "Market-related information" refers to data on fluctuations and trends in the stock market and financial markets, and is used to optimize asset allocation.

[0351] The embodiment for carrying out this invention is configured as follows.

[0352] First, the user operates a device, such as a smartphone or computer, to input attribute information related to their asset management. This information includes age, asset status, and investment goals. This initial information is entered through a user-friendly interface provided by the application on the device.

[0353] Next, the terminal receives attribute information obtained from the user and formats it into an appropriate data format, such as JSON. At this stage, the terminal utilizes its communication capabilities to send the organized information to the server via the internet. Standard internet protocols are used for communication.

[0354] The server stores the received attribute information in a database. Relational database management systems such as MySQL or PostgreSQL are used as the database. Simultaneously, the server runs an emotion engine to analyze and infer the current emotional state based on past data and user history. The emotion engine utilizes text analysis libraries and machine learning frameworks, such as NLTK or Scikit-learn.

[0355] Subsequently, the server uses a generative AI model to calculate the optimal asset allocation, taking the inferred emotional state and user attribute information as input. The generative AI model is built using deep learning frameworks such as TensorFlow or PyTorch. Emotional state plays a particularly important role in this process; for example, if the user is determined to be in an emotional state of risk aversion, a more conservative asset allocation will be concluded.

[0356] The calculated asset allocation results are processed by the server into advice and sent to the terminal for visual display. The terminal displays this advice using graphs and charts, providing the user with reference information to make investment decisions. Tools such as Python's Matplotlib and JavaScript's D3.js are used for specific visualizations.

[0357] As a concrete example, suppose a 40-year-old user is considering asset management aimed at short-term returns. If this user's emotional engine detects market anxiety, the generative model may suggest a low-risk asset allocation and advise increasing the proportion of safe assets such as bonds. In this way, the user can engage in rational asset management that takes their emotions into consideration.

[0358] An example of a prompt message to use with a generative AI model might be, "Calculate the optimal asset allocation considering the user's emotional state."

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The user enters attribute information related to asset management using a terminal. This information includes the user's age, asset status, and investment goals. This attribute information is collected through an input form on the terminal and formatted in JSON format. Once the input is complete, the terminal sends the data to the server via the internet.

[0362] Step 2:

[0363] The server analyzes attribute information received from the terminal and stores it in a database. This database creates user-specific records based on a data structure generated using SQL queries. Simultaneously, the server prepares input data to infer emotional states by comparing past data and logs to run the emotion engine.

[0364] Step 3:

[0365] The server uses an emotion engine to infer the user's current emotional state based on collected data. This inference of emotional state employs machine learning algorithms and analyzes text and historical data using natural language processing libraries. The output of this calculation generates emotional state parameters, such as risk tolerance.

[0366] Step 4:

[0367] The server passes emotional state and attribute information as input data to a generating AI model to calculate the optimal asset allocation. This model is implemented using a deep learning framework and adjusts the balance of risk and return according to the emotional state. As a result of the calculation, a proposed portfolio is output, showing its composition ratios and details.

[0368] Step 5:

[0369] The server processes the generated asset allocation results into a visual advice format. This process uses data visualization tools, such as Matplotlib, to generate graphs. The generated advice is then sent to the user's terminal in an easy-to-understand format.

[0370] Step 6:

[0371] The terminal displays advice sent from the server on the user interface. The information displayed on the terminal includes details of the proposed asset allocation and sentiment analysis results. Based on this, the user can consider an asset management strategy and make decisions.

[0372] (Application Example 2)

[0373] 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."

[0374] In today's consumer environment, consumers are often influenced by their emotions at the time of purchase, making them prone to irrational decisions. Therefore, there is a need for systems that support purchasing decisions based on the consumer's emotional state. In particular, there is a need to evaluate the impact of emotions on purchasing decisions and provide more appropriate purchasing advice.

[0375] 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.

[0376] In this invention, the server includes means equipped with an emotion engine for evaluating and analyzing emotional states, communication means for receiving attribute information obtained from a user, and processing means for applying a generative model to determine the optimal asset allocation based on the attribute information. This makes it possible to provide appropriate advice regarding purchase decisions while taking the user's emotions into consideration.

[0377] "Communication means" refers to devices or methods for receiving attribute information from users and exchanging information bidirectionally with a server.

[0378] "Processing means" refers to a method or apparatus that calculates the optimal asset allocation by applying a generative model based on received attribute information.

[0379] "Display means" refers to devices or methods for presenting the asset allocation determined by the generative model to the user.

[0380] An "emotional engine" is a device or method designed to evaluate and analyze a user's emotional state.

[0381] A "user" is an entity that provides attribute information and receives advice from the system.

[0382] A "generative model" is a mathematical or computational method for calculating the optimal asset allocation using attribute information and market-related data.

[0383] "Attribute information" refers to personal data about the user, including information such as age, asset status, and investment goals.

[0384] "Market-related data" refers to information about current market conditions and economic indicators that are considered when calculating asset allocation.

[0385] One embodiment of the present invention requires a server. This server comprises several program modules, including an emotion engine. The emotion engine is designed to infer the emotional state of a user and uses an existing dataset in combination with a machine learning algorithm (e.g., TensorFlow).

[0386] Users operate devices such as smartphones and computers to input attribute information. This information includes age, financial status, and investment goals. This information is transmitted to a server via communication means. The server stores the received attribute information in a database and uses an emotion engine to evaluate the user's emotional state.

[0387] Furthermore, the server uses a generative model to calculate the optimal asset allocation by combining attribute information, emotional states, and market-related data. This process uses AI technology to simulate multiple scenarios and generate the most effective asset strategy.

[0388] Finally, the calculation results are presented to the user in the form of advice. Through a display mechanism, suggestions including specific asset allocation and sentiment analysis results are sent to the user's device. This advice helps users avoid emotion-based misjudgments and make more logical decisions.

[0389] For example, suppose a user is planning to buy a new home appliance, and the emotion engine detects an optimistic emotional state. In this case, the server can then provide advice to reconsider the purchase, taking into account general market prices and past purchase data.

[0390] Example prompt: "Based on the user's recent emotional state and past purchase history, please suggest the best advice regarding the purchase of the following items."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] Users enter attribute information using their smartphones or computers. This information includes age, financial status, and investment goals. The entered data is transmitted to the server via communication means. The attribute information is formatted and prepared for the next processing step.

[0394] Step 2:

[0395] The server stores the received attribute information in its own database. Processing this database enables centralized management of user data. This ensures that the base data necessary for analysis by the emotion engine is secured.

[0396] Step 3:

[0397] The server uses an emotion engine to infer the user's emotional state from the received attribute information. During this process, an AI algorithm operates based on past user input data and history to evaluate the current emotional state. The output is a parameter representing the user's emotional state, which is used in the subsequent asset allocation calculation.

[0398] Step 4:

[0399] Based on emotional state parameters and attribute information, the server uses a generative AI model to calculate the optimal asset allocation. Market-related data is also incorporated, and multiple scenarios are simulated to calculate an allocation strategy that takes risk into account. As a result of the calculation, a proposed asset allocation plan is generated.

[0400] Step 5:

[0401] The server processes the generated asset allocation plan into an advice format and sends it to the user's terminal. The user is then presented with specific asset allocations and sentiment analysis results via a display device. The outputted advice assists the user in decision-making and helps them avoid irrational judgments driven by emotions.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention provides a system for optimizing asset management. Specifically, it optimizes a user's asset allocation using a generative AI model and presents the results to the user. This system consists of communication means, processing means, and display means.

[0419] First, the user enters their personal information using a terminal. This information includes the user's age, current asset status, and investment objectives. The terminal formats this information and sends it to the server via a communication method.

[0420] Next, the server processes the received attribute information and prepares it for input into the generative model. At this time, it also retrieves the latest market-related data via API. The generative model utilizes machine learning techniques to calculate the optimal asset allocation for the user. The calculated results are adjusted based on the user's risk tolerance and current market conditions.

[0421] This generated asset allocation information is transmitted from the server to the terminal and presented to the user through a display device. The presented information includes a detailed explanation of the optimal investment ratio suggested by the generation model, the reasoning behind it, and the anticipated risks. Based on this information, the user can make specific investment decisions through the 401K management site.

[0422] For example, if a 30-year-old user starts investing with the goal of saving for their child's education, the generated model might suggest a high-risk, high-return allocation based on their age and asset situation. In this case, the advice would likely recommend a high allocation to domestic stocks and a certain percentage of investment in bonds. The user, having received this information, can then adjust their actual investment settings by adjusting the proportions to specific asset classes.

[0423] In this way, the system provides an environment where even users without specialized knowledge can easily start using it, supporting effective asset building.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] Users input attribute information related to asset management (age, asset status, investment objectives, etc.) into a dedicated interface using their own devices.

[0427] Step 2:

[0428] The terminal converts the entered attribute information into a predetermined format and verifies the data's integrity. If there are no integrity issues, it sends the information to the server using a communication method.

[0429] Step 3:

[0430] The server receives attribute information sent from the terminal and securely stores it in the database. After confirming that the information has been successfully received, it communicates with external systems such as APIs to obtain market-related data necessary for processing.

[0431] Step 4:

[0432] The server preprocesses the received attribute information and acquired market-related data, preparing them for input into the generative model. Preprocessing includes data normalization and imputation of missing values.

[0433] Step 5:

[0434] The server uses a generative model to calculate the optimal asset allocation for the user. At this stage, the model generates an optimized investment plan based on the user's risk tolerance and objectives.

[0435] Step 6:

[0436] The server converts the asset allocation information obtained from the generative model into an advice format that is easy for the user to understand. This advice includes explanations of risk assessments and investment strategies.

[0437] Step 7:

[0438] The server sends asset allocation information, including advice, to the terminal. Security measures, such as data encryption, are applied during transmission.

[0439] Step 8:

[0440] The terminal displays the received advice to the user. Based on the optimization information presented, the user decides to take specific operational procedures.

[0441] (Example 1)

[0442] 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."

[0443] Optimizing asset management requires specialized knowledge and an understanding of market trends, making it difficult for many users to make sufficiently appropriate decisions. As a result, the effectiveness of asset management may decrease, and the expected results may not be achieved. Furthermore, it is necessary to quickly present asset allocations that reflect the latest market information while utilizing user-specific attribute information.

[0444] 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.

[0445] In this invention, the server includes information communication means for receiving attribute information acquired from the user, information processing means for applying a generative model that combines market-related data to determine the optimal asset allocation, and information display means for visually presenting the determined asset allocation. This makes it possible for users to effectively manage their assets even without specialized knowledge.

[0446] "Information and communication means" refers to a means for receiving attribute information from users, communicating with external data providers as needed, and exchanging information bidirectionally.

[0447] "Information processing means" refers to a means for determining the optimal asset allocation by combining received attribute information and market-related data and applying a generative model.

[0448] An "information display means" is a means of visually presenting the asset allocation results determined by a generative model to the user, and providing detailed information including the reasons and risks.

[0449] A "generative model" is an algorithm that uses machine learning techniques to calculate the optimal asset allocation for a user.

[0450] "Market-related data" refers to data obtained from external data providers, such as stock indices, exchange rates, and interest rate information, that pertain to external factors influencing asset management.

[0451] "Attribute information" refers to personal information provided by users, such as age, asset status, and investment objectives, which is necessary to determine individual asset allocation.

[0452] This invention provides a system that uses user attribute information and market-related data to generate an AI model and present an optimal asset allocation. Its specific form is described below.

[0453] Users input their personal information using a terminal equipped with an interface. This terminal is equipped with application software that provides a format to facilitate data entry. The inputted personal information includes the user's age, asset status, and investment purpose.

[0454] The terminal converts the entered attribute information into a standardized data format and sends it to the server via the internet. This transmission is performed using a secure and reliable protocol (e.g., HTTPS).

[0455] The server analyzes the received attribute information and also acquires market-related data via an external data provider. This market-related data includes financial data necessary for calculating asset allocation, such as stock price indices, exchange rates, and interest rate information. After collecting this data, the server passes it to the generating AI model.

[0456] The generative AI model utilizes machine learning techniques (e.g., neural networks) to calculate the optimal asset allocation for the user. The generated asset allocation results are further adjusted based on the user's risk tolerance and market trends.

[0457] The adjusted results are sent back to the device and presented to the user visually. The device displays the results in an easy-to-understand format, using graphs and charts to explain the rationale and risks of the asset allocation. This allows users to intuitively understand the information and make decisions, even without specialized knowledge.

[0458] For example, if a 30-year-old user starts investing for educational fund purposes, the AI ​​model might suggest a high-risk, high-return allocation based on attribute information and market data. In this case, a high allocation to domestic stocks and a certain percentage of investment in bonds might be recommended. The user then uses this information to execute their specific investments through the 401K investment platform.

[0459] An example of a prompt would be: "If a 30-year-old user is starting to invest for the purpose of saving for their education, please suggest the optimal asset allocation. Please consider the current market conditions and the user's risk tolerance." Based on this prompt, the generating AI model calculates the asset allocation.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The user enters their personal information into the terminal's input screen. This personal information includes age, asset status, and investment objectives. The entered data is converted into digital data according to the terminal's formatting rules. The output is the formatted personal information.

[0463] Step 2:

[0464] The terminal receives formatted attribute information and transmits this information to the server using an information communication method. Secure internet protocols such as HTTPS are used for transmission. The output is a data packet containing the attribute information.

[0465] Step 3:

[0466] The server receives attribute information and prepares it for analysis. Here, data validation and normalization are performed, and missing or inaccurate data are corrected or imputed. The output is validated and normalized attribute information.

[0467] Step 4:

[0468] The server retrieves market-related data from an external data provider. This process involves API calls to obtain the latest financial data (stock indices, exchange rates, interest rates, etc.). The output is the most up-to-date market-related data.

[0469] Step 5:

[0470] The server combines attribute information and market-related data and inputs it into a generative AI model. The generative AI model uses machine learning techniques to calculate the optimal asset allocation based on the input data. As a result of this calculation, an investment strategy suitable for the user is generated. The output is the generated asset allocation result.

[0471] Step 6:

[0472] The server reviews the generated asset allocation results and makes further adjustments based on the user's risk tolerance and market conditions. This establishes the most appropriate investment strategy for the user. The output is the adjusted asset allocation.

[0473] Step 7:

[0474] The server sends the adjusted asset allocation to the terminal. A secure protocol is used for this communication, ensuring user privacy. The output is the asset allocation information sent to the user's terminal.

[0475] Step 8:

[0476] The terminal visually displays the received asset allocation information, visualizing the data as charts and graphs. Based on this, users can check detailed information (risk assessment and underlying reasons). The output is the visualized asset allocation information presented to the user.

[0477] Step 9:

[0478] Based on the asset allocation information presented on the device, users make specific investment decisions. This includes adjusting different investment ratios and making investment decisions through the 401K management platform. The output is the user's final investment decision.

[0479] (Application Example 1)

[0480] 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."

[0481] Modern consumers face the challenge of difficulty in obtaining optimal investment strategies in real time by leveraging their personal attribute information and electronic transaction history. Traditional asset management systems cannot provide real-time advice tailored to each user's individual transaction situation, making it difficult to optimize asset management. Therefore, there is a need for a system that provides personalized investment advice in real time.

[0482] 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.

[0483] In this invention, the server includes communication means for receiving attribute information and electronic transaction history acquired from the user, processing means for applying a generative model for determining the optimal asset allocation based on the attribute information and market-related information, and display means for presenting the asset allocation determined by the generative model to the user and providing real-time advice. As a result, consumers can receive optimized asset management and real-time advice based on attribute information and transaction history, enabling them to execute more effective investment strategies.

[0484] "Attribute information" refers to information related to an individual user, such as data on age, asset status, and investment objectives.

[0485] "Electronic transaction history" refers to a record of an electronic economic activity conducted by a user, including information such as purchase history and details of expenditures.

[0486] "Communication means" refers to a system for sending and receiving information bidirectionally via a network such as the internet.

[0487] "Market-related information" refers to various data concerning financial markets, including information such as stock prices, exchange rates, and economic indicators.

[0488] A "generative model" is an algorithm built using machine learning techniques to calculate the optimal asset allocation based on specific input data.

[0489] The "processing means" refers to the system component for analyzing data based on received information, and has the function of performing calculations, including the application of generative models.

[0490] "Display means" refers to an interface that visually presents calculated results or suggested information to the user.

[0491] "Real-time advice" refers to immediate investment suggestions tailored to the user's current financial situation and market data.

[0492] The system that realizes this invention utilizes user attribute information and electronic transaction history to propose an optimal asset allocation. The system is equipped with a communication means that receives information from users via smartphones and communication terminals and transmits it to a server. The server uses a high-performance processing unit and applies a generated AI model utilizing machine learning libraries such as Python and TensorFlow to analyze the collected data.

[0493] First, users enter attribute information such as their age, asset status, and investment objectives through a smartphone application. This information is transmitted to the server via the internet. Next, the server retrieves the latest market-related information via an API and prepares to integrate this data and input it into a generative model.

[0494] The data is processed by a generative AI model, and real-time asset allocation is determined based on the user's individual investment situation. This asset allocation is sent back to the smartphone in JSON format and displayed. Real-time advice is updated instantly based on electronic trading history using data streaming technologies such as Firebase.

[0495] As a concrete example, a generative AI model might provide advice such as, "I recommend reinvesting a certain percentage of your total purchases at convenience stores this week into stocks." Another example of a prompt to the generative AI model is, "Given the user's attribute information (age: 30, asset status: 5 million yen, investment purpose: education funds), please propose the optimal asset allocation taking into account the latest market data."

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] Users input attribute information such as age, asset status, and investment objectives using a smartphone application. This input information is transmitted to a server via communication means. This input information serves as the basic data necessary for optimizing asset allocation.

[0499] Step 2:

[0500] The server retrieves the latest market-related data via an API. This data includes financial market information such as stock prices and exchange rates. This market-related data is combined with user attribute information and used to create input datasets for generative models.

[0501] Step 3:

[0502] The server uses Python and machine learning libraries such as TensorFlow to input user attribute information and market-related data into an AI model. Based on this input data, the model calculates the optimal asset allocation. This calculation result is adjusted to account for the user's individual risk tolerance and market conditions.

[0503] Step 4:

[0504] The server formats the optimal asset allocation results obtained by the generated AI model into JSON format and sends them to the smartphone. These results are displayed visually in the application on the smartphone. The display includes details of the investment proportions, recommended investment strategies, and explanations of the anticipated risks.

[0505] Step 5:

[0506] Users can view asset allocation information displayed on their smartphones and monitor their electronic transaction history in real time. Using Firebase or similar data streaming technologies, they receive real-time investment advice based on their history. Based on this advice, users can then take specific investment actions.

[0507] 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.

[0508] This invention provides a system for more precisely optimizing asset management, and in particular, a system that can make investment decisions while taking into account the user's emotional state. The system is composed of communication means, processing means, display means, and an emotion engine.

[0509] First, the user operates the terminal to input attribute information related to their asset management. This includes age, asset status, and investment goals. Once the input is complete, the terminal formats the information and sends it to the server via a communication method.

[0510] Next, the server receives the transmitted attribute information and stores it in the database. Simultaneously, the emotion engine analyzes the user's past input data and history to infer their current emotional state. This analysis result is provided to the processing unit as an emotional state parameter.

[0511] Based on this emotional state and attribute information, the server uses a generative model to calculate the optimal asset allocation. The emotional state contributes to adjusting the risk tolerance of the investment; for example, a more conservative allocation is set when the user is unwilling to take risks. By combining this with market-related data, an even more accurate asset allocation is proposed.

[0512] The server then processes the calculation results into an advice format and sends it to the terminal. The advice includes details of the generated asset allocation and the results of the associated sentiment analysis, helping the user understand how they should be configured.

[0513] As a concrete example, consider a 40-year-old user who wants to invest their assets with the aim of short-term returns. If the emotion engine recognizes anxiety about the current market, in addition to the usual information, the generative model will suggest a conservative allocation with reduced risk. In this case, advice such as increasing the proportion of bonds may be provided. In this way, the user can avoid emotional misjudgments and achieve logical yet emotionally balanced asset management.

[0514] This system supports scientific and rational operation while incorporating human factors such as emotional fluctuations.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] The user uses their device to input attribute information related to asset management (age, asset status, investment goals, etc.) into the interface. They also input a short questionnaire and feedback to assess their emotions.

[0518] Step 2:

[0519] The terminal formats the entered information and verifies data integrity. After verification, it sends the information to the server using a communication method.

[0520] Step 3:

[0521] The server stores attribute information and sentiment data received from the terminal in a database. After receiving the data, it communicates with relevant APIs to retrieve the latest market-related data.

[0522] Step 4:

[0523] The server runs an emotion engine that analyzes the user's current emotional state using received information and historical data. The analysis results are used to adjust risk tolerance.

[0524] Step 5:

[0525] The server inputs attribute information, emotional states, and market-related data into a generative model to determine the optimal asset allocation. Here, emotional analysis is incorporated to dynamically adjust the user's risk tolerance and generate an individually tailored investment strategy.

[0526] Step 6:

[0527] The server converts the generated asset allocation results into text-based advice, creating content that is easy for the user to understand. The advice also includes explanations related to the estimated emotional state.

[0528] Step 7:

[0529] The server sends an advice message to the terminal. Communication is conducted quickly while ensuring security.

[0530] Step 8:

[0531] The terminal displays the received advice and asset allocation information to the user. Based on this, the user can more carefully visit the 401K management site and proceed with the switching procedure.

[0532] (Example 2)

[0533] 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."

[0534] In optimizing asset management, there is a need for a system that can provide more precise and personalized asset allocation proposals while preventing misjudgments based on the user's emotions. Existing methods do not take into account the user's emotional state and are insufficient in adjusting risk tolerance.

[0535] 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.

[0536] In this invention, the server includes an analysis means for inferring the user's emotional state, a processing means for combining the inferred emotional state with attribute information and applying a generative model to determine the optimal asset allocation, and a display means for presenting the asset allocation determined by the generative model to the user in a visual display format. This makes it possible to propose a highly optimized asset allocation that takes into account the individual emotional state of each user.

[0537] "Analysis means" refers to a device or method that has the function of inferring the emotional state of a user from past data and behavioral history.

[0538] "Attribute information" refers to data that shows details about a user's asset management, including age, asset status, and investment goals.

[0539] A "generative model" is an algorithm or program that calculates the optimal asset allocation based on the user's attribute information and emotional state.

[0540] A "visual display format" is a format that uses visual elements such as graphs and charts to display the results of the generated asset allocation proposal in a user-friendly way.

[0541] "Communication means" refers to a device or method that utilizes network technologies such as the Internet for the bidirectional exchange of data.

[0542] "Market-related information" refers to data on fluctuations and trends in the stock market and financial markets, and is used to optimize asset allocation.

[0543] The embodiment for carrying out this invention is configured as follows.

[0544] First, the user operates a device, such as a smartphone or computer, to input attribute information related to their asset management. This information includes age, asset status, and investment goals. This initial information is entered through a user-friendly interface provided by the application on the device.

[0545] Next, the terminal receives attribute information obtained from the user and formats it into an appropriate data format, such as JSON. At this stage, the terminal utilizes its communication capabilities to send the organized information to the server via the internet. Standard internet protocols are used for communication.

[0546] The server stores the received attribute information in a database. Relational database management systems such as MySQL or PostgreSQL are used as the database. Simultaneously, the server runs an emotion engine to analyze and infer the current emotional state based on past data and user history. The emotion engine utilizes text analysis libraries and machine learning frameworks, such as NLTK or Scikit-learn.

[0547] Subsequently, the server uses a generative AI model to calculate the optimal asset allocation, taking the inferred emotional state and user attribute information as input. The generative AI model is built using deep learning frameworks such as TensorFlow or PyTorch. Emotional state plays a particularly important role in this process; for example, if the user is determined to be in an emotional state of risk aversion, a more conservative asset allocation will be concluded.

[0548] The calculated asset allocation results are processed by the server into advice and sent to the terminal for visual display. The terminal displays this advice using graphs and charts, providing the user with reference information to make investment decisions. Tools such as Python's Matplotlib and JavaScript's D3.js are used for specific visualizations.

[0549] As a concrete example, suppose a 40-year-old user is considering asset management aimed at short-term returns. If this user's emotional engine detects market anxiety, the generative model may suggest a low-risk asset allocation and advise increasing the proportion of safe assets such as bonds. In this way, the user can engage in rational asset management that takes their emotions into consideration.

[0550] An example of a prompt message to use with a generative AI model might be, "Calculate the optimal asset allocation considering the user's emotional state."

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The user enters attribute information related to asset management using a terminal. This information includes the user's age, asset status, and investment goals. This attribute information is collected through an input form on the terminal and formatted in JSON format. Once the input is complete, the terminal sends the data to the server via the internet.

[0554] Step 2:

[0555] The server analyzes attribute information received from the terminal and stores it in a database. This database creates user-specific records based on a data structure generated using SQL queries. Simultaneously, the server prepares input data to infer emotional states by comparing past data and logs to run the emotion engine.

[0556] Step 3:

[0557] The server uses an emotion engine to infer the user's current emotional state based on collected data. This inference of emotional state employs machine learning algorithms and analyzes text and historical data using natural language processing libraries. The output of this calculation generates emotional state parameters, such as risk tolerance.

[0558] Step 4:

[0559] The server passes emotional state and attribute information as input data to a generating AI model to calculate the optimal asset allocation. This model is implemented using a deep learning framework and adjusts the balance of risk and return according to the emotional state. As a result of the calculation, a proposed portfolio is output, showing its composition ratios and details.

[0560] Step 5:

[0561] The server processes the generated asset allocation results into a visual advice format. This process uses data visualization tools, such as Matplotlib, to generate graphs. The generated advice is then sent to the user's terminal in an easy-to-understand format.

[0562] Step 6:

[0563] The terminal displays advice sent from the server on the user interface. The information displayed on the terminal includes details of the proposed asset allocation and sentiment analysis results. Based on this, the user can consider an asset management strategy and make decisions.

[0564] (Application Example 2)

[0565] 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."

[0566] In today's consumer environment, consumers are often influenced by their emotions at the time of purchase, making them prone to irrational decisions. Therefore, there is a need for systems that support purchasing decisions based on the consumer's emotional state. In particular, there is a need to evaluate the impact of emotions on purchasing decisions and provide more appropriate purchasing advice.

[0567] 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.

[0568] In this invention, the server includes means equipped with an emotion engine for evaluating and analyzing emotional states, communication means for receiving attribute information obtained from a user, and processing means for applying a generative model to determine the optimal asset allocation based on the attribute information. This makes it possible to provide appropriate advice regarding purchase decisions while taking the user's emotions into consideration.

[0569] "Communication means" refers to devices or methods for receiving attribute information from users and exchanging information bidirectionally with a server.

[0570] "Processing means" refers to a method or apparatus that calculates the optimal asset allocation by applying a generative model based on received attribute information.

[0571] "Display means" refers to devices or methods for presenting the asset allocation determined by the generative model to the user.

[0572] An "emotional engine" is a device or method designed to evaluate and analyze a user's emotional state.

[0573] A "user" is an entity that provides attribute information and receives advice from the system.

[0574] A "generative model" is a mathematical or computational method for calculating the optimal asset allocation using attribute information and market-related data.

[0575] "Attribute information" refers to personal data about the user, including information such as age, asset status, and investment goals.

[0576] "Market-related data" refers to information about current market conditions and economic indicators that are considered when calculating asset allocation.

[0577] One embodiment of the present invention requires a server. This server comprises several program modules, including an emotion engine. The emotion engine is designed to infer the emotional state of a user and uses an existing dataset in combination with a machine learning algorithm (e.g., TensorFlow).

[0578] Users operate devices such as smartphones and computers to input attribute information. This information includes age, financial status, and investment goals. This information is transmitted to a server via communication means. The server stores the received attribute information in a database and uses an emotion engine to evaluate the user's emotional state.

[0579] Furthermore, the server uses a generative model to calculate the optimal asset allocation by combining attribute information, emotional states, and market-related data. This process uses AI technology to simulate multiple scenarios and generate the most effective asset strategy.

[0580] Finally, the calculation results are presented to the user in the form of advice. Through a display mechanism, suggestions including specific asset allocation and sentiment analysis results are sent to the user's device. This advice helps users avoid emotion-based misjudgments and make more logical decisions.

[0581] For example, suppose a user is planning to buy a new home appliance, and the emotion engine detects an optimistic emotional state. In this case, the server can then provide advice to reconsider the purchase, taking into account general market prices and past purchase data.

[0582] Example prompt: "Based on the user's recent emotional state and past purchase history, please suggest the best advice regarding the purchase of the following items."

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] Users enter attribute information using their smartphones or computers. This information includes age, financial status, and investment goals. The entered data is transmitted to the server via communication means. The attribute information is formatted and prepared for the next processing step.

[0586] Step 2:

[0587] The server stores the received attribute information in its own database. Processing this database enables centralized management of user data. This ensures that the base data necessary for analysis by the emotion engine is secured.

[0588] Step 3:

[0589] The server uses an emotion engine to infer the user's emotional state from the received attribute information. During this process, an AI algorithm operates based on past user input data and history to evaluate the current emotional state. The output is a parameter representing the user's emotional state, which is used in the subsequent asset allocation calculation.

[0590] Step 4:

[0591] Based on emotional state parameters and attribute information, the server uses a generative AI model to calculate the optimal asset allocation. Market-related data is also incorporated, and multiple scenarios are simulated to calculate an allocation strategy that takes risk into account. As a result of the calculation, a proposed asset allocation plan is generated.

[0592] Step 5:

[0593] The server processes the generated asset allocation plan into an advice format and sends it to the user's terminal. The user is then presented with specific asset allocations and sentiment analysis results via a display device. The outputted advice assists the user in decision-making and helps them avoid irrational judgments driven by emotions.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] This invention provides a system for optimizing asset management. Specifically, it optimizes a user's asset allocation using a generative AI model and presents the results to the user. This system consists of communication means, processing means, and display means.

[0612] First, the user enters their personal information using a terminal. This information includes the user's age, current asset status, and investment objectives. The terminal formats this information and sends it to the server via a communication method.

[0613] Next, the server processes the received attribute information and prepares it for input into the generative model. At this time, it also retrieves the latest market-related data via API. The generative model utilizes machine learning techniques to calculate the optimal asset allocation for the user. The calculated results are adjusted based on the user's risk tolerance and current market conditions.

[0614] This generated asset allocation information is transmitted from the server to the terminal and presented to the user through a display device. The presented information includes a detailed explanation of the optimal investment ratio suggested by the generation model, the reasoning behind it, and the anticipated risks. Based on this information, the user can make specific investment decisions through the 401K management site.

[0615] For example, if a 30-year-old user starts investing with the goal of saving for their child's education, the generated model might suggest a high-risk, high-return allocation based on their age and asset situation. In this case, the advice would likely recommend a high allocation to domestic stocks and a certain percentage of investment in bonds. The user, having received this information, can then adjust their actual investment settings by adjusting the proportions to specific asset classes.

[0616] In this way, the system provides an environment where even users without specialized knowledge can easily start using it, supporting effective asset building.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] Users input attribute information related to asset management (age, asset status, investment objectives, etc.) into a dedicated interface using their own devices.

[0620] Step 2:

[0621] The terminal converts the entered attribute information into a predetermined format and verifies the data's integrity. If there are no integrity issues, it sends the information to the server using a communication method.

[0622] Step 3:

[0623] The server receives attribute information sent from the terminal and securely stores it in the database. After confirming that the information has been successfully received, it communicates with external systems such as APIs to obtain market-related data necessary for processing.

[0624] Step 4:

[0625] The server preprocesses the received attribute information and acquired market-related data, preparing them for input into the generative model. Preprocessing includes data normalization and imputation of missing values.

[0626] Step 5:

[0627] The server uses a generative model to calculate the optimal asset allocation for the user. At this stage, the model generates an optimized investment plan based on the user's risk tolerance and objectives.

[0628] Step 6:

[0629] The server converts the asset allocation information obtained from the generative model into an advice format that is easy for the user to understand. This advice includes explanations of risk assessments and investment strategies.

[0630] Step 7:

[0631] The server sends asset allocation information, including advice, to the terminal. Security measures, such as data encryption, are applied during transmission.

[0632] Step 8:

[0633] The terminal displays the received advice to the user. Based on the optimization information presented, the user decides to take specific operational procedures.

[0634] (Example 1)

[0635] 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".

[0636] Optimizing asset management requires specialized knowledge and an understanding of market trends, making it difficult for many users to make sufficiently appropriate decisions. As a result, the effectiveness of asset management may decrease, and the expected results may not be achieved. Furthermore, it is necessary to quickly present asset allocations that reflect the latest market information while utilizing user-specific attribute information.

[0637] 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.

[0638] In this invention, the server includes information communication means for receiving attribute information acquired from the user, information processing means for applying a generative model that combines market-related data to determine the optimal asset allocation, and information display means for visually presenting the determined asset allocation. This makes it possible for users to effectively manage their assets even without specialized knowledge.

[0639] "Information and communication means" refers to a means for receiving attribute information from users, communicating with external data providers as needed, and exchanging information bidirectionally.

[0640] "Information processing means" refers to a means for determining the optimal asset allocation by combining received attribute information and market-related data and applying a generative model.

[0641] An "information display means" is a means of visually presenting the asset allocation results determined by a generative model to the user, and providing detailed information including the reasons and risks.

[0642] A "generative model" is an algorithm that uses machine learning techniques to calculate the optimal asset allocation for a user.

[0643] "Market-related data" refers to data obtained from external data providers, such as stock indices, exchange rates, and interest rate information, that pertain to external factors influencing asset management.

[0644] "Attribute information" refers to personal information provided by users, such as age, asset status, and investment objectives, which is necessary to determine individual asset allocation.

[0645] This invention provides a system that uses user attribute information and market-related data to generate an AI model and present an optimal asset allocation. Its specific form is described below.

[0646] Users input their personal information using a terminal equipped with an interface. This terminal is equipped with application software that provides a format to facilitate data entry. The inputted personal information includes the user's age, asset status, and investment purpose.

[0647] The terminal converts the entered attribute information into a standardized data format and sends it to the server via the internet. This transmission is performed using a secure and reliable protocol (e.g., HTTPS).

[0648] The server analyzes the received attribute information and also acquires market-related data via an external data provider. This market-related data includes financial data necessary for calculating asset allocation, such as stock price indices, exchange rates, and interest rate information. After collecting this data, the server passes it to the generating AI model.

[0649] The generative AI model utilizes machine learning techniques (e.g., neural networks) to calculate the optimal asset allocation for the user. The generated asset allocation results are further adjusted based on the user's risk tolerance and market trends.

[0650] The adjusted results are sent back to the device and presented to the user visually. The device displays the results in an easy-to-understand format, using graphs and charts to explain the rationale and risks of the asset allocation. This allows users to intuitively understand the information and make decisions, even without specialized knowledge.

[0651] For example, if a 30-year-old user starts investing for educational fund purposes, the AI ​​model might suggest a high-risk, high-return allocation based on attribute information and market data. In this case, a high allocation to domestic stocks and a certain percentage of investment in bonds might be recommended. The user then uses this information to execute their specific investments through the 401K investment platform.

[0652] An example of a prompt would be: "If a 30-year-old user is starting to invest for the purpose of saving for their education, please suggest the optimal asset allocation. Please consider the current market conditions and the user's risk tolerance." Based on this prompt, the generating AI model calculates the asset allocation.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The user enters their personal information into the terminal's input screen. This personal information includes age, asset status, and investment objectives. The entered data is converted into digital data according to the terminal's formatting rules. The output is the formatted personal information.

[0656] Step 2:

[0657] The terminal receives formatted attribute information and transmits this information to the server using an information communication method. Secure internet protocols such as HTTPS are used for transmission. The output is a data packet containing the attribute information.

[0658] Step 3:

[0659] The server receives attribute information and prepares it for analysis. Here, data validation and normalization are performed, and missing or inaccurate data are corrected or imputed. The output is validated and normalized attribute information.

[0660] Step 4:

[0661] The server retrieves market-related data from an external data provider. This process involves API calls to obtain the latest financial data (stock indices, exchange rates, interest rates, etc.). The output is the most up-to-date market-related data.

[0662] Step 5:

[0663] The server combines attribute information and market-related data and inputs it into a generative AI model. The generative AI model uses machine learning techniques to calculate the optimal asset allocation based on the input data. As a result of this calculation, an investment strategy suitable for the user is generated. The output is the generated asset allocation result.

[0664] Step 6:

[0665] The server reviews the generated asset allocation results and makes further adjustments based on the user's risk tolerance and market conditions. This establishes the most appropriate investment strategy for the user. The output is the adjusted asset allocation.

[0666] Step 7:

[0667] The server sends the adjusted asset allocation to the terminal. A secure protocol is used for this communication, ensuring user privacy. The output is the asset allocation information sent to the user's terminal.

[0668] Step 8:

[0669] The terminal visually displays the received asset allocation information, visualizing the data as charts and graphs. Based on this, users can check detailed information (risk assessment and underlying reasons). The output is the visualized asset allocation information presented to the user.

[0670] Step 9:

[0671] Based on the asset allocation information presented on the device, users make specific investment decisions. This includes adjusting different investment ratios and making investment decisions through the 401K management platform. The output is the user's final investment decision.

[0672] (Application Example 1)

[0673] 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".

[0674] Modern consumers face the challenge of difficulty in obtaining optimal investment strategies in real time by leveraging their personal attribute information and electronic transaction history. Traditional asset management systems cannot provide real-time advice tailored to each user's individual transaction situation, making it difficult to optimize asset management. Therefore, there is a need for a system that provides personalized investment advice in real time.

[0675] 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.

[0676] In this invention, the server includes communication means for receiving attribute information and electronic transaction history acquired from the user, processing means for applying a generative model for determining the optimal asset allocation based on the attribute information and market-related information, and display means for presenting the asset allocation determined by the generative model to the user and providing real-time advice. As a result, consumers can receive optimized asset management and real-time advice based on attribute information and transaction history, enabling them to execute more effective investment strategies.

[0677] "Attribute information" refers to information related to an individual user, such as data on age, asset status, and investment objectives.

[0678] "Electronic transaction history" refers to a record of an electronic economic activity conducted by a user, including information such as purchase history and details of expenditures.

[0679] "Communication means" refers to a system for sending and receiving information bidirectionally via a network such as the internet.

[0680] "Market-related information" refers to various data concerning financial markets, including information such as stock prices, exchange rates, and economic indicators.

[0681] A "generative model" is an algorithm built using machine learning techniques to calculate the optimal asset allocation based on specific input data.

[0682] The "processing means" refers to the system component for analyzing data based on received information, and has the function of performing calculations, including the application of generative models.

[0683] "Display means" refers to an interface that visually presents calculated results or suggested information to the user.

[0684] "Real-time advice" refers to immediate investment suggestions tailored to the user's current financial situation and market data.

[0685] The system that realizes this invention utilizes user attribute information and electronic transaction history to propose an optimal asset allocation. The system is equipped with a communication means that receives information from users via smartphones and communication terminals and transmits it to a server. The server uses a high-performance processing unit and applies a generated AI model utilizing machine learning libraries such as Python and TensorFlow to analyze the collected data.

[0686] First, users enter attribute information such as their age, asset status, and investment objectives through a smartphone application. This information is transmitted to the server via the internet. Next, the server retrieves the latest market-related information via an API and prepares to integrate this data and input it into a generative model.

[0687] The data is processed by a generative AI model, and real-time asset allocation is determined based on the user's individual investment situation. This asset allocation is sent back to the smartphone in JSON format and displayed. Real-time advice is updated instantly based on electronic trading history using data streaming technologies such as Firebase.

[0688] As a concrete example, a generative AI model might provide advice such as, "I recommend reinvesting a certain percentage of your total purchases at convenience stores this week into stocks." Another example of a prompt to the generative AI model is, "Given the user's attribute information (age: 30, asset status: 5 million yen, investment purpose: education funds), please propose the optimal asset allocation taking into account the latest market data."

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] Users input attribute information such as age, asset status, and investment objectives using a smartphone application. This input information is transmitted to a server via communication means. This input information serves as the basic data necessary for optimizing asset allocation.

[0692] Step 2:

[0693] The server retrieves the latest market-related data via an API. This data includes financial market information such as stock prices and exchange rates. This market-related data is combined with user attribute information and used to create input datasets for generative models.

[0694] Step 3:

[0695] The server uses Python and machine learning libraries such as TensorFlow to input user attribute information and market-related data into an AI model. Based on this input data, the model calculates the optimal asset allocation. This calculation result is adjusted to account for the user's individual risk tolerance and market conditions.

[0696] Step 4:

[0697] The server formats the optimal asset allocation results obtained by the generated AI model into JSON format and sends them to the smartphone. These results are displayed visually in the application on the smartphone. The display includes details of the investment proportions, recommended investment strategies, and explanations of the anticipated risks.

[0698] Step 5:

[0699] Users can view asset allocation information displayed on their smartphones and monitor their electronic transaction history in real time. Using Firebase or similar data streaming technologies, they receive real-time investment advice based on their history. Based on this advice, users can then take specific investment actions.

[0700] 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.

[0701] This invention provides a system for more precisely optimizing asset management, and in particular, a system that can make investment decisions while taking into account the user's emotional state. The system is composed of communication means, processing means, display means, and an emotion engine.

[0702] First, the user operates the terminal to input attribute information related to their asset management. This includes age, asset status, and investment goals. Once the input is complete, the terminal formats the information and sends it to the server via a communication method.

[0703] Next, the server receives the transmitted attribute information and stores it in the database. Simultaneously, the emotion engine analyzes the user's past input data and history to infer their current emotional state. This analysis result is provided to the processing unit as an emotional state parameter.

[0704] Based on this emotional state and attribute information, the server uses a generative model to calculate the optimal asset allocation. The emotional state contributes to adjusting the risk tolerance of the investment; for example, a more conservative allocation is set when the user is unwilling to take risks. By combining this with market-related data, an even more accurate asset allocation is proposed.

[0705] The server then processes the calculation results into an advice format and sends it to the terminal. The advice includes details of the generated asset allocation and the results of the associated sentiment analysis, helping the user understand how they should be configured.

[0706] As a concrete example, consider a 40-year-old user who wants to invest their assets with the aim of short-term returns. If the emotion engine recognizes anxiety about the current market, in addition to the usual information, the generative model will suggest a conservative allocation with reduced risk. In this case, advice such as increasing the proportion of bonds may be provided. In this way, the user can avoid emotional misjudgments and achieve logical yet emotionally balanced asset management.

[0707] This system supports scientific and rational operation while incorporating human factors such as emotional fluctuations.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The user uses their device to input attribute information related to asset management (age, asset status, investment goals, etc.) into the interface. They also input a short questionnaire and feedback to assess their emotions.

[0711] Step 2:

[0712] The terminal formats the entered information and verifies data integrity. After verification, it sends the information to the server using a communication method.

[0713] Step 3:

[0714] The server stores attribute information and sentiment data received from the terminal in a database. After receiving the data, it communicates with relevant APIs to retrieve the latest market-related data.

[0715] Step 4:

[0716] The server runs an emotion engine that analyzes the user's current emotional state using received information and historical data. The analysis results are used to adjust risk tolerance.

[0717] Step 5:

[0718] The server inputs attribute information, emotional states, and market-related data into a generative model to determine the optimal asset allocation. Here, emotional analysis is incorporated to dynamically adjust the user's risk tolerance and generate an individually tailored investment strategy.

[0719] Step 6:

[0720] The server converts the generated asset allocation results into text-based advice, creating content that is easy for the user to understand. The advice also includes explanations related to the estimated emotional state.

[0721] Step 7:

[0722] The server sends an advice message to the terminal. Communication is conducted quickly while ensuring security.

[0723] Step 8:

[0724] The terminal displays the received advice and asset allocation information to the user. Based on this, the user can more carefully visit the 401K management site and proceed with the switching procedure.

[0725] (Example 2)

[0726] 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".

[0727] In optimizing asset management, there is a need for a system that can provide more precise and personalized asset allocation proposals while preventing misjudgments based on the user's emotions. Existing methods do not take into account the user's emotional state and are insufficient in adjusting risk tolerance.

[0728] 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.

[0729] In this invention, the server includes an analysis means for inferring the user's emotional state, a processing means for combining the inferred emotional state with attribute information and applying a generative model to determine the optimal asset allocation, and a display means for presenting the asset allocation determined by the generative model to the user in a visual display format. This makes it possible to propose a highly optimized asset allocation that takes into account the individual emotional state of each user.

[0730] "Analysis means" refers to a device or method that has the function of inferring the emotional state of a user from past data and behavioral history.

[0731] "Attribute information" refers to data that shows details about a user's asset management, including age, asset status, and investment goals.

[0732] A "generative model" is an algorithm or program that calculates the optimal asset allocation based on the user's attribute information and emotional state.

[0733] A "visual display format" is a format that uses visual elements such as graphs and charts to display the results of the generated asset allocation proposal in a user-friendly way.

[0734] "Communication means" refers to a device or method that utilizes network technologies such as the Internet for the bidirectional exchange of data.

[0735] "Market-related information" refers to data on fluctuations and trends in the stock market and financial markets, and is used to optimize asset allocation.

[0736] The embodiment for carrying out this invention is configured as follows.

[0737] First, the user operates a device, such as a smartphone or computer, to input attribute information related to their asset management. This information includes age, asset status, and investment goals. This initial information is entered through a user-friendly interface provided by the application on the device.

[0738] Next, the terminal receives attribute information obtained from the user and formats it into an appropriate data format, such as JSON. At this stage, the terminal utilizes its communication capabilities to send the organized information to the server via the internet. Standard internet protocols are used for communication.

[0739] The server stores the received attribute information in a database. Relational database management systems such as MySQL or PostgreSQL are used as the database. Simultaneously, the server runs an emotion engine to analyze and infer the current emotional state based on past data and user history. The emotion engine utilizes text analysis libraries and machine learning frameworks, such as NLTK or Scikit-learn.

[0740] Subsequently, the server uses a generative AI model to calculate the optimal asset allocation, taking the inferred emotional state and user attribute information as input. The generative AI model is built using deep learning frameworks such as TensorFlow or PyTorch. Emotional state plays a particularly important role in this process; for example, if the user is determined to be in an emotional state of risk aversion, a more conservative asset allocation will be concluded.

[0741] The calculated asset allocation results are processed by the server into advice and sent to the terminal for visual display. The terminal displays this advice using graphs and charts, providing the user with reference information to make investment decisions. Tools such as Python's Matplotlib and JavaScript's D3.js are used for specific visualizations.

[0742] As a concrete example, suppose a 40-year-old user is considering asset management aimed at short-term returns. If this user's emotional engine detects market anxiety, the generative model may suggest a low-risk asset allocation and advise increasing the proportion of safe assets such as bonds. In this way, the user can engage in rational asset management that takes their emotions into consideration.

[0743] An example of a prompt message to use with a generative AI model might be, "Calculate the optimal asset allocation considering the user's emotional state."

[0744] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0745] Step 1:

[0746] The user enters attribute information related to asset management using a terminal. This information includes the user's age, asset status, and investment goals. This attribute information is collected through an input form on the terminal and formatted in JSON format. Once the input is complete, the terminal sends the data to the server via the internet.

[0747] Step 2:

[0748] The server analyzes attribute information received from the terminal and stores it in a database. This database creates user-specific records based on a data structure generated using SQL queries. Simultaneously, the server prepares input data to infer emotional states by comparing past data and logs to run the emotion engine.

[0749] Step 3:

[0750] The server uses an emotion engine to infer the user's current emotional state based on collected data. This inference of emotional state employs machine learning algorithms and analyzes text and historical data using natural language processing libraries. The output of this calculation generates emotional state parameters, such as risk tolerance.

[0751] Step 4:

[0752] The server passes emotional state and attribute information as input data to a generating AI model to calculate the optimal asset allocation. This model is implemented using a deep learning framework and adjusts the balance of risk and return according to the emotional state. As a result of the calculation, a proposed portfolio is output, showing its composition ratios and details.

[0753] Step 5:

[0754] The server processes the generated asset allocation results into a visual advice format. This process uses data visualization tools, such as Matplotlib, to generate graphs. The generated advice is then sent to the user's terminal in an easy-to-understand format.

[0755] Step 6:

[0756] The terminal displays advice sent from the server on the user interface. The information displayed on the terminal includes details of the proposed asset allocation and sentiment analysis results. Based on this, the user can consider an asset management strategy and make decisions.

[0757] (Application Example 2)

[0758] 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".

[0759] In today's consumer environment, consumers are often influenced by their emotions at the time of purchase, making them prone to irrational decisions. Therefore, there is a need for systems that support purchasing decisions based on the consumer's emotional state. In particular, there is a need to evaluate the impact of emotions on purchasing decisions and provide more appropriate purchasing advice.

[0760] 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.

[0761] In this invention, the server includes means equipped with an emotion engine for evaluating and analyzing emotional states, communication means for receiving attribute information obtained from a user, and processing means for applying a generative model to determine the optimal asset allocation based on the attribute information. This makes it possible to provide appropriate advice regarding purchase decisions while taking the user's emotions into consideration.

[0762] "Communication means" refers to devices or methods for receiving attribute information from users and exchanging information bidirectionally with a server.

[0763] "Processing means" refers to a method or apparatus that calculates the optimal asset allocation by applying a generative model based on received attribute information.

[0764] "Display means" refers to devices or methods for presenting the asset allocation determined by the generative model to the user.

[0765] An "emotional engine" is a device or method designed to evaluate and analyze a user's emotional state.

[0766] A "user" is an entity that provides attribute information and receives advice from the system.

[0767] A "generative model" is a mathematical or computational method for calculating the optimal asset allocation using attribute information and market-related data.

[0768] "Attribute information" refers to personal data about the user, including information such as age, asset status, and investment goals.

[0769] "Market-related data" refers to information about current market conditions and economic indicators that are considered when calculating asset allocation.

[0770] One embodiment of the present invention requires a server. This server comprises several program modules, including an emotion engine. The emotion engine is designed to infer the emotional state of a user and uses an existing dataset in combination with a machine learning algorithm (e.g., TensorFlow).

[0771] Users operate devices such as smartphones and computers to input attribute information. This information includes age, financial status, and investment goals. This information is transmitted to a server via communication means. The server stores the received attribute information in a database and uses an emotion engine to evaluate the user's emotional state.

[0772] Furthermore, the server uses a generative model to calculate the optimal asset allocation by combining attribute information, emotional states, and market-related data. This process uses AI technology to simulate multiple scenarios and generate the most effective asset strategy.

[0773] Finally, the calculation results are presented to the user in the form of advice. Through a display mechanism, suggestions including specific asset allocation and sentiment analysis results are sent to the user's device. This advice helps users avoid emotion-based misjudgments and make more logical decisions.

[0774] For example, suppose a user is planning to buy a new home appliance, and the emotion engine detects an optimistic emotional state. In this case, the server can then provide advice to reconsider the purchase, taking into account general market prices and past purchase data.

[0775] Example prompt: "Based on the user's recent emotional state and past purchase history, please suggest the best advice regarding the purchase of the following items."

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] Users enter attribute information using their smartphones or computers. This information includes age, financial status, and investment goals. The entered data is transmitted to the server via communication means. The attribute information is formatted and prepared for the next processing step.

[0779] Step 2:

[0780] The server stores the received attribute information in its own database. Processing this database enables centralized management of user data. This ensures that the base data necessary for analysis by the emotion engine is secured.

[0781] Step 3:

[0782] The server uses an emotion engine to infer the user's emotional state from the received attribute information. During this process, an AI algorithm operates based on past user input data and history to evaluate the current emotional state. The output is a parameter representing the user's emotional state, which is used in the subsequent asset allocation calculation.

[0783] Step 4:

[0784] Based on emotional state parameters and attribute information, the server uses a generative AI model to calculate the optimal asset allocation. Market-related data is also incorporated, and multiple scenarios are simulated to calculate an allocation strategy that takes risk into account. As a result of the calculation, a proposed asset allocation plan is generated.

[0785] Step 5:

[0786] The server processes the generated asset allocation plan into an advice format and sends it to the user's terminal. The user is then presented with specific asset allocations and sentiment analysis results via a display device. The outputted advice assists the user in decision-making and helps them avoid irrational judgments driven by emotions.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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."

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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 as being incorporated by reference.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A communication means for receiving attribute information obtained from the user,

[0811] Processing means for applying a generative model to determine the optimal asset allocation based on the attribute information,

[0812] A display means for presenting the asset allocation determined by the aforementioned generation model to the user,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the processing means determines the optimal asset allocation by combining the attribute information with market-related data.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the communication means is capable of exchanging information bidirectionally with the user's terminal via the Internet.

[0818] "Example 1"

[0819] (Claim 1)

[0820] Information and communication means for receiving attribute information obtained from users,

[0821] Information processing means that combines the aforementioned attribute information and market-related data obtained from an external data provider to apply a generative model for determining the optimal asset allocation,

[0822] Information display means for visually presenting the asset allocation determined by the aforementioned generation model,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the information display means visually provides details regarding the reasons for asset allocation and the associated risks.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the information communication means can exchange information bidirectionally with the user's computing device via the Internet.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] A communication means for receiving attribute information and electronic transaction history obtained from users,

[0831] Processing means for applying a generative model to determine the optimal asset allocation based on the aforementioned attribute information and market-related information,

[0832] A display means for presenting the asset allocation determined by the aforementioned generation model to the user and providing real-time advice,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the processing means determines the optimal asset allocation based on both the attribute information and market-related information, and provides real-time investment suggestions by analyzing the electronic transaction history.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the communication means is capable of exchanging information bidirectionally with the user's communication terminal via the Internet and acquiring financial data including the user's transaction information.

[0838] "Example 2 of combining an emotion engine"

[0839] (Claim 1)

[0840] Analytical means for inferring the emotional state of users,

[0841] A processing means that combines the emotional state inferred by the analysis means with the attribute information and applies a generative model for determining the optimal asset allocation,

[0842] A display means for presenting the asset allocation determined by the aforementioned generation model to the user in a visual display format,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the processing means determines the optimal asset allocation by combining the attribute information and emotional state with market-related information.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the communication means is capable of exchanging information with a remote control device using bidirectional data communication.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A communication means for receiving attribute information obtained from the user,

[0851] Processing means for applying a generative model to determine the optimal asset allocation based on the attribute information,

[0852] An emotion engine for evaluating and analyzing emotional states,

[0853] A display means for presenting the asset allocation determined by the aforementioned generation model to the user,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the processing means determines the optimal asset allocation by combining the attribute information with market-related data and emotional state.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the communication means is capable of exchanging information bidirectionally with a user's information device via a communication network. [Explanation of Symbols]

[0859] 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 communication means for receiving attribute information obtained from the user, Processing means for applying a generative model to determine the optimal asset allocation based on the attribute information, A display means for presenting the asset allocation determined by the aforementioned generation model to the user, A system that includes this.

2. The system according to claim 1, wherein the processing means determines the optimal asset allocation by combining the attribute information with market-related data.

3. The system according to claim 1, wherein the communication means is capable of exchanging information bidirectionally with the user's terminal via the Internet.

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