System

Abstract

A system is provided.SOLUTION: The system according to claim 1, further comprising: means for determining a risk tolerance of the user and analyzing a gap between the current assets and the target amount based on the stored information; means for calculating an optimal allocation of the generated AI based on the determined risk tolerance and the analyzed result; means for notifying the user of the optimal allocation; and means for receiving and recording the allocation adjusted by the user in accordance with the notified allocation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Many people who participate in corporate defined contribution pension plans (401K) lack knowledge about asset management and are therefore unable to actively manage their assets. Finding the optimal asset allocation is particularly difficult for participants who do not have time to research asset management methods in their daily work and life. Furthermore, many participants find it troublesome to manage their own assets, resulting in them not being able to maximize investment gains. The challenge is to effectively solve these problems and enable participants to easily manage their assets optimally. [Means for solving the problem]

[0005] The present invention provides a system including: a means for a user to input data regarding age, current assets, target amount, and investment preference; a means for receiving the data input by the user and storing it in a database; a means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; a means for a generating AI to calculate an optimal asset allocation based on the results of the calculation and analysis; a means for notifying the user of the optimal asset allocation; and a means for receiving and recording an investment allocation adjusted by the user in accordance with the notified asset allocation. This system allows users to easily determine an asset allocation that is suitable for them and supports them in maximizing the investment returns of their corporate defined contribution pension plans. Furthermore, if the user approves the proposed investment allocation, the system further includes a means for providing a link and guide to the company's investment website, thereby reducing the complexity of the investment procedure.

[0006] "User" refers to an individual who is enrolled in a corporate defined contribution pension plan (401K).

[0007] "Age" refers to the user's age calculated from their date of birth.

[0008] "Current assets" refers to the total amount of assets in the corporate defined contribution pension plan currently held by the user.

[0009] The "target amount" refers to the amount of assets that the user wishes to achieve in the future.

[0010] "Management orientation" refers to an investment policy that reflects the user's investment style and risk tolerance.

[0011] "Data input means" refers to an interface for a user to input data regarding age, current assets, target amount, and investment orientation.

[0012] "Receiving means" refers to the function of the server receiving data entered by the user.

[0013] "Database" refers to a system that stores and manages received user data.

[0014] The "initial processing means" refers to the process of calculating risk tolerance and performing gap analysis based on received user data.

[0015] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and calculate optimal asset management allocations for users.

[0016] "Investment allocation notification means" refers to the function that notifies the user of the optimal asset investment allocation calculated by the generation AI.

[0017] "Adjustment means" refers to a function that allows the user to adjust the investment allocation in accordance with the notified asset investment allocation.

[0018] "Switching tools" refers to a function that provides guides and links to help users reflect their confirmed investment allocations on their company's 401K management site. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0033] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Specifically, it involves a series of processes in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generation AI calculates the optimal asset allocation based on that data. The processing of the program of this system is explained below with specific examples.

[0041] System Embodiments

[0042] 1. Data Entry

[0043] Device: Using a smartphone or PC application, users input their age, current assets, target amount, and investment preference (e.g., stable, balanced, aggressive).

[0044] Server: Receives the data entered by the device and stores it in a database.

[0045] 2. Initial Processing

[0046] Server: Calculates risk tolerance based on received user data. For example, if the user's age is 30, the risk tolerance is calculated to be relatively high. Also, if the user's current assets are 5 million yen and the target amount is 20 million yen, the gap is analyzed.

[0047] 3. Analysis by generative AI

[0048] Server: Based on the results of the initial processing, the generation AI analyzes the data based on the user's age, current assets, target amount, and investment preferences. For example, the generation AI calculates the optimal ratio of domestic stocks, international stocks, and bonds. For a young person with a balanced investment preference, the AI ​​may suggest an allocation of 40% domestic stocks, 40% international stocks, and 20% bonds.

[0049] 4. Advice Generation and Notification

[0050] Server: Based on the optimal investment allocation calculated by the generation AI, the server proposes specific investment details to the user. This advice includes the names of investment products and their purchase ratios.

[0051] Device: The generated advice is sent to the user's device and displayed on the application.

[0052] 5. User Feedback

[0053] Terminal: The user reviews the proposed investment allocation and adjusts their portfolio as needed. If the user approves the adjusted allocation, the information is sent to the server.

[0054] Server: Records approved operational allocations and performs re-analysis if necessary.

[0055] 6. Switching Procedures

[0056] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[0057] User: Follow the provided guide to access the company's 401K management site and perform the switching procedure based on the optimal investment allocation.

[0058] Specific examples

[0059] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[0060] 1. Device: The user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation and balanced orientation into the smartphone app.

[0061] 2. Server: Receives the data sent from the device and stores it in a database. Based on age, the risk tolerance is calculated as "medium," and the gap between current assets and the target amount is calculated as 15 million yen.

[0062] 3. Server: The generative AI analyzes the data and calculates the optimal asset allocation, for example, 40% domestic stocks, 40% international stocks, and 20% bonds.

[0063] 4. Server: Based on the calculation results, the server generates advice including specific investment details (for example, specific domestic stocks or bond product names) and notifies the user.

[0064] 5. Device: The user reviews the advice and either accepts the proposed allocation or fine-tunes it. If the user approves the allocation, the information is sent to the server.

[0065] 6. Server: Records approved allocations and performs reanalysis as needed. Additionally, provides links to 401K management sites and guides to simplify the switching process.

[0066] 7. User: Follow the provided guide to access the production site and perform the switching procedure with the new allocation.

[0067] As described above, this system includes specific means for providing users with optimal asset management allocations in a simple and effective manner, and supports maximizing the investment returns of corporate defined contribution pension plans.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0071] Step 2:

[0072] The terminal receives the data entered by the user and temporarily stores it in local memory. When the user confirms the data and presses the send button, the data is sent to the server.

[0073] Step 3:

[0074] The server receives the data sent from the device and stores it in a database for subsequent processing.

[0075] Step 4:

[0076] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium."

[0077] Step 5:

[0078] The server calculates the asset gap based on the user's current assets and target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the gap is calculated as 15 million yen.

[0079] Step 6:

[0080] The server sends the results of the initial processing to the generation AI, which then begins analysis to calculate the optimal asset allocation based on the user data.

[0081] Step 7:

[0082] The server receives the analysis results returned by the generation AI and generates a specific investment breakdown, such as advice like "40% domestic stocks, 40% international stocks, 20% bonds."

[0083] Step 8:

[0084] The server sends information to the terminal to notify the user of the generated advice.

[0085] Step 9:

[0086] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[0087] Step 10:

[0088] The user adjusts the investment allocation based on the advice and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[0089] Step 11:

[0090] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[0091] Step 12:

[0092] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[0093] Step 13:

[0094] The terminal displays the received links and guides to the user.

[0095] Step 14:

[0096] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching process.

[0097] Example 1

[0098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0099] In today's asset management environment, it is difficult for users to find the optimal asset allocation on their own, especially in systems like corporate defined contribution pension plans (401K), which require specialized knowledge. Furthermore, it is necessary to process a large amount of information at once to develop an appropriate investment strategy, which is time-consuming, and there are limited tools available to help users find the optimal solution for their risk and goals. This hinders users from optimal asset management.

[0100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0101] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generative AI model to calculate an optimal asset allocation based on the calculation and analysis results; means for inputting a prompt sentence to the generative AI model; means for notifying the user of the optimal asset allocation; and means for receiving and recording the investment allocation adjusted by the user in accordance with the notified asset allocation. This allows the user to find an optimal asset allocation simply and effectively.

[0102] "Users" are participants in corporate defined contribution pension plans (401K) who use the system to find asset allocations.

[0103] "Age" is information indicating the current age of the user.

[0104] "Current assets" is information indicating the current total amount of assets held by the user.

[0105] The "target amount" is information indicating the amount of money that the user wants to achieve as a result of asset management.

[0106] "Management orientation" is information indicating the asset management policy desired by the user, and there are types such as "stable orientation," "balanced orientation," and "aggressive."

[0107] "Data input means" refers to a terminal or application that allows a user to input their age, current assets, target amount, and investment preference.

[0108] "Data receiving and storing means" refers to the server's function of receiving data sent by users and storing it in a database.

[0109] The "risk tolerance calculation means" refers to the server's function of calculating how much risk a user can tolerate based on data such as the user's age and investment preferences.

[0110] "Gap analysis means" refers to a function of the server for analyzing the difference between the user's current assets and the target amount.

[0111] A "generative AI model" refers to an artificial intelligence model that calculates optimal asset management allocations based on user data.

[0112] A "prompt sentence" refers to the input sentence provided to a generative AI model when requesting data analysis.

[0113] "Optimal investment allocation notification means" refers to the server's function of notifying users of the optimal asset investment allocation calculated by the generative AI model.

[0114] The "means for receiving and recording operational allocation" refers to the function of the server that receives operational allocation adjusted by the user and records it.

[0115] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. This system involves a process in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generative AI model calculates the optimal asset allocation based on the data.

[0116] Hardware and software used

[0117] 1. Device:

[0118] Users enter data using an application on their smartphone or PC, which then sends information about the user's age, current assets, target amount, and investment preference to the server.

[0119] Specific software used for mobile application development is Swift (for iOS) and Java (for Android), while HTML, CSS, and JavaScript are used for web application development.

[0120] 2. Server:

[0121] The server receives the data sent by the user and stores it in a database, such as MongoDB or SQL Server.

[0122] The server calculates the user's risk tolerance based on the received data and uses languages ​​such as Python and R to analyze the gap between the current assets and the target amount. These calculations use data processing libraries such as Pandas and NumPy.

[0123] A generative AI model (e.g., OpenAI's GPT series) is used to analyze user data and calculate optimal asset allocation. The generative AI model uses an API, and the optimal allocation is obtained by entering a prompt.

[0124] Process example

[0125] 1. Data Entry:

[0126] Device: The user launches the smartphone application and inputs their age, current assets, target amount, and investment orientation (e.g., "30 years old," "5 million yen," "20 million yen," "balanced orientation").

[0127] 2. Data Receipt and Storage:

[0128] Server: Receives data sent from the device and stores it securely in a MongoDB database using the HTTPS protocol.

[0129] 3. Risk Tolerance Calculation and Gap Analysis:

[0130] Server: Calculates the user's risk tolerance based on the received data. For example, if the user is 30 years old, the risk tolerance is calculated as "medium." Also, the gap between the current assets of 5 million yen and the target amount of 20 million yen is calculated as 15 million yen.

[0131] 4. Data analysis with generative AI models:

[0132] Server: Enter the following prompt into the generative AI model:

[0133] "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is looking for a balanced investment. What is the optimal asset allocation?"

[0134] The generative AI model responds by providing an optimal asset allocation, such as 40% domestic stocks, 40% international stocks, and 20% bonds.

[0135] 5. Advice generation and notification:

[0136] Server: Based on the allocation obtained from the generative AI model, the server generates advice including specific investment products (e.g., domestic stocks: "ABC Company," international stocks: "XYZ Company," bonds: "Japanese Government Bonds") and pushes it to the user's device.

[0137] Device: The advice sent to the user's device can be displayed and the details can be checked.

[0138] 6. User Feedback and Records:

[0139] Terminal: The user checks the proposed allocation and adjusts it if necessary. Once the adjusted allocation is approved, the data is sent to the server.

[0140] Server: Records the approved data and sends prompts to the generation AI again if reanalysis is required based on user feedback.

[0141] This system not only helps users easily find the optimal asset allocation, but also provides specific and effective means to maximize the investment returns of defined contribution pension plans.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] Data Entry

[0145] Device: The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0146] Input: The user enters "30 years old," "5 million yen," "20 million yen," and "balanced" into the application.

[0147] Specific operation: The user enters the required data into the application's input form and clicks the "Submit" button, which sends the input data from the terminal to the server.

[0148] Output: The input data is sent to the server.

[0149] Step 2:

[0150] Data reception and storage

[0151] Server: The server receives the data sent from the device and stores it in a database such as MongoDB using the HTTPS protocol.

[0152] Input: The data "30 years old," "5 million yen," "20 million yen," and "balanced" are sent to the server.

[0153] What it does: The server temporarily stores the received data in memory and then saves it to a database using a secure protocol.

[0154] Output: User data stored in the database.

[0155] Step 3:

[0156] Risk tolerance calculation and gap analysis

[0157] Server: Based on the stored data, the server calculates the user's risk tolerance and analyzes the gap between the current assets and the target amount.

[0158] Input: Data stored in the database: "30 years old," "5 million yen," "20 million yen," and "balanced orientation."

[0159] Specific operation: Using Python's Pandas library, the conditional statement calculates the risk tolerance as "medium" if the user's age is 30. It also calculates the gap between the current assets of 5 million yen and the target amount of 20 million yen, determining it to be 15 million yen.

[0160] Output: Data for risk tolerance "medium" and gap "15 million yen".

[0161] Step 4:

[0162] Data analysis with generative AI models

[0163] Server: Based on risk tolerance and gap data, the server inputs prompt statements into the generative AI model to analyze the optimal asset allocation.

[0164] Input: Data for "30 years old", "5 million yen", "20 million yen", "Balanced", "Risk tolerance: Medium", "Gap: 15 million yen".

[0165] Specific operation: The following prompt is input to the generative AI model: "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is balanced. Please tell me the optimal asset allocation."

[0166] Output: Data obtained from the generative AI model: 40% domestic stocks, 40% international stocks, 20% bonds.

[0167] Step 5:

[0168] Advice generation and notification

[0169] Server: The server generates a specific investment breakdown based on the optimal allocation obtained from the generative AI model and notifies the user's device.

[0170] Input: Data of "40% domestic stocks, 40% foreign stocks, 20% bonds".

[0171] Specific operation: Based on the allocation data, a specific investment breakdown is generated, such as "Domestic stocks: ABC Company 40%," "International stocks: XYZ Company 40%," and "Bonds: Japanese government bonds 20%." This is then sent as a push notification to the user's device.

[0172] Output: Specific investment breakdown notified to the terminal.

[0173] Step 6:

[0174] User feedback and records

[0175] Terminal: The user checks the notified allocation and adjusts it if necessary. After approval, the adjusted allocation data is sent to the server.

[0176] Input: Adjusted allocation data such as "Domestic stocks: ABC Company 30%", "International stocks: XYZ Company 50%", and "Bonds: Japanese government bonds 20%".

[0177] What happens: The user adjusts the allocation in the application interface and presses the "Accept" button, which sends the data to the server.

[0178] Output: The adjusted allocation data is sent to the server and recorded.

[0179] Step 7:

[0180] Switching Procedures

[0181] Server: Based on the optimal allocation obtained from the generative AI model, the server provides users with links and guides to their company's 401K management site.

[0182] Input: Optimal allocation data and link information.

[0183] Specific operation: The server generates a guide document explaining the specific switching procedure for the user and a link to the operation site, and notifies the user's terminal.

[0184] Output: The guide document and links that the user receives.

[0185] Through these processing steps, users can easily find the optimal asset management allocation, providing specific and effective means for maximizing the investment returns of corporate defined contribution pension plans.

[0186] (Application example 1)

[0187] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0188] For participants in corporate defined contribution pension plans (401K), finding the optimal asset allocation for themselves requires knowledge and time. In particular, analysis and proposals based on multiple factors, such as age, current assets, target amount, and investment preferences, are required, and a simple and fast method for doing this is needed. Furthermore, the process for actually implementing the proposed investment allocation is complicated, and there is a lack of a means to smoothly complete the process. This creates challenges for participants, making it difficult to effectively optimize their investment allocation.

[0189] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0190] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generating AI to calculate an optimal asset allocation based on the calculation and analysis results; means for notifying the user of the optimal asset allocation; means for receiving and recording an investment allocation adjusted by the user in accordance with the notified asset allocation; means for providing a link and guide to a company's investment site based on the investment allocation approved by the user; and means for proposing an optimal asset allocation using a generating AI model on a smart device. This allows users to easily find an optimal asset allocation for themselves and quickly and easily put that investment allocation into practice.

[0191] A "user" is someone who uses a particular application or system.

[0192] A "smart device" is a portable electronic device that can connect to the Internet, such as a smartphone or tablet.

[0193] A "generative AI model" is a model that uses artificial intelligence algorithms to analyze data and generate optimal solutions to specific problems.

[0194] "Age" is information indicating the length of time since the user was born.

[0195] "Current assets" is information indicating the amount of total assets currently held by the user.

[0196] The "target amount" is information indicating the amount of assets that the user wishes to achieve in the future.

[0197] "Management preference" is data that indicates the investment risk profile and investment style preferred by the user.

[0198] A "database" is an organized digital storage system for storing user-entered data.

[0199] "Risk tolerance" is an indicator that indicates the investment risk level that a user can tolerate.

[0200] The "gap" indicates the difference between current assets and the target amount.

[0201] "Notification" is the act of providing information to a user from an application or system.

[0202] "Adjustment" refers to the act of the user making subtle changes to the proposed investment allocation.

[0203] A "link" is a reference to a particular website or online resource.

[0204] A "guide" is a document or tool that provides procedures or instructions for performing a particular operation or procedure.

[0205] This invention relates to a system for finding optimal asset allocation for participants of corporate defined contribution pension plans (401K). The system includes the following components:

[0206] 1. Data Entry:

[0207] Terminal: Users use a smart terminal to input data such as age, current assets, target amount, investment preference, etc. The application on the terminal guides them through this data input.

[0208] 2. Data Receipt and Storage:

[0209] Server: The entered data is sent to the server and stored in a database, which prepares the data necessary for subsequent processing.

[0210] 3. Risk Tolerance Calculation and Gap Analysis:

[0211] Server: Based on the saved data, calculate the user's risk tolerance and analyze the gap between their current assets and their target amount. For example, classify the risk tolerance as "high," "medium," or "low" based on the user's age, asset status, and investment objectives.

[0212] 4. Calculation of optimal asset allocation using generative AI:

[0213] Server: Using a generative AI model, it calculates the optimal asset allocation based on the results of calculations and analysis. For example, it proposes a specific investment allocation such as "40% domestic stocks, 40% international stocks, 20% bonds."

[0214] 5. Investment Allocation Notification:

[0215] Server: Once the optimal asset allocation is calculated, the server notifies the user of the results via their smart device. The user receives the notification and can check the proposed asset allocation on the application.

[0216] 6. User feedback and adjustments:

[0217] Terminal: The user can review the proposed allocation and make any necessary adjustments. If the user approves the adjustments, the information is sent to the server.

[0218] Server: Receives the adjusted investment allocation data, stores it in a database, and re-analyzes it as needed.

[0219] 7. Providing Links and Guides:

[0220] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[0221] Terminal: Using the provided links and guides, users can easily carry out the switching procedure.

[0222] Hardware and software used:

[0223] Hardware: Smart devices (e.g. smartphones, tablets)

[0224] Software: On the server side, we use web frameworks such as Flask to build the API, and OpenAI's API for data analysis.

[0225] Examples:

[0226] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and a balanced investment orientation, the generative AI model will use the following prompt to suggest the optimal asset allocation.

[0227] Example prompt sentence:

[0228] "If the user is 30 years old, has 5 million yen in current assets, 20 million yen in target assets, and has a balanced investment preference, please suggest the optimal asset allocation."

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] The user uses a smart device to enter their age, current assets, target amount, and investment preference into the application. This input data is entered using text fields and drop-down menus. The entered data is temporarily saved on the device. Input data: Age, current assets, target amount, investment preference. Output data: User data temporarily saved on the device.

[0232] Step 2:

[0233] Input data is sent from the terminal to the server. The server receives this data and saves it in the database. The server checks the integrity of the data and notifies the terminal that saving is complete. Input data: User data (age, current assets, target amount, investment orientation). Output data: User data saved in the database, notification of saving completion.

[0234] Step 3:

[0235] The server calculates the user's risk tolerance based on the data stored in the database. A fixed algorithm is used for the calculation to generate a risk profile that takes into account factors such as the user's age and financial situation. The result of this process is a calculated risk tolerance of either "high," "medium," or "low." Input data: User information in the database. Output data: Risk tolerance.

[0236] Step 4:

[0237] The server analyzes the gap between the current assets and the target amount. The required additional assets are calculated by subtracting the target amount from the current assets. This process calculates the increase in assets required for the user to reach the goal. Input data: User information in the database. Output data: Gap (additional assets).

[0238] Step 5:

[0239] The server uses a generative AI model to calculate the optimal asset management allocation based on the analysis results of risk tolerance and asset gap. The generative AI model uses OpenAI's API and generates a specific investment allocation (e.g., 40% domestic stocks, 40% international stocks, 20% bonds) using a specific prompt as input. Input data: risk tolerance, asset gap. Example prompt: "If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment preference, please suggest the optimal asset management allocation." Output data: optimal asset management allocation.

[0240] Step 6:

[0241] The server notifies the user's device of the calculated optimal asset allocation. This notification includes specific investment details and proposals. Input data: Optimal asset allocation. Output data: Notification to the user's device.

[0242] Step 7:

[0243] The terminal displays the received operational allocation notification to the user. The user can review this proposal and make fine adjustments as necessary. Once the user completes and approves the adjustments, the information is sent from the terminal to the server. Input data: Notified operational allocation, user adjustments. Output data: Approved operational allocation to the server.

[0244] Step 8:

[0245] The server generates a link to the company's operation site and a guide for switching procedures based on the operation allocation approved by the user, and sends them to the user's terminal. The link contains information that allows the user to easily access and operate it. Input data: Approved operation allocation. Output data: Link and guide.

[0246] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0247] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice. The program processing of this system is explained below with specific examples.

[0248] System Embodiments

[0249] 1. Data Entry

[0250] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0251] 2. Emotion recognition

[0252] The emotion engine built into the device analyzes the user's facial expressions and tone of voice while they are typing to recognize their current emotional state, collecting data such as whether they are feeling stressed or relaxed.

[0253] 3. Data Transmission

[0254] The terminal transmits the input data and the emotion data recognized by the emotion engine to the server.

[0255] 4. Data Storage

[0256] The server receives the data sent from the device and stores it in a database. The stored data includes age, current assets, target amount, investment preference, and emotional data.

[0257] 5. Initial Processing

[0258] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium." If the current assets are 5 million yen and the target amount is 20 million yen, the server analyzes the gap to be 15 million yen.

[0259] 6. Emotion correction

[0260] The server corrects the user's investment preference parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the server will correct the parameters to suggest low-risk investments.

[0261] 7. Generative AI Analysis

[0262] The server sends the corrected data to the generation AI, which then begins analysis to calculate the optimal asset management allocation.

[0263] 8. Advice Generation

[0264] The server generates a specific investment breakdown based on the analysis results returned by the AI. For example, it creates advice such as "40% domestic stocks, 40% international stocks, 20% bonds."

[0265] 9. Advice Notice

[0266] The server sends information to the terminal to notify the user of the generated advice.

[0267] 10. User Verification

[0268] The device displays the received advice to the user, who then confirms it. If the user is satisfied, they can approve the operational allocation as is, or make fine adjustments as necessary.

[0269] 11. Allocation Adjustments

[0270] The user adjusts the proposed operation details and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[0271] 12. Data Recording

[0272] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[0273] 13. Switching Procedures

[0274] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[0275] 14. User Execution

[0276] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching procedure with the new allocation.

[0277] Specific examples

[0278] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[0279] 1. On the terminal, the user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation / balanced orientation.

[0280] 2. The emotion engine analyzes the user's facial expressions and tone of voice during this input and recognizes that the user is feeling stressed.

[0281] 3. The device sends the input data and emotion data to the server.

[0282] 4. The server receives the input data and begins analysis.

[0283] 5. The server sets the risk tolerance to "medium" based on the user's age and calculates the gap between the current assets and the target amount to 15 million yen.

[0284] 6. The server adjusts the investment-oriented parameters based on the emotional data to suggest low-risk investments.

[0285] 7. The generation AI calculates the optimal asset management allocation based on the corrected data and suggests an allocation of, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[0286] 8. The server generates specific advice based on the calculation results and notifies the user.

[0287] 9. On the device, the user checks the advice and either accepts it or makes adjustments.

[0288] 10. The server records the final operational allocation and provides a link to guide the switching procedure.

[0289] 11. The user follows the provided link to complete the switching procedure based on the new allocation on the company's 401K management site.

[0290] In this way, by incorporating an emotion engine, the present invention is a system that can provide more precise asset management advice that takes into account the user's short-term emotional state, and can help maximize the investment returns of defined contribution pension plans.

[0291] The processing flow will be explained below.

[0292] Step 1:

[0293] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0294] Step 2:

[0295] The device temporarily stores the user's input data in its local memory. Once input is complete, the user presses the send button to send the data to the server.

[0296] Step 3:

[0297] The emotion engine installed in the device analyzes the user's facial expressions and tone of voice while inputting, and recognizes the user's emotional state (e.g., stress, relaxation, etc.). The emotion data is also simultaneously sent to the server.

[0298] Step 4:

[0299] The server receives the data sent from the terminal (age, current assets, target amount, investment orientation, emotional data) and stores it in a database.

[0300] Step 5:

[0301] The server calculates your risk tolerance based on your age. For example, if you are 30, your risk tolerance will be set to "medium."

[0302] Step 6:

[0303] The server calculates the asset gap based on the current assets and the target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the asset gap will be 15 million yen.

[0304] Step 7:

[0305] The server adjusts the user's operational parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the parameters are adjusted to reduce risk.

[0306] Step 8:

[0307] The server sends the corrected data to the AI, which then calculates the optimal asset allocation. For example, it suggests an allocation of 35% domestic stocks, 35% international stocks, and 30% bonds.

[0308] Step 9:

[0309] The server generates a detailed investment breakdown (for example, specific stocks and bond product names) based on the analysis results returned by the generation AI.

[0310] Step 10:

[0311] The server sends information to the terminal to notify the user of the generated advice.

[0312] Step 11:

[0313] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[0314] Step 12:

[0315] The user checks the proposed operation details, adjusts them, and then confirms the allocation. When the user presses the confirm button, the allocation data is sent from the terminal to the server.

[0316] Step 13:

[0317] The server receives the adjusted allocation of the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[0318] Step 14:

[0319] The server generates a link to the company's 401K operation site and a guide for the user to carry out the switching procedure, and transmits the generated link to the terminal.

[0320] Step 15:

[0321] The terminal displays the received links and guides to the user.

[0322] Step 16:

[0323] The user clicks on the provided link to access the company's 401K management site and follows the instructions to complete the switching procedure with the new allocation.

[0324] Example 2

[0325] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] Conventional asset management advice systems can only provide general advice based on the user's age and asset information, and are unable to provide advice that takes into account the user's individual emotional state. This makes it difficult to obtain optimal investment advice that reflects the user's emotions, such as suggesting high-risk investments even when the user is feeling stressed. Therefore, there is a need for a system that provides more personalized asset management advice that takes into account the user's emotional state.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for the user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; and means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount. This enables the risk profile to be corrected taking into account the user's emotional state. Furthermore, the server includes means for analyzing the user's facial expression and tone of voice during input and recognizing the user's current emotional state; means for correcting the user's investment orientation based on the recognized emotional state; and means for the generation AI to recalculate the optimal asset investment allocation using the corrected data. This makes it possible to provide personalized asset management advice based on the user's real-time emotional state.

[0328] "User" refers to an individual or corporation that uses this system.

[0329] "Age" is a data item that indicates the user's current age.

[0330] "Current assets" is a data item that indicates the amount of total assets currently owned by the user.

[0331] "Target amount" is a data item that indicates the amount of assets that the user wants to achieve in the future.

[0332] "Management orientation" is a data item that indicates the orientation regarding the investment style and risk tolerance desired by the user.

[0333] "Data input means" refers to an interface for a user to input data regarding age, current assets, target amount, and investment orientation.

[0334] "Data receiving means" refers to a means by which the server receives data input by the user.

[0335] "Database storage means" refers to means for storing received data in a database.

[0336] "Risk tolerance calculation means" refers to a means for calculating risk tolerance based on the user's age.

[0337] "Gap analysis tools" refer to tools for analyzing the difference between current assets and target amounts.

[0338] "Generative AI" refers to a system that uses artificial intelligence to calculate optimal asset management allocations.

[0339] "Emotion recognition means" refers to means for analyzing the facial expressions and tone of voice of the user while they are inputting information and recognizing the user's emotional state.

[0340] The "emotion correction means" refers to a means for correcting the user's operational orientation parameters based on the recognized emotional state.

[0341] The "asset management allocation notification means" refers to a means for notifying the user of the optimal asset management allocation.

[0342] The "management allocation receiving means" refers to a means for receiving the investment allocation adjusted by the user in accordance with the notified asset investment allocation.

[0343] "Investment allocation recording means" refers to a means for recording received investment allocations in a database.

[0344] "Recalculation means" refers to the means by which the generation AI recalculates the optimal asset management allocation using the corrected data.

[0345] The system of the present invention is designed to enable participants of corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice.

[0346] The main elements of the system consist of a user device, a server, and a generative AI model. User devices are general-purpose devices such as smartphones and PCs. The server is a high-performance computer system that uses MySQL as its database management system. For emotion recognition, a camera and microphone are used, and dedicated software for image and audio analysis is installed.

[0347] First, the user launches the application on their smartphone or PC and enters data such as their age, current assets, target amount, and investment preference. Next, while the user is entering information, the emotion engine analyzes their facial expressions and tone of voice to recognize their current emotional state. For example, if the user is frowning, it will be recognized as "stressed."

[0348] The device sends the input data and emotion data to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a database, calculates the user's risk tolerance based on their age, and analyzes the difference between their current assets and their target amount. For example, if a user is 30 years old, their current assets are 5 million yen, and their target amount is 20 million yen, the gap is calculated as 15 million yen.

[0349] The server adjusts the risk tolerance based on the emotional data. For example, if the user is stressed, it will recommend low-risk investments. The adjusted data is sent to the generative AI model, which calculates the optimal asset allocation. The prompt for the generative AI model is as follows:

[0350] User information: Age 30, current assets 5 million yen, target amount 20 million yen, investment orientation, balance orientation, emotional state: stress

[0351] Desired outcome: Recommend optimal asset allocation

[0352] The AI ​​then calculates the optimal asset allocation based on these prompts, returning a recommended allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds." The server receives the results and generates specific advice for the user. The generated advice is sent to the device, where the user can review it and either accept it as is or make fine adjustments as needed.

[0353] When the final allocation is determined, the server records it in the database, generates a link and guide to the company's 401K management site, and sends it to the terminal. The user clicks the provided link to access the company's 401K management site and completes the switching procedure with the new allocation.

[0354] As described above, the present invention is a system that provides precise asset management advice that takes into account the user's short-term emotional state, and maximizes the management efficiency of defined contribution pension plans.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] Step 1:

[0357] Data Entry

[0358] The user launches the application on their smartphone or PC and enters data on their age, current assets, target amount, and investment preference. Text boxes and pull-down menus are displayed in the input fields, and the user enters or selects the required information. Once the input is complete, the user clicks the submit button. The input data is used in the next step.

[0359] Input: Age, current assets, target amount, investment preference

[0360] Output: User input data

[0361] Step 2:

[0362] emotion recognition

[0363] The device's built-in emotion engine uses the camera and microphone to capture facial expressions and tone of voice while the user is entering data, and emotion recognition algorithms analyze this data to identify the user's current emotional state (e.g., "stressed" or "relaxed").

[0364] Input: User's facial expression data, tone of voice

[0365] Output: User emotion data

[0366] Step 3:

[0367] Data transmission

[0368] The device sends the data entered in step 1 and the emotion data recognized in step 2 to the server using a secure communication protocol (e.g., HTTPS). The data is packaged in JSON format packets and sent to the server.

[0369] Input: User input data, emotion data

[0370] Output: Data sent to the server

[0371] Step 4:

[0372] Data storage

[0373] The server receives the data packets sent from the device. The received data is stored in a database management system (e.g., MySQL). The database table contains user ID, age, current assets, target amount, investment orientation, and emotional data.

[0374] Input: Data sent from the terminal

[0375] Output: Data stored in the database

[0376] Step 5:

[0377] Initial Processing

[0378] The server calculates the risk tolerance level based on the user's age based on the stored data. At the same time, it calculates the difference (gap) between the current assets and the target amount. For example, if the user is 30 years old, the risk tolerance level is set to "medium," and the current assets are 5 million yen and the target amount is 20 million yen, the gap will be calculated as 15 million yen.

[0379] Input: User data read from the database

[0380] Output: Risk tolerance, gap analysis results

[0381] Step 6:

[0382] emotion correction

[0383] The server adjusts the risk tolerance based on the emotional data. For example, if the user is under stress, the server will adjust the risk tolerance to suggest lower-risk investments. The risk tolerance may be changed from "medium" to "low."

[0384] Input: Calculated risk tolerance, emotional data

[0385] Output: Adjusted risk tolerance

[0386] Step 7:

[0387] Generative AI analysis

[0388] The server sends the corrected data to the generative AI model and inputs a prompt to calculate the optimal asset management allocation. The prompt is "User information: age 30, current assets of 5 million yen, target amount of 20 million yen, investment orientation balanced orientation, emotional state: stressed. Desired result: Please suggest the optimal asset management allocation." The generative AI model calculates the optimal allocation based on this prompt.

[0389] Input: prompt statement

[0390] Output: Optimal asset allocation

[0391] Step 8:

[0392] Advice Generation

[0393] The server receives the results of the AI ​​generation and generates specific investment advice for the user. For example, it generates text and graphs suggesting an allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds."

[0394] Input: Investment allocation data from the generation AI

[0395] Output: Investment advice for the user

[0396] Step 9:

[0397] Advice Notice

[0398] The server sends the generated advice to the user's device, where it is displayed in a visually easy-to-understand format (graphs, text).

[0399] Enter: Investment Advice

[0400] Output: Advice notification to user terminal

[0401] Step 10:

[0402] User Verification

[0403] The terminal displays the received advice to the user. The user checks the displayed investment details and presses the "Approve" button if they are satisfied with the details, or the "Adjust" button if they wish to readjust.

[0404] Input: Investment advice from the server

[0405] Output: User review and approval or adjustment

[0406] Step 11:

[0407] Allocation adjustment

[0408] When the user presses the "Adjust" button, a screen will appear where they can adjust their investment details using sliders and text fields. The user can manually adjust the allocation to their desired level and press the "Confirm" button. The adjusted data will then be sent back to the server from the device.

[0409] Input: User's adjusted share

[0410] Output: Sends the allocated data to the server

[0411] Step 12:

[0412] Data Recording

[0413] The server receives the user's adjusted allocation data and records it in the database again. If necessary, it requests the generation AI to reanalyze it, generates new results, and notifies the user.

[0414] Input: User's adjusted allocation data

[0415] Output: Records in the database

[0416] Step 13:

[0417] Switching Procedures

[0418] The server creates a link to the company's 401K management site and a guide for the specific switching procedure, and sends it to the terminal. The procedure guide provides detailed explanations for each step.

[0419] Input: Final investment allocation

[0420] Output: Switching Procedure Guide

[0421] Step 14:

[0422] User execution

[0423] The user clicks on the provided link to access the company's 401K management site. Following the displayed guide, the user completes the switching procedure to the new allocation. Specifically, after logging in, the user enters the new asset allocation and presses the confirm / confirm button.

[0424] Input: Switching Procedure Guide

[0425] Output: The completed switching procedure

[0426] The above are the specific processing steps of the system of the present invention. Through this flow, personalized asset management advice that also takes into account the user's emotional state is provided.

[0427] (Application example 2)

[0428] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0429] Existing asset management support systems for corporate defined contribution pension plans (401K) provide investment advice without considering the user's emotional state, and therefore cannot suggest appropriate investment allocations when the user is stressed or relaxed. Furthermore, while short-term emotional fluctuations in users are likely to affect investment decisions, no systems exist that take this into account. As a result, it is difficult to provide optimal asset allocations for individual users.

[0430] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's facial expressions and tone of voice to collect emotional data and correcting asset management suggestions based on the emotional data, means for the user to input data regarding age, current assets, target amount, and investment orientation, and means for receiving the data input by the user and storing it in a database. This makes it possible to provide more accurate asset management advice that reflects the user's emotional state.

[0431] The "input means" is a means by which a user provides information regarding age, current assets, target amount, and investment preference to the system.

[0432] "Means for saving in a database" refers to a means for receiving and safely storing information entered by a user.

[0433] The "means for calculating risk tolerance" is a means for calculating how much risk a user can tolerate based on information such as the user's age.

[0434] The "means for analyzing the gap" is a means for evaluating the difference between the user's current assets and the target amount, and analyzing how to fill the gap.

[0435] The "means calculated by the generating AI" refers to the means by which the generating AI calculates the optimal asset management allocation based on the analysis results.

[0436] The "means for notifying" is a means for notifying the user of the calculated optimal asset management allocation.

[0437] The "means for receiving and recording" is a means for receiving and recording information on the operational allocation adjusted by the user.

[0438] The "means for collecting emotional data" is a means for analyzing the user's facial expressions and tone of voice to collect data about the user's emotional state.

[0439] The "correction means" is a means for appropriately correcting asset management proposals based on the collected emotional data.

[0440] This invention relates to a system for finding optimal asset allocations for participants in corporate defined contribution pension plans (401K). The system incorporates an emotion engine and provides personalized investment advice by recognizing the user's emotional state.

[0441] System configuration

[0442] 1. User Device

[0443] Input means: The user inputs information about age, current assets, target amount, and investment orientation.

[0444] Emotion recognition means: Using the camera and microphone built into the device, the user's facial expressions and tone of voice are analyzed to recognize their emotional state.

[0445] Notification means: The calculated optimal asset management allocation is notified to the user.

[0446] Display: Users can view and adjust asset allocation suggestions on their devices.

[0447] 2. Server

[0448] Data storage means: User information sent from the terminal is stored in a database.

[0449] Risk tolerance calculation method: Calculates risk tolerance based on information such as the user's age.

[0450] Gap analysis tool: Evaluate the difference between your current assets and your target amount and analyze how to close the gap.

[0451] Generation AI calculation method: Based on the analysis results, the generation AI calculates the optimal asset management allocation.

[0452] Adjustment receiving means: Receives and records information on the operational allocation adjusted by the user.

[0453] Emotion data collection means: Analyzes the emotional data sent from the device and corrects asset management suggestions.

[0454] Hardware and software used

[0455] 1. Hardware

[0456] Smartphone camera: Used to capture the user's facial expressions in real time.

[0457] Smartphone microphone: Used to analyze the user's voice tone.

[0458] 2. Software

[0459] OpenCV (cv2): An image processing library used to capture and analyze facial expressions.

[0460] EmotionEngine: An emotion engine that extracts emotional data by analyzing the user's facial expressions and voice tone.

[0461] requests: A Python library for making HTTP requests, used to send and receive data.

[0462] Specific examples

[0463] Let's say a user is 35 years old, has 6 million yen in current assets, a target amount of 25 million yen, and prefers low-risk investments. The emotion engine recognizes that the user is relaxed. Based on this, the system operates as follows:

[0464] 1. Data Entry

[0465] Users enter their age, current assets, target amount, and investment preferences into a smartphone app.

[0466] 2. Emotion recognition

[0467] Using the smartphone's camera and microphone, emotional data is collected by analyzing the user's facial expressions and voice.

[0468] 3. Data transmission and storage

[0469] The input data and emotion data are sent to a server and stored in a database.

[0470] 4. Risk tolerance and gap analysis

[0471] The server calculates the risk tolerance based on the user's age and evaluates the gap between the current assets and the target amount.

[0472] 5. Calculation by Generative AI

[0473] After adjusting investment preferences based on emotional data, the generative AI calculates the optimal asset allocation, suggesting, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[0474] 6. Notice and Adjustment

[0475] The calculated asset management allocation is notified to the user, who can then check and adjust it.

[0476] Example prompts

[0477] A 35-year-old user currently has assets of 6 million yen, a target amount of 25 million yen, and a low-risk investment orientation. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset allocation.

[0478] This makes it possible to provide highly accurate asset management advice that reflects the user's short-term emotional state.

[0479] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0480] Step 1:

[0481] The user launches the smartphone app and enters data such as age, current assets, target amount, and investment preference. The entered data is temporarily saved in the device. The data is entered by the user manually providing information for each item. The specific action of data entry is for the user to type the information into each input field.

[0482] Step 2:

[0483] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, thereby collecting the user's emotional data. Specifically, the camera captures the user's face and the microphone records their voice. The emotional data is then analyzed using EmotionEngine. This emotional data includes happiness or sadness as determined from the user's facial expressions and stress levels as determined from their voice tone.

[0484] Step 3:

[0485] The device sends input data and emotion data to the server. An HTTP request is used to send the data, and the data is sent in JSON format. Specifically, the requests library is used to send the data to the server. The input for this step is the user's input data and emotion data, and the output is data sent to the server.

[0486] Step 4:

[0487] The server stores the received data in a database. The input data here is the information sent from the terminal, and the output is the correct record in the database. Specifically, the received data is analyzed, converted into an appropriate format, and inserted into the database. The database management system (DBMS) used is, for example, MySQL.

[0488] Step 5:

[0489] The server calculates the risk tolerance based on the user's age. The input for this step is age data, and the output is a numerical value for risk tolerance. Specifically, it sets a risk level (low, medium, high) according to the user's age range. For example, those under 30 are considered high risk, those between 30 and 50 are considered medium risk, and those over 50 are considered low risk.

[0490] Step 6:

[0491] The server analyzes the gap between the current assets and the target amount. The input for this step is the current assets and target amount data, and the output is the calculation result of the asset gap (shortfall). Specifically, it subtracts the current assets from the target amount to calculate the shortfall.

[0492] Step 7:

[0493] The server corrects the operational orientation parameters based on the emotional data. Specifically, it analyzes the collected emotional data and corrects it to suggest low-risk operations if the user is feeling stressed. The input of this step is the emotional data, and the output is the corrected operational orientation parameters.

[0494] Step 8:

[0495] The server uses the generative AI model to calculate the optimal asset management allocation based on the corrected data. The inputs for this step are risk tolerance, asset gap, and corrected investment preference parameters, and the output is the investment breakdown. Specifically, input is given to the generative AI model using a prompt, which calculates the optimal allocation. An example prompt is, "A 35-year-old user has current assets of 6 million yen, a target amount of 25 million yen, and an investment preference for low risk. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset management allocation."

[0496] Step 9:

[0497] The server notifies the terminal of the calculated optimal asset management allocation. The input to this step is the calculation result of the generative AI model, and the output is notification information to the user terminal. Specifically, the notification information is formatted in JSON format and sent to the terminal via an HTTP request.

[0498] Step 10:

[0499] The terminal displays the received asset management allocation to the user, who can then confirm and adjust its contents. The input to this step is the notification information sent from the server, and the output is the investment allocation adjusted by the user. Specifically, the investment allocation is displayed on the terminal's UI, and an interface is provided for the user to adjust.

[0500] Step 11:

[0501] The user sends the adjusted operational allocation from the terminal to the server, which receives and records the information. The input of this step is the operational allocation after the user adjustment, and the output is a record in the database. The specific operation is to receive the user's adjusted data and save it in the database.

[0502] Step 12:

[0503] The server generates a link and guide to the company's 401K management site and sends it to the terminal. The input to this step is the user's final investment allocation decision information, and the output is link information with the guide. Specifically, it generates an appropriate link and creates and sends a guide to help the user complete the procedure smoothly.

[0504] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0505] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0506] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0507] [Second embodiment]

[0508] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0509] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0510] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0511] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0512] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0513] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0514] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0515] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0516] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0517] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0518] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0519] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0520] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Specifically, it involves a series of processes in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generation AI calculates the optimal asset allocation based on that data. The processing of the program of this system is explained below with specific examples.

[0521] System Embodiments

[0522] 1. Data Entry

[0523] Device: Using a smartphone or PC application, users input their age, current assets, target amount, and investment preference (e.g., stable, balanced, aggressive).

[0524] Server: Receives the data entered by the device and stores it in a database.

[0525] 2. Initial Processing

[0526] Server: Calculates risk tolerance based on received user data. For example, if the user's age is 30, the risk tolerance is calculated to be relatively high. Also, if the user's current assets are 5 million yen and the target amount is 20 million yen, the gap is analyzed.

[0527] 3. Analysis by generative AI

[0528] Server: Based on the results of the initial processing, the generation AI analyzes the data based on the user's age, current assets, target amount, and investment preferences. For example, the generation AI calculates the optimal ratio of domestic stocks, international stocks, and bonds. For a young person with a balanced investment preference, the AI ​​may suggest an allocation of 40% domestic stocks, 40% international stocks, and 20% bonds.

[0529] 4. Advice Generation and Notification

[0530] Server: Based on the optimal investment allocation calculated by the generation AI, the server proposes specific investment details to the user. This advice includes the names of investment products and their purchase ratios.

[0531] Device: The generated advice is sent to the user's device and displayed on the application.

[0532] 5. User Feedback

[0533] Terminal: The user reviews the proposed investment allocation and adjusts their portfolio as needed. If the user approves the adjusted allocation, the information is sent to the server.

[0534] Server: Records approved operational allocations and performs re-analysis if necessary.

[0535] 6. Switching Procedures

[0536] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[0537] User: Follow the provided guide to access the company's 401K management site and perform the switching procedure based on the optimal investment allocation.

[0538] Specific examples

[0539] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[0540] 1. Device: The user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation and balanced orientation into the smartphone app.

[0541] 2. Server: Receives the data sent from the device and stores it in a database. Based on age, the risk tolerance is calculated as "medium," and the gap between current assets and the target amount is calculated as 15 million yen.

[0542] 3. Server: The generative AI analyzes the data and calculates the optimal asset allocation, for example, 40% domestic stocks, 40% international stocks, and 20% bonds.

[0543] 4. Server: Based on the calculation results, the server generates advice including specific investment details (for example, specific domestic stocks or bond product names) and notifies the user.

[0544] 5. Device: The user reviews the advice and either accepts the proposed allocation or fine-tunes it. If the user approves the allocation, the information is sent to the server.

[0545] 6. Server: Records approved allocations and performs reanalysis as needed. Additionally, provides links to 401K management sites and guides to simplify the switching process.

[0546] 7. User: Follow the provided guide to access the production site and perform the switching procedure with the new allocation.

[0547] As described above, this system includes specific means for providing users with optimal asset management allocations in a simple and effective manner, and supports maximizing the investment returns of corporate defined contribution pension plans.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0551] Step 2:

[0552] The terminal receives the data entered by the user and temporarily stores it in local memory. When the user confirms the data and presses the send button, the data is sent to the server.

[0553] Step 3:

[0554] The server receives the data sent from the device and stores it in a database for subsequent processing.

[0555] Step 4:

[0556] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium."

[0557] Step 5:

[0558] The server calculates the asset gap based on the user's current assets and target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the gap is calculated as 15 million yen.

[0559] Step 6:

[0560] The server sends the results of the initial processing to the generation AI, which then begins analysis to calculate the optimal asset allocation based on the user data.

[0561] Step 7:

[0562] The server receives the analysis results returned by the generation AI and generates a specific investment breakdown, such as advice like "40% domestic stocks, 40% international stocks, 20% bonds."

[0563] Step 8:

[0564] The server sends information to the terminal to notify the user of the generated advice.

[0565] Step 9:

[0566] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[0567] Step 10:

[0568] The user adjusts the investment allocation based on the advice and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[0569] Step 11:

[0570] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[0571] Step 12:

[0572] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[0573] Step 13:

[0574] The terminal displays the received links and guides to the user.

[0575] Step 14:

[0576] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching process.

[0577] Example 1

[0578] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] In today's asset management environment, it is difficult for users to find the optimal asset allocation on their own, especially in systems like corporate defined contribution pension plans (401K), which require specialized knowledge. Furthermore, it is necessary to process a large amount of information at once to develop an appropriate investment strategy, which is time-consuming, and there are limited tools available to help users find the optimal solution for their risk and goals. This hinders users from optimal asset management.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0581] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generative AI model to calculate an optimal asset allocation based on the calculation and analysis results; means for inputting a prompt sentence to the generative AI model; means for notifying the user of the optimal asset allocation; and means for receiving and recording the investment allocation adjusted by the user in accordance with the notified asset allocation. This allows the user to find an optimal asset allocation simply and effectively.

[0582] "Users" are participants in corporate defined contribution pension plans (401K) who use the system to find asset allocations.

[0583] "Age" is information indicating the current age of the user.

[0584] "Current assets" is information indicating the current total amount of assets held by the user.

[0585] The "target amount" is information indicating the amount of money that the user wants to achieve as a result of asset management.

[0586] "Management orientation" is information indicating the asset management policy desired by the user, and there are types such as "stable orientation," "balanced orientation," and "aggressive."

[0587] "Data input means" refers to a terminal or application that allows a user to input their age, current assets, target amount, and investment preference.

[0588] "Data receiving and storing means" refers to the server's function of receiving data sent by users and storing it in a database.

[0589] The "risk tolerance calculation means" refers to the server's function of calculating how much risk a user can tolerate based on data such as the user's age and investment preferences.

[0590] "Gap analysis means" refers to a function of the server for analyzing the difference between the user's current assets and the target amount.

[0591] A "generative AI model" refers to an artificial intelligence model that calculates optimal asset management allocations based on user data.

[0592] A "prompt sentence" refers to the input sentence provided to a generative AI model when requesting data analysis.

[0593] "Optimal investment allocation notification means" refers to the server's function of notifying users of the optimal asset investment allocation calculated by the generative AI model.

[0594] The "means for receiving and recording operational allocation" refers to the function of the server that receives operational allocation adjusted by the user and records it.

[0595] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. This system involves a process in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generative AI model calculates the optimal asset allocation based on the data.

[0596] Hardware and software used

[0597] 1. Device:

[0598] Users enter data using an application on their smartphone or PC, which then sends information about the user's age, current assets, target amount, and investment preference to the server.

[0599] Specific software used for mobile application development is Swift (for iOS) and Java (for Android), while HTML, CSS, and JavaScript are used for web application development.

[0600] 2. Server:

[0601] The server receives the data sent by the user and stores it in a database, such as MongoDB or SQL Server.

[0602] The server calculates the user's risk tolerance based on the received data and uses languages ​​such as Python and R to analyze the gap between the current assets and the target amount. These calculations use data processing libraries such as Pandas and NumPy.

[0603] A generative AI model (e.g., OpenAI's GPT series) is used to analyze user data and calculate optimal asset allocation. The generative AI model uses an API, and the optimal allocation is obtained by entering a prompt.

[0604] Process example

[0605] 1. Data Entry:

[0606] Device: The user launches the smartphone application and inputs their age, current assets, target amount, and investment orientation (e.g., "30 years old," "5 million yen," "20 million yen," "balanced orientation").

[0607] 2. Data Receipt and Storage:

[0608] Server: Receives data sent from the device and stores it securely in a MongoDB database using the HTTPS protocol.

[0609] 3. Risk Tolerance Calculation and Gap Analysis:

[0610] Server: Calculates the user's risk tolerance based on the received data. For example, if the user is 30 years old, the risk tolerance is calculated as "medium." Also, the gap between the current assets of 5 million yen and the target amount of 20 million yen is calculated as 15 million yen.

[0611] 4. Data analysis with generative AI models:

[0612] Server: Enter the following prompt into the generative AI model:

[0613] "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is looking for a balanced investment. What is the optimal asset allocation?"

[0614] The generative AI model responds by providing an optimal asset allocation, such as 40% domestic stocks, 40% international stocks, and 20% bonds.

[0615] 5. Advice generation and notification:

[0616] Server: Based on the allocation obtained from the generative AI model, the server generates advice including specific investment products (e.g., domestic stocks: "ABC Company," international stocks: "XYZ Company," bonds: "Japanese Government Bonds") and pushes it to the user's device.

[0617] Device: The advice sent to the user's device can be displayed and the details can be checked.

[0618] 6. User Feedback and Records:

[0619] Terminal: The user checks the proposed allocation and adjusts it if necessary. Once the adjusted allocation is approved, the data is sent to the server.

[0620] Server: Records the approved data and sends prompts to the generation AI again if reanalysis is required based on user feedback.

[0621] This system not only helps users easily find the optimal asset allocation, but also provides specific and effective means to maximize the investment returns of defined contribution pension plans.

[0622] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0623] Step 1:

[0624] Data Entry

[0625] Device: The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0626] Input: The user enters "30 years old," "5 million yen," "20 million yen," and "balanced" into the application.

[0627] Specific operation: The user enters the required data into the application's input form and clicks the "Submit" button, which sends the input data from the terminal to the server.

[0628] Output: The input data is sent to the server.

[0629] Step 2:

[0630] Data reception and storage

[0631] Server: The server receives the data sent from the device and stores it in a database such as MongoDB using the HTTPS protocol.

[0632] Input: The data "30 years old," "5 million yen," "20 million yen," and "balanced" are sent to the server.

[0633] What it does: The server temporarily stores the received data in memory and then saves it to a database using a secure protocol.

[0634] Output: User data stored in the database.

[0635] Step 3:

[0636] Risk tolerance calculation and gap analysis

[0637] Server: Based on the stored data, the server calculates the user's risk tolerance and analyzes the gap between the current assets and the target amount.

[0638] Input: Data stored in the database: "30 years old," "5 million yen," "20 million yen," and "balanced orientation."

[0639] Specific operation: Using Python's Pandas library, the conditional statement calculates the risk tolerance as "medium" if the user's age is 30. It also calculates the gap between the current assets of 5 million yen and the target amount of 20 million yen, determining it to be 15 million yen.

[0640] Output: Data for risk tolerance "medium" and gap "15 million yen".

[0641] Step 4:

[0642] Data analysis with generative AI models

[0643] Server: Based on risk tolerance and gap data, the server inputs prompt statements into the generative AI model to analyze the optimal asset allocation.

[0644] Input: Data for "30 years old", "5 million yen", "20 million yen", "Balanced", "Risk tolerance: Medium", "Gap: 15 million yen".

[0645] Specific operation: The following prompt is input to the generative AI model: "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is balanced. Please tell me the optimal asset allocation."

[0646] Output: Data obtained from the generative AI model: 40% domestic stocks, 40% international stocks, 20% bonds.

[0647] Step 5:

[0648] Advice generation and notification

[0649] Server: The server generates a specific investment breakdown based on the optimal allocation obtained from the generative AI model and notifies the user's device.

[0650] Input: Data of "40% domestic stocks, 40% foreign stocks, 20% bonds".

[0651] Specific operation: Based on the allocation data, a specific investment breakdown is generated, such as "Domestic stocks: ABC Company 40%," "International stocks: XYZ Company 40%," and "Bonds: Japanese government bonds 20%." This is then sent as a push notification to the user's device.

[0652] Output: Specific investment breakdown notified to the terminal.

[0653] Step 6:

[0654] User feedback and records

[0655] Terminal: The user checks the notified allocation and adjusts it if necessary. After approval, the adjusted allocation data is sent to the server.

[0656] Input: Adjusted allocation data such as "Domestic stocks: ABC Company 30%", "International stocks: XYZ Company 50%", and "Bonds: Japanese government bonds 20%".

[0657] What happens: The user adjusts the allocation in the application interface and presses the "Accept" button, which sends the data to the server.

[0658] Output: The adjusted allocation data is sent to the server and recorded.

[0659] Step 7:

[0660] Switching Procedures

[0661] Server: Based on the optimal allocation obtained from the generative AI model, the server provides users with links and guides to their company's 401K management site.

[0662] Input: Optimal allocation data and link information.

[0663] Specific operation: The server generates a guide document explaining the specific switching procedure for the user and a link to the operation site, and notifies the user's terminal.

[0664] Output: The guide document and links that the user receives.

[0665] Through these processing steps, users can easily find the optimal asset management allocation, providing specific and effective means for maximizing the investment returns of corporate defined contribution pension plans.

[0666] (Application example 1)

[0667] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] For participants in corporate defined contribution pension plans (401K), finding the optimal asset allocation for themselves requires knowledge and time. In particular, analysis and proposals based on multiple factors, such as age, current assets, target amount, and investment preferences, are required, and a simple and fast method for doing this is needed. Furthermore, the process for actually implementing the proposed investment allocation is complicated, and there is a lack of a means to smoothly complete the process. This creates challenges for participants, making it difficult to effectively optimize their investment allocation.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0670] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generating AI to calculate an optimal asset allocation based on the calculation and analysis results; means for notifying the user of the optimal asset allocation; means for receiving and recording an investment allocation adjusted by the user in accordance with the notified asset allocation; means for providing a link and guide to a company's investment site based on the investment allocation approved by the user; and means for proposing an optimal asset allocation using a generating AI model on a smart device. This allows users to easily find an optimal asset allocation for themselves and quickly and easily put that investment allocation into practice.

[0671] A "user" is someone who uses a particular application or system.

[0672] A "smart device" is a portable electronic device that can connect to the Internet, such as a smartphone or tablet.

[0673] A "generative AI model" is a model that uses artificial intelligence algorithms to analyze data and generate optimal solutions to specific problems.

[0674] "Age" is information indicating the length of time since the user was born.

[0675] "Current assets" is information indicating the amount of total assets currently held by the user.

[0676] The "target amount" is information indicating the amount of assets that the user wishes to achieve in the future.

[0677] "Management preference" is data that indicates the investment risk profile and investment style preferred by the user.

[0678] A "database" is an organized digital storage system for storing user-entered data.

[0679] "Risk tolerance" is an indicator that indicates the investment risk level that a user can tolerate.

[0680] The "gap" indicates the difference between current assets and the target amount.

[0681] "Notification" is the act of providing information to a user from an application or system.

[0682] "Adjustment" refers to the act of the user making subtle changes to the proposed investment allocation.

[0683] A "link" is a reference to a particular website or online resource.

[0684] A "guide" is a document or tool that provides procedures or instructions for performing a particular operation or procedure.

[0685] This invention relates to a system for finding optimal asset allocation for participants of corporate defined contribution pension plans (401K). The system includes the following components:

[0686] 1. Data Entry:

[0687] Terminal: Users use a smart terminal to input data such as age, current assets, target amount, investment preference, etc. The application on the terminal guides them through this data input.

[0688] 2. Data Receipt and Storage:

[0689] Server: The entered data is sent to the server and stored in a database, which prepares the data necessary for subsequent processing.

[0690] 3. Risk Tolerance Calculation and Gap Analysis:

[0691] Server: Based on the saved data, calculate the user's risk tolerance and analyze the gap between their current assets and their target amount. For example, classify the risk tolerance as "high," "medium," or "low" based on the user's age, asset status, and investment objectives.

[0692] 4. Calculation of optimal asset allocation using generative AI:

[0693] Server: Using a generative AI model, it calculates the optimal asset allocation based on the results of calculations and analysis. For example, it proposes a specific investment allocation such as "40% domestic stocks, 40% international stocks, 20% bonds."

[0694] 5. Investment Allocation Notification:

[0695] Server: Once the optimal asset allocation is calculated, the server notifies the user of the results via their smart device. The user receives the notification and can check the proposed asset allocation on the application.

[0696] 6. User feedback and adjustments:

[0697] Terminal: The user can review the proposed allocation and make any necessary adjustments. If the user approves the adjustments, the information is sent to the server.

[0698] Server: Receives the adjusted investment allocation data, stores it in a database, and re-analyzes it as needed.

[0699] 7. Providing Links and Guides:

[0700] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[0701] Terminal: Using the provided links and guides, users can easily carry out the switching procedure.

[0702] Hardware and software used:

[0703] Hardware: Smart devices (e.g. smartphones, tablets)

[0704] Software: On the server side, we use web frameworks such as Flask to build the API, and OpenAI's API for data analysis.

[0705] Examples:

[0706] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and a balanced investment orientation, the generative AI model will use the following prompt to suggest the optimal asset allocation.

[0707] Example prompt sentence:

[0708] "If the user is 30 years old, has 5 million yen in current assets, 20 million yen in target assets, and has a balanced investment preference, please suggest the optimal asset allocation."

[0709] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0710] Step 1:

[0711] The user uses a smart device to enter their age, current assets, target amount, and investment preference into the application. This input data is entered using text fields and drop-down menus. The entered data is temporarily saved on the device. Input data: Age, current assets, target amount, investment preference. Output data: User data temporarily saved on the device.

[0712] Step 2:

[0713] Input data is sent from the terminal to the server. The server receives this data and saves it in the database. The server checks the integrity of the data and notifies the terminal that saving is complete. Input data: User data (age, current assets, target amount, investment orientation). Output data: User data saved in the database, notification of saving completion.

[0714] Step 3:

[0715] The server calculates the user's risk tolerance based on the data stored in the database. A fixed algorithm is used for the calculation to generate a risk profile that takes into account factors such as the user's age and financial situation. The result of this process is a calculated risk tolerance of either "high," "medium," or "low." Input data: User information in the database. Output data: Risk tolerance.

[0716] Step 4:

[0717] The server analyzes the gap between the current assets and the target amount. The required additional assets are calculated by subtracting the target amount from the current assets. This process calculates the increase in assets required for the user to reach the goal. Input data: User information in the database. Output data: Gap (additional assets).

[0718] Step 5:

[0719] The server uses a generative AI model to calculate the optimal asset management allocation based on the analysis results of risk tolerance and asset gap. The generative AI model uses OpenAI's API and generates a specific investment allocation (e.g., 40% domestic stocks, 40% international stocks, 20% bonds) using a specific prompt as input. Input data: risk tolerance, asset gap. Example prompt: "If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment preference, please suggest the optimal asset management allocation." Output data: optimal asset management allocation.

[0720] Step 6:

[0721] The server notifies the user's device of the calculated optimal asset allocation. This notification includes specific investment details and proposals. Input data: Optimal asset allocation. Output data: Notification to the user's device.

[0722] Step 7:

[0723] The terminal displays the received operational allocation notification to the user. The user can review this proposal and make fine adjustments as necessary. Once the user completes and approves the adjustments, the information is sent from the terminal to the server. Input data: Notified operational allocation, user adjustments. Output data: Approved operational allocation to the server.

[0724] Step 8:

[0725] The server generates a link to the company's operation site and a guide for switching procedures based on the operation allocation approved by the user, and sends them to the user's terminal. The link contains information that allows the user to easily access and operate it. Input data: Approved operation allocation. Output data: Link and guide.

[0726] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0727] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice. The program processing of this system is explained below with specific examples.

[0728] System Embodiments

[0729] 1. Data Entry

[0730] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0731] 2. Emotion recognition

[0732] The emotion engine built into the device analyzes the user's facial expressions and tone of voice while they are typing to recognize their current emotional state, collecting data such as whether they are feeling stressed or relaxed.

[0733] 3. Data Transmission

[0734] The terminal transmits the input data and the emotion data recognized by the emotion engine to the server.

[0735] 4. Data Storage

[0736] The server receives the data sent from the device and stores it in a database. The stored data includes age, current assets, target amount, investment preference, and emotional data.

[0737] 5. Initial Processing

[0738] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium." If the current assets are 5 million yen and the target amount is 20 million yen, the server analyzes the gap to be 15 million yen.

[0739] 6. Emotion correction

[0740] The server corrects the user's investment preference parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the server will correct the parameters to suggest low-risk investments.

[0741] 7. Generative AI Analysis

[0742] The server sends the corrected data to the generation AI, which then begins analysis to calculate the optimal asset management allocation.

[0743] 8. Advice Generation

[0744] The server generates a specific investment breakdown based on the analysis results returned by the AI. For example, it creates advice such as "40% domestic stocks, 40% international stocks, 20% bonds."

[0745] 9. Advice Notice

[0746] The server sends information to the terminal to notify the user of the generated advice.

[0747] 10. User Verification

[0748] The device displays the received advice to the user, who then confirms it. If the user is satisfied, they can approve the operational allocation as is, or make fine adjustments as necessary.

[0749] 11. Allocation Adjustments

[0750] The user adjusts the proposed operation details and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[0751] 12. Data Recording

[0752] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[0753] 13. Switching Procedures

[0754] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[0755] 14. User Execution

[0756] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching procedure with the new allocation.

[0757] Specific examples

[0758] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[0759] 1. On the terminal, the user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation / balanced orientation.

[0760] 2. The emotion engine analyzes the user's facial expressions and tone of voice during this input and recognizes that the user is feeling stressed.

[0761] 3. The device sends the input data and emotion data to the server.

[0762] 4. The server receives the input data and begins analysis.

[0763] 5. The server sets the risk tolerance to "medium" based on the user's age and calculates the gap between the current assets and the target amount to 15 million yen.

[0764] 6. The server adjusts the investment-oriented parameters based on the emotional data to suggest low-risk investments.

[0765] 7. The generation AI calculates the optimal asset management allocation based on the corrected data and suggests an allocation of, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[0766] 8. The server generates specific advice based on the calculation results and notifies the user.

[0767] 9. On the device, the user checks the advice and either accepts it or makes adjustments.

[0768] 10. The server records the final operational allocation and provides a link to guide the switching procedure.

[0769] 11. The user follows the provided link to complete the switching procedure based on the new allocation on the company's 401K management site.

[0770] In this way, by incorporating an emotion engine, the present invention is a system that can provide more precise asset management advice that takes into account the user's short-term emotional state, and can help maximize the investment returns of defined contribution pension plans.

[0771] The processing flow will be explained below.

[0772] Step 1:

[0773] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[0774] Step 2:

[0775] The device temporarily stores the user's input data in its local memory. Once input is complete, the user presses the send button to send the data to the server.

[0776] Step 3:

[0777] The emotion engine installed in the device analyzes the user's facial expressions and tone of voice while inputting, and recognizes the user's emotional state (e.g., stress, relaxation, etc.). The emotion data is also simultaneously sent to the server.

[0778] Step 4:

[0779] The server receives the data sent from the terminal (age, current assets, target amount, investment orientation, emotional data) and stores it in a database.

[0780] Step 5:

[0781] The server calculates your risk tolerance based on your age. For example, if you are 30, your risk tolerance will be set to "medium."

[0782] Step 6:

[0783] The server calculates the asset gap based on the current assets and the target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the asset gap will be 15 million yen.

[0784] Step 7:

[0785] The server adjusts the user's operational parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the parameters are adjusted to reduce risk.

[0786] Step 8:

[0787] The server sends the corrected data to the AI, which then calculates the optimal asset allocation. For example, it suggests an allocation of 35% domestic stocks, 35% international stocks, and 30% bonds.

[0788] Step 9:

[0789] The server generates a detailed investment breakdown (for example, specific stocks and bond product names) based on the analysis results returned by the generation AI.

[0790] Step 10:

[0791] The server sends information to the terminal to notify the user of the generated advice.

[0792] Step 11:

[0793] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[0794] Step 12:

[0795] The user checks the proposed operation details, adjusts them, and then confirms the allocation. When the user presses the confirm button, the allocation data is sent from the terminal to the server.

[0796] Step 13:

[0797] The server receives the adjusted allocation of the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[0798] Step 14:

[0799] The server generates a link to the company's 401K operation site and a guide for the user to carry out the switching procedure, and transmits the generated link to the terminal.

[0800] Step 15:

[0801] The terminal displays the received links and guides to the user.

[0802] Step 16:

[0803] The user clicks on the provided link to access the company's 401K management site and follows the instructions to complete the switching procedure with the new allocation.

[0804] Example 2

[0805] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0806] Conventional asset management advice systems can only provide general advice based on the user's age and asset information, and are unable to provide advice that takes into account the user's individual emotional state. This makes it difficult to obtain optimal investment advice that reflects the user's emotions, such as suggesting high-risk investments even when the user is feeling stressed. Therefore, there is a need for a system that provides more personalized asset management advice that takes into account the user's emotional state.

[0807] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for the user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; and means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount. This enables the risk profile to be corrected taking into account the user's emotional state. Furthermore, the server includes means for analyzing the user's facial expression and tone of voice during input and recognizing the user's current emotional state; means for correcting the user's investment orientation based on the recognized emotional state; and means for the generation AI to recalculate the optimal asset investment allocation using the corrected data. This makes it possible to provide personalized asset management advice based on the user's real-time emotional state.

[0808] "User" refers to an individual or corporation that uses this system.

[0809] "Age" is a data item that indicates the user's current age.

[0810] "Current assets" is a data item that indicates the amount of total assets currently owned by the user.

[0811] "Target amount" is a data item that indicates the amount of assets that the user wants to achieve in the future.

[0812] "Management orientation" is a data item that indicates the orientation regarding the investment style and risk tolerance desired by the user.

[0813] "Data input means" refers to an interface for a user to input data regarding age, current assets, target amount, and investment orientation.

[0814] "Data receiving means" refers to a means by which the server receives data input by the user.

[0815] "Database storage means" refers to means for storing received data in a database.

[0816] "Risk tolerance calculation means" refers to a means for calculating risk tolerance based on the user's age.

[0817] "Gap analysis tools" refer to tools for analyzing the difference between current assets and target amounts.

[0818] "Generative AI" refers to a system that uses artificial intelligence to calculate optimal asset management allocations.

[0819] "Emotion recognition means" refers to means for analyzing the facial expressions and tone of voice of the user while they are inputting information and recognizing the user's emotional state.

[0820] The "emotion correction means" refers to a means for correcting the user's operational orientation parameters based on the recognized emotional state.

[0821] The "asset management allocation notification means" refers to a means for notifying the user of the optimal asset management allocation.

[0822] The "management allocation receiving means" refers to a means for receiving the investment allocation adjusted by the user in accordance with the notified asset investment allocation.

[0823] "Investment allocation recording means" refers to a means for recording received investment allocations in a database.

[0824] "Recalculation means" refers to the means by which the generation AI recalculates the optimal asset management allocation using the corrected data.

[0825] The system of the present invention is designed to enable participants of corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice.

[0826] The main elements of the system consist of a user device, a server, and a generative AI model. User devices are general-purpose devices such as smartphones and PCs. The server is a high-performance computer system that uses MySQL as its database management system. For emotion recognition, a camera and microphone are used, and dedicated software for image and audio analysis is installed.

[0827] First, the user launches the application on their smartphone or PC and enters data such as their age, current assets, target amount, and investment preference. Next, while the user is entering information, the emotion engine analyzes their facial expressions and tone of voice to recognize their current emotional state. For example, if the user is frowning, it will be recognized as "stressed."

[0828] The device sends the input data and emotion data to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a database, calculates the user's risk tolerance based on their age, and analyzes the difference between their current assets and their target amount. For example, if a user is 30 years old, their current assets are 5 million yen, and their target amount is 20 million yen, the gap is calculated as 15 million yen.

[0829] The server adjusts the risk tolerance based on the emotional data. For example, if the user is stressed, it will recommend low-risk investments. The adjusted data is sent to the generative AI model, which calculates the optimal asset allocation. The prompt for the generative AI model is as follows:

[0830] User information: Age 30, current assets 5 million yen, target amount 20 million yen, investment orientation, balance orientation, emotional state: stress

[0831] Desired outcome: Recommend optimal asset allocation

[0832] The AI ​​then calculates the optimal asset allocation based on these prompts, returning a recommended allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds." The server receives the results and generates specific advice for the user. The generated advice is sent to the device, where the user can review it and either accept it as is or make fine adjustments as needed.

[0833] When the final allocation is determined, the server records it in the database, generates a link and guide to the company's 401K management site, and sends it to the terminal. The user clicks the provided link to access the company's 401K management site and completes the switching procedure with the new allocation.

[0834] As described above, the present invention is a system that provides precise asset management advice that takes into account the user's short-term emotional state, and maximizes the management efficiency of defined contribution pension plans.

[0835] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0836] Step 1:

[0837] Data Entry

[0838] The user launches the application on their smartphone or PC and enters data on their age, current assets, target amount, and investment preference. Text boxes and pull-down menus are displayed in the input fields, and the user enters or selects the required information. Once the input is complete, the user clicks the submit button. The input data is used in the next step.

[0839] Input: Age, current assets, target amount, investment preference

[0840] Output: User input data

[0841] Step 2:

[0842] emotion recognition

[0843] The device's built-in emotion engine uses the camera and microphone to capture facial expressions and tone of voice while the user is entering data, and emotion recognition algorithms analyze this data to identify the user's current emotional state (e.g., "stressed" or "relaxed").

[0844] Input: User's facial expression data, tone of voice

[0845] Output: User emotion data

[0846] Step 3:

[0847] Data transmission

[0848] The device sends the data entered in step 1 and the emotion data recognized in step 2 to the server using a secure communication protocol (e.g., HTTPS). The data is packaged in JSON format packets and sent to the server.

[0849] Input: User input data, emotion data

[0850] Output: Data sent to the server

[0851] Step 4:

[0852] Data storage

[0853] The server receives the data packets sent from the device. The received data is stored in a database management system (e.g., MySQL). The database table contains user ID, age, current assets, target amount, investment orientation, and emotional data.

[0854] Input: Data sent from the terminal

[0855] Output: Data stored in the database

[0856] Step 5:

[0857] Initial Processing

[0858] The server calculates the risk tolerance level based on the user's age based on the stored data. At the same time, it calculates the difference (gap) between the current assets and the target amount. For example, if the user is 30 years old, the risk tolerance level is set to "medium," and the current assets are 5 million yen and the target amount is 20 million yen, the gap will be calculated as 15 million yen.

[0859] Input: User data read from the database

[0860] Output: Risk tolerance, gap analysis results

[0861] Step 6:

[0862] emotion correction

[0863] The server adjusts the risk tolerance based on the emotional data. For example, if the user is under stress, the server will adjust the risk tolerance to suggest lower-risk investments. The risk tolerance may be changed from "medium" to "low."

[0864] Input: Calculated risk tolerance, emotional data

[0865] Output: Adjusted risk tolerance

[0866] Step 7:

[0867] Generative AI analysis

[0868] The server sends the corrected data to the generative AI model and inputs a prompt to calculate the optimal asset management allocation. The prompt is "User information: age 30, current assets of 5 million yen, target amount of 20 million yen, investment orientation balanced orientation, emotional state: stressed. Desired result: Please suggest the optimal asset management allocation." The generative AI model calculates the optimal allocation based on this prompt.

[0869] Input: prompt statement

[0870] Output: Optimal asset allocation

[0871] Step 8:

[0872] Advice Generation

[0873] The server receives the results of the AI ​​generation and generates specific investment advice for the user. For example, it generates text and graphs suggesting an allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds."

[0874] Input: Investment allocation data from the generation AI

[0875] Output: Investment advice for the user

[0876] Step 9:

[0877] Advice Notice

[0878] The server sends the generated advice to the user's device, where it is displayed in a visually easy-to-understand format (graphs, text).

[0879] Enter: Investment Advice

[0880] Output: Advice notification to user terminal

[0881] Step 10:

[0882] User Verification

[0883] The terminal displays the received advice to the user. The user checks the displayed investment details and presses the "Approve" button if they are satisfied with the details, or the "Adjust" button if they wish to readjust.

[0884] Input: Investment advice from the server

[0885] Output: User review and approval or adjustment

[0886] Step 11:

[0887] Allocation adjustment

[0888] When the user presses the "Adjust" button, a screen will appear where they can adjust their investment details using sliders and text fields. The user can manually adjust the allocation to their desired level and press the "Confirm" button. The adjusted data will then be sent back to the server from the device.

[0889] Input: User's adjusted share

[0890] Output: Sends the allocated data to the server

[0891] Step 12:

[0892] Data Recording

[0893] The server receives the user's adjusted allocation data and records it in the database again. If necessary, it requests the generation AI to reanalyze it, generates new results, and notifies the user.

[0894] Input: User's adjusted allocation data

[0895] Output: Records in the database

[0896] Step 13:

[0897] Switching Procedures

[0898] The server creates a link to the company's 401K management site and a guide for the specific switching procedure, and sends it to the terminal. The procedure guide provides detailed explanations for each step.

[0899] Input: Final investment allocation

[0900] Output: Switching Procedure Guide

[0901] Step 14:

[0902] User execution

[0903] The user clicks on the provided link to access the company's 401K management site. Following the displayed guide, the user completes the switching procedure to the new allocation. Specifically, after logging in, the user enters the new asset allocation and presses the confirm / confirm button.

[0904] Input: Switching Procedure Guide

[0905] Output: The completed switching procedure

[0906] The above are the specific processing steps of the system of the present invention. Through this flow, personalized asset management advice that also takes into account the user's emotional state is provided.

[0907] (Application example 2)

[0908] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0909] Existing asset management support systems for corporate defined contribution pension plans (401K) provide investment advice without considering the user's emotional state, and therefore cannot suggest appropriate investment allocations when the user is stressed or relaxed. Furthermore, while short-term emotional fluctuations in users are likely to affect investment decisions, no systems exist that take this into account. As a result, it is difficult to provide optimal asset allocations for individual users.

[0910] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's facial expressions and tone of voice to collect emotional data and correcting asset management suggestions based on the emotional data, means for the user to input data regarding age, current assets, target amount, and investment orientation, and means for receiving the data input by the user and storing it in a database. This makes it possible to provide more accurate asset management advice that reflects the user's emotional state.

[0911] The "input means" is a means by which a user provides information regarding age, current assets, target amount, and investment preference to the system.

[0912] "Means for saving in a database" refers to a means for receiving and safely storing information entered by a user.

[0913] The "means for calculating risk tolerance" is a means for calculating how much risk a user can tolerate based on information such as the user's age.

[0914] The "means for analyzing the gap" is a means for evaluating the difference between the user's current assets and the target amount, and analyzing how to fill the gap.

[0915] The "means calculated by the generating AI" refers to the means by which the generating AI calculates the optimal asset management allocation based on the analysis results.

[0916] The "means for notifying" is a means for notifying the user of the calculated optimal asset management allocation.

[0917] The "means for receiving and recording" is a means for receiving and recording information on the operational allocation adjusted by the user.

[0918] The "means for collecting emotional data" is a means for analyzing the user's facial expressions and tone of voice to collect data about the user's emotional state.

[0919] The "correction means" is a means for appropriately correcting asset management proposals based on the collected emotional data.

[0920] This invention relates to a system for finding optimal asset allocations for participants in corporate defined contribution pension plans (401K). The system incorporates an emotion engine and provides personalized investment advice by recognizing the user's emotional state.

[0921] System configuration

[0922] 1. User Device

[0923] Input means: The user inputs information about age, current assets, target amount, and investment orientation.

[0924] Emotion recognition means: Using the camera and microphone built into the device, the user's facial expressions and tone of voice are analyzed to recognize their emotional state.

[0925] Notification means: The calculated optimal asset management allocation is notified to the user.

[0926] Display: Users can view and adjust asset allocation suggestions on their devices.

[0927] 2. Server

[0928] Data storage means: User information sent from the terminal is stored in a database.

[0929] Risk tolerance calculation method: Calculates risk tolerance based on information such as the user's age.

[0930] Gap analysis tool: Evaluate the difference between your current assets and your target amount and analyze how to close the gap.

[0931] Generation AI calculation method: Based on the analysis results, the generation AI calculates the optimal asset management allocation.

[0932] Adjustment receiving means: Receives and records information on the operational allocation adjusted by the user.

[0933] Emotion data collection means: Analyzes the emotional data sent from the device and corrects asset management suggestions.

[0934] Hardware and software used

[0935] 1. Hardware

[0936] Smartphone camera: Used to capture the user's facial expressions in real time.

[0937] Smartphone microphone: Used to analyze the user's voice tone.

[0938] 2. Software

[0939] OpenCV (cv2): An image processing library used to capture and analyze facial expressions.

[0940] EmotionEngine: An emotion engine that extracts emotional data by analyzing the user's facial expressions and voice tone.

[0941] requests: A Python library for making HTTP requests, used to send and receive data.

[0942] Specific examples

[0943] Let's say a user is 35 years old, has 6 million yen in current assets, a target amount of 25 million yen, and prefers low-risk investments. The emotion engine recognizes that the user is relaxed. Based on this, the system operates as follows:

[0944] 1. Data Entry

[0945] Users enter their age, current assets, target amount, and investment preferences into a smartphone app.

[0946] 2. Emotion recognition

[0947] Using the smartphone's camera and microphone, emotional data is collected by analyzing the user's facial expressions and voice.

[0948] 3. Data transmission and storage

[0949] The input data and emotion data are sent to a server and stored in a database.

[0950] 4. Risk tolerance and gap analysis

[0951] The server calculates the risk tolerance based on the user's age and evaluates the gap between the current assets and the target amount.

[0952] 5. Calculation by Generative AI

[0953] After adjusting investment preferences based on emotional data, the generative AI calculates the optimal asset allocation, suggesting, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[0954] 6. Notice and Adjustment

[0955] The calculated asset management allocation is notified to the user, who can then check and adjust it.

[0956] Example prompts

[0957] A 35-year-old user currently has assets of 6 million yen, a target amount of 25 million yen, and a low-risk investment orientation. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset allocation.

[0958] This makes it possible to provide highly accurate asset management advice that reflects the user's short-term emotional state.

[0959] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0960] Step 1:

[0961] The user launches the smartphone app and enters data such as age, current assets, target amount, and investment preference. The entered data is temporarily saved in the device. The data is entered by the user manually providing information for each item. The specific action of data entry is for the user to type the information into each input field.

[0962] Step 2:

[0963] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, thereby collecting the user's emotional data. Specifically, the camera captures the user's face and the microphone records their voice. The emotional data is then analyzed using EmotionEngine. This emotional data includes happiness or sadness as determined from the user's facial expressions and stress levels as determined from their voice tone.

[0964] Step 3:

[0965] The device sends input data and emotion data to the server. An HTTP request is used to send the data, and the data is sent in JSON format. Specifically, the requests library is used to send the data to the server. The input for this step is the user's input data and emotion data, and the output is data sent to the server.

[0966] Step 4:

[0967] The server stores the received data in a database. The input data here is the information sent from the terminal, and the output is the correct record in the database. Specifically, the received data is analyzed, converted into an appropriate format, and inserted into the database. The database management system (DBMS) used is, for example, MySQL.

[0968] Step 5:

[0969] The server calculates the risk tolerance based on the user's age. The input for this step is age data, and the output is a numerical value for risk tolerance. Specifically, it sets a risk level (low, medium, high) according to the user's age range. For example, those under 30 are considered high risk, those between 30 and 50 are considered medium risk, and those over 50 are considered low risk.

[0970] Step 6:

[0971] The server analyzes the gap between the current assets and the target amount. The input for this step is the current assets and target amount data, and the output is the calculation result of the asset gap (shortfall). Specifically, it subtracts the current assets from the target amount to calculate the shortfall.

[0972] Step 7:

[0973] The server corrects the operational orientation parameters based on the emotional data. Specifically, it analyzes the collected emotional data and corrects it to suggest low-risk operations if the user is feeling stressed. The input of this step is the emotional data, and the output is the corrected operational orientation parameters.

[0974] Step 8:

[0975] The server uses the generative AI model to calculate the optimal asset management allocation based on the corrected data. The inputs for this step are risk tolerance, asset gap, and corrected investment preference parameters, and the output is the investment breakdown. Specifically, input is given to the generative AI model using a prompt, which calculates the optimal allocation. An example prompt is, "A 35-year-old user has current assets of 6 million yen, a target amount of 25 million yen, and an investment preference for low risk. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset management allocation."

[0976] Step 9:

[0977] The server notifies the terminal of the calculated optimal asset management allocation. The input to this step is the calculation result of the generative AI model, and the output is notification information to the user terminal. Specifically, the notification information is formatted in JSON format and sent to the terminal via an HTTP request.

[0978] Step 10:

[0979] The terminal displays the received asset management allocation to the user, who can then confirm and adjust its contents. The input to this step is the notification information sent from the server, and the output is the investment allocation adjusted by the user. Specifically, the investment allocation is displayed on the terminal's UI, and an interface is provided for the user to adjust.

[0980] Step 11:

[0981] The user sends the adjusted operational allocation from the terminal to the server, which receives and records the information. The input of this step is the operational allocation after the user adjustment, and the output is a record in the database. The specific operation is to receive the user's adjusted data and save it in the database.

[0982] Step 12:

[0983] The server generates a link and guide to the company's 401K management site and sends it to the terminal. The input to this step is the user's final investment allocation decision information, and the output is link information with the guide. Specifically, it generates an appropriate link and creates and sends a guide to help the user complete the procedure smoothly.

[0984] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0985] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0986] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0987] [Third embodiment]

[0988] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0989] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0990] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0991] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0992] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0993] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0994] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0995] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0996] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0997] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0998] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0999] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1000] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Specifically, it involves a series of processes in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generation AI calculates the optimal asset allocation based on that data. The processing of the program of this system is explained below with specific examples.

[1001] System Embodiments

[1002] 1. Data Entry

[1003] Device: Using a smartphone or PC application, users input their age, current assets, target amount, and investment preference (e.g., stable, balanced, aggressive).

[1004] Server: Receives the data entered by the device and stores it in a database.

[1005] 2. Initial Processing

[1006] Server: Calculates risk tolerance based on received user data. For example, if the user's age is 30, the risk tolerance is calculated to be relatively high. Also, if the user's current assets are 5 million yen and the target amount is 20 million yen, the gap is analyzed.

[1007] 3. Analysis by generative AI

[1008] Server: Based on the results of the initial processing, the generation AI analyzes the data based on the user's age, current assets, target amount, and investment preferences. For example, the generation AI calculates the optimal ratio of domestic stocks, international stocks, and bonds. For a young person with a balanced investment preference, the AI ​​may suggest an allocation of 40% domestic stocks, 40% international stocks, and 20% bonds.

[1009] 4. Advice Generation and Notification

[1010] Server: Based on the optimal investment allocation calculated by the generation AI, the server proposes specific investment details to the user. This advice includes the names of investment products and their purchase ratios.

[1011] Device: The generated advice is sent to the user's device and displayed on the application.

[1012] 5. User Feedback

[1013] Terminal: The user reviews the proposed investment allocation and adjusts their portfolio as needed. If the user approves the adjusted allocation, the information is sent to the server.

[1014] Server: Records approved operational allocations and performs re-analysis if necessary.

[1015] 6. Switching Procedures

[1016] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[1017] User: Follow the provided guide to access the company's 401K management site and perform the switching procedure based on the optimal investment allocation.

[1018] Specific examples

[1019] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[1020] 1. Device: The user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation and balanced orientation into the smartphone app.

[1021] 2. Server: Receives the data sent from the device and stores it in a database. Based on age, the risk tolerance is calculated as "medium," and the gap between current assets and the target amount is calculated as 15 million yen.

[1022] 3. Server: The generative AI analyzes the data and calculates the optimal asset allocation, for example, 40% domestic stocks, 40% international stocks, and 20% bonds.

[1023] 4. Server: Based on the calculation results, the server generates advice including specific investment details (for example, specific domestic stocks or bond product names) and notifies the user.

[1024] 5. Device: The user reviews the advice and either accepts the proposed allocation or fine-tunes it. If the user approves the allocation, the information is sent to the server.

[1025] 6. Server: Records approved allocations and performs reanalysis as needed. Additionally, provides links to 401K management sites and guides to simplify the switching process.

[1026] 7. User: Follow the provided guide to access the production site and perform the switching procedure with the new allocation.

[1027] As described above, this system includes specific means for providing users with optimal asset management allocations in a simple and effective manner, and supports maximizing the investment returns of corporate defined contribution pension plans.

[1028] The processing flow will be explained below.

[1029] Step 1:

[1030] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1031] Step 2:

[1032] The terminal receives the data entered by the user and temporarily stores it in local memory. When the user confirms the data and presses the send button, the data is sent to the server.

[1033] Step 3:

[1034] The server receives the data sent from the device and stores it in a database for subsequent processing.

[1035] Step 4:

[1036] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium."

[1037] Step 5:

[1038] The server calculates the asset gap based on the user's current assets and target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the gap is calculated as 15 million yen.

[1039] Step 6:

[1040] The server sends the results of the initial processing to the generation AI, which then begins analysis to calculate the optimal asset allocation based on the user data.

[1041] Step 7:

[1042] The server receives the analysis results returned by the generation AI and generates a specific investment breakdown, such as advice like "40% domestic stocks, 40% international stocks, 20% bonds."

[1043] Step 8:

[1044] The server sends information to the terminal to notify the user of the generated advice.

[1045] Step 9:

[1046] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[1047] Step 10:

[1048] The user adjusts the investment allocation based on the advice and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[1049] Step 11:

[1050] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[1051] Step 12:

[1052] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[1053] Step 13:

[1054] The terminal displays the received links and guides to the user.

[1055] Step 14:

[1056] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching process.

[1057] Example 1

[1058] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1059] In today's asset management environment, it is difficult for users to find the optimal asset allocation on their own, especially in systems like corporate defined contribution pension plans (401K), which require specialized knowledge. Furthermore, it is necessary to process a large amount of information at once to develop an appropriate investment strategy, which is time-consuming, and there are limited tools available to help users find the optimal solution for their risk and goals. This hinders users from optimal asset management.

[1060] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1061] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generative AI model to calculate an optimal asset allocation based on the calculation and analysis results; means for inputting a prompt sentence to the generative AI model; means for notifying the user of the optimal asset allocation; and means for receiving and recording the investment allocation adjusted by the user in accordance with the notified asset allocation. This allows the user to find an optimal asset allocation simply and effectively.

[1062] "Users" are participants in corporate defined contribution pension plans (401K) who use the system to find asset allocations.

[1063] "Age" is information indicating the current age of the user.

[1064] "Current assets" is information indicating the current total amount of assets held by the user.

[1065] The "target amount" is information indicating the amount of money that the user wants to achieve as a result of asset management.

[1066] "Management orientation" is information indicating the asset management policy desired by the user, and there are types such as "stable orientation," "balanced orientation," and "aggressive."

[1067] "Data input means" refers to a terminal or application that allows a user to input their age, current assets, target amount, and investment preference.

[1068] "Data receiving and storing means" refers to the server's function of receiving data sent by users and storing it in a database.

[1069] The "risk tolerance calculation means" refers to the server's function of calculating how much risk a user can tolerate based on data such as the user's age and investment preferences.

[1070] "Gap analysis means" refers to a function of the server for analyzing the difference between the user's current assets and the target amount.

[1071] A "generative AI model" refers to an artificial intelligence model that calculates optimal asset management allocations based on user data.

[1072] A "prompt sentence" refers to the input sentence provided to a generative AI model when requesting data analysis.

[1073] "Optimal investment allocation notification means" refers to the server's function of notifying users of the optimal asset investment allocation calculated by the generative AI model.

[1074] The "means for receiving and recording operational allocation" refers to the function of the server that receives operational allocation adjusted by the user and records it.

[1075] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. This system involves a process in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generative AI model calculates the optimal asset allocation based on the data.

[1076] Hardware and software used

[1077] 1. Device:

[1078] Users enter data using an application on their smartphone or PC, which then sends information about the user's age, current assets, target amount, and investment preference to the server.

[1079] Specific software used for mobile application development is Swift (for iOS) and Java (for Android), while HTML, CSS, and JavaScript are used for web application development.

[1080] 2. Server:

[1081] The server receives the data sent by the user and stores it in a database, such as MongoDB or SQL Server.

[1082] The server calculates the user's risk tolerance based on the received data and uses languages ​​such as Python and R to analyze the gap between the current assets and the target amount. These calculations use data processing libraries such as Pandas and NumPy.

[1083] A generative AI model (e.g., OpenAI's GPT series) is used to analyze user data and calculate optimal asset allocation. The generative AI model uses an API, and the optimal allocation is obtained by entering a prompt.

[1084] Process example

[1085] 1. Data Entry:

[1086] Device: The user launches the smartphone application and inputs their age, current assets, target amount, and investment orientation (e.g., "30 years old," "5 million yen," "20 million yen," "balanced orientation").

[1087] 2. Data Receipt and Storage:

[1088] Server: Receives data sent from the device and stores it securely in a MongoDB database using the HTTPS protocol.

[1089] 3. Risk Tolerance Calculation and Gap Analysis:

[1090] Server: Calculates the user's risk tolerance based on the received data. For example, if the user is 30 years old, the risk tolerance is calculated as "medium." Also, the gap between the current assets of 5 million yen and the target amount of 20 million yen is calculated as 15 million yen.

[1091] 4. Data analysis with generative AI models:

[1092] Server: Enter the following prompt into the generative AI model:

[1093] "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is looking for a balanced investment. What is the optimal asset allocation?"

[1094] The generative AI model responds by providing an optimal asset allocation, such as 40% domestic stocks, 40% international stocks, and 20% bonds.

[1095] 5. Advice generation and notification:

[1096] Server: Based on the allocation obtained from the generative AI model, the server generates advice including specific investment products (e.g., domestic stocks: "ABC Company," international stocks: "XYZ Company," bonds: "Japanese Government Bonds") and pushes it to the user's device.

[1097] Device: The advice sent to the user's device can be displayed and the details can be checked.

[1098] 6. User Feedback and Records:

[1099] Terminal: The user checks the proposed allocation and adjusts it if necessary. Once the adjusted allocation is approved, the data is sent to the server.

[1100] Server: Records the approved data and sends prompts to the generation AI again if reanalysis is required based on user feedback.

[1101] This system not only helps users easily find the optimal asset allocation, but also provides specific and effective means to maximize the investment returns of defined contribution pension plans.

[1102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1103] Step 1:

[1104] Data Entry

[1105] Device: The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1106] Input: The user enters "30 years old," "5 million yen," "20 million yen," and "balanced" into the application.

[1107] Specific operation: The user enters the required data into the application's input form and clicks the "Submit" button, which sends the input data from the terminal to the server.

[1108] Output: The input data is sent to the server.

[1109] Step 2:

[1110] Data reception and storage

[1111] Server: The server receives the data sent from the device and stores it in a database such as MongoDB using the HTTPS protocol.

[1112] Input: The data "30 years old," "5 million yen," "20 million yen," and "balanced" are sent to the server.

[1113] What it does: The server temporarily stores the received data in memory and then saves it to a database using a secure protocol.

[1114] Output: User data stored in the database.

[1115] Step 3:

[1116] Risk tolerance calculation and gap analysis

[1117] Server: Based on the stored data, the server calculates the user's risk tolerance and analyzes the gap between the current assets and the target amount.

[1118] Input: Data stored in the database: "30 years old," "5 million yen," "20 million yen," and "balanced orientation."

[1119] Specific operation: Using Python's Pandas library, the conditional statement calculates the risk tolerance as "medium" if the user's age is 30. It also calculates the gap between the current assets of 5 million yen and the target amount of 20 million yen, determining it to be 15 million yen.

[1120] Output: Data for risk tolerance "medium" and gap "15 million yen".

[1121] Step 4:

[1122] Data analysis with generative AI models

[1123] Server: Based on risk tolerance and gap data, the server inputs prompt statements into the generative AI model to analyze the optimal asset allocation.

[1124] Input: Data for "30 years old", "5 million yen", "20 million yen", "Balanced", "Risk tolerance: Medium", "Gap: 15 million yen".

[1125] Specific operation: The following prompt is input to the generative AI model: "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is balanced. Please tell me the optimal asset allocation."

[1126] Output: Data obtained from the generative AI model: 40% domestic stocks, 40% international stocks, 20% bonds.

[1127] Step 5:

[1128] Advice generation and notification

[1129] Server: The server generates a specific investment breakdown based on the optimal allocation obtained from the generative AI model and notifies the user's device.

[1130] Input: Data of "40% domestic stocks, 40% foreign stocks, 20% bonds".

[1131] Specific operation: Based on the allocation data, a specific investment breakdown is generated, such as "Domestic stocks: ABC Company 40%," "International stocks: XYZ Company 40%," and "Bonds: Japanese government bonds 20%." This is then sent as a push notification to the user's device.

[1132] Output: Specific investment breakdown notified to the terminal.

[1133] Step 6:

[1134] User feedback and records

[1135] Terminal: The user checks the notified allocation and adjusts it if necessary. After approval, the adjusted allocation data is sent to the server.

[1136] Input: Adjusted allocation data such as "Domestic stocks: ABC Company 30%", "International stocks: XYZ Company 50%", and "Bonds: Japanese government bonds 20%".

[1137] What happens: The user adjusts the allocation in the application interface and presses the "Accept" button, which sends the data to the server.

[1138] Output: The adjusted allocation data is sent to the server and recorded.

[1139] Step 7:

[1140] Switching Procedures

[1141] Server: Based on the optimal allocation obtained from the generative AI model, the server provides users with links and guides to their company's 401K management site.

[1142] Input: Optimal allocation data and link information.

[1143] Specific operation: The server generates a guide document explaining the specific switching procedure for the user and a link to the operation site, and notifies the user's terminal.

[1144] Output: The guide document and links that the user receives.

[1145] Through these processing steps, users can easily find the optimal asset management allocation, providing specific and effective means for maximizing the investment returns of corporate defined contribution pension plans.

[1146] (Application example 1)

[1147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1148] For participants in corporate defined contribution pension plans (401K), finding the optimal asset allocation for themselves requires knowledge and time. In particular, analysis and proposals based on multiple factors, such as age, current assets, target amount, and investment preferences, are required, and a simple and fast method for doing this is needed. Furthermore, the process for actually implementing the proposed investment allocation is complicated, and there is a lack of a means to smoothly complete the process. This creates challenges for participants, making it difficult to effectively optimize their investment allocation.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1150] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generating AI to calculate an optimal asset allocation based on the calculation and analysis results; means for notifying the user of the optimal asset allocation; means for receiving and recording an investment allocation adjusted by the user in accordance with the notified asset allocation; means for providing a link and guide to a company's investment site based on the investment allocation approved by the user; and means for proposing an optimal asset allocation using a generating AI model on a smart device. This allows users to easily find an optimal asset allocation for themselves and quickly and easily put that investment allocation into practice.

[1151] A "user" is someone who uses a particular application or system.

[1152] A "smart device" is a portable electronic device that can connect to the Internet, such as a smartphone or tablet.

[1153] A "generative AI model" is a model that uses artificial intelligence algorithms to analyze data and generate optimal solutions to specific problems.

[1154] "Age" is information indicating the length of time since the user was born.

[1155] "Current assets" is information indicating the amount of total assets currently held by the user.

[1156] The "target amount" is information indicating the amount of assets that the user wishes to achieve in the future.

[1157] "Management preference" is data that indicates the investment risk profile and investment style preferred by the user.

[1158] A "database" is an organized digital storage system for storing user-entered data.

[1159] "Risk tolerance" is an indicator that indicates the investment risk level that a user can tolerate.

[1160] The "gap" indicates the difference between current assets and the target amount.

[1161] "Notification" is the act of providing information to a user from an application or system.

[1162] "Adjustment" refers to the act of the user making subtle changes to the proposed investment allocation.

[1163] A "link" is a reference to a particular website or online resource.

[1164] A "guide" is a document or tool that provides procedures or instructions for performing a particular operation or procedure.

[1165] This invention relates to a system for finding optimal asset allocation for participants of corporate defined contribution pension plans (401K). The system includes the following components:

[1166] 1. Data Entry:

[1167] Terminal: Users use a smart terminal to input data such as age, current assets, target amount, investment preference, etc. The application on the terminal guides them through this data input.

[1168] 2. Data Receipt and Storage:

[1169] Server: The entered data is sent to the server and stored in a database, which prepares the data necessary for subsequent processing.

[1170] 3. Risk Tolerance Calculation and Gap Analysis:

[1171] Server: Based on the saved data, calculate the user's risk tolerance and analyze the gap between their current assets and their target amount. For example, classify the risk tolerance as "high," "medium," or "low" based on the user's age, asset status, and investment objectives.

[1172] 4. Calculation of optimal asset allocation using generative AI:

[1173] Server: Using a generative AI model, it calculates the optimal asset allocation based on the results of calculations and analysis. For example, it proposes a specific investment allocation such as "40% domestic stocks, 40% international stocks, 20% bonds."

[1174] 5. Investment Allocation Notification:

[1175] Server: Once the optimal asset allocation is calculated, the server notifies the user of the results via their smart device. The user receives the notification and can check the proposed asset allocation on the application.

[1176] 6. User feedback and adjustments:

[1177] Terminal: The user can review the proposed allocation and make any necessary adjustments. If the user approves the adjustments, the information is sent to the server.

[1178] Server: Receives the adjusted investment allocation data, stores it in a database, and re-analyzes it as needed.

[1179] 7. Providing Links and Guides:

[1180] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[1181] Terminal: Using the provided links and guides, users can easily carry out the switching procedure.

[1182] Hardware and software used:

[1183] Hardware: Smart devices (e.g. smartphones, tablets)

[1184] Software: On the server side, we use web frameworks such as Flask to build the API, and OpenAI's API for data analysis.

[1185] Examples:

[1186] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and a balanced investment orientation, the generative AI model will use the following prompt to suggest the optimal asset allocation.

[1187] Example prompt sentence:

[1188] "If the user is 30 years old, has 5 million yen in current assets, 20 million yen in target assets, and has a balanced investment preference, please suggest the optimal asset allocation."

[1189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1190] Step 1:

[1191] The user uses a smart device to enter their age, current assets, target amount, and investment preference into the application. This input data is entered using text fields and drop-down menus. The entered data is temporarily saved on the device. Input data: Age, current assets, target amount, investment preference. Output data: User data temporarily saved on the device.

[1192] Step 2:

[1193] Input data is sent from the terminal to the server. The server receives this data and saves it in the database. The server checks the integrity of the data and notifies the terminal that saving is complete. Input data: User data (age, current assets, target amount, investment orientation). Output data: User data saved in the database, notification of saving completion.

[1194] Step 3:

[1195] The server calculates the user's risk tolerance based on the data stored in the database. A fixed algorithm is used for the calculation to generate a risk profile that takes into account factors such as the user's age and financial situation. The result of this process is a calculated risk tolerance of either "high," "medium," or "low." Input data: User information in the database. Output data: Risk tolerance.

[1196] Step 4:

[1197] The server analyzes the gap between the current assets and the target amount. The required additional assets are calculated by subtracting the target amount from the current assets. This process calculates the increase in assets required for the user to reach the goal. Input data: User information in the database. Output data: Gap (additional assets).

[1198] Step 5:

[1199] The server uses a generative AI model to calculate the optimal asset management allocation based on the analysis results of risk tolerance and asset gap. The generative AI model uses OpenAI's API and generates a specific investment allocation (e.g., 40% domestic stocks, 40% international stocks, 20% bonds) using a specific prompt as input. Input data: risk tolerance, asset gap. Example prompt: "If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment preference, please suggest the optimal asset management allocation." Output data: optimal asset management allocation.

[1200] Step 6:

[1201] The server notifies the user's device of the calculated optimal asset allocation. This notification includes specific investment details and proposals. Input data: Optimal asset allocation. Output data: Notification to the user's device.

[1202] Step 7:

[1203] The terminal displays the received operational allocation notification to the user. The user can review this proposal and make fine adjustments as necessary. Once the user completes and approves the adjustments, the information is sent from the terminal to the server. Input data: Notified operational allocation, user adjustments. Output data: Approved operational allocation to the server.

[1204] Step 8:

[1205] The server generates a link to the company's operation site and a guide for switching procedures based on the operation allocation approved by the user, and sends them to the user's terminal. The link contains information that allows the user to easily access and operate it. Input data: Approved operation allocation. Output data: Link and guide.

[1206] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1207] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice. The program processing of this system is explained below with specific examples.

[1208] System Embodiments

[1209] 1. Data Entry

[1210] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1211] 2. Emotion recognition

[1212] The emotion engine built into the device analyzes the user's facial expressions and tone of voice while they are typing to recognize their current emotional state, collecting data such as whether they are feeling stressed or relaxed.

[1213] 3. Data Transmission

[1214] The terminal transmits the input data and the emotion data recognized by the emotion engine to the server.

[1215] 4. Data Storage

[1216] The server receives the data sent from the device and stores it in a database. The stored data includes age, current assets, target amount, investment preference, and emotional data.

[1217] 5. Initial Processing

[1218] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium." If the current assets are 5 million yen and the target amount is 20 million yen, the server analyzes the gap to be 15 million yen.

[1219] 6. Emotion correction

[1220] The server corrects the user's investment preference parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the server will correct the parameters to suggest low-risk investments.

[1221] 7. Generative AI Analysis

[1222] The server sends the corrected data to the generation AI, which then begins analysis to calculate the optimal asset management allocation.

[1223] 8. Advice Generation

[1224] The server generates a specific investment breakdown based on the analysis results returned by the AI. For example, it creates advice such as "40% domestic stocks, 40% international stocks, 20% bonds."

[1225] 9. Advice Notice

[1226] The server sends information to the terminal to notify the user of the generated advice.

[1227] 10. User Verification

[1228] The device displays the received advice to the user, who then confirms it. If the user is satisfied, they can approve the operational allocation as is, or make fine adjustments as necessary.

[1229] 11. Allocation Adjustments

[1230] The user adjusts the proposed operation details and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[1231] 12. Data Recording

[1232] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[1233] 13. Switching Procedures

[1234] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[1235] 14. User Execution

[1236] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching procedure with the new allocation.

[1237] Specific examples

[1238] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[1239] 1. On the terminal, the user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation / balanced orientation.

[1240] 2. The emotion engine analyzes the user's facial expressions and tone of voice during this input and recognizes that the user is feeling stressed.

[1241] 3. The device sends the input data and emotion data to the server.

[1242] 4. The server receives the input data and begins analysis.

[1243] 5. The server sets the risk tolerance to "medium" based on the user's age and calculates the gap between the current assets and the target amount to 15 million yen.

[1244] 6. The server adjusts the investment-oriented parameters based on the emotional data to suggest low-risk investments.

[1245] 7. The generation AI calculates the optimal asset management allocation based on the corrected data and suggests an allocation of, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[1246] 8. The server generates specific advice based on the calculation results and notifies the user.

[1247] 9. On the device, the user checks the advice and either accepts it or makes adjustments.

[1248] 10. The server records the final operational allocation and provides a link to guide the switching procedure.

[1249] 11. The user follows the provided link to complete the switching procedure based on the new allocation on the company's 401K management site.

[1250] In this way, by incorporating an emotion engine, the present invention is a system that can provide more precise asset management advice that takes into account the user's short-term emotional state, and can help maximize the investment returns of defined contribution pension plans.

[1251] The processing flow will be explained below.

[1252] Step 1:

[1253] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1254] Step 2:

[1255] The device temporarily stores the user's input data in its local memory. Once input is complete, the user presses the send button to send the data to the server.

[1256] Step 3:

[1257] The emotion engine installed in the device analyzes the user's facial expressions and tone of voice while inputting, and recognizes the user's emotional state (e.g., stress, relaxation, etc.). The emotion data is also simultaneously sent to the server.

[1258] Step 4:

[1259] The server receives the data sent from the terminal (age, current assets, target amount, investment orientation, emotional data) and stores it in a database.

[1260] Step 5:

[1261] The server calculates your risk tolerance based on your age. For example, if you are 30, your risk tolerance will be set to "medium."

[1262] Step 6:

[1263] The server calculates the asset gap based on the current assets and the target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the asset gap will be 15 million yen.

[1264] Step 7:

[1265] The server adjusts the user's operational parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the parameters are adjusted to reduce risk.

[1266] Step 8:

[1267] The server sends the corrected data to the AI, which then calculates the optimal asset allocation. For example, it suggests an allocation of 35% domestic stocks, 35% international stocks, and 30% bonds.

[1268] Step 9:

[1269] The server generates a detailed investment breakdown (for example, specific stocks and bond product names) based on the analysis results returned by the generation AI.

[1270] Step 10:

[1271] The server sends information to the terminal to notify the user of the generated advice.

[1272] Step 11:

[1273] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[1274] Step 12:

[1275] The user checks the proposed operation details, adjusts them, and then confirms the allocation. When the user presses the confirm button, the allocation data is sent from the terminal to the server.

[1276] Step 13:

[1277] The server receives the adjusted allocation of the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[1278] Step 14:

[1279] The server generates a link to the company's 401K operation site and a guide for the user to carry out the switching procedure, and transmits the generated link to the terminal.

[1280] Step 15:

[1281] The terminal displays the received links and guides to the user.

[1282] Step 16:

[1283] The user clicks on the provided link to access the company's 401K management site and follows the instructions to complete the switching procedure with the new allocation.

[1284] Example 2

[1285] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1286] Conventional asset management advice systems can only provide general advice based on the user's age and asset information, and are unable to provide advice that takes into account the user's individual emotional state. This makes it difficult to obtain optimal investment advice that reflects the user's emotions, such as suggesting high-risk investments even when the user is feeling stressed. Therefore, there is a need for a system that provides more personalized asset management advice that takes into account the user's emotional state.

[1287] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for the user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; and means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount. This enables the risk profile to be corrected taking into account the user's emotional state. Furthermore, the server includes means for analyzing the user's facial expression and tone of voice during input and recognizing the user's current emotional state; means for correcting the user's investment orientation based on the recognized emotional state; and means for the generation AI to recalculate the optimal asset investment allocation using the corrected data. This makes it possible to provide personalized asset management advice based on the user's real-time emotional state.

[1288] "User" refers to an individual or corporation that uses this system.

[1289] "Age" is a data item that indicates the user's current age.

[1290] "Current assets" is a data item that indicates the amount of total assets currently owned by the user.

[1291] "Target amount" is a data item that indicates the amount of assets that the user wants to achieve in the future.

[1292] "Management orientation" is a data item that indicates the orientation regarding the investment style and risk tolerance desired by the user.

[1293] "Data input means" refers to an interface for a user to input data regarding age, current assets, target amount, and investment orientation.

[1294] "Data receiving means" refers to a means by which the server receives data input by the user.

[1295] "Database storage means" refers to means for storing received data in a database.

[1296] "Risk tolerance calculation means" refers to a means for calculating risk tolerance based on the user's age.

[1297] "Gap analysis tools" refer to tools for analyzing the difference between current assets and target amounts.

[1298] "Generative AI" refers to a system that uses artificial intelligence to calculate optimal asset management allocations.

[1299] "Emotion recognition means" refers to means for analyzing the facial expressions and tone of voice of the user while they are inputting information and recognizing the user's emotional state.

[1300] The "emotion correction means" refers to a means for correcting the user's operational orientation parameters based on the recognized emotional state.

[1301] The "asset management allocation notification means" refers to a means for notifying the user of the optimal asset management allocation.

[1302] The "management allocation receiving means" refers to a means for receiving the investment allocation adjusted by the user in accordance with the notified asset investment allocation.

[1303] "Investment allocation recording means" refers to a means for recording received investment allocations in a database.

[1304] "Recalculation means" refers to the means by which the generation AI recalculates the optimal asset management allocation using the corrected data.

[1305] The system of the present invention is designed to enable participants of corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice.

[1306] The main elements of the system consist of a user device, a server, and a generative AI model. User devices are general-purpose devices such as smartphones and PCs. The server is a high-performance computer system that uses MySQL as its database management system. For emotion recognition, a camera and microphone are used, and dedicated software for image and audio analysis is installed.

[1307] First, the user launches the application on their smartphone or PC and enters data such as their age, current assets, target amount, and investment preference. Next, while the user is entering information, the emotion engine analyzes their facial expressions and tone of voice to recognize their current emotional state. For example, if the user is frowning, it will be recognized as "stressed."

[1308] The device sends the input data and emotion data to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a database, calculates the user's risk tolerance based on their age, and analyzes the difference between their current assets and their target amount. For example, if a user is 30 years old, their current assets are 5 million yen, and their target amount is 20 million yen, the gap is calculated as 15 million yen.

[1309] The server adjusts the risk tolerance based on the emotional data. For example, if the user is stressed, it will recommend low-risk investments. The adjusted data is sent to the generative AI model, which calculates the optimal asset allocation. The prompt for the generative AI model is as follows:

[1310] User information: Age 30, current assets 5 million yen, target amount 20 million yen, investment orientation, balance orientation, emotional state: stress

[1311] Desired outcome: Recommend optimal asset allocation

[1312] The AI ​​then calculates the optimal asset allocation based on these prompts, returning a recommended allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds." The server receives the results and generates specific advice for the user. The generated advice is sent to the device, where the user can review it and either accept it as is or make fine adjustments as needed.

[1313] When the final allocation is determined, the server records it in the database, generates a link and guide to the company's 401K management site, and sends it to the terminal. The user clicks the provided link to access the company's 401K management site and completes the switching procedure with the new allocation.

[1314] As described above, the present invention is a system that provides precise asset management advice that takes into account the user's short-term emotional state, and maximizes the management efficiency of defined contribution pension plans.

[1315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1316] Step 1:

[1317] Data Entry

[1318] The user launches the application on their smartphone or PC and enters data on their age, current assets, target amount, and investment preference. Text boxes and pull-down menus are displayed in the input fields, and the user enters or selects the required information. Once the input is complete, the user clicks the submit button. The input data is used in the next step.

[1319] Input: Age, current assets, target amount, investment preference

[1320] Output: User input data

[1321] Step 2:

[1322] emotion recognition

[1323] The device's built-in emotion engine uses the camera and microphone to capture facial expressions and tone of voice while the user is entering data, and emotion recognition algorithms analyze this data to identify the user's current emotional state (e.g., "stressed" or "relaxed").

[1324] Input: User's facial expression data, tone of voice

[1325] Output: User emotion data

[1326] Step 3:

[1327] Data transmission

[1328] The device sends the data entered in step 1 and the emotion data recognized in step 2 to the server using a secure communication protocol (e.g., HTTPS). The data is packaged in JSON format packets and sent to the server.

[1329] Input: User input data, emotion data

[1330] Output: Data sent to the server

[1331] Step 4:

[1332] Data storage

[1333] The server receives the data packets sent from the device. The received data is stored in a database management system (e.g., MySQL). The database table contains user ID, age, current assets, target amount, investment orientation, and emotional data.

[1334] Input: Data sent from the terminal

[1335] Output: Data stored in the database

[1336] Step 5:

[1337] Initial Processing

[1338] The server calculates the risk tolerance level based on the user's age based on the stored data. At the same time, it calculates the difference (gap) between the current assets and the target amount. For example, if the user is 30 years old, the risk tolerance level is set to "medium," and the current assets are 5 million yen and the target amount is 20 million yen, the gap will be calculated as 15 million yen.

[1339] Input: User data read from the database

[1340] Output: Risk tolerance, gap analysis results

[1341] Step 6:

[1342] emotion correction

[1343] The server adjusts the risk tolerance based on the emotional data. For example, if the user is under stress, the server will adjust the risk tolerance to suggest lower-risk investments. The risk tolerance may be changed from "medium" to "low."

[1344] Input: Calculated risk tolerance, emotional data

[1345] Output: Adjusted risk tolerance

[1346] Step 7:

[1347] Generative AI analysis

[1348] The server sends the corrected data to the generative AI model and inputs a prompt to calculate the optimal asset management allocation. The prompt is "User information: age 30, current assets of 5 million yen, target amount of 20 million yen, investment orientation balanced orientation, emotional state: stressed. Desired result: Please suggest the optimal asset management allocation." The generative AI model calculates the optimal allocation based on this prompt.

[1349] Input: prompt statement

[1350] Output: Optimal asset allocation

[1351] Step 8:

[1352] Advice Generation

[1353] The server receives the results of the AI ​​generation and generates specific investment advice for the user. For example, it generates text and graphs suggesting an allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds."

[1354] Input: Investment allocation data from the generation AI

[1355] Output: Investment advice for the user

[1356] Step 9:

[1357] Advice Notice

[1358] The server sends the generated advice to the user's device, where it is displayed in a visually easy-to-understand format (graphs, text).

[1359] Enter: Investment Advice

[1360] Output: Advice notification to user terminal

[1361] Step 10:

[1362] User Verification

[1363] The terminal displays the received advice to the user. The user checks the displayed investment details and presses the "Approve" button if they are satisfied with the details, or the "Adjust" button if they wish to readjust.

[1364] Input: Investment advice from the server

[1365] Output: User review and approval or adjustment

[1366] Step 11:

[1367] Allocation adjustment

[1368] When the user presses the "Adjust" button, a screen will appear where they can adjust their investment details using sliders and text fields. The user can manually adjust the allocation to their desired level and press the "Confirm" button. The adjusted data will then be sent back to the server from the device.

[1369] Input: User's adjusted share

[1370] Output: Sends the allocated data to the server

[1371] Step 12:

[1372] Data Recording

[1373] The server receives the user's adjusted allocation data and records it in the database again. If necessary, it requests the generation AI to reanalyze it, generates new results, and notifies the user.

[1374] Input: User's adjusted allocation data

[1375] Output: Records in the database

[1376] Step 13:

[1377] Switching Procedures

[1378] The server creates a link to the company's 401K management site and a guide for the specific switching procedure, and sends it to the terminal. The procedure guide provides detailed explanations for each step.

[1379] Input: Final investment allocation

[1380] Output: Switching Procedure Guide

[1381] Step 14:

[1382] User execution

[1383] The user clicks on the provided link to access the company's 401K management site. Following the displayed guide, the user completes the switching procedure to the new allocation. Specifically, after logging in, the user enters the new asset allocation and presses the confirm / confirm button.

[1384] Input: Switching Procedure Guide

[1385] Output: The completed switching procedure

[1386] The above are the specific processing steps of the system of the present invention. Through this flow, personalized asset management advice that also takes into account the user's emotional state is provided.

[1387] (Application example 2)

[1388] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1389] Existing asset management support systems for corporate defined contribution pension plans (401K) provide investment advice without considering the user's emotional state, and therefore cannot suggest appropriate investment allocations when the user is stressed or relaxed. Furthermore, while short-term emotional fluctuations in users are likely to affect investment decisions, no systems exist that take this into account. As a result, it is difficult to provide optimal asset allocations for individual users.

[1390] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's facial expressions and tone of voice to collect emotional data and correcting asset management suggestions based on the emotional data, means for the user to input data regarding age, current assets, target amount, and investment orientation, and means for receiving the data input by the user and storing it in a database. This makes it possible to provide more accurate asset management advice that reflects the user's emotional state.

[1391] The "input means" is a means by which a user provides information regarding age, current assets, target amount, and investment preference to the system.

[1392] "Means for saving in a database" refers to a means for receiving and safely storing information entered by a user.

[1393] The "means for calculating risk tolerance" is a means for calculating how much risk a user can tolerate based on information such as the user's age.

[1394] The "means for analyzing the gap" is a means for evaluating the difference between the user's current assets and the target amount, and analyzing how to fill the gap.

[1395] The "means calculated by the generating AI" refers to the means by which the generating AI calculates the optimal asset management allocation based on the analysis results.

[1396] The "means for notifying" is a means for notifying the user of the calculated optimal asset management allocation.

[1397] The "means for receiving and recording" is a means for receiving and recording information on the operational allocation adjusted by the user.

[1398] The "means for collecting emotional data" is a means for analyzing the user's facial expressions and tone of voice to collect data about the user's emotional state.

[1399] The "correction means" is a means for appropriately correcting asset management proposals based on the collected emotional data.

[1400] This invention relates to a system for finding optimal asset allocations for participants in corporate defined contribution pension plans (401K). The system incorporates an emotion engine and provides personalized investment advice by recognizing the user's emotional state.

[1401] System configuration

[1402] 1. User Device

[1403] Input means: The user inputs information about age, current assets, target amount, and investment orientation.

[1404] Emotion recognition means: Using the camera and microphone built into the device, the user's facial expressions and tone of voice are analyzed to recognize their emotional state.

[1405] Notification means: The calculated optimal asset management allocation is notified to the user.

[1406] Display: Users can view and adjust asset allocation suggestions on their devices.

[1407] 2. Server

[1408] Data storage means: User information sent from the terminal is stored in a database.

[1409] Risk tolerance calculation method: Calculates risk tolerance based on information such as the user's age.

[1410] Gap analysis tool: Evaluate the difference between your current assets and your target amount and analyze how to close the gap.

[1411] Generation AI calculation method: Based on the analysis results, the generation AI calculates the optimal asset management allocation.

[1412] Adjustment receiving means: Receives and records information on the operational allocation adjusted by the user.

[1413] Emotion data collection means: Analyzes the emotional data sent from the device and corrects asset management suggestions.

[1414] Hardware and software used

[1415] 1. Hardware

[1416] Smartphone camera: Used to capture the user's facial expressions in real time.

[1417] Smartphone microphone: Used to analyze the user's voice tone.

[1418] 2. Software

[1419] OpenCV (cv2): An image processing library used to capture and analyze facial expressions.

[1420] EmotionEngine: An emotion engine that extracts emotional data by analyzing the user's facial expressions and voice tone.

[1421] requests: A Python library for making HTTP requests, used to send and receive data.

[1422] Specific examples

[1423] Let's say a user is 35 years old, has 6 million yen in current assets, a target amount of 25 million yen, and prefers low-risk investments. The emotion engine recognizes that the user is relaxed. Based on this, the system operates as follows:

[1424] 1. Data Entry

[1425] Users enter their age, current assets, target amount, and investment preferences into a smartphone app.

[1426] 2. Emotion recognition

[1427] Using the smartphone's camera and microphone, emotional data is collected by analyzing the user's facial expressions and voice.

[1428] 3. Data transmission and storage

[1429] The input data and emotion data are sent to a server and stored in a database.

[1430] 4. Risk tolerance and gap analysis

[1431] The server calculates the risk tolerance based on the user's age and evaluates the gap between the current assets and the target amount.

[1432] 5. Calculation by Generative AI

[1433] After adjusting investment preferences based on emotional data, the generative AI calculates the optimal asset allocation, suggesting, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[1434] 6. Notice and Adjustment

[1435] The calculated asset management allocation is notified to the user, who can then check and adjust it.

[1436] Example prompts

[1437] A 35-year-old user currently has assets of 6 million yen, a target amount of 25 million yen, and a low-risk investment orientation. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset allocation.

[1438] This makes it possible to provide highly accurate asset management advice that reflects the user's short-term emotional state.

[1439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1440] Step 1:

[1441] The user launches the smartphone app and enters data such as age, current assets, target amount, and investment preference. The entered data is temporarily saved in the device. The data is entered by the user manually providing information for each item. The specific action of data entry is for the user to type the information into each input field.

[1442] Step 2:

[1443] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, thereby collecting the user's emotional data. Specifically, the camera captures the user's face and the microphone records their voice. The emotional data is then analyzed using EmotionEngine. This emotional data includes happiness or sadness as determined from the user's facial expressions and stress levels as determined from their voice tone.

[1444] Step 3:

[1445] The device sends input data and emotion data to the server. An HTTP request is used to send the data, and the data is sent in JSON format. Specifically, the requests library is used to send the data to the server. The input for this step is the user's input data and emotion data, and the output is data sent to the server.

[1446] Step 4:

[1447] The server stores the received data in a database. The input data here is the information sent from the terminal, and the output is the correct record in the database. Specifically, the received data is analyzed, converted into an appropriate format, and inserted into the database. The database management system (DBMS) used is, for example, MySQL.

[1448] Step 5:

[1449] The server calculates the risk tolerance based on the user's age. The input for this step is age data, and the output is a numerical value for risk tolerance. Specifically, it sets a risk level (low, medium, high) according to the user's age range. For example, those under 30 are considered high risk, those between 30 and 50 are considered medium risk, and those over 50 are considered low risk.

[1450] Step 6:

[1451] The server analyzes the gap between the current assets and the target amount. The input for this step is the current assets and target amount data, and the output is the calculation result of the asset gap (shortfall). Specifically, it subtracts the current assets from the target amount to calculate the shortfall.

[1452] Step 7:

[1453] The server corrects the operational orientation parameters based on the emotional data. Specifically, it analyzes the collected emotional data and corrects it to suggest low-risk operations if the user is feeling stressed. The input of this step is the emotional data, and the output is the corrected operational orientation parameters.

[1454] Step 8:

[1455] The server uses the generative AI model to calculate the optimal asset management allocation based on the corrected data. The inputs for this step are risk tolerance, asset gap, and corrected investment preference parameters, and the output is the investment breakdown. Specifically, input is given to the generative AI model using a prompt, which calculates the optimal allocation. An example prompt is, "A 35-year-old user has current assets of 6 million yen, a target amount of 25 million yen, and an investment preference for low risk. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset management allocation."

[1456] Step 9:

[1457] The server notifies the terminal of the calculated optimal asset management allocation. The input to this step is the calculation result of the generative AI model, and the output is notification information to the user terminal. Specifically, the notification information is formatted in JSON format and sent to the terminal via an HTTP request.

[1458] Step 10:

[1459] The terminal displays the received asset management allocation to the user, who can then confirm and adjust its contents. The input to this step is the notification information sent from the server, and the output is the investment allocation adjusted by the user. Specifically, the investment allocation is displayed on the terminal's UI, and an interface is provided for the user to adjust.

[1460] Step 11:

[1461] The user sends the adjusted operational allocation from the terminal to the server, which receives and records the information. The input of this step is the operational allocation after the user adjustment, and the output is a record in the database. The specific operation is to receive the user's adjusted data and save it in the database.

[1462] Step 12:

[1463] The server generates a link and guide to the company's 401K management site and sends it to the terminal. The input to this step is the user's final investment allocation decision information, and the output is link information with the guide. Specifically, it generates an appropriate link and creates and sends a guide to help the user complete the procedure smoothly.

[1464] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1465] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1466] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1467] [Fourth embodiment]

[1468] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1469] 7, a 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.

[1470] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1471] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1472] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1473] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1474] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1475] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1476] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1477] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1478] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1479] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1480] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1481] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Specifically, it involves a series of processes in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generation AI calculates the optimal asset allocation based on that data. The processing of the program of this system is explained below with specific examples.

[1482] System Embodiments

[1483] 1. Data Entry

[1484] Device: Using a smartphone or PC application, users input their age, current assets, target amount, and investment preference (e.g., stable, balanced, aggressive).

[1485] Server: Receives the data entered by the device and stores it in a database.

[1486] 2. Initial Processing

[1487] Server: Calculates risk tolerance based on received user data. For example, if the user's age is 30, the risk tolerance is calculated to be relatively high. Also, if the user's current assets are 5 million yen and the target amount is 20 million yen, the gap is analyzed.

[1488] 3. Analysis by generative AI

[1489] Server: Based on the results of the initial processing, the generation AI analyzes the data based on the user's age, current assets, target amount, and investment preferences. For example, the generation AI calculates the optimal ratio of domestic stocks, international stocks, and bonds. For a young person with a balanced investment preference, the AI ​​may suggest an allocation of 40% domestic stocks, 40% international stocks, and 20% bonds.

[1490] 4. Advice Generation and Notification

[1491] Server: Based on the optimal investment allocation calculated by the generation AI, the server proposes specific investment details to the user. This advice includes the names of investment products and their purchase ratios.

[1492] Device: The generated advice is sent to the user's device and displayed on the application.

[1493] 5. User Feedback

[1494] Terminal: The user reviews the proposed investment allocation and adjusts their portfolio as needed. If the user approves the adjusted allocation, the information is sent to the server.

[1495] Server: Records approved operational allocations and performs re-analysis if necessary.

[1496] 6. Switching Procedures

[1497] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[1498] User: Follow the provided guide to access the company's 401K management site and perform the switching procedure based on the optimal investment allocation.

[1499] Specific examples

[1500] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[1501] 1. Device: The user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation and balanced orientation into the smartphone app.

[1502] 2. Server: Receives the data sent from the device and stores it in a database. Based on age, the risk tolerance is calculated as "medium," and the gap between current assets and the target amount is calculated as 15 million yen.

[1503] 3. Server: The generative AI analyzes the data and calculates the optimal asset allocation, for example, 40% domestic stocks, 40% international stocks, and 20% bonds.

[1504] 4. Server: Based on the calculation results, the server generates advice including specific investment details (for example, specific domestic stocks or bond product names) and notifies the user.

[1505] 5. Device: The user reviews the advice and either accepts the proposed allocation or fine-tunes it. If the user approves the allocation, the information is sent to the server.

[1506] 6. Server: Records approved allocations and performs reanalysis as needed. Additionally, provides links to 401K management sites and guides to simplify the switching process.

[1507] 7. User: Follow the provided guide to access the production site and perform the switching procedure with the new allocation.

[1508] As described above, this system includes specific means for providing users with optimal asset management allocations in a simple and effective manner, and supports maximizing the investment returns of corporate defined contribution pension plans.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1512] Step 2:

[1513] The terminal receives the data entered by the user and temporarily stores it in local memory. When the user confirms the data and presses the send button, the data is sent to the server.

[1514] Step 3:

[1515] The server receives the data sent from the device and stores it in a database for subsequent processing.

[1516] Step 4:

[1517] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium."

[1518] Step 5:

[1519] The server calculates the asset gap based on the user's current assets and target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the gap is calculated as 15 million yen.

[1520] Step 6:

[1521] The server sends the results of the initial processing to the generation AI, which then begins analysis to calculate the optimal asset allocation based on the user data.

[1522] Step 7:

[1523] The server receives the analysis results returned by the generation AI and generates a specific investment breakdown, such as advice like "40% domestic stocks, 40% international stocks, 20% bonds."

[1524] Step 8:

[1525] The server sends information to the terminal to notify the user of the generated advice.

[1526] Step 9:

[1527] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[1528] Step 10:

[1529] The user adjusts the investment allocation based on the advice and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[1530] Step 11:

[1531] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[1532] Step 12:

[1533] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[1534] Step 13:

[1535] The terminal displays the received links and guides to the user.

[1536] Step 14:

[1537] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching process.

[1538] Example 1

[1539] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1540] In today's asset management environment, it is difficult for users to find the optimal asset allocation on their own, especially in systems like corporate defined contribution pension plans (401K), which require specialized knowledge. Furthermore, it is necessary to process a large amount of information at once to develop an appropriate investment strategy, which is time-consuming, and there are limited tools available to help users find the optimal solution for their risk and goals. This hinders users from optimal asset management.

[1541] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1542] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generative AI model to calculate an optimal asset allocation based on the calculation and analysis results; means for inputting a prompt sentence to the generative AI model; means for notifying the user of the optimal asset allocation; and means for receiving and recording the investment allocation adjusted by the user in accordance with the notified asset allocation. This allows the user to find an optimal asset allocation simply and effectively.

[1543] "Users" are participants in corporate defined contribution pension plans (401K) who use the system to find asset allocations.

[1544] "Age" is information indicating the current age of the user.

[1545] "Current assets" is information indicating the current total amount of assets held by the user.

[1546] The "target amount" is information indicating the amount of money that the user wants to achieve as a result of asset management.

[1547] "Management orientation" is information indicating the asset management policy desired by the user, and there are types such as "stable orientation," "balanced orientation," and "aggressive."

[1548] "Data input means" refers to a terminal or application that allows a user to input their age, current assets, target amount, and investment preference.

[1549] "Data receiving and storing means" refers to the server's function of receiving data sent by users and storing it in a database.

[1550] The "risk tolerance calculation means" refers to the server's function of calculating how much risk a user can tolerate based on data such as the user's age and investment preferences.

[1551] "Gap analysis means" refers to a function of the server for analyzing the difference between the user's current assets and the target amount.

[1552] A "generative AI model" refers to an artificial intelligence model that calculates optimal asset management allocations based on user data.

[1553] A "prompt sentence" refers to the input sentence provided to a generative AI model when requesting data analysis.

[1554] "Optimal investment allocation notification means" refers to the server's function of notifying users of the optimal asset investment allocation calculated by the generative AI model.

[1555] The "means for receiving and recording operational allocation" refers to the function of the server that receives operational allocation adjusted by the user and records it.

[1556] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. This system involves a process in which a user inputs data regarding their age, current assets, target amount, and investment preference, and a generative AI model calculates the optimal asset allocation based on the data.

[1557] Hardware and software used

[1558] 1. Device:

[1559] Users enter data using an application on their smartphone or PC, which then sends information about the user's age, current assets, target amount, and investment preference to the server.

[1560] Specific software used for mobile application development is Swift (for iOS) and Java (for Android), while HTML, CSS, and JavaScript are used for web application development.

[1561] 2. Server:

[1562] The server receives the data sent by the user and stores it in a database, such as MongoDB or SQL Server.

[1563] The server calculates the user's risk tolerance based on the received data and uses languages ​​such as Python and R to analyze the gap between the current assets and the target amount. These calculations use data processing libraries such as Pandas and NumPy.

[1564] A generative AI model (e.g., OpenAI's GPT series) is used to analyze user data and calculate optimal asset allocation. The generative AI model uses an API, and the optimal allocation is obtained by entering a prompt.

[1565] Process example

[1566] 1. Data Entry:

[1567] Device: The user launches the smartphone application and inputs their age, current assets, target amount, and investment orientation (e.g., "30 years old," "5 million yen," "20 million yen," "balanced orientation").

[1568] 2. Data Receipt and Storage:

[1569] Server: Receives data sent from the device and stores it securely in a MongoDB database using the HTTPS protocol.

[1570] 3. Risk Tolerance Calculation and Gap Analysis:

[1571] Server: Calculates the user's risk tolerance based on the received data. For example, if the user is 30 years old, the risk tolerance is calculated as "medium." Also, the gap between the current assets of 5 million yen and the target amount of 20 million yen is calculated as 15 million yen.

[1572] 4. Data analysis with generative AI models:

[1573] Server: Enter the following prompt into the generative AI model:

[1574] "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is looking for a balanced investment. What is the optimal asset allocation?"

[1575] The generative AI model responds by providing an optimal asset allocation, such as 40% domestic stocks, 40% international stocks, and 20% bonds.

[1576] 5. Advice generation and notification:

[1577] Server: Based on the allocation obtained from the generative AI model, the server generates advice including specific investment products (e.g., domestic stocks: "ABC Company," international stocks: "XYZ Company," bonds: "Japanese Government Bonds") and pushes it to the user's device.

[1578] Device: The advice sent to the user's device can be displayed and the details can be checked.

[1579] 6. User Feedback and Records:

[1580] Terminal: The user checks the proposed allocation and adjusts it if necessary. Once the adjusted allocation is approved, the data is sent to the server.

[1581] Server: Records the approved data and sends prompts to the generation AI again if reanalysis is required based on user feedback.

[1582] This system not only helps users easily find the optimal asset allocation, but also provides specific and effective means to maximize the investment returns of defined contribution pension plans.

[1583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1584] Step 1:

[1585] Data Entry

[1586] Device: The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1587] Input: The user enters "30 years old," "5 million yen," "20 million yen," and "balanced" into the application.

[1588] Specific operation: The user enters the required data into the application's input form and clicks the "Submit" button, which sends the input data from the terminal to the server.

[1589] Output: The input data is sent to the server.

[1590] Step 2:

[1591] Data reception and storage

[1592] Server: The server receives the data sent from the device and stores it in a database such as MongoDB using the HTTPS protocol.

[1593] Input: The data "30 years old," "5 million yen," "20 million yen," and "balanced" are sent to the server.

[1594] What it does: The server temporarily stores the received data in memory and then saves it to a database using a secure protocol.

[1595] Output: User data stored in the database.

[1596] Step 3:

[1597] Risk tolerance calculation and gap analysis

[1598] Server: Based on the stored data, the server calculates the user's risk tolerance and analyzes the gap between the current assets and the target amount.

[1599] Input: Data stored in the database: "30 years old," "5 million yen," "20 million yen," and "balanced orientation."

[1600] Specific operation: Using Python's Pandas library, the conditional statement calculates the risk tolerance as "medium" if the user's age is 30. It also calculates the gap between the current assets of 5 million yen and the target amount of 20 million yen, determining it to be 15 million yen.

[1601] Output: Data for risk tolerance "medium" and gap "15 million yen".

[1602] Step 4:

[1603] Data analysis with generative AI models

[1604] Server: Based on risk tolerance and gap data, the server inputs prompt statements into the generative AI model to analyze the optimal asset allocation.

[1605] Input: Data for "30 years old", "5 million yen", "20 million yen", "Balanced", "Risk tolerance: Medium", "Gap: 15 million yen".

[1606] Specific operation: The following prompt is input to the generative AI model: "The user is 30 years old, currently has assets of 5 million yen, a goal of 20 million yen, and is balanced. Please tell me the optimal asset allocation."

[1607] Output: Data obtained from the generative AI model: 40% domestic stocks, 40% international stocks, 20% bonds.

[1608] Step 5:

[1609] Advice generation and notification

[1610] Server: The server generates a specific investment breakdown based on the optimal allocation obtained from the generative AI model and notifies the user's device.

[1611] Input: Data of "40% domestic stocks, 40% foreign stocks, 20% bonds".

[1612] Specific operation: Based on the allocation data, a specific investment breakdown is generated, such as "Domestic stocks: ABC Company 40%," "International stocks: XYZ Company 40%," and "Bonds: Japanese government bonds 20%." This is then sent as a push notification to the user's device.

[1613] Output: Specific investment breakdown notified to the terminal.

[1614] Step 6:

[1615] User feedback and records

[1616] Terminal: The user checks the notified allocation and adjusts it if necessary. After approval, the adjusted allocation data is sent to the server.

[1617] Input: Adjusted allocation data such as "Domestic stocks: ABC Company 30%", "International stocks: XYZ Company 50%", and "Bonds: Japanese government bonds 20%".

[1618] What happens: The user adjusts the allocation in the application interface and presses the "Accept" button, which sends the data to the server.

[1619] Output: The adjusted allocation data is sent to the server and recorded.

[1620] Step 7:

[1621] Switching Procedures

[1622] Server: Based on the optimal allocation obtained from the generative AI model, the server provides users with links and guides to their company's 401K management site.

[1623] Input: Optimal allocation data and link information.

[1624] Specific operation: The server generates a guide document explaining the specific switching procedure for the user and a link to the operation site, and notifies the user's terminal.

[1625] Output: The guide document and links that the user receives.

[1626] Through these processing steps, users can easily find the optimal asset management allocation, providing specific and effective means for maximizing the investment returns of corporate defined contribution pension plans.

[1627] (Application example 1)

[1628] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1629] For participants in corporate defined contribution pension plans (401K), finding the optimal asset allocation for themselves requires knowledge and time. In particular, analysis and proposals based on multiple factors, such as age, current assets, target amount, and investment preferences, are required, and a simple and fast method for doing this is needed. Furthermore, the process for actually implementing the proposed investment allocation is complicated, and there is a lack of a means to smoothly complete the process. This creates challenges for participants, making it difficult to effectively optimize their investment allocation.

[1630] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1631] In this invention, the server includes: means for a user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount; means for a generating AI to calculate an optimal asset allocation based on the calculation and analysis results; means for notifying the user of the optimal asset allocation; means for receiving and recording an investment allocation adjusted by the user in accordance with the notified asset allocation; means for providing a link and guide to a company's investment site based on the investment allocation approved by the user; and means for proposing an optimal asset allocation using a generating AI model on a smart device. This allows users to easily find an optimal asset allocation for themselves and quickly and easily put that investment allocation into practice.

[1632] A "user" is someone who uses a particular application or system.

[1633] A "smart device" is a portable electronic device that can connect to the Internet, such as a smartphone or tablet.

[1634] A "generative AI model" is a model that uses artificial intelligence algorithms to analyze data and generate optimal solutions to specific problems.

[1635] "Age" is information indicating the length of time since the user was born.

[1636] "Current assets" is information indicating the amount of total assets currently held by the user.

[1637] The "target amount" is information indicating the amount of assets that the user wishes to achieve in the future.

[1638] "Management preference" is data that indicates the investment risk profile and investment style preferred by the user.

[1639] A "database" is an organized digital storage system for storing user-entered data.

[1640] "Risk tolerance" is an indicator that indicates the investment risk level that a user can tolerate.

[1641] The "gap" indicates the difference between current assets and the target amount.

[1642] "Notification" is the act of providing information to a user from an application or system.

[1643] "Adjustment" refers to the act of the user making subtle changes to the proposed investment allocation.

[1644] A "link" is a reference to a particular website or online resource.

[1645] A "guide" is a document or tool that provides procedures or instructions for performing a particular operation or procedure.

[1646] This invention relates to a system for finding optimal asset allocation for participants of corporate defined contribution pension plans (401K). The system includes the following components:

[1647] 1. Data Entry:

[1648] Terminal: Users use a smart terminal to input data such as age, current assets, target amount, investment preference, etc. The application on the terminal guides them through this data input.

[1649] 2. Data Receipt and Storage:

[1650] Server: The entered data is sent to the server and stored in a database, which prepares the data necessary for subsequent processing.

[1651] 3. Risk Tolerance Calculation and Gap Analysis:

[1652] Server: Based on the saved data, calculate the user's risk tolerance and analyze the gap between their current assets and their target amount. For example, classify the risk tolerance as "high," "medium," or "low" based on the user's age, asset status, and investment objectives.

[1653] 4. Calculation of optimal asset allocation using generative AI:

[1654] Server: Using a generative AI model, it calculates the optimal asset allocation based on the results of calculations and analysis. For example, it proposes a specific investment allocation such as "40% domestic stocks, 40% international stocks, 20% bonds."

[1655] 5. Investment Allocation Notification:

[1656] Server: Once the optimal asset allocation is calculated, the server notifies the user of the results via their smart device. The user receives the notification and can check the proposed asset allocation on the application.

[1657] 6. User feedback and adjustments:

[1658] Terminal: The user can review the proposed allocation and make any necessary adjustments. If the user approves the adjustments, the information is sent to the server.

[1659] Server: Receives the adjusted investment allocation data, stores it in a database, and re-analyzes it as needed.

[1660] 7. Providing Links and Guides:

[1661] Server: If the user approves the optimal investment allocation, the server provides a link to the company's 401K investment site and a guide for completing the switching procedure.

[1662] Terminal: Using the provided links and guides, users can easily carry out the switching procedure.

[1663] Hardware and software used:

[1664] Hardware: Smart devices (e.g. smartphones, tablets)

[1665] Software: On the server side, we use web frameworks such as Flask to build the API, and OpenAI's API for data analysis.

[1666] Examples:

[1667] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and a balanced investment orientation, the generative AI model will use the following prompt to suggest the optimal asset allocation.

[1668] Example prompt sentence:

[1669] "If the user is 30 years old, has 5 million yen in current assets, 20 million yen in target assets, and has a balanced investment preference, please suggest the optimal asset allocation."

[1670] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1671] Step 1:

[1672] The user uses a smart device to enter their age, current assets, target amount, and investment preference into the application. This input data is entered using text fields and drop-down menus. The entered data is temporarily saved on the device. Input data: Age, current assets, target amount, investment preference. Output data: User data temporarily saved on the device.

[1673] Step 2:

[1674] Input data is sent from the terminal to the server. The server receives this data and saves it in the database. The server checks the integrity of the data and notifies the terminal that saving is complete. Input data: User data (age, current assets, target amount, investment orientation). Output data: User data saved in the database, notification of saving completion.

[1675] Step 3:

[1676] The server calculates the user's risk tolerance based on the data stored in the database. A fixed algorithm is used for the calculation to generate a risk profile that takes into account factors such as the user's age and financial situation. The result of this process is a calculated risk tolerance of either "high," "medium," or "low." Input data: User information in the database. Output data: Risk tolerance.

[1677] Step 4:

[1678] The server analyzes the gap between the current assets and the target amount. The required additional assets are calculated by subtracting the target amount from the current assets. This process calculates the increase in assets required for the user to reach the goal. Input data: User information in the database. Output data: Gap (additional assets).

[1679] Step 5:

[1680] The server uses a generative AI model to calculate the optimal asset management allocation based on the analysis results of risk tolerance and asset gap. The generative AI model uses OpenAI's API and generates a specific investment allocation (e.g., 40% domestic stocks, 40% international stocks, 20% bonds) using a specific prompt as input. Input data: risk tolerance, asset gap. Example prompt: "If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment preference, please suggest the optimal asset management allocation." Output data: optimal asset management allocation.

[1681] Step 6:

[1682] The server notifies the user's device of the calculated optimal asset allocation. This notification includes specific investment details and proposals. Input data: Optimal asset allocation. Output data: Notification to the user's device.

[1683] Step 7:

[1684] The terminal displays the received operational allocation notification to the user. The user can review this proposal and make fine adjustments as necessary. Once the user completes and approves the adjustments, the information is sent from the terminal to the server. Input data: Notified operational allocation, user adjustments. Output data: Approved operational allocation to the server.

[1685] Step 8:

[1686] The server generates a link to the company's operation site and a guide for switching procedures based on the operation allocation approved by the user, and sends them to the user's terminal. The link contains information that allows the user to easily access and operate it. Input data: Approved operation allocation. Output data: Link and guide.

[1687] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1688] The system of the present invention is designed to enable participants in corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice. The program processing of this system is explained below with specific examples.

[1689] System Embodiments

[1690] 1. Data Entry

[1691] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1692] 2. Emotion recognition

[1693] The emotion engine built into the device analyzes the user's facial expressions and tone of voice while they are typing to recognize their current emotional state, collecting data such as whether they are feeling stressed or relaxed.

[1694] 3. Data Transmission

[1695] The terminal transmits the input data and the emotion data recognized by the emotion engine to the server.

[1696] 4. Data Storage

[1697] The server receives the data sent from the device and stores it in a database. The stored data includes age, current assets, target amount, investment preference, and emotional data.

[1698] 5. Initial Processing

[1699] The server calculates the risk tolerance based on the user's age. For example, if the user is 30 years old, the risk tolerance is set to "medium." If the current assets are 5 million yen and the target amount is 20 million yen, the server analyzes the gap to be 15 million yen.

[1700] 6. Emotion correction

[1701] The server corrects the user's investment preference parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the server will correct the parameters to suggest low-risk investments.

[1702] 7. Generative AI Analysis

[1703] The server sends the corrected data to the generation AI, which then begins analysis to calculate the optimal asset management allocation.

[1704] 8. Advice Generation

[1705] The server generates a specific investment breakdown based on the analysis results returned by the AI. For example, it creates advice such as "40% domestic stocks, 40% international stocks, 20% bonds."

[1706] 9. Advice Notice

[1707] The server sends information to the terminal to notify the user of the generated advice.

[1708] 10. User Verification

[1709] The device displays the received advice to the user, who then confirms it. If the user is satisfied, they can approve the operational allocation as is, or make fine adjustments as necessary.

[1710] 11. Allocation Adjustments

[1711] The user adjusts the proposed operation details and presses the confirm button. The adjusted allocation is sent from the terminal to the server.

[1712] 12. Data Recording

[1713] The server receives the adjusted allocation data from the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[1714] 13. Switching Procedures

[1715] The server generates a link to the company's 401K management site and a guide for switching procedures for the user, and transmits them to the terminal.

[1716] 14. User Execution

[1717] The user clicks on the provided link to access the company's 401K management site and follows the guide to complete the switching procedure with the new allocation.

[1718] Specific examples

[1719] If the user is 30 years old, has current assets of 5 million yen, a target amount of 20 million yen, and has a balanced investment orientation, the following steps will be taken.

[1720] 1. On the terminal, the user enters their age, current assets of 5 million yen, target amount of 20 million yen, and investment orientation / balanced orientation.

[1721] 2. The emotion engine analyzes the user's facial expressions and tone of voice during this input and recognizes that the user is feeling stressed.

[1722] 3. The device sends the input data and emotion data to the server.

[1723] 4. The server receives the input data and begins analysis.

[1724] 5. The server sets the risk tolerance to "medium" based on the user's age and calculates the gap between the current assets and the target amount to 15 million yen.

[1725] 6. The server adjusts the investment-oriented parameters based on the emotional data to suggest low-risk investments.

[1726] 7. The generation AI calculates the optimal asset management allocation based on the corrected data and suggests an allocation of, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[1727] 8. The server generates specific advice based on the calculation results and notifies the user.

[1728] 9. On the device, the user checks the advice and either accepts it or makes adjustments.

[1729] 10. The server records the final operational allocation and provides a link to guide the switching procedure.

[1730] 11. The user follows the provided link to complete the switching procedure based on the new allocation on the company's 401K management site.

[1731] In this way, by incorporating an emotion engine, the present invention is a system that can provide more precise asset management advice that takes into account the user's short-term emotional state, and can help maximize the investment returns of defined contribution pension plans.

[1732] The processing flow will be explained below.

[1733] Step 1:

[1734] The user launches the application on their smartphone or computer and enters data on their age, current assets, target amount, and investment preferences.

[1735] Step 2:

[1736] The device temporarily stores the user's input data in its local memory. Once input is complete, the user presses the send button to send the data to the server.

[1737] Step 3:

[1738] The emotion engine installed in the device analyzes the user's facial expressions and tone of voice while inputting, and recognizes the user's emotional state (e.g., stress, relaxation, etc.). The emotion data is also simultaneously sent to the server.

[1739] Step 4:

[1740] The server receives the data sent from the terminal (age, current assets, target amount, investment orientation, emotional data) and stores it in a database.

[1741] Step 5:

[1742] The server calculates your risk tolerance based on your age. For example, if you are 30, your risk tolerance will be set to "medium."

[1743] Step 6:

[1744] The server calculates the asset gap based on the current assets and the target amount. For example, if the current assets are 5 million yen and the target amount is 20 million yen, the asset gap will be 15 million yen.

[1745] Step 7:

[1746] The server adjusts the user's operational parameters based on the emotion data obtained from the emotion engine. For example, if the user is feeling stressed, the parameters are adjusted to reduce risk.

[1747] Step 8:

[1748] The server sends the corrected data to the AI, which then calculates the optimal asset allocation. For example, it suggests an allocation of 35% domestic stocks, 35% international stocks, and 30% bonds.

[1749] Step 9:

[1750] The server generates a detailed investment breakdown (for example, specific stocks and bond product names) based on the analysis results returned by the generation AI.

[1751] Step 10:

[1752] The server sends information to the terminal to notify the user of the generated advice.

[1753] Step 11:

[1754] The device displays the received advice to the user, who can then review the advice and adjust their portfolio as necessary.

[1755] Step 12:

[1756] The user checks the proposed operation details, adjusts them, and then confirms the allocation. When the user presses the confirm button, the allocation data is sent from the terminal to the server.

[1757] Step 13:

[1758] The server receives the adjusted allocation of the user and records it in the database. If necessary, it may request the generation AI to reanalyze it again.

[1759] Step 14:

[1760] The server generates a link to the company's 401K operation site and a guide for the user to carry out the switching procedure, and transmits the generated link to the terminal.

[1761] Step 15:

[1762] The terminal displays the received links and guides to the user.

[1763] Step 16:

[1764] The user clicks on the provided link to access the company's 401K management site and follows the instructions to complete the switching procedure with the new allocation.

[1765] Example 2

[1766] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1767] Conventional asset management advice systems can only provide general advice based on the user's age and asset information, and are unable to provide advice that takes into account the user's individual emotional state. This makes it difficult to obtain optimal investment advice that reflects the user's emotions, such as suggesting high-risk investments even when the user is feeling stressed. Therefore, there is a need for a system that provides more personalized asset management advice that takes into account the user's emotional state.

[1768] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for the user to input data regarding age, current assets, target amount, and investment orientation; means for receiving the data input by the user and storing it in a database; and means for calculating the user's risk tolerance based on the stored data and analyzing the gap between the current assets and the target amount. This enables the risk profile to be corrected taking into account the user's emotional state. Furthermore, the server includes means for analyzing the user's facial expression and tone of voice during input and recognizing the user's current emotional state; means for correcting the user's investment orientation based on the recognized emotional state; and means for the generation AI to recalculate the optimal asset investment allocation using the corrected data. This makes it possible to provide personalized asset management advice based on the user's real-time emotional state.

[1769] "User" refers to an individual or corporation that uses this system.

[1770] "Age" is a data item that indicates the user's current age.

[1771] "Current assets" is a data item that indicates the amount of total assets currently owned by the user.

[1772] "Target amount" is a data item that indicates the amount of assets that the user wants to achieve in the future.

[1773] "Management orientation" is a data item that indicates the orientation regarding the investment style and risk tolerance desired by the user.

[1774] "Data input means" refers to an interface for a user to input data regarding age, current assets, target amount, and investment orientation.

[1775] "Data receiving means" refers to a means by which the server receives data input by the user.

[1776] "Database storage means" refers to means for storing received data in a database.

[1777] "Risk tolerance calculation means" refers to a means for calculating risk tolerance based on the user's age.

[1778] "Gap analysis tools" refer to tools for analyzing the difference between current assets and target amounts.

[1779] "Generative AI" refers to a system that uses artificial intelligence to calculate optimal asset management allocations.

[1780] "Emotion recognition means" refers to means for analyzing the facial expressions and tone of voice of the user while they are inputting information and recognizing the user's emotional state.

[1781] The "emotion correction means" refers to a means for correcting the user's operational orientation parameters based on the recognized emotional state.

[1782] The "asset management allocation notification means" refers to a means for notifying the user of the optimal asset management allocation.

[1783] The "management allocation receiving means" refers to a means for receiving the investment allocation adjusted by the user in accordance with the notified asset investment allocation.

[1784] "Investment allocation recording means" refers to a means for recording received investment allocations in a database.

[1785] "Recalculation means" refers to the means by which the generation AI recalculates the optimal asset management allocation using the corrected data.

[1786] The system of the present invention is designed to enable participants of corporate defined contribution pension plans (401K) to easily find the optimal asset allocation. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized investment advice.

[1787] The main elements of the system consist of a user device, a server, and a generative AI model. User devices are general-purpose devices such as smartphones and PCs. The server is a high-performance computer system that uses MySQL as its database management system. For emotion recognition, a camera and microphone are used, and dedicated software for image and audio analysis is installed.

[1788] First, the user launches the application on their smartphone or PC and enters data such as their age, current assets, target amount, and investment preference. Next, while the user is entering information, the emotion engine analyzes their facial expressions and tone of voice to recognize their current emotional state. For example, if the user is frowning, it will be recognized as "stressed."

[1789] The device sends the input data and emotion data to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a database, calculates the user's risk tolerance based on their age, and analyzes the difference between their current assets and their target amount. For example, if a user is 30 years old, their current assets are 5 million yen, and their target amount is 20 million yen, the gap is calculated as 15 million yen.

[1790] The server adjusts the risk tolerance based on the emotional data. For example, if the user is stressed, it will recommend low-risk investments. The adjusted data is sent to the generative AI model, which calculates the optimal asset allocation. The prompt for the generative AI model is as follows:

[1791] User information: Age 30, current assets 5 million yen, target amount 20 million yen, investment orientation, balance orientation, emotional state: stress

[1792] Desired outcome: Recommend optimal asset allocation

[1793] The AI ​​then calculates the optimal asset allocation based on these prompts, returning a recommended allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds." The server receives the results and generates specific advice for the user. The generated advice is sent to the device, where the user can review it and either accept it as is or make fine adjustments as needed.

[1794] When the final allocation is determined, the server records it in the database, generates a link and guide to the company's 401K management site, and sends it to the terminal. The user clicks the provided link to access the company's 401K management site and completes the switching procedure with the new allocation.

[1795] As described above, the present invention is a system that provides precise asset management advice that takes into account the user's short-term emotional state, and maximizes the management efficiency of defined contribution pension plans.

[1796] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1797] Step 1:

[1798] Data Entry

[1799] The user launches the application on their smartphone or PC and enters data on their age, current assets, target amount, and investment preference. Text boxes and pull-down menus are displayed in the input fields, and the user enters or selects the required information. Once the input is complete, the user clicks the submit button. The input data is used in the next step.

[1800] Input: Age, current assets, target amount, investment preference

[1801] Output: User input data

[1802] Step 2:

[1803] emotion recognition

[1804] The device's built-in emotion engine uses the camera and microphone to capture facial expressions and tone of voice while the user is entering data, and emotion recognition algorithms analyze this data to identify the user's current emotional state (e.g., "stressed" or "relaxed").

[1805] Input: User's facial expression data, tone of voice

[1806] Output: User emotion data

[1807] Step 3:

[1808] Data transmission

[1809] The device sends the data entered in step 1 and the emotion data recognized in step 2 to the server using a secure communication protocol (e.g., HTTPS). The data is packaged in JSON format packets and sent to the server.

[1810] Input: User input data, emotion data

[1811] Output: Data sent to the server

[1812] Step 4:

[1813] Data storage

[1814] The server receives the data packets sent from the device. The received data is stored in a database management system (e.g., MySQL). The database table contains user ID, age, current assets, target amount, investment orientation, and emotional data.

[1815] Input: Data sent from the terminal

[1816] Output: Data stored in the database

[1817] Step 5:

[1818] Initial Processing

[1819] The server calculates the risk tolerance level based on the user's age based on the stored data. At the same time, it calculates the difference (gap) between the current assets and the target amount. For example, if the user is 30 years old, the risk tolerance level is set to "medium," and the current assets are 5 million yen and the target amount is 20 million yen, the gap will be calculated as 15 million yen.

[1820] Input: User data read from the database

[1821] Output: Risk tolerance, gap analysis results

[1822] Step 6:

[1823] emotion correction

[1824] The server adjusts the risk tolerance based on the emotional data. For example, if the user is under stress, the server will adjust the risk tolerance to suggest lower-risk investments. The risk tolerance may be changed from "medium" to "low."

[1825] Input: Calculated risk tolerance, emotional data

[1826] Output: Adjusted risk tolerance

[1827] Step 7:

[1828] Generative AI analysis

[1829] The server sends the corrected data to the generative AI model and inputs a prompt to calculate the optimal asset management allocation. The prompt is "User information: age 30, current assets of 5 million yen, target amount of 20 million yen, investment orientation balanced orientation, emotional state: stressed. Desired result: Please suggest the optimal asset management allocation." The generative AI model calculates the optimal allocation based on this prompt.

[1830] Input: prompt statement

[1831] Output: Optimal asset allocation

[1832] Step 8:

[1833] Advice Generation

[1834] The server receives the results of the AI ​​generation and generates specific investment advice for the user. For example, it generates text and graphs suggesting an allocation such as "35% domestic stocks, 35% international stocks, and 30% bonds."

[1835] Input: Investment allocation data from the generation AI

[1836] Output: Investment advice for the user

[1837] Step 9:

[1838] Advice Notice

[1839] The server sends the generated advice to the user's device, where it is displayed in a visually easy-to-understand format (graphs, text).

[1840] Enter: Investment Advice

[1841] Output: Advice notification to user terminal

[1842] Step 10:

[1843] User Verification

[1844] The terminal displays the received advice to the user. The user checks the displayed investment details and presses the "Approve" button if they are satisfied with the details, or the "Adjust" button if they wish to readjust.

[1845] Input: Investment advice from the server

[1846] Output: User review and approval or adjustment

[1847] Step 11:

[1848] Allocation adjustment

[1849] When the user presses the "Adjust" button, a screen will appear where they can adjust their investment details using sliders and text fields. The user can manually adjust the allocation to their desired level and press the "Confirm" button. The adjusted data will then be sent back to the server from the device.

[1850] Input: User's adjusted share

[1851] Output: Sends the allocated data to the server

[1852] Step 12:

[1853] Data Recording

[1854] The server receives the user's adjusted allocation data and records it in the database again. If necessary, it requests the generation AI to reanalyze it, generates new results, and notifies the user.

[1855] Input: User's adjusted allocation data

[1856] Output: Records in the database

[1857] Step 13:

[1858] Switching Procedures

[1859] The server creates a link to the company's 401K management site and a guide for the specific switching procedure, and sends it to the terminal. The procedure guide provides detailed explanations for each step.

[1860] Input: Final investment allocation

[1861] Output: Switching Procedure Guide

[1862] Step 14:

[1863] User execution

[1864] The user clicks on the provided link to access the company's 401K management site. Following the displayed guide, the user completes the switching procedure to the new allocation. Specifically, after logging in, the user enters the new asset allocation and presses the confirm / confirm button.

[1865] Input: Switching Procedure Guide

[1866] Output: The completed switching procedure

[1867] The above are the specific processing steps of the system of the present invention. Through this flow, personalized asset management advice that also takes into account the user's emotional state is provided.

[1868] (Application example 2)

[1869] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1870] Existing asset management support systems for corporate defined contribution pension plans (401K) provide investment advice without considering the user's emotional state, and therefore cannot suggest appropriate investment allocations when the user is stressed or relaxed. Furthermore, while short-term emotional fluctuations in users are likely to affect investment decisions, no systems exist that take this into account. As a result, it is difficult to provide optimal asset allocations for individual users.

[1871] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's facial expressions and tone of voice to collect emotional data and correcting asset management suggestions based on the emotional data, means for the user to input data regarding age, current assets, target amount, and investment orientation, and means for receiving the data input by the user and storing it in a database. This makes it possible to provide more accurate asset management advice that reflects the user's emotional state.

[1872] The "input means" is a means by which a user provides information regarding age, current assets, target amount, and investment preference to the system.

[1873] "Means for saving in a database" refers to a means for receiving and safely storing information entered by a user.

[1874] The "means for calculating risk tolerance" is a means for calculating how much risk a user can tolerate based on information such as the user's age.

[1875] The "means for analyzing the gap" is a means for evaluating the difference between the user's current assets and the target amount, and analyzing how to fill the gap.

[1876] The "means calculated by the generating AI" refers to the means by which the generating AI calculates the optimal asset management allocation based on the analysis results.

[1877] The "means for notifying" is a means for notifying the user of the calculated optimal asset management allocation.

[1878] The "means for receiving and recording" is a means for receiving and recording information on the operational allocation adjusted by the user.

[1879] The "means for collecting emotional data" is a means for analyzing the user's facial expressions and tone of voice to collect data about the user's emotional state.

[1880] The "correction means" is a means for appropriately correcting asset management proposals based on the collected emotional data.

[1881] This invention relates to a system for finding optimal asset allocations for participants in corporate defined contribution pension plans (401K). The system incorporates an emotion engine and provides personalized investment advice by recognizing the user's emotional state.

[1882] System configuration

[1883] 1. User Device

[1884] Input means: The user inputs information about age, current assets, target amount, and investment orientation.

[1885] Emotion recognition means: Using the camera and microphone built into the device, the user's facial expressions and tone of voice are analyzed to recognize their emotional state.

[1886] Notification means: The calculated optimal asset management allocation is notified to the user.

[1887] Display: Users can view and adjust asset allocation suggestions on their devices.

[1888] 2. Server

[1889] Data storage means: User information sent from the terminal is stored in a database.

[1890] Risk tolerance calculation method: Calculates risk tolerance based on information such as the user's age.

[1891] Gap analysis tool: Evaluate the difference between your current assets and your target amount and analyze how to close the gap.

[1892] Generation AI calculation method: Based on the analysis results, the generation AI calculates the optimal asset management allocation.

[1893] Adjustment receiving means: Receives and records information on the operational allocation adjusted by the user.

[1894] Emotion data collection means: Analyzes the emotional data sent from the device and corrects asset management suggestions.

[1895] Hardware and software used

[1896] 1. Hardware

[1897] Smartphone camera: Used to capture the user's facial expressions in real time.

[1898] Smartphone microphone: Used to analyze the user's voice tone.

[1899] 2. Software

[1900] OpenCV (cv2): An image processing library used to capture and analyze facial expressions.

[1901] EmotionEngine: An emotion engine that extracts emotional data by analyzing the user's facial expressions and voice tone.

[1902] requests: A Python library for making HTTP requests, used to send and receive data.

[1903] Specific examples

[1904] Let's say a user is 35 years old, has 6 million yen in current assets, a target amount of 25 million yen, and prefers low-risk investments. The emotion engine recognizes that the user is relaxed. Based on this, the system operates as follows:

[1905] 1. Data Entry

[1906] Users enter their age, current assets, target amount, and investment preferences into a smartphone app.

[1907] 2. Emotion recognition

[1908] Using the smartphone's camera and microphone, emotional data is collected by analyzing the user's facial expressions and voice.

[1909] 3. Data transmission and storage

[1910] The input data and emotion data are sent to a server and stored in a database.

[1911] 4. Risk tolerance and gap analysis

[1912] The server calculates the risk tolerance based on the user's age and evaluates the gap between the current assets and the target amount.

[1913] 5. Calculation by Generative AI

[1914] After adjusting investment preferences based on emotional data, the generative AI calculates the optimal asset allocation, suggesting, for example, 35% domestic stocks, 35% international stocks, and 30% bonds.

[1915] 6. Notice and Adjustment

[1916] The calculated asset management allocation is notified to the user, who can then check and adjust it.

[1917] Example prompts

[1918] A 35-year-old user currently has assets of 6 million yen, a target amount of 25 million yen, and a low-risk investment orientation. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset allocation.

[1919] This makes it possible to provide highly accurate asset management advice that reflects the user's short-term emotional state.

[1920] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1921] Step 1:

[1922] The user launches the smartphone app and enters data such as age, current assets, target amount, and investment preference. The entered data is temporarily saved in the device. The data is entered by the user manually providing information for each item. The specific action of data entry is for the user to type the information into each input field.

[1923] Step 2:

[1924] The device uses the smartphone's camera and microphone to capture the user's facial expressions and voice, thereby collecting the user's emotional data. Specifically, the camera captures the user's face and the microphone records their voice. The emotional data is then analyzed using EmotionEngine. This emotional data includes happiness or sadness as determined from the user's facial expressions and stress levels as determined from their voice tone.

[1925] Step 3:

[1926] The device sends input data and emotion data to the server. An HTTP request is used to send the data, and the data is sent in JSON format. Specifically, the requests library is used to send the data to the server. The input for this step is the user's input data and emotion data, and the output is data sent to the server.

[1927] Step 4:

[1928] The server stores the received data in a database. The input data here is the information sent from the terminal, and the output is the correct record in the database. Specifically, the received data is analyzed, converted into an appropriate format, and inserted into the database. The database management system (DBMS) used is, for example, MySQL.

[1929] Step 5:

[1930] The server calculates the risk tolerance based on the user's age. The input for this step is age data, and the output is a numerical value for risk tolerance. Specifically, it sets a risk level (low, medium, high) according to the user's age range. For example, those under 30 are considered high risk, those between 30 and 50 are considered medium risk, and those over 50 are considered low risk.

[1931] Step 6:

[1932] The server analyzes the gap between the current assets and the target amount. The input for this step is the current assets and target amount data, and the output is the calculation result of the asset gap (shortfall). Specifically, it subtracts the current assets from the target amount to calculate the shortfall.

[1933] Step 7:

[1934] The server corrects the operational orientation parameters based on the emotional data. Specifically, it analyzes the collected emotional data and corrects it to suggest low-risk operations if the user is feeling stressed. The input of this step is the emotional data, and the output is the corrected operational orientation parameters.

[1935] Step 8:

[1936] The server uses the generative AI model to calculate the optimal asset management allocation based on the corrected data. The inputs for this step are risk tolerance, asset gap, and corrected investment preference parameters, and the output is the investment breakdown. Specifically, input is given to the generative AI model using a prompt, which calculates the optimal allocation. An example prompt is, "A 35-year-old user has current assets of 6 million yen, a target amount of 25 million yen, and an investment preference for low risk. The emotion engine recognizes that the user is relaxed. Please suggest the optimal asset management allocation."

[1937] Step 9:

[1938] The server notifies the terminal of the calculated optimal asset management allocation. The input to this step is the calculation result of the generative AI model, and the output is notification information to the user terminal. Specifically, the notification information is formatted in JSON format and sent to the terminal via an HTTP request.

[1939] Step 10:

[1940] The terminal displays the received asset management allocation to the user, who can then confirm and adjust its contents. The input to this step is the notification information sent from the server, and the output is the investment allocation adjusted by the user. Specifically, the investment allocation is displayed on the terminal's UI, and an interface is provided for the user to adjust.

[1941] Step 11:

[1942] The user sends the adjusted operational allocation from the terminal to the server, which receives and records the information. The input of this step is the operational allocation after the user adjustment, and the output is a record in the database. The specific operation is to receive the user's adjusted data and save it in the database.

[1943] Step 12:

[1944] The server generates a link and guide to the company's 401K management site and sends it to the terminal. The input to this step is the user's final investment allocation decision information, and the output is link information with the guide. Specifically, it generates an appropriate link and creates and sends a guide to help the user complete the procedure smoothly.

[1945] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1946] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1947] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1948] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1949] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1950] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1951] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1952] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1953] The emotion map defines two emotions tha...

Claims

1. A means for a user to input data regarding age, current assets, target amount, and investment preference; means for receiving the data entered by the user and storing it in a database; A means for calculating the user's risk tolerance level based on the stored data and analyzing the gap between the current assets and the target amount; A means for the generation AI to calculate the optimal asset management allocation based on the calculation and analysis results; means for notifying a user of the optimal asset management allocation; A means for receiving and recording the investment allocation adjusted by the user in accordance with the notified asset investment allocation; A system including:

2. The system according to claim 1 , further comprising means for displaying the asset allocation proposal to the user on a user terminal.

3. 10. The system of claim 1, further comprising means for providing a link and guide to a company's investment site if the asset allocation proposal for the user is approved.

Images