System

Abstract

A system is provided.SOLUTION: A system, comprising: means for inputting expense data and income data from a user; means for transmitting the input data to a server; means for receiving and storing the data in a database; means for pre-processing the stored data; means for generating an investment strategy using an artificial intelligence engine for analyzing the pre-processed data; means for notifying the user of the generated investment strategy; and means for immediately answering an investment management question from the user.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 today have questions and concerns about asset management for their retirement years, but find it difficult to obtain appropriate information and advice. Traditional asset management services also struggle to provide optimal investment strategies tailored to each user's risk tolerance and goals. Furthermore, privacy protection is insufficient, leaving many users concerned about their private information being shared with others. To address these issues, a system is needed that provides optimal asset management support while protecting users' privacy. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including a means for inputting expenditure data and income data from a user, a means for transmitting the input data to a server, a means for receiving the data and storing it in a database, a means for preprocessing the stored data, a means for generating an investment strategy using an AI engine for analyzing the preprocessed data, a means for notifying the user of the generated investment strategy, and a means for providing instant answers to questions from the user regarding asset management. The AI ​​engine performs clustering and generates a risk profile based on the user's expenditure data and income data, thereby optimizing the investment strategy. Furthermore, the server encrypts and stores the user's data to protect the user's privacy. This allows the user to receive an appropriate investment strategy with peace of mind.

[0006] "User" refers to any individual or legal entity that uses the System.

[0007] "Expense Data" refers to information about the amount of money a user spends and the categories of products purchased over a certain period of time.

[0008] "Income Data" refers to information about the amount and breakdown of income a User has earned within a certain period of time.

[0009] "Server" refers to a computer system that receives, stores, processes, and manages data sent by users.

[0010] A "database" refers to a collection of information that stores collected data systematically and allows it to be efficiently searched and used as needed.

[0011] "Preprocessing" refers to the initial steps in data processing, where data is shaped and cleaned to make it suitable for analysis.

[0012] An "artificial intelligence engine" refers to software that has the ability to analyze collected data and generate optimal investment strategies for users.

[0013] An "investment strategy" refers to a plan for determining optimal asset allocation and investment targets based on a user's risk tolerance and goals.

[0014] "Encryption" refers to a technology that converts data using a specific algorithm to protect it from unauthorized access.

[0015] "Clustering" refers to a technique for classifying data into several groups based on properties or characteristics.

[0016] "Risk profile" refers to an indicator that includes a risk assessment based on a user's risk tolerance and investment goals. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] Overall system configuration

[0039] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0040] Data input from the user

[0041] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and financial situation.

[0042] Data transmission and storage

[0043] The device encrypts the data entered by the user and sends it securely to the server, which immediately stores the data in a database. The database also records metadata for each piece of data, such as the user ID and timestamp. The data is then formatted and cleaned as needed to prepare it for analysis.

[0044] AI-based data analysis and investment strategy generation

[0045] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[0046] Notification and display of investment strategies

[0047] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[0048] Example: User scenario

[0049] Users enter their monthly expenditure information through the app, such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The device sends this data to the server, which receives it and stores it in an encrypted form in a database.

[0050] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[0051] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, for example, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[0052] As a result, users can receive an appropriate investment strategy tailored to their individual financial situation and risk tolerance, allowing them to invest with peace of mind while alleviating any concerns or doubts they may have.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[0056] Step 2:

[0057] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[0058] Step 3:

[0059] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[0060] Step 4:

[0061] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and timestamp.

[0062] Step 5:

[0063] The server preprocesses the stored data, specifically by imputing missing values, converting non-numeric data, and removing unnecessary data.

[0064] Step 6:

[0065] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[0066] Step 7:

[0067] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify user spending patterns, such as users whose spending is concentrated on rent and food.

[0068] Step 8:

[0069] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[0070] Step 9:

[0071] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[0072] Step 10:

[0073] The server receives the investment strategy generated by the AI ​​engine and notifies the user, including recommended portfolios, predicted returns, and risk assessments.

[0074] Step 11:

[0075] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[0076] Step 12:

[0077] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[0078] Step 13:

[0079] The terminal sends the user's questions to the server.

[0080] Step 14:

[0081] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[0082] Step 15:

[0083] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[0084] Step 16:

[0085] The server generates a response and sends it back to the user.

[0086] Step 17:

[0087] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[0088] Through this process, users will receive an appropriate investment strategy based on their financial situation, allowing them to immediately resolve any doubts or concerns they may have about asset management.

[0089] Example 1

[0090] 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."

[0091] In modern society, personal asset management is extremely important. However, it is generally difficult for individual users to find the optimal investment strategy. In particular, there is a lack of systems that assess risk based on users' spending and income data and provide individually optimized investment strategies. This poses the challenge of users being unable to make appropriate investment decisions or accurately understand the risks involved in asset management. Furthermore, it is important to manage data while protecting users' privacy.

[0092] 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.

[0093] In this invention, the server includes means for inputting expenditure data and income data from a user, means for encrypting the input data at a terminal and transmitting it to the server, means for receiving the data and storing it in a database, means for cleaning and formatting the stored data, means for generating clustering and an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying and displaying the generated investment strategy to the user, and means for instantly answering questions about asset management from the user. This allows the user to receive an individually optimized investment strategy and makes it easier to accurately understand the risks involved in asset management. Furthermore, data encryption also protects the user's privacy.

[0094] "User" means any person or entity that utilizes the system to input expenditure and income data and receive optimal investment strategies.

[0095] "Expense data" refers to data that includes information on the amounts and items of expenditures that a user has made over a specific period of time.

[0096] "Income Data" means data that includes information about the amount and type of income a User has earned during a particular period of time.

[0097] A "terminal" is an electronic device used by a user to input data and send it to a server, including smartphones, tablets, and personal computers.

[0098] "Encryption" is a technology that converts data using a specific algorithm so that the data being transmitted cannot be deciphered by a third party.

[0099] "Server" means a computer system that receives, stores, and processes data submitted by users and generates investment strategies using an artificial intelligence engine.

[0100] A "database" is a collection of information, including user expenditure data and income data, stored on a server, and is an organized storage system.

[0101] "Cleaning" refers to preprocessing work such as deleting unnecessary data and standardizing formats in order to improve the quality and reliability of data.

[0102] "Formatting" is the process of converting data into a form suitable for analysis in order to perform data analysis.

[0103] An "artificial intelligence engine" is a software program that contains machine learning algorithms and models to analyze user data and generate optimal investment strategies.

[0104] "Clustering" is a data analysis technique that groups similar data together to understand user spending patterns.

[0105] An "investment strategy" is a plan that includes asset allocation and investment recommendations optimized based on a user's risk tolerance and goals.

[0106] "Notification" means the act of transmitting investment strategies and other related information from the server to the user.

[0107] "Display" refers to the act of visually showing the notified information on the user terminal.

[0108] A "question" is an action in which a user sends a question about asset management to the server.

[0109] "Immediate response" refers to the act of quickly providing appropriate information and answers to questions from users.

[0110] The above definitions clarify the meaning of important words in this system.

[0111] The present invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. This system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0112] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation.

[0113] Next, the terminal encrypts the entered data and sends it to the server using a secure method. Encryption is performed using technologies such as AES (Advanced Encryption Standard). Transmission is performed using the HTTPS protocol, ensuring data protection. For example, data such as a rent of 100,000 yen is encrypted with AES before being sent to the server.

[0114] The server receives the encrypted data and stores it in a database. When stored, metadata such as user IDs and timestamps are also recorded. It also cleans (deletes unnecessary data and standardizes formats) and formats (converts data into a form suitable for analysis).

[0115] The server then passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[0116] The AI ​​engine performs detailed analysis based on the data it receives. First, it normalizes expenditure and income data and classifies users into several categories using a clustering algorithm (e.g., k-means clustering). Then, it uses machine learning models (e.g., random forests, neural networks) to generate investment strategies suited to each individual user. For example, safe investments are suggested for low-risk users.

[0117] The resulting investment strategies are managed by a server and organized for each user. The server stores information based on the user ID and generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash."

[0118] Finally, the device displays the investment strategy data received from the server to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." Furthermore, when a user asks a question, for example, "What are the returns of index funds over the past three years?", it sends a prompt to the server, and the server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[0119] The system allows users to receive individually optimized investment strategies and accurately understand the risks involved in managing their assets, while protecting user privacy through data encryption and the use of the HTTPS protocol.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Specific explanation of processing steps

[0122] Step 1: Data input from the user

[0123] Users enter monthly expenditure and income data using a dedicated application or web interface. Specifically, they enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen into the app. This input data is important information that will be used for subsequent analysis.

[0124] Input: User's expenditure data and income data (e.g. rent 100,000 yen, food expenses 50,000 yen, income 250,000 yen)

[0125] Output: Data for encryption and transmission

[0126] Step 2: Encrypt and send data

[0127] The terminal encrypts the data entered by the user. The encryption technology is AES (Advanced Encryption Standard), and the data is sent to the server using the HTTPS protocol. For example, data such as "rent 100,000 yen" is sent to the server in AES encrypted form.

[0128] Input: Unencrypted data entered by the user

[0129] Output: Encrypted data

[0130] Step 3: Receiving and storing data

[0131] The server receives the encrypted data and stores it in a database using security technology. When the data is stored, metadata such as the user ID and timestamp are also recorded. For example, "Rent of 100,000 yen" is stored in the database as "Expense category: Rent, Amount: 100,000 yen, User ID: 12345, Timestamp: 2023-10-01."

[0132] Input: Encrypted data and metadata

[0133] Output: Saved database entry

[0134] Step 4: Preprocessing the data

[0135] The server cleans and formats the stored data to make it suitable for analysis. Cleaning involves deleting unnecessary data and standardizing formats. Formatting involves converting the data into a format suitable for analysis. For example, this involves correcting incomplete date data and standardizing data formats.

[0136] Input: Stored raw data

[0137] Output: Preprocessed data

[0138] Step 5: Passing data to the AI ​​engine

[0139] The server passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[0140] Input: Preprocessed data

[0141] Output: Data passed to the AI ​​engine

[0142] Step 6: Data analysis and investment strategy generation using an AI engine

[0143] The AI ​​engine performs detailed analysis based on the data it receives. It normalizes expenditure and income data and classifies users into categories using clustering algorithms such as k-means clustering. It then uses machine learning models (e.g., random forests and neural networks) to generate investment strategies tailored to each individual user. For example, safe investments are suggested for low-risk users.

[0144] Input: Data passed to the AI ​​engine

[0145] Output: Generated investment strategy

[0146] Step 7: Managing and sending investment strategies on the server

[0147] The server organizes and stores data for each user based on the investment strategy received from the AI ​​engine. For example, for user ID 12345, it generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash" and manages it within the server.

[0148] Input: Investment strategy received from the AI ​​engine

[0149] Output: Organized investment strategy data

[0150] Step 8: Notify and display the investment strategy to users

[0151] The device receives investment strategy data from the server and displays it to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." If the user has a question about an investment, for example, "What are the returns on index funds over the past three years?", the device sends the question to the server. The server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[0152] Input: Investment strategies received from the server, queries from users

[0153] Output: Investment strategies and answers displayed to the user

[0154] For example, after a user enters their spending information such as rent and food, the device encrypts the data and sends it to the server, which then receives and processes it to generate an optimal investment strategy, which is then notified and displayed to the user. This process allows users to easily find an investment strategy that suits their financial situation.

[0155] (Application example 1)

[0156] 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."

[0157] Conventional investment strategy proposal systems require users to manually input income and expenditure data, which is time-consuming and increases the risk of input errors. Furthermore, they are unable to utilize data from the electronic payment services users use on a daily basis, making it difficult to propose real-time investment strategies based on that data. Furthermore, they are also required to respond quickly to questions about asset management from users.

[0158] 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.

[0159] In this invention, the server includes means for inputting expenditure data and income data from a user, means for transmitting the data to the server, means for receiving the data and storing it in a database, means for preprocessing the stored data, means for generating an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying the user of the generated investment strategy, means for automatically collecting the user's expenditure data through an electronic payment service, and means for instantly responding to questions from the user regarding asset management. This enables real-time investment strategy proposals utilizing electronic payment data while reducing the user's effort. Furthermore, detailed analysis by the artificial intelligence engine can propose optimal investment strategies suited to the user.

[0160] definition statement

[0161] "User" refers to any person or entity that inputs expenditure and income data and receives investment strategy suggestions.

[0162] "Expense Data" refers to information about the money a user spends in their daily life or business activities.

[0163] "Income Data" refers to information about the income earned by a User in their daily life or business activities.

[0164] "Server" refers to the computer system that receives and stores user data, as well as pre-processing and analyzing the data for the artificial intelligence engine.

[0165] "Database" refers to a data storage system for organizing, storing, and managing data within a server.

[0166] "Preprocessing" refers to the process of data shaping and cleaning to prepare stored data in a form suitable for analysis.

[0167] "Artificial intelligence engine" refers to a program or algorithm that analyzes a user's expenditure and income data and generates an optimal investment strategy.

[0168] "Investment Strategy" refers to a specific investment allocation plan suggested to you based on your financial situation and risk tolerance.

[0169] "Electronic Payment Service" means an internet-based payment system through which users can pay for goods and services.

[0170] "Clustering" refers to a data analysis technique that classifies a large number of data points into several groups based on their similarities.

[0171] "Risk Profile" refers to an assessment analysis generated based on a user's risk tolerance and financial situation.

[0172] "Encryption" refers to the process of converting data into a form that cannot be understood by third parties, with the purpose of protecting the data.

[0173] "Privacy protection" refers to measures taken to prevent users' personal information and data from being accessed, used, or disclosed in an unauthorized manner.

[0174] MODE FOR CARRYING OUT THE INVENTION

[0175] Overall system configuration

[0176] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0177] Data input from the user

[0178] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation. It is also possible to automatically collect user expenditure data by using electronic payment services.

[0179] Data transmission and storage

[0180] The user's device encrypts the data entered and sends it securely to the server. The server immediately stores the data in a database, which also records metadata such as the user ID and timestamp for each piece of data. The data is then formatted and cleaned as needed to prepare it for analysis.

[0181] AI-based data analysis and investment strategy generation

[0182] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[0183] Notification and display of investment strategies

[0184] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[0185] User Scenarios

[0186] Users enter their monthly expenditure information through the app. By using an electronic payment service, expenditure data is automatically collected. For example, details such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen are entered. The device sends this data to a server, which receives it and stores it in an encrypted form in a database.

[0187] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[0188] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[0189] Hardware and software used

[0190] Hardware: Server (using cloud services)

[0191] Software: Python, Pandas, Sklearn, REST API

[0192] Prompt Sentence Examples

[0193] "I want to create an application that suggests investment strategies based on monthly expenditure and income data. Specifically, this will include data collection, data cleaning, clustering, generating optimal investment strategies, and notifying the user."

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Program processing flow

[0196] Step 1:

[0197] Data Entry and Collection

[0198] Users enter expenditure and income data using a dedicated application or a web interface. Users' expenditure data is also collected automatically by using an electronic payment service. As a result, input data includes both manual input and automatic collection. Input data includes details such as rent, food expenses, entertainment expenses, and income.

[0199] Input: User spending and income data

[0200] Output: Collected data objects

[0201] Step 2:

[0202] Data Transmission and Encryption

[0203] The device encrypts the collected data and transmits it to the server in a secure manner, which involves transforming the data using an encryption algorithm and transmitting it to the server's endpoint.

[0204] Input: Collected data objects

[0205] Output: Encrypted data

[0206] Step 3:

[0207] Receiving and storing data

[0208] The server receives the encrypted data and stores it in a database, where it is decrypted and recorded along with metadata such as the user ID and timestamp.

[0209] Input: Encrypted data

[0210] Output: Data stored in the database

[0211] Step 4:

[0212] Data Preprocessing

[0213] The server preprocesses the stored data, which includes data formatting and cleaning, such as standardizing data formats, imputing missing values, and detecting and correcting outliers.

[0214] Input: Data stored in a database

[0215] Output: Preprocessed data

[0216] Step 5:

[0217] Data analysis using an AI engine

[0218] The server then passes the preprocessed data to an AI engine for further analysis, including data normalization, clustering, and risk profile generation, which categorizes the user's spending patterns into several categories and generates an investment strategy suited to each individual user.

[0219] Input: Preprocessed data

[0220] Output: Generated investment strategy

[0221] Step 6:

[0222] Investment Strategy Notification

[0223] The server notifies the user of the investment strategy generated by the AI ​​engine, and the user can view detailed information such as the recommended portfolio, projected returns, and risk assessment through an app or web interface.

[0224] Input: Generated investment strategy

[0225] Output: Investment strategy communicated to the user

[0226] Step 7:

[0227] Responding to user questions

[0228] Users submit questions about asset management through the application. The device sends the questions to the server, which uses an AI engine to generate appropriate answers. The device then displays the answers to the user.

[0229] Input: User question

[0230] Output: Answer by AI engine

[0231] Through these steps, users can receive the optimal investment strategy based on their individual financial situation and risk tolerance, and real-time investment suggestions can be made using data automatically collected through the electronic payment service.

[0232] 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.

[0233] Overall system configuration

[0234] This invention provides a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[0235] Data input from the user

[0236] Users enter expenditure and income data through a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen.

[0237] Data transmission and storage

[0238] The device verifies the entered data, encrypts it, and sends it to the server, which receives it and stores it securely in a database, along with metadata such as the user ID and a timestamp.

[0239] AI-based data analysis and investment strategy generation

[0240] The server passes the stored data to the AI ​​engine, which normalizes the expenditure and income data and applies a clustering algorithm to identify spending patterns. It evaluates the user's expenditure-to-income ratio and creates a risk profile. It generates an optimal investment strategy based on the user's risk tolerance and suggests a specific portfolio (e.g., 60% bonds, 30% index funds, and 10% cash).

[0241] Applying the Emotion Engine

[0242] The server passes the text and voice contained in the user's input or question to the emotion engine, which uses natural language processing technology to recognize the user's emotions. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize the user's anxiety.

[0243] Coordinating and informing investment strategies

[0244] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, for a user with high anxiety, the AI ​​engine will increase low-risk investments. The adjusted investment strategy is finally generated.

[0245] The server then communicates this adjusted investment strategy to the user, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice.

[0246] Example: User scenario

[0247] Users enter their monthly expenditure information through the app. For example, rent is 100,000 yen, food is 50,000 yen, entertainment is 20,000 yen, and income is 250,000 yen. The device encrypts this data and sends it to the server. The server receives the data and stores it in a database in encrypted form.

[0248] The server passes the stored data to an AI engine, which then normalizes and clusters the spending data. For example, it identifies users whose spending is concentrated on rent and food. The AI ​​engine also assesses risk and suggests optimal investment strategies.

[0249] At the same time, the server passes the user's comments and questions to the emotion engine. For example, if a user enters, "I'm worried about the recent fluctuations in stock prices," the emotion engine analyzes this comment and recognizes the user's anxiety. As a result, the AI ​​engine makes adjustments to increase the number of low-risk items.

[0250] The server then notifies the user of an adjusted investment strategy based on this. For example, it displays details such as, "Taking your current risk profile into consideration, we propose a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." If the user asks, "What are the returns of this index fund over the past three years?", the device sends the question to the server, which uses its AI engine and emotion engine to generate an appropriate answer and immediately responds to the user. For example, it may notify the user that "The average return of this index fund over the past three years is 5.2%."

[0251] In this way, by combining the emotion engine, users can receive the optimal investment strategy tailored to their emotional state, allowing them to manage their assets with greater peace of mind.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[0255] Step 2:

[0256] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[0257] Step 3:

[0258] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[0259] Step 4:

[0260] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and a timestamp.

[0261] Step 5:

[0262] The server preprocesses the stored data, for example by imputing missing values, converting non-numeric data, and removing unnecessary data.

[0263] Step 6:

[0264] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[0265] Step 7:

[0266] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify users' spending patterns, such as users whose spending is concentrated on rent and food.

[0267] Step 8:

[0268] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[0269] Step 9:

[0270] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[0271] Step 10:

[0272] The server notifies the user of the generated investment strategy, including the recommended portfolio, predicted returns, and risk assessment.

[0273] Step 11:

[0274] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[0275] Step 12:

[0276] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[0277] Step 13:

[0278] The terminal sends the user's questions to the server.

[0279] Step 14:

[0280] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[0281] Step 15:

[0282] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[0283] Step 16:

[0284] The server generates a response and sends it back to the user.

[0285] Step 17:

[0286] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[0287] Step 18:

[0288] The server passes the text and voice contained in the user's input and questions to the emotion engine, which analyzes them and recognizes the user's emotions.

[0289] Step 19:

[0290] The emotion engine assesses the user's emotional state (e.g., anxiety, relief) and provides the results to the AI ​​engine.

[0291] Step 20:

[0292] The AI ​​engine takes into account emotional state information and adjusts investment strategies, for example, if a user is feeling anxious, it will adjust to a lower-risk investment strategy to alleviate those feelings.

[0293] Step 21:

[0294] The server then notifies the user of the adjusted investment strategy again, for example, by displaying a message saying, "Taking your emotional state into consideration, we propose a new portfolio with reduced risk."

[0295] This series of steps will result in a system that can provide optimal investment strategies that take into account not only the user's financial situation but also their emotional state.

[0296] Example 2

[0297] 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."

[0298] Conventional investment strategy proposal systems performed risk assessment and clustering based on users' expenditure and income data, but did not optimize investment strategies taking into account emotional fluctuations. As a result, when users felt anxious or worried about market fluctuations or their personal circumstances, the investment strategy could not be adjusted to take those emotions into account, resulting in a decrease in user satisfaction. Furthermore, sufficient measures were required to ensure the security and privacy of the collected data.

[0299] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0300] In this invention, the server includes a means for inputting expenditure data and income data from a user, a means for the terminal to verify the input data, encrypt it, and transmit it to the server, and a means for the server to receive the data and store it in a database. This enables the user's expenditure data and income data to be safely collected and stored while ensuring security and privacy. The server also includes a means for preprocessing the stored data, clustering and normalizing the expenditure data and income data using an artificial intelligence engine, and generating a risk profile. The artificial intelligence engine optimizes an investment strategy based on the generated risk profile. The server also provides the user's input and questions to an emotion engine, which analyzes the user's emotions and returns an emotion tag, allowing the AI ​​engine to adjust the investment strategy taking the emotion tag into account. This makes it possible to provide an investment strategy that reflects the user's current emotional state, thereby improving the user's investment performance and satisfaction.

[0301] "User" means an individual or legal entity that uses the system to input expenditure and income data and receive investment strategy suggestions.

[0302] A "server" is a computer system that receives, stores, and processes data sent by users.

[0303] A "terminal" is a device used by a user to input and transmit data, such as a smartphone or a personal computer.

[0304] "Expenditure Data" refers to data that indicates the amount of money a user spends on various consumption activities.

[0305] "Income Data" means data indicating the income earned by a user over a certain period of time.

[0306] "Encryption" is the process of transforming data with a specific algorithm to ensure its security.

[0307] A "database" is a collection of information that stores data in an organized manner and allows it to be searched and used when needed.

[0308] "Preprocessing" refers to the process of organizing and normalizing data before performing data analysis.

[0309] An "artificial intelligence engine" is software that uses machine learning and data analysis techniques to analyze data and generate investment strategies.

[0310] "Clustering" is a technique for grouping data based on specific criteria and identifying patterns.

[0311] "Risk Profile" is a criterion for assessing a user's risk tolerance and determining their investment strategy.

[0312] An "emotion engine" is software that uses natural language processing technology to analyze emotions from user input text and add emotion tags.

[0313] An "emotion tag" is a label that is added to the emotion engine to identify the user's emotion and indicate the result.

[0314] An "investment strategy" is a plan that suggests optimal asset allocation and investment products based on a user's financial data and risk profile.

[0315] This invention relates to a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[0316] Users enter expense and income data through a dedicated application or web interface. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The software used could be a mobile application or a web browser.

[0317] The terminal validates the data entered by the user. Validation includes data format and range checks. For example, an error message is displayed if income is less than expenses or if non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and transmits it securely to the server.

[0318] The server receives the encrypted data sent from the device and temporarily decrypts it in memory. It then re-encrypts it before saving it to the database. The saved data also includes metadata such as the user ID, input date and time, and data type.

[0319] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[0320] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It evaluates the user's income and expenditure ratios and creates a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[0321] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[0322] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. Specifically, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[0323] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[0324] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, projected returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[0325] Prompt Sentence Examples

[0326] An example of a prompt sentence is, "I have entered the following data: rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, and income 250,000 yen. Please suggest an investment strategy with low risk."

[0327] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0328] Step 1:

[0329] Users use a dedicated application or web interface to enter monthly expenditure and income data. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen.

[0330] Input: User expenditure and income data

[0331] Output: The input data

[0332] Step 2:

[0333] The terminal validates the entered data, checking the data format and range, and displays an error message if, for example, income is less than expenses or non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and sends it securely to the server.

[0334] Input: Data entered by the user

[0335] Output: Encrypted data

[0336] Step 3:

[0337] The server receives the encrypted data sent from the device. It temporarily decrypts it in memory, then re-encrypts it and stores it in the database. The stored data also includes metadata such as the user ID, input date and time, and data type.

[0338] Input: Encrypted data

[0339] Output: Data stored in the database

[0340] Step 4:

[0341] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[0342] Input: Saved data

[0343] Output: Data provided to the AI ​​engine

[0344] Step 5:

[0345] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It then evaluates the user's income and expenditure ratios to create a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[0346] Input: Preprocessed expenditure and income data

[0347] Output: Optimized investment strategy

[0348] Step 6:

[0349] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[0350] Input: User input or questions

[0351] Output: Data provided to the emotion engine

[0352] Step 7:

[0353] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. For example, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[0354] Input: User's text data

[0355] Output: Emotion tag

[0356] Step 8:

[0357] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[0358] Input: emotion tag

[0359] Output: Recalibrated investment strategy

[0360] Step 9:

[0361] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, predicted returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio with 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[0362] Input: Reworked investment strategy and user question

[0363] Output: User notification and response

[0364] (Application example 2)

[0365] 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."

[0366] Conventional investment strategy proposal systems provide investment strategies based on users' expenditure and income data, but they do not optimize the strategies based on the user's emotional state, which means they are unable to address the user's anxiety and risk tolerance. This can lead to emotions affecting the user's investment behavior, resulting in suboptimal investment results. There is also a need for a system that can securely manage user data and instantly answer questions about asset management.

[0367] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0368] In this invention, the server includes means for inputting expenditure data and income data from a user, means for preprocessing the data, means for generating an investment strategy using an artificial intelligence engine, means for identifying the emotional state of the user using an emotion engine for analyzing the user's emotions, means for adjusting the investment strategy based on the emotional state of the user, and means for providing immediate answers to questions about asset management from the user. This enables the provision of an investment strategy that takes the emotional state of the user into consideration, fast and secure data management, and immediate answers.

[0369] "Expense Data" refers to information about financial expenditures used by a user in their daily lives.

[0370] "Income Data" means information about a User's financial income from work or other sources.

[0371] A "server" is a computer system located in a remote location that receives and stores data and provides services to users.

[0372] An "artificial intelligence engine" is a technology that uses programs and algorithms installed on a server to perform data analysis and automated decision-making.

[0373] An "emotion engine" is a technology for analyzing and recognizing emotions from a user's text or voice.

[0374] An "investment strategy" is a plan that suggests optimal asset allocation and investment destinations for users' investments.

[0375] A "database" is a collection of information that allows information to be efficiently stored and quickly retrieved when needed.

[0376] "Encryption" is the process of transforming data using a specific algorithm to protect it from third parties.

[0377] "Clustering" is a technique for dividing data into groups so that data within the same group have similar characteristics.

[0378] A "risk profile" is information that assesses a user's risk tolerance and financial situation and indicates how much risk they are willing to take.

[0379] "User" means an individual or organization that uses the system to input expenditure and income data and receive investment strategy proposals.

[0380] "Notification" is an action taken by a system to provide information to a user.

[0381] "Privacy" is the right to protect personal information from being disclosed to third parties.

[0382] The "question answering means" is a function for providing immediate and appropriate answers to inquiries from users.

[0383] The present invention provides a system that uses an artificial intelligence engine and an emotion engine to propose an optimal investment strategy based on a user's expenditure data and income data. Specific embodiments are described below.

[0384] Overall system configuration

[0385] The system mainly consists of the following components:

[0386] 1. User terminal: A device such as a smartphone that provides a means for users to enter spending and income data.

[0387] 2. Server: Receives input data and stores it securely. Specific technologies used here include database systems and data encryption technology.

[0388] 3. AI Engine: Performs data analysis such as normalization, clustering, and generating risk profiles to generate investment strategies.

[0389] 4. Emotion Engine: Analyzes text and speech in user inputs and questions to identify the user's emotional state. Natural language processing techniques are used.

[0390] Data processing flow

[0391] Users enter their daily expenditure and income data through a smartphone application, which is then encrypted by the device and sent to a server, where it is received and securely stored in a database.

[0392] Data analysis

[0393] The server receives the stored data and passes it to the AI ​​engine, which first normalizes the data and applies a clustering algorithm to identify spending patterns, then generates a risk profile and creates an investment strategy based on it.

[0394] Emotion analysis

[0395] The server also forwards user comments and questions to the emotion engine, which analyzes them and identifies the user's emotional state. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize anxiety.

[0396] Adjusting investment strategies

[0397] Based on the results of the emotion engine, the AI ​​engine will adjust the investment strategy taking into account the user's emotional state, and can suggest lower-risk investment strategies for users with high anxiety.

[0398] User notification and response

[0399] The final investment strategy is then sent to the server, which then notifies the user of the strategy. This notification includes specific investment allocations, risk assessments, and advice. The system also has the ability to instantly answer questions from users about asset management.

[0400] Specific examples

[0401] When a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to a server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[0402] Prompt Sentence Examples

[0403] "I'm worried about the recent market fluctuations."

[0404] In response, the AI ​​engine will adjust the investment strategy taking into account the results of the sentiment engine, allowing users to manage their assets with greater peace of mind.

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] Users enter expenditure and income data through a smartphone application. Specifically, users enter data such as monthly rent, food expenses, entertainment expenses, and income into the app. This data will be used for subsequent analysis, so it must be entered accurately. The input data is stored in standard formats such as JSON and CSV.

[0408] Step 2:

[0409] The device encrypts the input data and sends it to the server using an encryption method such as AES-256. The data, including metadata such as the user ID and timestamp, is sent to the server using a secure communication protocol (e.g., HTTPS).

[0410] Step 3:

[0411] The server securely stores the received data in a database. The data is not stored in its original format, but may be further encrypted to maintain data integrity. The stored data also includes the user ID and the date and time of input.

[0412] Step 4:

[0413] The server preprocesses the stored data and passes it to the AI ​​engine. Preprocessing includes normalizing the data (e.g., scaling by standard deviation) and imputing missing data (e.g., imputing by mean value), making the data suitable for analysis.

[0414] Step 5:

[0415] The AI ​​engine analyzes the pre-processed data and applies clustering algorithms (e.g., K-means) to identify spending patterns. It also evaluates the user's spending-to-income ratio and generates a risk profile. Based on this, it generates an investment strategy (e.g., 60% bonds, 30% index funds, 10% cash).

[0416] Step 6:

[0417] The server passes the user's comments and questions to the emotion engine, which uses natural language processing techniques (e.g., the BERT model) to analyze the text and identify the user's emotional state (e.g., anxiety, satisfaction, expectation, etc.).

[0418] Step 7:

[0419] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, if the user feels anxious, it will increase low-risk investments.

[0420] Step 8:

[0421] The server notifies the user of the adjusted investment strategy, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice. If the user then asks additional questions, the questions are analyzed again and an immediate answer is provided.

[0422] For example, if a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to the server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[0423] 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.

[0424] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0425] 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.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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. 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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."

[0439] Overall system configuration

[0440] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0441] Data input from the user

[0442] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and financial situation.

[0443] Data transmission and storage

[0444] The device encrypts the data entered by the user and sends it securely to the server, which immediately stores the data in a database. The database also records metadata for each piece of data, such as the user ID and timestamp. The data is then formatted and cleaned as needed to prepare it for analysis.

[0445] AI-based data analysis and investment strategy generation

[0446] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[0447] Notification and display of investment strategies

[0448] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[0449] Example: User scenario

[0450] Users enter their monthly expenditure information through the app, such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The device sends this data to the server, which receives it and stores it in an encrypted form in a database.

[0451] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[0452] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, for example, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[0453] As a result, users can receive an appropriate investment strategy tailored to their individual financial situation and risk tolerance, allowing them to invest with peace of mind while alleviating any concerns or doubts they may have.

[0454] The processing flow will be explained below.

[0455] Step 1:

[0456] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[0457] Step 2:

[0458] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[0459] Step 3:

[0460] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[0461] Step 4:

[0462] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and timestamp.

[0463] Step 5:

[0464] The server preprocesses the stored data, specifically by imputing missing values, converting non-numeric data, and removing unnecessary data.

[0465] Step 6:

[0466] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[0467] Step 7:

[0468] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify user spending patterns, such as users whose spending is concentrated on rent and food.

[0469] Step 8:

[0470] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[0471] Step 9:

[0472] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[0473] Step 10:

[0474] The server receives the investment strategy generated by the AI ​​engine and notifies the user, including recommended portfolios, predicted returns, and risk assessments.

[0475] Step 11:

[0476] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[0477] Step 12:

[0478] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[0479] Step 13:

[0480] The terminal sends the user's questions to the server.

[0481] Step 14:

[0482] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[0483] Step 15:

[0484] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[0485] Step 16:

[0486] The server generates a response and sends it back to the user.

[0487] Step 17:

[0488] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[0489] Through this process, users will receive an appropriate investment strategy based on their financial situation, allowing them to immediately resolve any doubts or concerns they may have about asset management.

[0490] Example 1

[0491] 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."

[0492] In modern society, personal asset management is extremely important. However, it is generally difficult for individual users to find the optimal investment strategy. In particular, there is a lack of systems that assess risk based on users' spending and income data and provide individually optimized investment strategies. This poses the challenge of users being unable to make appropriate investment decisions or accurately understand the risks involved in asset management. Furthermore, it is important to manage data while protecting users' privacy.

[0493] 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.

[0494] In this invention, the server includes means for inputting expenditure data and income data from a user, means for encrypting the input data at a terminal and transmitting it to the server, means for receiving the data and storing it in a database, means for cleaning and formatting the stored data, means for generating clustering and an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying and displaying the generated investment strategy to the user, and means for instantly answering questions about asset management from the user. This allows the user to receive an individually optimized investment strategy and makes it easier to accurately understand the risks involved in asset management. Furthermore, data encryption also protects the user's privacy.

[0495] "User" means any person or entity that utilizes the system to input expenditure and income data and receive optimal investment strategies.

[0496] "Expense data" refers to data that includes information on the amounts and items of expenditures that a user has made over a specific period of time.

[0497] "Income Data" means data that includes information about the amount and type of income a User has earned during a particular period of time.

[0498] A "terminal" is an electronic device used by a user to input data and send it to a server, including smartphones, tablets, and personal computers.

[0499] "Encryption" is a technology that converts data using a specific algorithm so that the data being transmitted cannot be deciphered by a third party.

[0500] "Server" means a computer system that receives, stores, and processes data submitted by users and generates investment strategies using an artificial intelligence engine.

[0501] A "database" is a collection of information, including user expenditure data and income data, stored on a server, and is an organized storage system.

[0502] "Cleaning" refers to preprocessing work such as deleting unnecessary data and standardizing formats in order to improve the quality and reliability of data.

[0503] "Formatting" is the process of converting data into a form suitable for analysis in order to perform data analysis.

[0504] An "artificial intelligence engine" is a software program that contains machine learning algorithms and models to analyze user data and generate optimal investment strategies.

[0505] "Clustering" is a data analysis technique that groups similar data together to understand user spending patterns.

[0506] An "investment strategy" is a plan that includes asset allocation and investment recommendations optimized based on a user's risk tolerance and goals.

[0507] "Notification" means the act of transmitting investment strategies and other related information from the server to the user.

[0508] "Display" refers to the act of visually showing the notified information on the user terminal.

[0509] A "question" is an action in which a user sends a question about asset management to the server.

[0510] "Immediate response" refers to the act of quickly providing appropriate information and answers to questions from users.

[0511] The above definitions clarify the meaning of important words in this system.

[0512] The present invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. This system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0513] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation.

[0514] Next, the terminal encrypts the entered data and sends it to the server using a secure method. Encryption is performed using technologies such as AES (Advanced Encryption Standard). Transmission is performed using the HTTPS protocol, ensuring data protection. For example, data such as a rent of 100,000 yen is encrypted with AES before being sent to the server.

[0515] The server receives the encrypted data and stores it in a database. When stored, metadata such as user IDs and timestamps are also recorded. It also cleans (deletes unnecessary data and standardizes formats) and formats (converts data into a form suitable for analysis).

[0516] The server then passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[0517] The AI ​​engine performs detailed analysis based on the data it receives. First, it normalizes expenditure and income data and classifies users into several categories using a clustering algorithm (e.g., k-means clustering). Then, it uses machine learning models (e.g., random forests, neural networks) to generate investment strategies suited to each individual user. For example, safe investments are suggested for low-risk users.

[0518] The resulting investment strategies are managed by a server and organized for each user. The server stores information based on the user ID and generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash."

[0519] Finally, the device displays the investment strategy data received from the server to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." Furthermore, when a user asks a question, for example, "What are the returns of index funds over the past three years?", it sends a prompt to the server, and the server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[0520] The system allows users to receive individually optimized investment strategies and accurately understand the risks involved in managing their assets, while protecting user privacy through data encryption and the use of the HTTPS protocol.

[0521] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0522] Specific explanation of processing steps

[0523] Step 1: Data input from the user

[0524] Users enter monthly expenditure and income data using a dedicated application or web interface. Specifically, they enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen into the app. This input data is important information that will be used for subsequent analysis.

[0525] Input: User's expenditure data and income data (e.g. rent 100,000 yen, food expenses 50,000 yen, income 250,000 yen)

[0526] Output: Data for encryption and transmission

[0527] Step 2: Encrypt and send data

[0528] The terminal encrypts the data entered by the user. The encryption technology is AES (Advanced Encryption Standard), and the data is sent to the server using the HTTPS protocol. For example, data such as "rent 100,000 yen" is sent to the server in AES encrypted form.

[0529] Input: Unencrypted data entered by the user

[0530] Output: Encrypted data

[0531] Step 3: Receiving and storing data

[0532] The server receives the encrypted data and stores it in a database using security technology. When the data is stored, metadata such as the user ID and timestamp are also recorded. For example, "Rent of 100,000 yen" is stored in the database as "Expense category: Rent, Amount: 100,000 yen, User ID: 12345, Timestamp: 2023-10-01."

[0533] Input: Encrypted data and metadata

[0534] Output: Saved database entry

[0535] Step 4: Preprocessing the data

[0536] The server cleans and formats the stored data to make it suitable for analysis. Cleaning involves deleting unnecessary data and standardizing formats. Formatting involves converting the data into a format suitable for analysis. For example, this involves correcting incomplete date data and standardizing data formats.

[0537] Input: Stored raw data

[0538] Output: Preprocessed data

[0539] Step 5: Passing data to the AI ​​engine

[0540] The server passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[0541] Input: Preprocessed data

[0542] Output: Data passed to the AI ​​engine

[0543] Step 6: Data analysis and investment strategy generation using an AI engine

[0544] The AI ​​engine performs detailed analysis based on the data it receives. It normalizes expenditure and income data and classifies users into categories using clustering algorithms such as k-means clustering. It then uses machine learning models (e.g., random forests and neural networks) to generate investment strategies tailored to each individual user. For example, safe investments are suggested for low-risk users.

[0545] Input: Data passed to the AI ​​engine

[0546] Output: Generated investment strategy

[0547] Step 7: Managing and sending investment strategies on the server

[0548] The server organizes and stores data for each user based on the investment strategy received from the AI ​​engine. For example, for user ID 12345, it generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash" and manages it within the server.

[0549] Input: Investment strategy received from the AI ​​engine

[0550] Output: Organized investment strategy data

[0551] Step 8: Notify and display the investment strategy to users

[0552] The device receives investment strategy data from the server and displays it to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." If the user has a question about an investment, for example, "What are the returns on index funds over the past three years?", the device sends the question to the server. The server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[0553] Input: Investment strategies received from the server, queries from users

[0554] Output: Investment strategies and answers displayed to the user

[0555] For example, after a user enters their spending information such as rent and food, the device encrypts the data and sends it to the server, which then receives and processes it to generate an optimal investment strategy, which is then notified and displayed to the user. This process allows users to easily find an investment strategy that suits their financial situation.

[0556] (Application example 1)

[0557] 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."

[0558] Conventional investment strategy proposal systems require users to manually input income and expenditure data, which is time-consuming and increases the risk of input errors. Furthermore, they are unable to utilize data from the electronic payment services users use on a daily basis, making it difficult to propose real-time investment strategies based on that data. Furthermore, they are also required to respond quickly to questions about asset management from users.

[0559] 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.

[0560] In this invention, the server includes means for inputting expenditure data and income data from a user, means for transmitting the data to the server, means for receiving the data and storing it in a database, means for preprocessing the stored data, means for generating an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying the user of the generated investment strategy, means for automatically collecting the user's expenditure data through an electronic payment service, and means for instantly responding to questions from the user regarding asset management. This enables real-time investment strategy proposals utilizing electronic payment data while reducing the user's effort. Furthermore, detailed analysis by the artificial intelligence engine can propose optimal investment strategies suited to the user.

[0561] definition statement

[0562] "User" refers to any person or entity that inputs expenditure and income data and receives investment strategy suggestions.

[0563] "Expense Data" refers to information about the money a user spends in their daily life or business activities.

[0564] "Income Data" refers to information about the income earned by a User in their daily life or business activities.

[0565] "Server" refers to the computer system that receives and stores user data, as well as pre-processing and analyzing the data for the artificial intelligence engine.

[0566] "Database" refers to a data storage system for organizing, storing, and managing data within a server.

[0567] "Preprocessing" refers to the process of data shaping and cleaning to prepare stored data in a form suitable for analysis.

[0568] "Artificial intelligence engine" refers to a program or algorithm that analyzes a user's expenditure and income data and generates an optimal investment strategy.

[0569] "Investment Strategy" refers to a specific investment allocation plan suggested to you based on your financial situation and risk tolerance.

[0570] "Electronic Payment Service" means an internet-based payment system through which users can pay for goods and services.

[0571] "Clustering" refers to a data analysis technique that classifies a large number of data points into several groups based on their similarities.

[0572] "Risk Profile" refers to an assessment analysis generated based on a user's risk tolerance and financial situation.

[0573] "Encryption" refers to the process of converting data into a form that cannot be understood by third parties, with the purpose of protecting the data.

[0574] "Privacy protection" refers to measures taken to prevent users' personal information and data from being accessed, used, or disclosed in an unauthorized manner.

[0575] MODE FOR CARRYING OUT THE INVENTION

[0576] Overall system configuration

[0577] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0578] Data input from the user

[0579] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation. It is also possible to automatically collect user expenditure data by using electronic payment services.

[0580] Data transmission and storage

[0581] The user's device encrypts the data entered and sends it securely to the server. The server immediately stores the data in a database, which also records metadata such as the user ID and timestamp for each piece of data. The data is then formatted and cleaned as needed to prepare it for analysis.

[0582] AI-based data analysis and investment strategy generation

[0583] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[0584] Notification and display of investment strategies

[0585] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[0586] User Scenarios

[0587] Users enter their monthly expenditure information through the app. By using an electronic payment service, expenditure data is automatically collected. For example, details such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen are entered. The device sends this data to a server, which receives it and stores it in an encrypted form in a database.

[0588] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[0589] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[0590] Hardware and software used

[0591] Hardware: Server (using cloud services)

[0592] Software: Python, Pandas, Sklearn, REST API

[0593] Prompt Sentence Examples

[0594] "I want to create an application that suggests investment strategies based on monthly expenditure and income data. Specifically, this will include data collection, data cleaning, clustering, generating optimal investment strategies, and notifying the user."

[0595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0596] Program processing flow

[0597] Step 1:

[0598] Data Entry and Collection

[0599] Users enter expenditure and income data using a dedicated application or a web interface. Users' expenditure data is also collected automatically by using an electronic payment service. As a result, input data includes both manual input and automatic collection. Input data includes details such as rent, food expenses, entertainment expenses, and income.

[0600] Input: User spending and income data

[0601] Output: Collected data objects

[0602] Step 2:

[0603] Data Transmission and Encryption

[0604] The device encrypts the collected data and transmits it to the server in a secure manner, which involves transforming the data using an encryption algorithm and transmitting it to the server's endpoint.

[0605] Input: Collected data objects

[0606] Output: Encrypted data

[0607] Step 3:

[0608] Receiving and storing data

[0609] The server receives the encrypted data and stores it in a database, where it is decrypted and recorded along with metadata such as the user ID and timestamp.

[0610] Input: Encrypted data

[0611] Output: Data stored in the database

[0612] Step 4:

[0613] Data Preprocessing

[0614] The server preprocesses the stored data, which includes data formatting and cleaning, such as standardizing data formats, imputing missing values, and detecting and correcting outliers.

[0615] Input: Data stored in a database

[0616] Output: Preprocessed data

[0617] Step 5:

[0618] Data analysis using an AI engine

[0619] The server then passes the preprocessed data to an AI engine for further analysis, including data normalization, clustering, and risk profile generation, which categorizes the user's spending patterns into several categories and generates an investment strategy suited to each individual user.

[0620] Input: Preprocessed data

[0621] Output: Generated investment strategy

[0622] Step 6:

[0623] Investment Strategy Notification

[0624] The server notifies the user of the investment strategy generated by the AI ​​engine, and the user can view detailed information such as the recommended portfolio, projected returns, and risk assessment through an app or web interface.

[0625] Input: Generated investment strategy

[0626] Output: Investment strategy communicated to the user

[0627] Step 7:

[0628] Responding to user questions

[0629] Users submit questions about asset management through the application. The device sends the questions to the server, which uses an AI engine to generate appropriate answers. The device then displays the answers to the user.

[0630] Input: User question

[0631] Output: Answer by AI engine

[0632] Through these steps, users can receive the optimal investment strategy based on their individual financial situation and risk tolerance, and real-time investment suggestions can be made using data automatically collected through the electronic payment service.

[0633] 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.

[0634] Overall system configuration

[0635] This invention provides a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[0636] Data input from the user

[0637] Users enter expenditure and income data through a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen.

[0638] Data transmission and storage

[0639] The device verifies the entered data, encrypts it, and sends it to the server, which receives it and stores it securely in a database, along with metadata such as the user ID and a timestamp.

[0640] AI-based data analysis and investment strategy generation

[0641] The server passes the stored data to the AI ​​engine, which normalizes the expenditure and income data and applies a clustering algorithm to identify spending patterns. It evaluates the user's expenditure-to-income ratio and creates a risk profile. It generates an optimal investment strategy based on the user's risk tolerance and suggests a specific portfolio (e.g., 60% bonds, 30% index funds, and 10% cash).

[0642] Applying the Emotion Engine

[0643] The server passes the text and voice contained in the user's input or question to the emotion engine, which uses natural language processing technology to recognize the user's emotions. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize the user's anxiety.

[0644] Coordinating and informing investment strategies

[0645] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, for a user with high anxiety, the AI ​​engine will increase low-risk investments. The adjusted investment strategy is finally generated.

[0646] The server then communicates this adjusted investment strategy to the user, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice.

[0647] Example: User scenario

[0648] Users enter their monthly expenditure information through the app. For example, rent is 100,000 yen, food is 50,000 yen, entertainment is 20,000 yen, and income is 250,000 yen. The device encrypts this data and sends it to the server. The server receives the data and stores it in a database in encrypted form.

[0649] The server passes the stored data to an AI engine, which then normalizes and clusters the spending data. For example, it identifies users whose spending is concentrated on rent and food. The AI ​​engine also assesses risk and suggests optimal investment strategies.

[0650] At the same time, the server passes the user's comments and questions to the emotion engine. For example, if a user enters, "I'm worried about the recent fluctuations in stock prices," the emotion engine analyzes this comment and recognizes the user's anxiety. As a result, the AI ​​engine makes adjustments to increase the number of low-risk items.

[0651] The server then notifies the user of an adjusted investment strategy based on this. For example, it displays details such as, "Taking your current risk profile into consideration, we propose a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." If the user asks, "What are the returns of this index fund over the past three years?", the device sends the question to the server, which uses its AI engine and emotion engine to generate an appropriate answer and immediately responds to the user. For example, it may notify the user that "The average return of this index fund over the past three years is 5.2%."

[0652] In this way, by combining the emotion engine, users can receive the optimal investment strategy tailored to their emotional state, allowing them to manage their assets with greater peace of mind.

[0653] The processing flow will be explained below.

[0654] Step 1:

[0655] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[0656] Step 2:

[0657] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[0658] Step 3:

[0659] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[0660] Step 4:

[0661] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and a timestamp.

[0662] Step 5:

[0663] The server preprocesses the stored data, for example by imputing missing values, converting non-numeric data, and removing unnecessary data.

[0664] Step 6:

[0665] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[0666] Step 7:

[0667] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify users' spending patterns, such as users whose spending is concentrated on rent and food.

[0668] Step 8:

[0669] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[0670] Step 9:

[0671] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[0672] Step 10:

[0673] The server notifies the user of the generated investment strategy, including the recommended portfolio, predicted returns, and risk assessment.

[0674] Step 11:

[0675] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[0676] Step 12:

[0677] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[0678] Step 13:

[0679] The terminal sends the user's questions to the server.

[0680] Step 14:

[0681] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[0682] Step 15:

[0683] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[0684] Step 16:

[0685] The server generates a response and sends it back to the user.

[0686] Step 17:

[0687] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[0688] Step 18:

[0689] The server passes the text and voice contained in the user's input and questions to the emotion engine, which analyzes them and recognizes the user's emotions.

[0690] Step 19:

[0691] The emotion engine assesses the user's emotional state (e.g., anxiety, relief) and provides the results to the AI ​​engine.

[0692] Step 20:

[0693] The AI ​​engine takes into account emotional state information and adjusts investment strategies, for example, if a user is feeling anxious, it will adjust to a lower-risk investment strategy to alleviate those feelings.

[0694] Step 21:

[0695] The server then notifies the user of the adjusted investment strategy again, for example, by displaying a message saying, "Taking your emotional state into consideration, we propose a new portfolio with reduced risk."

[0696] This series of steps will result in a system that can provide optimal investment strategies that take into account not only the user's financial situation but also their emotional state.

[0697] Example 2

[0698] 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."

[0699] Conventional investment strategy proposal systems performed risk assessment and clustering based on users' expenditure and income data, but did not optimize investment strategies taking into account emotional fluctuations. As a result, when users felt anxious or worried about market fluctuations or their personal circumstances, the investment strategy could not be adjusted to take those emotions into account, resulting in a decrease in user satisfaction. Furthermore, sufficient measures were required to ensure the security and privacy of the collected data.

[0700] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0701] In this invention, the server includes a means for inputting expenditure data and income data from a user, a means for the terminal to verify the input data, encrypt it, and transmit it to the server, and a means for the server to receive the data and store it in a database. This enables the user's expenditure data and income data to be safely collected and stored while ensuring security and privacy. The server also includes a means for preprocessing the stored data, clustering and normalizing the expenditure data and income data using an artificial intelligence engine, and generating a risk profile. The artificial intelligence engine optimizes an investment strategy based on the generated risk profile. The server also provides the user's input and questions to an emotion engine, which analyzes the user's emotions and returns an emotion tag, allowing the AI ​​engine to adjust the investment strategy taking the emotion tag into account. This makes it possible to provide an investment strategy that reflects the user's current emotional state, thereby improving the user's investment performance and satisfaction.

[0702] "User" means an individual or legal entity that uses the system to input expenditure and income data and receive investment strategy suggestions.

[0703] A "server" is a computer system that receives, stores, and processes data sent by users.

[0704] A "terminal" is a device used by a user to input and transmit data, such as a smartphone or a personal computer.

[0705] "Expenditure Data" refers to data that indicates the amount of money a user spends on various consumption activities.

[0706] "Income Data" means data indicating the income earned by a user over a certain period of time.

[0707] "Encryption" is the process of transforming data with a specific algorithm to ensure its security.

[0708] A "database" is a collection of information that stores data in an organized manner and allows it to be searched and used when needed.

[0709] "Preprocessing" refers to the process of organizing and normalizing data before performing data analysis.

[0710] An "artificial intelligence engine" is software that uses machine learning and data analysis techniques to analyze data and generate investment strategies.

[0711] "Clustering" is a technique for grouping data based on specific criteria and identifying patterns.

[0712] "Risk Profile" is a criterion for assessing a user's risk tolerance and determining their investment strategy.

[0713] An "emotion engine" is software that uses natural language processing technology to analyze emotions from user input text and add emotion tags.

[0714] An "emotion tag" is a label that is added to the emotion engine to identify the user's emotion and indicate the result.

[0715] An "investment strategy" is a plan that suggests optimal asset allocation and investment products based on a user's financial data and risk profile.

[0716] This invention relates to a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[0717] Users enter expense and income data through a dedicated application or web interface. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The software used could be a mobile application or a web browser.

[0718] The terminal validates the data entered by the user. Validation includes data format and range checks. For example, an error message is displayed if income is less than expenses or if non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and transmits it securely to the server.

[0719] The server receives the encrypted data sent from the device and temporarily decrypts it in memory. It then re-encrypts it before saving it to the database. The saved data also includes metadata such as the user ID, input date and time, and data type.

[0720] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[0721] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It evaluates the user's income and expenditure ratios and creates a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[0722] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[0723] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. Specifically, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[0724] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[0725] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, projected returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[0726] Prompt Sentence Examples

[0727] An example of a prompt sentence is, "I have entered the following data: rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, and income 250,000 yen. Please suggest an investment strategy with low risk."

[0728] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0729] Step 1:

[0730] Users use a dedicated application or web interface to enter monthly expenditure and income data. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen.

[0731] Input: User expenditure and income data

[0732] Output: The input data

[0733] Step 2:

[0734] The terminal validates the entered data, checking the data format and range, and displays an error message if, for example, income is less than expenses or non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and sends it securely to the server.

[0735] Input: Data entered by the user

[0736] Output: Encrypted data

[0737] Step 3:

[0738] The server receives the encrypted data sent from the device. It temporarily decrypts it in memory, then re-encrypts it and stores it in the database. The stored data also includes metadata such as the user ID, input date and time, and data type.

[0739] Input: Encrypted data

[0740] Output: Data stored in the database

[0741] Step 4:

[0742] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[0743] Input: Saved data

[0744] Output: Data provided to the AI ​​engine

[0745] Step 5:

[0746] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It then evaluates the user's income and expenditure ratios to create a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[0747] Input: Preprocessed expenditure and income data

[0748] Output: Optimized investment strategy

[0749] Step 6:

[0750] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[0751] Input: User input or questions

[0752] Output: Data provided to the emotion engine

[0753] Step 7:

[0754] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. For example, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[0755] Input: User's text data

[0756] Output: Emotion tag

[0757] Step 8:

[0758] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[0759] Input: emotion tag

[0760] Output: Recalibrated investment strategy

[0761] Step 9:

[0762] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, predicted returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio with 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[0763] Input: Reworked investment strategy and user question

[0764] Output: User notification and response

[0765] (Application example 2)

[0766] 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."

[0767] Conventional investment strategy proposal systems provide investment strategies based on users' expenditure and income data, but they do not optimize the strategies based on the user's emotional state, which means they are unable to address the user's anxiety and risk tolerance. This can lead to emotions affecting the user's investment behavior, resulting in suboptimal investment results. There is also a need for a system that can securely manage user data and instantly answer questions about asset management.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0769] In this invention, the server includes means for inputting expenditure data and income data from a user, means for preprocessing the data, means for generating an investment strategy using an artificial intelligence engine, means for identifying the emotional state of the user using an emotion engine for analyzing the user's emotions, means for adjusting the investment strategy based on the emotional state of the user, and means for providing immediate answers to questions about asset management from the user. This enables the provision of an investment strategy that takes the emotional state of the user into consideration, fast and secure data management, and immediate answers.

[0770] "Expense Data" refers to information about financial expenditures used by a user in their daily lives.

[0771] "Income Data" means information about a User's financial income from work or other sources.

[0772] A "server" is a computer system located in a remote location that receives and stores data and provides services to users.

[0773] An "artificial intelligence engine" is a technology that uses programs and algorithms installed on a server to perform data analysis and automated decision-making.

[0774] An "emotion engine" is a technology for analyzing and recognizing emotions from a user's text or voice.

[0775] An "investment strategy" is a plan that suggests optimal asset allocation and investment destinations for users' investments.

[0776] A "database" is a collection of information that allows information to be efficiently stored and quickly retrieved when needed.

[0777] "Encryption" is the process of transforming data using a specific algorithm to protect it from third parties.

[0778] "Clustering" is a technique for dividing data into groups so that data within the same group have similar characteristics.

[0779] A "risk profile" is information that assesses a user's risk tolerance and financial situation and indicates how much risk they are willing to take.

[0780] "User" means an individual or organization that uses the system to input expenditure and income data and receive investment strategy proposals.

[0781] "Notification" is an action taken by a system to provide information to a user.

[0782] "Privacy" is the right to protect personal information from being disclosed to third parties.

[0783] The "question answering means" is a function for providing immediate and appropriate answers to inquiries from users.

[0784] The present invention provides a system that uses an artificial intelligence engine and an emotion engine to propose an optimal investment strategy based on a user's expenditure data and income data. Specific embodiments are described below.

[0785] Overall system configuration

[0786] The system mainly consists of the following components:

[0787] 1. User terminal: A device such as a smartphone that provides a means for users to enter spending and income data.

[0788] 2. Server: Receives input data and stores it securely. Specific technologies used here include database systems and data encryption technology.

[0789] 3. AI Engine: Performs data analysis such as normalization, clustering, and generating risk profiles to generate investment strategies.

[0790] 4. Emotion Engine: Analyzes text and speech in user inputs and questions to identify the user's emotional state. Natural language processing techniques are used.

[0791] Data processing flow

[0792] Users enter their daily expenditure and income data through a smartphone application, which is then encrypted by the device and sent to a server, where it is received and securely stored in a database.

[0793] Data analysis

[0794] The server receives the stored data and passes it to the AI ​​engine, which first normalizes the data and applies a clustering algorithm to identify spending patterns, then generates a risk profile and creates an investment strategy based on it.

[0795] Emotion analysis

[0796] The server also forwards user comments and questions to the emotion engine, which analyzes them and identifies the user's emotional state. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize anxiety.

[0797] Adjusting investment strategies

[0798] Based on the results of the emotion engine, the AI ​​engine will adjust the investment strategy taking into account the user's emotional state, and can suggest lower-risk investment strategies for users with high anxiety.

[0799] User notification and response

[0800] The final investment strategy is then sent to the server, which then notifies the user of the strategy. This notification includes specific investment allocations, risk assessments, and advice. The system also has the ability to instantly answer questions from users about asset management.

[0801] Specific examples

[0802] When a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to a server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[0803] Prompt Sentence Examples

[0804] "I'm worried about the recent market fluctuations."

[0805] In response, the AI ​​engine will adjust the investment strategy taking into account the results of the sentiment engine, allowing users to manage their assets with greater peace of mind.

[0806] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0807] Step 1:

[0808] Users enter expenditure and income data through a smartphone application. Specifically, users enter data such as monthly rent, food expenses, entertainment expenses, and income into the app. This data will be used for subsequent analysis, so it must be entered accurately. The input data is stored in standard formats such as JSON and CSV.

[0809] Step 2:

[0810] The device encrypts the input data and sends it to the server using an encryption method such as AES-256. The data, including metadata such as the user ID and timestamp, is sent to the server using a secure communication protocol (e.g., HTTPS).

[0811] Step 3:

[0812] The server securely stores the received data in a database. The data is not stored in its original format, but may be further encrypted to maintain data integrity. The stored data also includes the user ID and the date and time of input.

[0813] Step 4:

[0814] The server preprocesses the stored data and passes it to the AI ​​engine. Preprocessing includes normalizing the data (e.g., scaling by standard deviation) and imputing missing data (e.g., imputing by mean value), making the data suitable for analysis.

[0815] Step 5:

[0816] The AI ​​engine analyzes the pre-processed data and applies clustering algorithms (e.g., K-means) to identify spending patterns. It also evaluates the user's spending-to-income ratio and generates a risk profile. Based on this, it generates an investment strategy (e.g., 60% bonds, 30% index funds, 10% cash).

[0817] Step 6:

[0818] The server passes the user's comments and questions to the emotion engine, which uses natural language processing techniques (e.g., the BERT model) to analyze the text and identify the user's emotional state (e.g., anxiety, satisfaction, expectation, etc.).

[0819] Step 7:

[0820] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, if the user feels anxious, it will increase low-risk investments.

[0821] Step 8:

[0822] The server notifies the user of the adjusted investment strategy, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice. If the user then asks additional questions, the questions are analyzed again and an immediate answer is provided.

[0823] For example, if a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to the server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] [Third embodiment]

[0828] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0829] 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.

[0830] 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).

[0831] 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.

[0832] 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.

[0833] 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).

[0834] 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. 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] Overall system configuration

[0841] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0842] Data input from the user

[0843] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and financial situation.

[0844] Data transmission and storage

[0845] The device encrypts the data entered by the user and sends it securely to the server, which immediately stores the data in a database. The database also records metadata for each piece of data, such as the user ID and timestamp. The data is then formatted and cleaned as needed to prepare it for analysis.

[0846] AI-based data analysis and investment strategy generation

[0847] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[0848] Notification and display of investment strategies

[0849] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[0850] Example: User scenario

[0851] Users enter their monthly expenditure information through the app, such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The device sends this data to the server, which receives it and stores it in an encrypted form in a database.

[0852] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[0853] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, for example, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[0854] As a result, users can receive an appropriate investment strategy tailored to their individual financial situation and risk tolerance, allowing them to invest with peace of mind while alleviating any concerns or doubts they may have.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[0858] Step 2:

[0859] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[0860] Step 3:

[0861] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[0862] Step 4:

[0863] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and timestamp.

[0864] Step 5:

[0865] The server preprocesses the stored data, specifically by imputing missing values, converting non-numeric data, and removing unnecessary data.

[0866] Step 6:

[0867] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[0868] Step 7:

[0869] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify user spending patterns, such as users whose spending is concentrated on rent and food.

[0870] Step 8:

[0871] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[0872] Step 9:

[0873] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[0874] Step 10:

[0875] The server receives the investment strategy generated by the AI ​​engine and notifies the user, including recommended portfolios, predicted returns, and risk assessments.

[0876] Step 11:

[0877] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[0878] Step 12:

[0879] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[0880] Step 13:

[0881] The terminal sends the user's questions to the server.

[0882] Step 14:

[0883] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[0884] Step 15:

[0885] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[0886] Step 16:

[0887] The server generates a response and sends it back to the user.

[0888] Step 17:

[0889] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[0890] Through this process, users will receive an appropriate investment strategy based on their financial situation, allowing them to immediately resolve any doubts or concerns they may have about asset management.

[0891] Example 1

[0892] 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."

[0893] In modern society, personal asset management is extremely important. However, it is generally difficult for individual users to find the optimal investment strategy. In particular, there is a lack of systems that assess risk based on users' spending and income data and provide individually optimized investment strategies. This poses the challenge of users being unable to make appropriate investment decisions or accurately understand the risks involved in asset management. Furthermore, it is important to manage data while protecting users' privacy.

[0894] 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.

[0895] In this invention, the server includes means for inputting expenditure data and income data from a user, means for encrypting the input data at a terminal and transmitting it to the server, means for receiving the data and storing it in a database, means for cleaning and formatting the stored data, means for generating clustering and an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying and displaying the generated investment strategy to the user, and means for instantly answering questions about asset management from the user. This allows the user to receive an individually optimized investment strategy and makes it easier to accurately understand the risks involved in asset management. Furthermore, data encryption also protects the user's privacy.

[0896] "User" means any person or entity that utilizes the system to input expenditure and income data and receive optimal investment strategies.

[0897] "Expense data" refers to data that includes information on the amounts and items of expenditures that a user has made over a specific period of time.

[0898] "Income Data" means data that includes information about the amount and type of income a User has earned during a particular period of time.

[0899] A "terminal" is an electronic device used by a user to input data and send it to a server, including smartphones, tablets, and personal computers.

[0900] "Encryption" is a technology that converts data using a specific algorithm so that the data being transmitted cannot be deciphered by a third party.

[0901] "Server" means a computer system that receives, stores, and processes data submitted by users and generates investment strategies using an artificial intelligence engine.

[0902] A "database" is a collection of information, including user expenditure data and income data, stored on a server, and is an organized storage system.

[0903] "Cleaning" refers to preprocessing work such as deleting unnecessary data and standardizing formats in order to improve the quality and reliability of data.

[0904] "Formatting" is the process of converting data into a form suitable for analysis in order to perform data analysis.

[0905] An "artificial intelligence engine" is a software program that contains machine learning algorithms and models to analyze user data and generate optimal investment strategies.

[0906] "Clustering" is a data analysis technique that groups similar data together to understand user spending patterns.

[0907] An "investment strategy" is a plan that includes asset allocation and investment recommendations optimized based on a user's risk tolerance and goals.

[0908] "Notification" means the act of transmitting investment strategies and other related information from the server to the user.

[0909] "Display" refers to the act of visually showing the notified information on the user terminal.

[0910] A "question" is an action in which a user sends a question about asset management to the server.

[0911] "Immediate response" refers to the act of quickly providing appropriate information and answers to questions from users.

[0912] The above definitions clarify the meaning of important words in this system.

[0913] The present invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. This system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0914] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation.

[0915] Next, the terminal encrypts the entered data and sends it to the server using a secure method. Encryption is performed using technologies such as AES (Advanced Encryption Standard). Transmission is performed using the HTTPS protocol, ensuring data protection. For example, data such as a rent of 100,000 yen is encrypted with AES before being sent to the server.

[0916] The server receives the encrypted data and stores it in a database. When stored, metadata such as user IDs and timestamps are also recorded. It also cleans (deletes unnecessary data and standardizes formats) and formats (converts data into a form suitable for analysis).

[0917] The server then passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[0918] The AI ​​engine performs detailed analysis based on the data it receives. First, it normalizes expenditure and income data and classifies users into several categories using a clustering algorithm (e.g., k-means clustering). Then, it uses machine learning models (e.g., random forests, neural networks) to generate investment strategies suited to each individual user. For example, safe investments are suggested for low-risk users.

[0919] The resulting investment strategies are managed by a server and organized for each user. The server stores information based on the user ID and generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash."

[0920] Finally, the device displays the investment strategy data received from the server to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." Furthermore, when a user asks a question, for example, "What are the returns of index funds over the past three years?", it sends a prompt to the server, and the server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[0921] The system allows users to receive individually optimized investment strategies and accurately understand the risks involved in managing their assets, while protecting user privacy through data encryption and the use of the HTTPS protocol.

[0922] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0923] Specific explanation of processing steps

[0924] Step 1: Data input from the user

[0925] Users enter monthly expenditure and income data using a dedicated application or web interface. Specifically, they enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen into the app. This input data is important information that will be used for subsequent analysis.

[0926] Input: User's expenditure data and income data (e.g. rent 100,000 yen, food expenses 50,000 yen, income 250,000 yen)

[0927] Output: Data for encryption and transmission

[0928] Step 2: Encrypt and send data

[0929] The terminal encrypts the data entered by the user. The encryption technology is AES (Advanced Encryption Standard), and the data is sent to the server using the HTTPS protocol. For example, data such as "rent 100,000 yen" is sent to the server in AES encrypted form.

[0930] Input: Unencrypted data entered by the user

[0931] Output: Encrypted data

[0932] Step 3: Receiving and storing data

[0933] The server receives the encrypted data and stores it in a database using security technology. When the data is stored, metadata such as the user ID and timestamp are also recorded. For example, "Rent of 100,000 yen" is stored in the database as "Expense category: Rent, Amount: 100,000 yen, User ID: 12345, Timestamp: 2023-10-01."

[0934] Input: Encrypted data and metadata

[0935] Output: Saved database entry

[0936] Step 4: Preprocessing the data

[0937] The server cleans and formats the stored data to make it suitable for analysis. Cleaning involves deleting unnecessary data and standardizing formats. Formatting involves converting the data into a format suitable for analysis. For example, this involves correcting incomplete date data and standardizing data formats.

[0938] Input: Stored raw data

[0939] Output: Preprocessed data

[0940] Step 5: Passing data to the AI ​​engine

[0941] The server passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[0942] Input: Preprocessed data

[0943] Output: Data passed to the AI ​​engine

[0944] Step 6: Data analysis and investment strategy generation using an AI engine

[0945] The AI ​​engine performs detailed analysis based on the data it receives. It normalizes expenditure and income data and classifies users into categories using clustering algorithms such as k-means clustering. It then uses machine learning models (e.g., random forests and neural networks) to generate investment strategies tailored to each individual user. For example, safe investments are suggested for low-risk users.

[0946] Input: Data passed to the AI ​​engine

[0947] Output: Generated investment strategy

[0948] Step 7: Managing and sending investment strategies on the server

[0949] The server organizes and stores data for each user based on the investment strategy received from the AI ​​engine. For example, for user ID 12345, it generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash" and manages it within the server.

[0950] Input: Investment strategy received from the AI ​​engine

[0951] Output: Organized investment strategy data

[0952] Step 8: Notify and display the investment strategy to users

[0953] The device receives investment strategy data from the server and displays it to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." If the user has a question about an investment, for example, "What are the returns on index funds over the past three years?", the device sends the question to the server. The server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[0954] Input: Investment strategies received from the server, queries from users

[0955] Output: Investment strategies and answers displayed to the user

[0956] For example, after a user enters their spending information such as rent and food, the device encrypts the data and sends it to the server, which then receives and processes it to generate an optimal investment strategy, which is then notified and displayed to the user. This process allows users to easily find an investment strategy that suits their financial situation.

[0957] (Application example 1)

[0958] 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."

[0959] Conventional investment strategy proposal systems require users to manually input income and expenditure data, which is time-consuming and increases the risk of input errors. Furthermore, they are unable to utilize data from the electronic payment services users use on a daily basis, making it difficult to propose real-time investment strategies based on that data. Furthermore, they are also required to respond quickly to questions about asset management from users.

[0960] 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.

[0961] In this invention, the server includes means for inputting expenditure data and income data from a user, means for transmitting the data to the server, means for receiving the data and storing it in a database, means for preprocessing the stored data, means for generating an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying the user of the generated investment strategy, means for automatically collecting the user's expenditure data through an electronic payment service, and means for instantly responding to questions from the user regarding asset management. This enables real-time investment strategy proposals utilizing electronic payment data while reducing the user's effort. Furthermore, detailed analysis by the artificial intelligence engine can propose optimal investment strategies suited to the user.

[0962] definition statement

[0963] "User" refers to any person or entity that inputs expenditure and income data and receives investment strategy suggestions.

[0964] "Expense Data" refers to information about the money a user spends in their daily life or business activities.

[0965] "Income Data" refers to information about the income earned by a User in their daily life or business activities.

[0966] "Server" refers to the computer system that receives and stores user data, as well as pre-processing and analyzing the data for the artificial intelligence engine.

[0967] "Database" refers to a data storage system for organizing, storing, and managing data within a server.

[0968] "Preprocessing" refers to the process of data shaping and cleaning to prepare stored data in a form suitable for analysis.

[0969] "Artificial intelligence engine" refers to a program or algorithm that analyzes a user's expenditure and income data and generates an optimal investment strategy.

[0970] "Investment Strategy" refers to a specific investment allocation plan suggested to you based on your financial situation and risk tolerance.

[0971] "Electronic Payment Service" means an internet-based payment system through which users can pay for goods and services.

[0972] "Clustering" refers to a data analysis technique that classifies a large number of data points into several groups based on their similarities.

[0973] "Risk Profile" refers to an assessment analysis generated based on a user's risk tolerance and financial situation.

[0974] "Encryption" refers to the process of converting data into a form that cannot be understood by third parties, with the purpose of protecting the data.

[0975] "Privacy protection" refers to measures taken to prevent users' personal information and data from being accessed, used, or disclosed in an unauthorized manner.

[0976] MODE FOR CARRYING OUT THE INVENTION

[0977] Overall system configuration

[0978] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[0979] Data input from the user

[0980] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation. It is also possible to automatically collect user expenditure data by using electronic payment services.

[0981] Data transmission and storage

[0982] The user's device encrypts the data entered and sends it securely to the server. The server immediately stores the data in a database, which also records metadata such as the user ID and timestamp for each piece of data. The data is then formatted and cleaned as needed to prepare it for analysis.

[0983] AI-based data analysis and investment strategy generation

[0984] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[0985] Notification and display of investment strategies

[0986] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[0987] User Scenarios

[0988] Users enter their monthly expenditure information through the app. By using an electronic payment service, expenditure data is automatically collected. For example, details such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen are entered. The device sends this data to a server, which receives it and stores it in an encrypted form in a database.

[0989] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[0990] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[0991] Hardware and software used

[0992] Hardware: Server (using cloud services)

[0993] Software: Python, Pandas, Sklearn, REST API

[0994] Prompt Sentence Examples

[0995] "I want to create an application that suggests investment strategies based on monthly expenditure and income data. Specifically, this will include data collection, data cleaning, clustering, generating optimal investment strategies, and notifying the user."

[0996] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0997] Program processing flow

[0998] Step 1:

[0999] Data Entry and Collection

[1000] Users enter expenditure and income data using a dedicated application or a web interface. Users' expenditure data is also collected automatically by using an electronic payment service. As a result, input data includes both manual input and automatic collection. Input data includes details such as rent, food expenses, entertainment expenses, and income.

[1001] Input: User spending and income data

[1002] Output: Collected data objects

[1003] Step 2:

[1004] Data Transmission and Encryption

[1005] The device encrypts the collected data and transmits it to the server in a secure manner, which involves transforming the data using an encryption algorithm and transmitting it to the server's endpoint.

[1006] Input: Collected data objects

[1007] Output: Encrypted data

[1008] Step 3:

[1009] Receiving and storing data

[1010] The server receives the encrypted data and stores it in a database, where it is decrypted and recorded along with metadata such as the user ID and timestamp.

[1011] Input: Encrypted data

[1012] Output: Data stored in the database

[1013] Step 4:

[1014] Data Preprocessing

[1015] The server preprocesses the stored data, which includes data formatting and cleaning, such as standardizing data formats, imputing missing values, and detecting and correcting outliers.

[1016] Input: Data stored in a database

[1017] Output: Preprocessed data

[1018] Step 5:

[1019] Data analysis using an AI engine

[1020] The server then passes the preprocessed data to an AI engine for further analysis, including data normalization, clustering, and risk profile generation, which categorizes the user's spending patterns into several categories and generates an investment strategy suited to each individual user.

[1021] Input: Preprocessed data

[1022] Output: Generated investment strategy

[1023] Step 6:

[1024] Investment Strategy Notification

[1025] The server notifies the user of the investment strategy generated by the AI ​​engine, and the user can view detailed information such as the recommended portfolio, projected returns, and risk assessment through an app or web interface.

[1026] Input: Generated investment strategy

[1027] Output: Investment strategy communicated to the user

[1028] Step 7:

[1029] Responding to user questions

[1030] Users submit questions about asset management through the application. The device sends the questions to the server, which uses an AI engine to generate appropriate answers. The device then displays the answers to the user.

[1031] Input: User question

[1032] Output: Answer by AI engine

[1033] Through these steps, users can receive the optimal investment strategy based on their individual financial situation and risk tolerance, and real-time investment suggestions can be made using data automatically collected through the electronic payment service.

[1034] 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.

[1035] Overall system configuration

[1036] This invention provides a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[1037] Data input from the user

[1038] Users enter expenditure and income data through a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen.

[1039] Data transmission and storage

[1040] The device verifies the entered data, encrypts it, and sends it to the server, which receives it and stores it securely in a database, along with metadata such as the user ID and a timestamp.

[1041] AI-based data analysis and investment strategy generation

[1042] The server passes the stored data to the AI ​​engine, which normalizes the expenditure and income data and applies a clustering algorithm to identify spending patterns. It evaluates the user's expenditure-to-income ratio and creates a risk profile. It generates an optimal investment strategy based on the user's risk tolerance and suggests a specific portfolio (e.g., 60% bonds, 30% index funds, and 10% cash).

[1043] Applying the Emotion Engine

[1044] The server passes the text and voice contained in the user's input or question to the emotion engine, which uses natural language processing technology to recognize the user's emotions. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize the user's anxiety.

[1045] Coordinating and informing investment strategies

[1046] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, for a user with high anxiety, the AI ​​engine will increase low-risk investments. The adjusted investment strategy is finally generated.

[1047] The server then communicates this adjusted investment strategy to the user, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice.

[1048] Example: User scenario

[1049] Users enter their monthly expenditure information through the app. For example, rent is 100,000 yen, food is 50,000 yen, entertainment is 20,000 yen, and income is 250,000 yen. The device encrypts this data and sends it to the server. The server receives the data and stores it in a database in encrypted form.

[1050] The server passes the stored data to an AI engine, which then normalizes and clusters the spending data. For example, it identifies users whose spending is concentrated on rent and food. The AI ​​engine also assesses risk and suggests optimal investment strategies.

[1051] At the same time, the server passes the user's comments and questions to the emotion engine. For example, if a user enters, "I'm worried about the recent fluctuations in stock prices," the emotion engine analyzes this comment and recognizes the user's anxiety. As a result, the AI ​​engine makes adjustments to increase the number of low-risk items.

[1052] The server then notifies the user of an adjusted investment strategy based on this. For example, it displays details such as, "Taking your current risk profile into consideration, we propose a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." If the user asks, "What are the returns of this index fund over the past three years?", the device sends the question to the server, which uses its AI engine and emotion engine to generate an appropriate answer and immediately responds to the user. For example, it may notify the user that "The average return of this index fund over the past three years is 5.2%."

[1053] In this way, by combining the emotion engine, users can receive the optimal investment strategy tailored to their emotional state, allowing them to manage their assets with greater peace of mind.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[1057] Step 2:

[1058] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[1059] Step 3:

[1060] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[1061] Step 4:

[1062] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and a timestamp.

[1063] Step 5:

[1064] The server preprocesses the stored data, for example by imputing missing values, converting non-numeric data, and removing unnecessary data.

[1065] Step 6:

[1066] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[1067] Step 7:

[1068] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify users' spending patterns, such as users whose spending is concentrated on rent and food.

[1069] Step 8:

[1070] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[1071] Step 9:

[1072] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[1073] Step 10:

[1074] The server notifies the user of the generated investment strategy, including the recommended portfolio, predicted returns, and risk assessment.

[1075] Step 11:

[1076] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[1077] Step 12:

[1078] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[1079] Step 13:

[1080] The terminal sends the user's questions to the server.

[1081] Step 14:

[1082] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[1083] Step 15:

[1084] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[1085] Step 16:

[1086] The server generates a response and sends it back to the user.

[1087] Step 17:

[1088] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[1089] Step 18:

[1090] The server passes the text and voice contained in the user's input and questions to the emotion engine, which analyzes them and recognizes the user's emotions.

[1091] Step 19:

[1092] The emotion engine assesses the user's emotional state (e.g., anxiety, relief) and provides the results to the AI ​​engine.

[1093] Step 20:

[1094] The AI ​​engine takes into account emotional state information and adjusts investment strategies, for example, if a user is feeling anxious, it will adjust to a lower-risk investment strategy to alleviate those feelings.

[1095] Step 21:

[1096] The server then notifies the user of the adjusted investment strategy again, for example, by displaying a message saying, "Taking your emotional state into consideration, we propose a new portfolio with reduced risk."

[1097] This series of steps will result in a system that can provide optimal investment strategies that take into account not only the user's financial situation but also their emotional state.

[1098] Example 2

[1099] 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."

[1100] Conventional investment strategy proposal systems performed risk assessment and clustering based on users' expenditure and income data, but did not optimize investment strategies taking into account emotional fluctuations. As a result, when users felt anxious or worried about market fluctuations or their personal circumstances, the investment strategy could not be adjusted to take those emotions into account, resulting in a decrease in user satisfaction. Furthermore, sufficient measures were required to ensure the security and privacy of the collected data.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1102] In this invention, the server includes a means for inputting expenditure data and income data from a user, a means for the terminal to verify the input data, encrypt it, and transmit it to the server, and a means for the server to receive the data and store it in a database. This enables the user's expenditure data and income data to be safely collected and stored while ensuring security and privacy. The server also includes a means for preprocessing the stored data, clustering and normalizing the expenditure data and income data using an artificial intelligence engine, and generating a risk profile. The artificial intelligence engine optimizes an investment strategy based on the generated risk profile. The server also provides the user's input and questions to an emotion engine, which analyzes the user's emotions and returns an emotion tag, allowing the AI ​​engine to adjust the investment strategy taking the emotion tag into account. This makes it possible to provide an investment strategy that reflects the user's current emotional state, thereby improving the user's investment performance and satisfaction.

[1103] "User" means an individual or legal entity that uses the system to input expenditure and income data and receive investment strategy suggestions.

[1104] A "server" is a computer system that receives, stores, and processes data sent by users.

[1105] A "terminal" is a device used by a user to input and transmit data, such as a smartphone or a personal computer.

[1106] "Expenditure Data" refers to data that indicates the amount of money a user spends on various consumption activities.

[1107] "Income Data" means data indicating the income earned by a user over a certain period of time.

[1108] "Encryption" is the process of transforming data with a specific algorithm to ensure its security.

[1109] A "database" is a collection of information that stores data in an organized manner and allows it to be searched and used when needed.

[1110] "Preprocessing" refers to the process of organizing and normalizing data before performing data analysis.

[1111] An "artificial intelligence engine" is software that uses machine learning and data analysis techniques to analyze data and generate investment strategies.

[1112] "Clustering" is a technique for grouping data based on specific criteria and identifying patterns.

[1113] "Risk Profile" is a criterion for assessing a user's risk tolerance and determining their investment strategy.

[1114] An "emotion engine" is software that uses natural language processing technology to analyze emotions from user input text and add emotion tags.

[1115] An "emotion tag" is a label that is added to the emotion engine to identify the user's emotion and indicate the result.

[1116] An "investment strategy" is a plan that suggests optimal asset allocation and investment products based on a user's financial data and risk profile.

[1117] This invention relates to a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[1118] Users enter expense and income data through a dedicated application or web interface. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The software used could be a mobile application or a web browser.

[1119] The terminal validates the data entered by the user. Validation includes data format and range checks. For example, an error message is displayed if income is less than expenses or if non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and transmits it securely to the server.

[1120] The server receives the encrypted data sent from the device and temporarily decrypts it in memory. It then re-encrypts it before saving it to the database. The saved data also includes metadata such as the user ID, input date and time, and data type.

[1121] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[1122] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It evaluates the user's income and expenditure ratios and creates a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[1123] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[1124] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. Specifically, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[1125] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[1126] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, projected returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[1127] Prompt Sentence Examples

[1128] An example of a prompt sentence is, "I have entered the following data: rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, and income 250,000 yen. Please suggest an investment strategy with low risk."

[1129] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1130] Step 1:

[1131] Users use a dedicated application or web interface to enter monthly expenditure and income data. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen.

[1132] Input: User expenditure and income data

[1133] Output: The input data

[1134] Step 2:

[1135] The terminal validates the entered data, checking the data format and range, and displays an error message if, for example, income is less than expenses or non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and sends it securely to the server.

[1136] Input: Data entered by the user

[1137] Output: Encrypted data

[1138] Step 3:

[1139] The server receives the encrypted data sent from the device. It temporarily decrypts it in memory, then re-encrypts it and stores it in the database. The stored data also includes metadata such as the user ID, input date and time, and data type.

[1140] Input: Encrypted data

[1141] Output: Data stored in the database

[1142] Step 4:

[1143] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[1144] Input: Saved data

[1145] Output: Data provided to the AI ​​engine

[1146] Step 5:

[1147] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It then evaluates the user's income and expenditure ratios to create a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[1148] Input: Preprocessed expenditure and income data

[1149] Output: Optimized investment strategy

[1150] Step 6:

[1151] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[1152] Input: User input or questions

[1153] Output: Data provided to the emotion engine

[1154] Step 7:

[1155] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. For example, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[1156] Input: User's text data

[1157] Output: Emotion tag

[1158] Step 8:

[1159] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[1160] Input: emotion tag

[1161] Output: Recalibrated investment strategy

[1162] Step 9:

[1163] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, predicted returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio with 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[1164] Input: Reworked investment strategy and user question

[1165] Output: User notification and response

[1166] (Application example 2)

[1167] 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."

[1168] Conventional investment strategy proposal systems provide investment strategies based on users' expenditure and income data, but they do not optimize the strategies based on the user's emotional state, which means they are unable to address the user's anxiety and risk tolerance. This can lead to emotions affecting the user's investment behavior, resulting in suboptimal investment results. There is also a need for a system that can securely manage user data and instantly answer questions about asset management.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1170] In this invention, the server includes means for inputting expenditure data and income data from a user, means for preprocessing the data, means for generating an investment strategy using an artificial intelligence engine, means for identifying the emotional state of the user using an emotion engine for analyzing the user's emotions, means for adjusting the investment strategy based on the emotional state of the user, and means for providing immediate answers to questions about asset management from the user. This enables the provision of an investment strategy that takes the emotional state of the user into consideration, fast and secure data management, and immediate answers.

[1171] "Expense Data" refers to information about financial expenditures used by a user in their daily lives.

[1172] "Income Data" means information about a User's financial income from work or other sources.

[1173] A "server" is a computer system located in a remote location that receives and stores data and provides services to users.

[1174] An "artificial intelligence engine" is a technology that uses programs and algorithms installed on a server to perform data analysis and automated decision-making.

[1175] An "emotion engine" is a technology for analyzing and recognizing emotions from a user's text or voice.

[1176] An "investment strategy" is a plan that suggests optimal asset allocation and investment destinations for users' investments.

[1177] A "database" is a collection of information that allows information to be efficiently stored and quickly retrieved when needed.

[1178] "Encryption" is the process of transforming data using a specific algorithm to protect it from third parties.

[1179] "Clustering" is a technique for dividing data into groups so that data within the same group have similar characteristics.

[1180] A "risk profile" is information that assesses a user's risk tolerance and financial situation and indicates how much risk they are willing to take.

[1181] "User" means an individual or organization that uses the system to input expenditure and income data and receive investment strategy proposals.

[1182] "Notification" is an action taken by a system to provide information to a user.

[1183] "Privacy" is the right to protect personal information from being disclosed to third parties.

[1184] The "question answering means" is a function for providing immediate and appropriate answers to inquiries from users.

[1185] The present invention provides a system that uses an artificial intelligence engine and an emotion engine to propose an optimal investment strategy based on a user's expenditure data and income data. Specific embodiments are described below.

[1186] Overall system configuration

[1187] The system mainly consists of the following components:

[1188] 1. User terminal: A device such as a smartphone that provides a means for users to enter spending and income data.

[1189] 2. Server: Receives input data and stores it securely. Specific technologies used here include database systems and data encryption technology.

[1190] 3. AI Engine: Performs data analysis such as normalization, clustering, and generating risk profiles to generate investment strategies.

[1191] 4. Emotion Engine: Analyzes text and speech in user inputs and questions to identify the user's emotional state. Natural language processing techniques are used.

[1192] Data processing flow

[1193] Users enter their daily expenditure and income data through a smartphone application, which is then encrypted by the device and sent to a server, where it is received and securely stored in a database.

[1194] Data analysis

[1195] The server receives the stored data and passes it to the AI ​​engine, which first normalizes the data and applies a clustering algorithm to identify spending patterns, then generates a risk profile and creates an investment strategy based on it.

[1196] Emotion analysis

[1197] The server also forwards user comments and questions to the emotion engine, which analyzes them and identifies the user's emotional state. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize anxiety.

[1198] Adjusting investment strategies

[1199] Based on the results of the emotion engine, the AI ​​engine can adjust investment strategies taking into account the user's emotional state, suggesting lower-risk investment strategies for users with high anxiety.

[1200] User notification and response

[1201] The final investment strategy is then sent to the server, which then notifies the user of the specific investment allocation, risk assessment, and advice. The system also has the ability to instantly answer any questions users may have about asset management.

[1202] Specific examples

[1203] When a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to a server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[1204] Prompt Sentence Examples

[1205] "I'm worried about the recent market fluctuations."

[1206] In response, the AI ​​engine will adjust the investment strategy taking into account the results of the sentiment engine, allowing users to manage their assets with greater peace of mind.

[1207] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1208] Step 1:

[1209] Users enter expenditure and income data through a smartphone application. Specifically, users enter data such as monthly rent, food expenses, entertainment expenses, and income into the app. This data will be used for subsequent analysis, so it must be entered accurately. The input data is stored in standard formats such as JSON and CSV.

[1210] Step 2:

[1211] The device encrypts the input data and sends it to the server using an encryption method such as AES-256. The data, including metadata such as the user ID and timestamp, is sent to the server using a secure communication protocol (e.g., HTTPS).

[1212] Step 3:

[1213] The server securely stores the received data in a database. The data is not stored in its original format, but may be further encrypted to maintain data integrity. The stored data also includes the user ID and the date and time of input.

[1214] Step 4:

[1215] The server preprocesses the stored data and passes it to the AI ​​engine. Preprocessing includes normalizing the data (e.g., scaling by standard deviation) and imputing missing data (e.g., imputing by mean value), making the data suitable for analysis.

[1216] Step 5:

[1217] The AI ​​engine analyzes the pre-processed data and applies clustering algorithms (e.g., K-means) to identify spending patterns. It also evaluates the user's spending-to-income ratio and generates a risk profile. Based on this, it generates an investment strategy (e.g., 60% bonds, 30% index funds, 10% cash).

[1218] Step 6:

[1219] The server passes the user's comments and questions to the emotion engine, which uses natural language processing techniques (e.g., the BERT model) to analyze the text and identify the user's emotional state (e.g., anxiety, satisfaction, expectation, etc.).

[1220] Step 7:

[1221] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, if the user feels anxious, it will increase low-risk investments.

[1222] Step 8:

[1223] The server notifies the user of the adjusted investment strategy, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice. If the user then asks additional questions, the questions are analyzed again and an immediate answer is provided.

[1224] For example, if a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to the server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] [Fourth embodiment]

[1229] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1230] 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.

[1231] 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).

[1232] 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.

[1233] 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.

[1234] 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).

[1235] 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. 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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."

[1242] Overall system configuration

[1243] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[1244] Data input from the user

[1245] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and financial situation.

[1246] Data transmission and storage

[1247] The device encrypts the data entered by the user and sends it securely to the server, which immediately stores the data in a database. The database also records metadata for each piece of data, such as the user ID and timestamp. The data is then formatted and cleaned as needed to prepare it for analysis.

[1248] AI-based data analysis and investment strategy generation

[1249] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[1250] Notification and display of investment strategies

[1251] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[1252] Example: User scenario

[1253] Users enter their monthly expenditure information through the app, such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The device sends this data to the server, which receives it and stores it in an encrypted form in a database.

[1254] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[1255] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, for example, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[1256] As a result, users can receive an appropriate investment strategy tailored to their individual financial situation and risk tolerance, allowing them to invest with peace of mind while alleviating any concerns or doubts they may have.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[1260] Step 2:

[1261] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[1262] Step 3:

[1263] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[1264] Step 4:

[1265] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and timestamp.

[1266] Step 5:

[1267] The server preprocesses the stored data, specifically by imputing missing values, converting non-numeric data, and removing unnecessary data.

[1268] Step 6:

[1269] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[1270] Step 7:

[1271] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify user spending patterns, such as users whose spending is concentrated on rent and food.

[1272] Step 8:

[1273] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[1274] Step 9:

[1275] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[1276] Step 10:

[1277] The server receives the investment strategy generated by the AI ​​engine and notifies the user, including recommended portfolios, predicted returns, and risk assessments.

[1278] Step 11:

[1279] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[1280] Step 12:

[1281] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[1282] Step 13:

[1283] The terminal sends the user's questions to the server.

[1284] Step 14:

[1285] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[1286] Step 15:

[1287] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[1288] Step 16:

[1289] The server generates a response and sends it back to the user.

[1290] Step 17:

[1291] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[1292] Through this process, users will receive an appropriate investment strategy based on their financial situation, allowing them to immediately resolve any doubts or concerns they may have about asset management.

[1293] Example 1

[1294] 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."

[1295] In modern society, personal asset management is extremely important. However, it is generally difficult for individual users to find the optimal investment strategy. In particular, there is a lack of systems that assess risk based on users' spending and income data and provide individually optimized investment strategies. This poses the challenge of users being unable to make appropriate investment decisions or accurately understand the risks involved in asset management. Furthermore, it is important to manage data while protecting users' privacy.

[1296] 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.

[1297] In this invention, the server includes means for inputting expenditure data and income data from a user, means for encrypting the input data at a terminal and transmitting it to the server, means for receiving the data and storing it in a database, means for cleaning and formatting the stored data, means for generating clustering and an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying and displaying the generated investment strategy to the user, and means for instantly answering questions about asset management from the user. This allows the user to receive an individually optimized investment strategy and makes it easier to accurately understand the risks involved in asset management. Furthermore, data encryption also protects the user's privacy.

[1298] "User" means any person or entity that utilizes the system to input expenditure and income data and receive optimal investment strategies.

[1299] "Expense data" refers to data that includes information on the amounts and items of expenditures that a user has made over a specific period of time.

[1300] "Income Data" means data that includes information about the amount and type of income a User has earned during a particular period of time.

[1301] A "terminal" is an electronic device used by a user to input data and send it to a server, including smartphones, tablets, and personal computers.

[1302] "Encryption" is a technology that converts data using a specific algorithm so that the data being transmitted cannot be deciphered by a third party.

[1303] "Server" means a computer system that receives, stores, and processes data submitted by users and generates investment strategies using an artificial intelligence engine.

[1304] A "database" is a collection of information, including user expenditure data and income data, stored on a server, and is an organized storage system.

[1305] "Cleaning" refers to preprocessing work such as deleting unnecessary data and standardizing formats in order to improve the quality and reliability of data.

[1306] "Formatting" is the process of converting data into a form suitable for analysis in order to perform data analysis.

[1307] An "artificial intelligence engine" is a software program that contains machine learning algorithms and models to analyze user data and generate optimal investment strategies.

[1308] "Clustering" is a data analysis technique that groups similar data together to understand user spending patterns.

[1309] An "investment strategy" is a plan that includes asset allocation and investment recommendations optimized based on a user's risk tolerance and goals.

[1310] "Notification" means the act of transmitting investment strategies and other related information from the server to the user.

[1311] "Display" refers to the act of visually showing the notified information on the user terminal.

[1312] A "question" is an action in which a user sends a question about asset management to the server.

[1313] "Immediate response" refers to the act of quickly providing appropriate information and answers to questions from users.

[1314] The above definitions clarify the meaning of important words in this system.

[1315] The present invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. This system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[1316] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation.

[1317] Next, the terminal encrypts the entered data and sends it to the server using a secure method. Encryption is performed using technologies such as AES (Advanced Encryption Standard). Transmission is performed using the HTTPS protocol, ensuring data protection. For example, data such as a rent of 100,000 yen is encrypted with AES before being sent to the server.

[1318] The server receives the encrypted data and stores it in a database. When stored, metadata such as user IDs and timestamps are also recorded. It also cleans (deletes unnecessary data and standardizes formats) and formats (converts data into a form suitable for analysis).

[1319] The server then passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[1320] The AI ​​engine performs detailed analysis based on the data it receives. First, it normalizes expenditure and income data and classifies users into several categories using a clustering algorithm (e.g., k-means clustering). Then, it uses machine learning models (e.g., random forests, neural networks) to generate investment strategies suited to each individual user. For example, safe investments are suggested for low-risk users.

[1321] The resulting investment strategies are managed by a server and organized for each user. The server stores information based on the user ID and generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash."

[1322] Finally, the device displays the investment strategy data received from the server to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." Furthermore, when a user asks a question, for example, "What are the returns of index funds over the past three years?", it sends a prompt to the server, and the server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[1323] The system allows users to receive individually optimized investment strategies and accurately understand the risks involved in managing their assets, while protecting user privacy through data encryption and the use of the HTTPS protocol.

[1324] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1325] Specific explanation of processing steps

[1326] Step 1: Data input from the user

[1327] Users enter monthly expenditure and income data using a dedicated application or web interface. Specifically, they enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen into the app. This input data is important information that will be used for subsequent analysis.

[1328] Input: User's expenditure data and income data (e.g. rent 100,000 yen, food expenses 50,000 yen, income 250,000 yen)

[1329] Output: Data for encryption and transmission

[1330] Step 2: Encrypt and send data

[1331] The terminal encrypts the data entered by the user. The encryption technology is AES (Advanced Encryption Standard), and the data is sent to the server using the HTTPS protocol. For example, data such as "rent 100,000 yen" is sent to the server in AES encrypted form.

[1332] Input: Unencrypted data entered by the user

[1333] Output: Encrypted data

[1334] Step 3: Receiving and storing data

[1335] The server receives the encrypted data and stores it in a database using security technology. When the data is stored, metadata such as the user ID and timestamp are also recorded. For example, "Rent of 100,000 yen" is stored in the database as "Expense category: Rent, Amount: 100,000 yen, User ID: 12345, Timestamp: 2023-10-01."

[1336] Input: Encrypted data and metadata

[1337] Output: Saved database entry

[1338] Step 4: Preprocessing the data

[1339] The server cleans and formats the stored data to make it suitable for analysis. Cleaning involves deleting unnecessary data and standardizing formats. Formatting involves converting the data into a format suitable for analysis. For example, this involves correcting incomplete date data and standardizing data formats.

[1340] Input: Stored raw data

[1341] Output: Preprocessed data

[1342] Step 5: Passing data to the AI ​​engine

[1343] The server passes the preprocessed data to the AI ​​engine. The data is sent to the AI ​​engine via HTTP API. For example, data such as "rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, income 250,000 yen" is sent in JSON format.

[1344] Input: Preprocessed data

[1345] Output: Data passed to the AI ​​engine

[1346] Step 6: Data analysis and investment strategy generation using an AI engine

[1347] The AI ​​engine performs detailed analysis based on the data it receives. It normalizes expenditure and income data and classifies users into categories using clustering algorithms such as k-means clustering. It then uses machine learning models (e.g., random forests and neural networks) to generate investment strategies tailored to each individual user. For example, safe investments are suggested for low-risk users.

[1348] Input: Data passed to the AI ​​engine

[1349] Output: Generated investment strategy

[1350] Step 7: Managing and sending investment strategies on the server

[1351] The server organizes and stores data for each user based on the investment strategy received from the AI ​​engine. For example, for user ID 12345, it generates data such as "recommended portfolio: 60% bonds, 30% index funds, 10% cash" and manages it within the server.

[1352] Input: Investment strategy received from the AI ​​engine

[1353] Output: Organized investment strategy data

[1354] Step 8: Notify and display the investment strategy to users

[1355] The device receives investment strategy data from the server and displays it to the user. For example, when a user opens the app, it displays information such as "Recommended portfolio: 60% bonds, 30% index funds, 10% cash." If the user has a question about an investment, for example, "What are the returns on index funds over the past three years?", the device sends the question to the server. The server uses an AI engine to generate an appropriate answer, which the device then displays to the user.

[1356] Input: Investment strategies received from the server, queries from users

[1357] Output: Investment strategies and answers displayed to the user

[1358] For example, after a user enters their spending information such as rent and food, the device encrypts the data and sends it to the server, which then receives and processes it to generate an optimal investment strategy, which is then notified and displayed to the user. This process allows users to easily find an investment strategy that suits their financial situation.

[1359] (Application example 1)

[1360] 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."

[1361] Conventional investment strategy proposal systems require users to manually input income and expenditure data, which is time-consuming and increases the risk of input errors. Furthermore, they are unable to utilize data from the electronic payment services users use on a daily basis, making it difficult to propose real-time investment strategies based on that data. Furthermore, they are also required to respond quickly to questions about asset management from users.

[1362] 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.

[1363] In this invention, the server includes means for inputting expenditure data and income data from a user, means for transmitting the data to the server, means for receiving the data and storing it in a database, means for preprocessing the stored data, means for generating an investment strategy using an artificial intelligence engine for analyzing the preprocessed data, means for notifying the user of the generated investment strategy, means for automatically collecting the user's expenditure data through an electronic payment service, and means for instantly responding to questions from the user regarding asset management. This enables real-time investment strategy proposals utilizing electronic payment data while reducing the user's effort. Furthermore, detailed analysis by the artificial intelligence engine can propose optimal investment strategies suited to the user.

[1364] definition statement

[1365] "User" refers to any person or entity that inputs expenditure and income data and receives investment strategy suggestions.

[1366] "Expense Data" refers to information about the money a user spends in their daily life or business activities.

[1367] "Income Data" refers to information about the income earned by a User in their daily life or business activities.

[1368] "Server" refers to the computer system that receives and stores user data, as well as pre-processing and analyzing the data for the artificial intelligence engine.

[1369] "Database" refers to a data storage system for organizing, storing, and managing data within a server.

[1370] "Preprocessing" refers to the process of data shaping and cleaning to prepare stored data in a form suitable for analysis.

[1371] "Artificial intelligence engine" refers to a program or algorithm that analyzes a user's expenditure and income data and generates an optimal investment strategy.

[1372] "Investment Strategy" refers to a specific investment allocation plan suggested to you based on your financial situation and risk tolerance.

[1373] "Electronic Payment Service" means an internet-based payment system through which users can pay for goods and services.

[1374] "Clustering" refers to a data analysis technique that classifies a large number of data points into several groups based on their similarities.

[1375] "Risk Profile" refers to an assessment analysis generated based on a user's risk tolerance and financial situation.

[1376] "Encryption" refers to the process of converting data into a form that cannot be understood by third parties, with the purpose of protecting the data.

[1377] "Privacy protection" refers to measures taken to prevent users' personal information and data from being accessed, used, or disclosed in an unauthorized manner.

[1378] MODE FOR CARRYING OUT THE INVENTION

[1379] Overall system configuration

[1380] This invention provides a system that uses an artificial intelligence engine to propose optimal investment strategies based on a user's expenditure and income data. The system is primarily composed of three main components: a user terminal, a server, and an AI engine.

[1381] Data input from the user

[1382] First, the user enters expenditure and income data using a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen. This data is important information that reflects the user's behavioral patterns and economic situation. It is also possible to automatically collect user expenditure data by using electronic payment services.

[1383] Data transmission and storage

[1384] The user's device encrypts the data entered and sends it securely to the server. The server immediately stores the data in a database, which also records metadata such as the user ID and timestamp for each piece of data. The data is then formatted and cleaned as needed to prepare it for analysis.

[1385] AI-based data analysis and investment strategy generation

[1386] The server passes the stored data to the AI ​​engine, which then performs detailed analysis based on this data. Specifically, it normalizes expenditure and income data and performs clustering. Clustering allows the user's spending patterns to be classified into several categories, generating an investment strategy suited to each individual user. It also creates a risk profile and optimizes investments based on the user's risk tolerance and goals.

[1387] Notification and display of investment strategies

[1388] The server then notifies the user of the generated investment strategy. Users can view detailed information such as the recommended portfolio, projected returns, and risk assessment through the app or web interface. For example, a user with a low risk tolerance might be suggested a portfolio with 60% bonds, 30% index funds, and 10% cash.

[1389] User Scenarios

[1390] Users enter their monthly expenditure information through the app. By using an electronic payment service, expenditure data is automatically collected. For example, details such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen are entered. The device sends this data to a server, which receives it and stores it in an encrypted form in a database.

[1391] The server passes the saved data to an AI engine, which categorizes the spending data and evaluates the user's risk tolerance. For example, if a user's main spending is on entertainment, the AI ​​engine will suggest low-risk, stable investments. Specifically, it might generate a portfolio with 40% index funds, 40% individual stocks, and 20% cash.

[1392] The server then notifies the user of this information again, and the user can review the details of the proposed portfolio through the app. If the user has questions or concerns about an investment, they can ask, "What are the returns on index funds over the past three years?" The device sends this question to the server, which then has the AI ​​engine generate an appropriate answer. The device receives the answer and immediately displays it to the user.

[1393] Hardware and software used

[1394] Hardware: Server (using cloud services)

[1395] Software: Python, Pandas, Sklearn, REST API

[1396] Prompt Sentence Examples

[1397] "I want to create an application that suggests investment strategies based on monthly expenditure and income data. Specifically, this will include data collection, data cleaning, clustering, generating optimal investment strategies, and notifying the user."

[1398] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1399] Program processing flow

[1400] Step 1:

[1401] Data Entry and Collection

[1402] Users enter expenditure and income data using a dedicated application or a web interface. Users' expenditure data is also collected automatically by using an electronic payment service. As a result, input data includes both manual input and automatic collection. Input data includes details such as rent, food expenses, entertainment expenses, and income.

[1403] Input: User spending and income data

[1404] Output: Collected data objects

[1405] Step 2:

[1406] Data Transmission and Encryption

[1407] The device encrypts the collected data and transmits it to the server in a secure manner, which involves transforming the data using an encryption algorithm and transmitting it to the server's endpoint.

[1408] Input: Collected data objects

[1409] Output: Encrypted data

[1410] Step 3:

[1411] Receiving and storing data

[1412] The server receives the encrypted data and stores it in a database, where it is decrypted and recorded along with metadata such as the user ID and timestamp.

[1413] Input: Encrypted data

[1414] Output: Data stored in the database

[1415] Step 4:

[1416] Data Preprocessing

[1417] The server preprocesses the stored data, which includes data formatting and cleaning, such as standardizing data formats, imputing missing values, and detecting and correcting outliers.

[1418] Input: Data stored in a database

[1419] Output: Preprocessed data

[1420] Step 5:

[1421] Data analysis using an AI engine

[1422] The server then passes the preprocessed data to an AI engine for further analysis, including data normalization, clustering, and risk profile generation, which categorizes the user's spending patterns into several categories and generates an investment strategy suited to each individual user.

[1423] Input: Preprocessed data

[1424] Output: Generated investment strategy

[1425] Step 6:

[1426] Investment Strategy Notification

[1427] The server notifies the user of the investment strategy generated by the AI ​​engine, and the user can view detailed information such as the recommended portfolio, projected returns, and risk assessment through an app or web interface.

[1428] Input: Generated investment strategy

[1429] Output: Investment strategy communicated to the user

[1430] Step 7:

[1431] Responding to user questions

[1432] Users submit questions about asset management through the application. The device sends the questions to the server, which uses an AI engine to generate appropriate answers. The device then displays the answers to the user.

[1433] Input: User question

[1434] Output: Answer by AI engine

[1435] Through these steps, users can receive the optimal investment strategy based on their individual financial situation and risk tolerance, and real-time investment suggestions can be made using data automatically collected through the electronic payment service.

[1436] 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.

[1437] Overall system configuration

[1438] This invention provides a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[1439] Data input from the user

[1440] Users enter expenditure and income data through a dedicated application or web interface. For example, rent is 100,000 yen, food expenses are 50,000 yen, entertainment expenses are 20,000 yen, and income is 250,000 yen.

[1441] Data transmission and storage

[1442] The device verifies the entered data, encrypts it, and sends it to the server, which receives it and stores it securely in a database, along with metadata such as the user ID and a timestamp.

[1443] AI-based data analysis and investment strategy generation

[1444] The server passes the stored data to the AI ​​engine, which normalizes the expenditure and income data and applies a clustering algorithm to identify spending patterns. It evaluates the user's expenditure-to-income ratio and creates a risk profile. It generates an optimal investment strategy based on the user's risk tolerance and suggests a specific portfolio (e.g., 60% bonds, 30% index funds, and 10% cash).

[1445] Applying the Emotion Engine

[1446] The server passes the text and voice contained in the user's input or question to the emotion engine, which uses natural language processing technology to recognize the user's emotions. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize the user's anxiety.

[1447] Coordinating and informing investment strategies

[1448] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, for a user with high anxiety, the AI ​​engine will increase low-risk investments. The adjusted investment strategy is finally generated.

[1449] The server then communicates this adjusted investment strategy to the user, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice.

[1450] Example: User scenario

[1451] Users enter their monthly expenditure information through the app. For example, rent is 100,000 yen, food is 50,000 yen, entertainment is 20,000 yen, and income is 250,000 yen. The device encrypts this data and sends it to the server. The server receives the data and stores it in a database in encrypted form.

[1452] The server passes the stored data to an AI engine, which then normalizes and clusters the spending data. For example, it identifies users whose spending is concentrated on rent and food. The AI ​​engine also assesses risk and suggests optimal investment strategies.

[1453] At the same time, the server passes the user's comments and questions to the emotion engine. For example, if a user enters, "I'm worried about the recent fluctuations in stock prices," the emotion engine analyzes this comment and recognizes the user's anxiety. As a result, the AI ​​engine makes adjustments to increase the number of low-risk items.

[1454] The server then notifies the user of an adjusted investment strategy based on this. For example, it displays details such as, "Taking your current risk profile into consideration, we propose a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." If the user asks, "What are the returns of this index fund over the past three years?", the device sends the question to the server, which uses its AI engine and emotion engine to generate an appropriate answer and immediately responds to the user. For example, it may notify the user that "The average return of this index fund over the past three years is 5.2%."

[1455] In this way, by combining the emotion engine, users can receive the optimal investment strategy tailored to their emotional state, allowing them to manage their assets with greater peace of mind.

[1456] The processing flow will be explained below.

[1457] Step 1:

[1458] The user launches the application and enters expense data (e.g., rent 100,000 yen, food 50,000 yen, entertainment 20,000 yen) and income data (e.g., income 250,000 yen).

[1459] Step 2:

[1460] The terminal checks the format and content of the data entered, for example verifying that expenditure and income data are entered correctly in numerical format.

[1461] Step 3:

[1462] The device encrypts the verified data and sends it to the server, thereby preventing unauthorized access and information leaks during the data transmission process.

[1463] Step 4:

[1464] The server receives the encrypted data and stores it in a database, along with metadata such as the user ID and a timestamp.

[1465] Step 5:

[1466] The server preprocesses the stored data, for example by imputing missing values, converting non-numeric data, and removing unnecessary data.

[1467] Step 6:

[1468] The server passes the preprocessed data to the AI ​​engine, which then begins analyzing the data.

[1469] Step 7:

[1470] The AI ​​engine normalizes expense and income data and applies clustering algorithms to identify users' spending patterns, such as users whose spending is concentrated on rent and food.

[1471] Step 8:

[1472] The AI ​​engine generates a risk profile, assessing a user's risk tolerance based on their income-to-expense ratio and history. For example, a user with a high risk tolerance might be recommended a portfolio focused on stocks.

[1473] Step 9:

[1474] The AI ​​engine generates an optimal investment strategy based on the user's risk tolerance and goals, suggesting a portfolio (e.g., 60% bonds, 30% index funds, 10% cash).

[1475] Step 10:

[1476] The server notifies the user of the generated investment strategy, including the recommended portfolio, predicted returns, and risk assessment.

[1477] Step 11:

[1478] The terminal displays the investment strategy to the user, who can review the details and either accept the proposal or request an amendment.

[1479] Step 12:

[1480] A user submits a financial management question using the application's chat function. For example, "What are the returns on index funds over the past three years?"

[1481] Step 13:

[1482] The terminal sends the user's questions to the server.

[1483] Step 14:

[1484] The server passes the question to the AI ​​engine and asks it to generate an appropriate answer.

[1485] Step 15:

[1486] The AI ​​engine searches data based on the question and generates an appropriate answer, for example, calculating the average return over the past three years.

[1487] Step 16:

[1488] The server generates a response and sends it back to the user.

[1489] Step 17:

[1490] The device instantly displays the answer to the user, for example, "This index fund's average return over the past three years is 5.2%."

[1491] Step 18:

[1492] The server passes the text and voice contained in the user's input and questions to the emotion engine, which analyzes them and recognizes the user's emotions.

[1493] Step 19:

[1494] The emotion engine assesses the user's emotional state (e.g., anxiety, relief) and provides the results to the AI ​​engine.

[1495] Step 20:

[1496] The AI ​​engine takes into account emotional state information and adjusts investment strategies, for example, if a user is feeling anxious, it will adjust to a lower-risk investment strategy to alleviate those feelings.

[1497] Step 21:

[1498] The server then notifies the user of the adjusted investment strategy again, for example, by displaying a message saying, "Taking your emotional state into consideration, we propose a new portfolio with reduced risk."

[1499] This series of steps will result in a system that can provide optimal investment strategies that take into account not only the user's financial situation but also their emotional state.

[1500] Example 2

[1501] 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."

[1502] Conventional investment strategy proposal systems performed risk assessment and clustering based on users' expenditure and income data, but did not optimize investment strategies taking into account emotional fluctuations. As a result, when users felt anxious or worried about market fluctuations or their personal circumstances, the investment strategy could not be adjusted to take those emotions into account, resulting in a decrease in user satisfaction. Furthermore, sufficient measures were required to ensure the security and privacy of the collected data.

[1503] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1504] In this invention, the server includes a means for inputting expenditure data and income data from a user, a means for the terminal to verify the input data, encrypt it, and transmit it to the server, and a means for the server to receive the data and store it in a database. This enables the user's expenditure data and income data to be safely collected and stored while ensuring security and privacy. The server also includes a means for preprocessing the stored data, clustering and normalizing the expenditure data and income data using an artificial intelligence engine, and generating a risk profile. The artificial intelligence engine optimizes an investment strategy based on the generated risk profile. The server also provides the user's input and questions to an emotion engine, which analyzes the user's emotions and returns an emotion tag, allowing the AI ​​engine to adjust the investment strategy taking the emotion tag into account. This makes it possible to provide an investment strategy that reflects the user's current emotional state, thereby improving the user's investment performance and satisfaction.

[1505] "User" means an individual or legal entity that uses the system to input expenditure and income data and receive investment strategy suggestions.

[1506] A "server" is a computer system that receives, stores, and processes data sent by users.

[1507] A "terminal" is a device used by a user to input and transmit data, such as a smartphone or a personal computer.

[1508] "Expenditure Data" refers to data that indicates the amount of money a user spends on various consumption activities.

[1509] "Income Data" means data indicating the income earned by a user over a certain period of time.

[1510] "Encryption" is the process of transforming data with a specific algorithm to ensure its security.

[1511] A "database" is a collection of information that stores data in an organized manner and allows it to be searched and used when needed.

[1512] "Preprocessing" refers to the process of organizing and normalizing data before performing data analysis.

[1513] An "artificial intelligence engine" is software that uses machine learning and data analysis techniques to analyze data and generate investment strategies.

[1514] "Clustering" is a technique for grouping data based on specific criteria and identifying patterns.

[1515] "Risk Profile" is a criterion for assessing a user's risk tolerance and determining their investment strategy.

[1516] An "emotion engine" is software that uses natural language processing technology to analyze emotions from user input text and add emotion tags.

[1517] An "emotion tag" is a label that is added to the emotion engine to identify the user's emotion and indicate the result.

[1518] An "investment strategy" is a plan that suggests optimal asset allocation and investment products based on a user's financial data and risk profile.

[1519] This invention relates to a system that uses an artificial intelligence engine and an emotion engine to propose optimal investment strategies based on user expenditure and income data. The system is primarily composed of four main components: a user terminal, a server, an AI engine, and an emotion engine.

[1520] Users enter expense and income data through a dedicated application or web interface. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen. The software used could be a mobile application or a web browser.

[1521] The terminal validates the data entered by the user. Validation includes data format and range checks. For example, an error message is displayed if income is less than expenses or if non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and transmits it securely to the server.

[1522] The server receives the encrypted data sent from the device and temporarily decrypts it in memory. It then re-encrypts it before saving it to the database. The saved data also includes metadata such as the user ID, input date and time, and data type.

[1523] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[1524] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It evaluates the user's income and expenditure ratios and creates a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[1525] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[1526] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. Specifically, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[1527] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[1528] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, projected returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio consisting of 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[1529] Prompt Sentence Examples

[1530] An example of a prompt sentence is, "I have entered the following data: rent 100,000 yen, food expenses 50,000 yen, entertainment expenses 20,000 yen, and income 250,000 yen. Please suggest an investment strategy with low risk."

[1531] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1532] Step 1:

[1533] Users use a dedicated application or web interface to enter monthly expenditure and income data. For example, a user might enter information such as rent of 100,000 yen, food expenses of 50,000 yen, entertainment expenses of 20,000 yen, and income of 250,000 yen.

[1534] Input: User expenditure and income data

[1535] Output: The input data

[1536] Step 2:

[1537] The terminal validates the entered data, checking the data format and range, and displays an error message if, for example, income is less than expenses or non-numeric data is entered. If the data is correct, the terminal AES encrypts the data and sends it securely to the server.

[1538] Input: Data entered by the user

[1539] Output: Encrypted data

[1540] Step 3:

[1541] The server receives the encrypted data sent from the device. It temporarily decrypts it in memory, then re-encrypts it and stores it in the database. The stored data also includes metadata such as the user ID, input date and time, and data type.

[1542] Input: Encrypted data

[1543] Output: Data stored in the database

[1544] Step 4:

[1545] The server periodically, or upon user request, provides the expense and income data stored in the database to the AI ​​engine, converting it into the required format and sending it through a secure API.

[1546] Input: Saved data

[1547] Output: Data provided to the AI ​​engine

[1548] Step 5:

[1549] The AI ​​engine normalizes the data received from the server and uses clustering algorithms (e.g., K-means or DBSCAN) to identify spending patterns. It then evaluates the user's income and expenditure ratios to create a risk profile. It then generates an optimal investment strategy based on the user's risk tolerance. For example, it calculates a portfolio such as "60% bonds, 30% index funds, and 10% cash."

[1550] Input: Preprocessed expenditure and income data

[1551] Output: Optimized investment strategy

[1552] Step 6:

[1553] The server provides the text and voice contained in the user's input and questions to the emotion engine via real-time API communication.

[1554] Input: User input or questions

[1555] Output: Data provided to the emotion engine

[1556] Step 7:

[1557] The emotion engine uses natural language processing technology to analyze emotions from the user's text data. For example, it understands the context of the text and adds emotion tags such as "worry," "anxiety," "optimism," and "satisfaction." For example, if a user enters "I'm worried about recent stock price fluctuations," the emotion engine will return the tag "anxiety" to the server.

[1558] Input: User's text data

[1559] Output: Emotion tag

[1560] Step 8:

[1561] The server passes the emotion tags obtained from the emotion engine to the AI ​​engine and asks it to readjust the investment strategy taking this into account. For example, based on the emotion tag of "anxiety," the server restructures the portfolio to increase the proportion of low-risk investment products.

[1562] Input: emotion tag

[1563] Output: Recalibrated investment strategy

[1564] Step 9:

[1565] The server then notifies the user of the final adjusted investment strategy. The notification includes a recommended portfolio, predicted returns, risk assessment, and sentiment-based advice. For example, it displays details such as, "Taking your current risk profile into consideration, we suggest a portfolio with 60% bonds, 30% index funds, and 10% cash." Additionally, if the user asks additional questions, the server instantly responds using its AI and sentiment engines. For example, if the user asks, "What are the returns of this index fund over the past three years?", the server will notify the user, "The average return of this index fund over the past three years is 5.2%."

[1566] Input: Reworked investment strategy and user question

[1567] Output: User notification and response

[1568] (Application example 2)

[1569] 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."

[1570] Conventional investment strategy proposal systems provide investment strategies based on users' expenditure and income data, but they do not optimize the strategies based on the user's emotional state, which means they are unable to address the user's anxiety and risk tolerance. This can lead to emotions affecting the user's investment behavior, resulting in suboptimal investment results. There is also a need for a system that can securely manage user data and instantly answer questions about asset management.

[1571] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1572] In this invention, the server includes means for inputting expenditure data and income data from a user, means for preprocessing the data, means for generating an investment strategy using an artificial intelligence engine, means for identifying the emotional state of the user using an emotion engine for analyzing the user's emotions, means for adjusting the investment strategy based on the emotional state of the user, and means for providing immediate answers to questions about asset management from the user. This enables the provision of an investment strategy that takes the emotional state of the user into consideration, fast and secure data management, and immediate answers.

[1573] "Expense Data" refers to information about financial expenditures used by a user in their daily lives.

[1574] "Income Data" means information about a User's financial income from work or other sources.

[1575] A "server" is a computer system located in a remote location that receives and stores data and provides services to users.

[1576] An "artificial intelligence engine" is a technology that uses programs and algorithms installed on a server to perform data analysis and automated decision-making.

[1577] An "emotion engine" is a technology for analyzing and recognizing emotions from a user's text or voice.

[1578] An "investment strategy" is a plan that suggests optimal asset allocation and investment destinations for users' investments.

[1579] A "database" is a collection of information that allows information to be efficiently stored and quickly retrieved when needed.

[1580] "Encryption" is the process of transforming data using a specific algorithm to protect it from third parties.

[1581] "Clustering" is a technique for dividing data into groups so that data within the same group have similar characteristics.

[1582] A "risk profile" is information that assesses a user's risk tolerance and financial situation and indicates how much risk they are willing to take.

[1583] "User" means an individual or organization that uses the system to input expenditure and income data and receive investment strategy proposals.

[1584] "Notification" is an action taken by a system to provide information to a user.

[1585] "Privacy" is the right to protect personal information from being disclosed to third parties.

[1586] The "question answering means" is a function for providing immediate and appropriate answers to inquiries from users.

[1587] The present invention provides a system that uses an artificial intelligence engine and an emotion engine to propose an optimal investment strategy based on a user's expenditure data and income data. Specific embodiments are described below.

[1588] Overall system configuration

[1589] The system mainly consists of the following components:

[1590] 1. User terminal: A device such as a smartphone that provides a means for users to enter spending and income data.

[1591] 2. Server: Receives input data and stores it securely. Specific technologies used here include database systems and data encryption technology.

[1592] 3. AI Engine: Performs data analysis such as normalization, clustering, and generating risk profiles to generate investment strategies.

[1593] 4. Emotion Engine: Analyzes text and speech in user inputs and questions to identify the user's emotional state. Natural language processing techniques are used.

[1594] Data processing flow

[1595] Users enter their daily expenditure and income data through a smartphone application, which is then encrypted by the device and sent to a server, where it is received and securely stored in a database.

[1596] Data analysis

[1597] The server receives the stored data and passes it to the AI ​​engine, which first normalizes the data and applies a clustering algorithm to identify spending patterns, then generates a risk profile and creates an investment strategy based on it.

[1598] Emotion analysis

[1599] The server also forwards user comments and questions to the emotion engine, which analyzes them and identifies the user's emotional state. For example, if a user types, "I'm worried about the recent market fluctuations," the emotion engine will recognize anxiety.

[1600] Adjusting investment strategies

[1601] Based on the results of the emotion engine, the AI ​​engine will adjust the investment strategy taking into account the user's emotional state, and can suggest lower-risk investment strategies for users with high anxiety.

[1602] User notification and response

[1603] The final investment strategy is then sent to the server, which then notifies the user of the strategy. This notification includes specific investment allocations, risk assessments, and advice. The system also has the ability to instantly answer questions from users about asset management.

[1604] Specific examples

[1605] When a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to a server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[1606] Prompt Sentence Examples

[1607] "I'm worried about the recent market fluctuations."

[1608] In response, the AI ​​engine will adjust the investment strategy taking into account the results of the sentiment engine, allowing users to manage their assets with greater peace of mind.

[1609] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1610] Step 1:

[1611] Users enter expenditure and income data through a smartphone application. Specifically, users enter data such as monthly rent, food expenses, entertainment expenses, and income into the app. This data will be used for subsequent analysis, so it must be entered accurately. The input data is stored in standard formats such as JSON and CSV.

[1612] Step 2:

[1613] The device encrypts the input data and sends it to the server using an encryption method such as AES-256. The data, including metadata such as the user ID and timestamp, is sent to the server using a secure communication protocol (e.g., HTTPS).

[1614] Step 3:

[1615] The server securely stores the received data in a database. The data is not stored in its original format, but may be further encrypted to maintain data integrity. The stored data also includes the user ID and the date and time of input.

[1616] Step 4:

[1617] The server preprocesses the stored data and passes it to the AI ​​engine. Preprocessing includes normalizing the data (e.g., scaling by standard deviation) and imputing missing data (e.g., imputing by mean value), making the data suitable for analysis.

[1618] Step 5:

[1619] The AI ​​engine analyzes the pre-processed data and applies clustering algorithms (e.g., K-means) to identify spending patterns. It also evaluates the user's spending-to-income ratio and generates a risk profile. Based on this, it generates an investment strategy (e.g., 60% bonds, 30% index funds, 10% cash).

[1620] Step 6:

[1621] The server passes the user's comments and questions to the emotion engine, which uses natural language processing techniques (e.g., the BERT model) to analyze the text and identify the user's emotional state (e.g., anxiety, satisfaction, expectation, etc.).

[1622] Step 7:

[1623] The AI ​​engine takes into account the results of the emotion engine and adjusts the investment strategy based on the user's emotional state. For example, if the user feels anxious, it will increase low-risk investments.

[1624] Step 8:

[1625] The server notifies the user of the adjusted investment strategy, including recommended portfolios, projected returns, risk assessments, and sentiment-based advice. If the user then asks additional questions, the questions are analyzed again and an immediate answer is provided.

[1626] For example, if a user enters into the app, "This month I spent 100,000 yen on rent, 50,000 yen on food, and 20,000 yen on entertainment," the data is encrypted and sent to the server. An AI engine on the server analyzes the data and generates an investment strategy such as "30% bonds, 50% index funds, and 20% cash." If a user comments, "I'm worried about the recent market fluctuations," the emotion engine recognizes the anxiety, and the AI ​​engine adjusts the strategy to "40% bonds, 40% index funds, and 20% cash" and notifies the user.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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).

[1634] 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.

[1635] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1636] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1637] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1638] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1639] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1640] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1641] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1642] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1643] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1644] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1645] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1646] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1647] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1648] The following is further disclosed regarding the above embodiment.

[1649] (Claim 1)

[1650] means for inputting expenditure data and income data from a user;

[1651] means for transmitting the input data to a server;

[1652] means for receiving and storing the data in a database;

[1653] means for preprocessing the stored data;

[1654] means for generating an investment strategy using an artificial intelligence engine for analyzing the pre-processed data;

[1655] means for informing the user of the generated investment strategy;

[1656] A means to instantly answer questions from users about asset management,

[1657] A system including:

[1658] (Claim 2)

[1659] The system of claim 1, wherein the artificial intelligence engine optimizes investment strategies by performing clustering and generating risk profiles based on the user's expenditure data and income data.

[1660] (Claim 3)

[1661] 2. The system according to claim 1, wherein the server encrypts and stores user data to protect user privacy.

[1662] "Example 1"

[1663] (Claim 1)

[1664] means for inputting expenditure data and income data from a user;

[1665] A means for encrypting input data at the terminal and transmitting the data to a server;

[1666] means for receiving and storing the data in a database;

[1667] a means of cleaning and formatting the stored data;

[1668] means for generating clustering and investment strategies using an artificial intelligence engine for analyzing the pre-processed data;

[1669] means for informing and displaying the generated investment strategy to the user;

[1670] A means to instantly answer questions from users about asset management,

[1671] A system including:

[1672] (Claim 2)

[1673] The system of claim 1, characterized in that the artificial intelligence engine optimizes investment strategies by performing clustering and generating risk profiles based on the user's expenditure data and income data.

[1674] (Claim 3)

[1675] The system according to claim 1, characterized in that the server encrypts and stores user data to protect user privacy.

[1676] "Application Example 1"

[1677] (Claim 1)

[1678] means for inputting expenditure data and income data from a user;

[1679] means for transmitting the input data to a server;

[1680] means for receiving and storing the data in a database;

[1681] means for preprocessing the stored data;

[1682] means for generating an investment strategy using an artificial intelligence engine for analyzing the pre-processed data;

[1683] means for informing the user of the generated investment strategy;

[1684] means of automatically collecting user spending data through electronic payment services;

[1685] A means to instantly answer questions from users about asset management,

[1686] A system including:

[1687] (Claim 2)

[1688] The system described in claim 1, characterized in that the artificial intelligence engine optimizes investment strategies by performing clustering and generating risk profiles based on the user's expenditure data and income data, and automatically suggests investment strategies suitable for the user.

[1689] (Claim 3)

[1690] 2. The system according to claim 1, wherein the server encrypts and stores user data to protect user privacy.

[1691] "Example 2: Combining Emotion Engines"

[1692] (Claim 1)

[1693] means for inputting expenditure data and income data from a user;

[1694] A means for the terminal to verify the input data, encrypt it and send it to the server;

[1695] a means for the server to receive the data and store it in a database;

[1696] means for preprocessing the stored data;

[1697] a means by which the server provides the preprocessed data to the artificial intelligence engine;

[1698] means for clustering and normalizing the expenditure data and the income data using an artificial intelligence engine to generate a risk profile;

[1699] a means for optimizing an investment strategy based on the generated risk profile;

[1700] A means for the server to provide user input and questions to the emotion engine;

[1701] A means for the emotion engine to analyze the user's emotion and return emotion tags to the server;

[1702] A means for the artificial intelligence engine to adjust investment strategies taking into account sentiment tags;

[1703] a means by which the server notifies the user of the adjusted investment strategy;

[1704] A means to instantly answer questions from users about asset management,

[1705] A system including:

[1706] (Claim 2)

[1707] The system of claim 1, wherein the artificial intelligence engine optimizes investment strategies by performing clustering and generating risk profiles based on the user's expenditure data and income data.

[1708] (Claim 3)

[1709] 2. The system according to claim 1, wherein the server encrypts and stores user data to protect user privacy.

[1710] "Application example 2 when combining emotion engines"

[1711] (Claim 1)

[1712] means for inputting expenditure data and income data from a user;

[1713] means for transmitting the input data to a server;

[1714] means for receiving and storing the data in a database;

[1715] means for preprocessing the stored data;

[1716] means for generating an investment strategy using an artificial intelligence engine for analyzing the pre-processed data;

[1717] means for informing the user of the generated investment strategy;

[1718] means for identifying an emotional state of the user using an emotion engine for analyzing the user's emotions;

[1719] a means for adjusting an investment strategy based on a user's emotional state;

[1720] A means to instantly answer questions from users about asset management,

[1721] A system including:

[1722] (Claim 2)

[1723] The system of claim 1, wherein the artificial intelligence engine optimizes investment strategies by performing clustering and generating risk profiles based on the user's expenditure data and income data.

[1724] (Claim 3)

[1725] 2. The system according to claim 1, wherein the server encrypts and stores user data to protect user privacy. [Explanation of symbols]

[1726] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for inputting expenditure data and income data from a user; means for transmitting the input data to a server; means for receiving and storing the data in a database; means for preprocessing the stored data; means for generating an investment strategy using an artificial intelligence engine for analyzing the pre-processed data; means for informing the user of the generated investment strategy; A means to instantly answer questions from users about asset management, A system including:

2. 2. The system of claim 1, wherein the artificial intelligence engine optimizes investment strategies by performing clustering and generating risk profiles based on user expenditure data and income data.

3. 2. The system according to claim 1, wherein the server encrypts and stores user data to protect user privacy.

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