system

Abstract

We provide the system. [Solution] A means for the user to input their target asset amount, Means for collecting users' financial information, Means of obtaining external financial information, A means for generating an asset formation plan based on the user's financial information and the external financial information, A means of visually presenting the generated asset formation plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, many people are worried about future asset formation. Especially due to the lack of education regarding finance and economy, it is difficult to individually formulate an effective asset formation plan. Under such circumstances, there is a need to provide a specific and feasible asset formation plan for each individual and a system that supports future economic stability.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes means for the user to input a target asset amount, means for collecting the user's financial information, means for acquiring external financial information, generates an asset formation plan based on this information, and means for visually presenting the generated plan. The system further includes means for receiving feedback from the user and adjusting the plan accordingly, and means for proposing an optimized asset formation strategy using artificial intelligence.

[0006] A "user" is an individual or group that uses the system to set their own asset building goals and receives advice and plans to achieve those goals.

[0007] "Target asset amount" refers to a specific financial goal that the user hopes to achieve in the future.

[0008] "Financial information" refers to personal or corporate economic data related to wealth creation, such as a user's annual income, savings, living expenses, and existing investments.

[0009] "External financial information" refers to information about external economic conditions that affect wealth creation, such as interest rates, stock market trends, tax systems, and inflation rates.

[0010] An "asset building plan" is a comprehensive plan that includes savings strategies, investment methods, and risk management necessary for a user to achieve their goals.

[0011] "Visual presentation methods" refer to methods of displaying the generated asset formation plan as graphs or charts so that users can easily understand it.

[0012] "Feedback" refers to the opinions and requests for revisions that users provide regarding the proposed asset building plan.

[0013] "Artificial intelligence" is a technology that analyzes large amounts of data and proposes optimal asset building strategies to users. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

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

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

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

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0035] The system for implementing this invention is designed so that a user can input their asset building goals and receive the optimal plan for achieving those goals. This system mainly consists of two components: a terminal and a server.

[0036] On the terminal, the user enters specific asset-building goals, such as "save 20 million yen in 10 years." Simultaneously, they also enter financial information such as age, annual income, current savings, and existing investment status. This information is then sent to the server.

[0037] The server first researches external financial information, such as the latest interest rates and market trends, via the internet. This allows it to obtain the most up-to-date data necessary for the user to achieve their goals. Next, the server uses AI to generate an optimal asset building plan based on the information received from the user and the information obtained from external sources. This plan includes savings plans, investment strategies, and risk management. The server then converts the generated plan into a visually easy-to-understand format, such as graphs and charts, and sends it to the user's device.

[0038] The terminal displays information received from the server to the user. Based on this, the user can make decisions regarding their asset building. Furthermore, the user can provide feedback on the presented plan, for example, requesting modifications such as "I want to reduce the risk further." This feedback is sent back to the server, which then adjusts and optimizes the plan based on it.

[0039] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server will consider the user's current income and expenses, as well as their projected future expenses, and then present a specific plan such as "maintain monthly savings of 80,000 yen while investing a certain percentage in mutual funds." In this way, the present invention provides an environment in which users can continue to build assets based on a concrete action plan.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters their asset-building goals into the terminal. Specifically, they fill in financial data such as the target amount, desired age to achieve it, current savings, and annual income into the input form.

[0043] Step 2:

[0044] The terminal sends information entered by the user to the server. This information includes specific goals and details about the financial situation.

[0045] Step 3:

[0046] The server retrieves external financial information. This includes current interest rates, stock market trends, and inflation rates, which are collected and analyzed from the internet.

[0047] Step 4:

[0048] The server stores the user's financial information and external financial information in a database and prepares to generate an optimal asset building plan based on this information.

[0049] Step 5:

[0050] The server uses AI to generate an optimal asset building plan to help users achieve their goals. This plan includes recommendations for savings amounts, investment allocation, and risk management methods.

[0051] Step 6:

[0052] The server visualizes the generated asset building plan. Specifically, it creates an overview of the plan as graphs and charts, and presents it in a way that is easy for the user to understand.

[0053] Step 7:

[0054] The terminal displays visualization data received from the server to the user. The user then uses this to review their asset building strategy and take appropriate action if necessary.

[0055] Step 8:

[0056] Users input feedback about their asset building plan into the terminal. For example, they might input that they prefer a plan with reduced risk.

[0057] Step 9:

[0058] The device sends user feedback to the server.

[0059] Step 10:

[0060] The server incorporates user feedback and readjusts the plan. Steps 5-7 are repeated as needed to provide the user with an optimized plan.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Developing an optimal asset building plan tailored to each individual user's financial situation and market conditions is complex and difficult, and there is a challenge in that it is difficult for ordinary users without specialized knowledge to easily obtain effective methods.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting and receiving the user's financial information at a terminal, means for acquiring external financial information, means for generating an asset formation plan using a generation AI model, and means for visually converting the generated asset formation plan and presenting it at the terminal. This makes it possible for users to obtain a rational and specific asset formation plan based on market trends and their own financial situation, even without specialized knowledge.

[0066] A "user" is the entity that accesses the system and inputs their own financial information and asset building goals.

[0067] "Asset amount" refers to the specific financial target that the user wishes to achieve.

[0068] "Financial information" refers to data that shows an individual's economic status, including the user's age, annual income, current savings, and existing investment status.

[0069] A "terminal" is an interface device used by users to input information and receive suggestions from a server.

[0070] A "server" is a central processing unit that processes information from users and external data sources and generates asset formation plans using a generated AI model.

[0071] "External financial information" refers to financial data from external sources, such as the latest interest rates and market trends, obtained via the internet.

[0072] A "generative AI model" refers to an artificial intelligence algorithm that generates asset building plans using collected data.

[0073] An "asset building plan" is an actionable proposal that includes savings plans and investment strategies generated based on user input information and external financial information.

[0074] "Visual presentation" means displaying generated information on a device in the form of graphs, charts, or other formats, so that users can understand it intuitively.

[0075] "Feedback" refers to the opinions and suggestions for improvement that users provide regarding the asset building plan presented.

[0076] "Adjusting" means modifying existing asset building plans as needed, taking user feedback into consideration.

[0077] Users access the asset building support system using their own devices. These devices provide an interface for inputting financial information such as target asset amount, age, annual income, current savings, and existing investment status. This allows users to easily send the necessary information to the system.

[0078] The terminal is responsible for transmitting collected user information to the server. The server obtains external financial information via the internet, including the latest interest rates and market trends. This information is used as reference data to generate asset building plans.

[0079] The server is equipped with a generative AI model that generates an optimal asset building plan based on the user's financial information and external financial information. This AI model proposes savings plans and investment strategies tailored to the user's circumstances and has the flexibility to adjust the plan.

[0080] The generated asset building plan is visualized and sent to the device for presentation to the user. The user can evaluate their asset building strategy based on the presented graphs and charts and provide feedback as needed. This feedback is sent back to the server for further adjustments to the plan.

[0081] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server can use an AI model to generate and present a plan such as "maintaining a monthly savings of 80,000 yen while investing a certain percentage in mutual funds."

[0082] An example of a prompt for the generating AI model would be text such as, "I would like a low-risk plan to save 10 million yen by the age of 30." In this way, the user can obtain a specific and realistic asset building plan.

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

[0084] Step 1:

[0085] Users access the system using a terminal and input financial information such as their asset building goals, age, annual income, current savings, and existing investment status. The entered information is organized as data within the terminal and formatted in preparation for transmission.

[0086] Step 2:

[0087] The device sends the information entered by the user to the server. This data includes the user's goals and financial status. The device encrypts the data during transmission to ensure it reaches the server securely.

[0088] Step 3:

[0089] The server analyzes the user's financial information and retrieves external financial information via the internet. This includes the latest interest rates and market trends. This external information is obtained via API and stored in a database for combination with user information.

[0090] Step 4:

[0091] The server integrates the user's financial information with external financial information and generates an optimal asset building plan using a generative AI model. The AI ​​model evaluates the input data and presents the most suitable savings plan and investment strategy for the user. This generates a concrete action plan from the data.

[0092] Step 5:

[0093] The generated asset building plan is visualized on the server. Here, data visualization tools are used to create graphs and charts. The visual representation transforms the information into a format that users can easily understand.

[0094] Step 6:

[0095] The server sends a visualized plan to the terminal. The terminal displays the received information to the user, presenting a visual plan. The user then makes their own decisions based on this plan.

[0096] Step 7:

[0097] Users provide feedback on the presented plan through their device. For example, they may want to reduce risk or adjust investment ratios. User feedback is collected on the device and sent back to the server.

[0098] Step 8:

[0099] The server receives feedback from the user and uses a generated AI model to readjust the asset building plan. The readjusted plan is further visualized and presented to the user again. This allows for continuous plan improvement tailored to the user's needs.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] A challenge is that users often lack access to optimal plans for efficiently achieving their wealth-building goals. Furthermore, financial transactions are complex and cumbersome, and the numerous procedures required for wealth building can lead to a lack of progress. Therefore, it is necessary to provide means to simplify and automate the wealth-building process.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for the user to input a target asset amount, means for collecting the user's financial information, means for acquiring external financial information, means for generating an asset formation plan, means for visually presenting the generated asset formation plan, and means for automating financial transactions for asset management through electronic commerce means. This enables the user to effectively plan their asset formation, automatically reflect an actionable plan in transactions, and efficiently achieve their goals.

[0105] "A means for users to input their target asset amount" refers to an interface that allows users to set specific asset goals and provide those figures to the system.

[0106] "Means for collecting user financial information" refers to functions for obtaining financial data such as the user's age, annual income, savings, and existing investment status.

[0107] "Means of obtaining external financial information" refers to functions for obtaining external financial market information, such as market trends and interest rates, via the internet.

[0108] The "means for generating asset formation plans" refer to a function that uses AI to create a plan for achieving goals, based on the user's financial information and external financial information.

[0109] "Means of visually presenting the generated asset formation plan" refers to a function that displays the generated plan using graphs and charts to make it easier for the user to understand.

[0110] "Means of automating financial transactions for asset management through electronic commerce" refers to a function that automates the execution of investments and savings based on a user's asset formation plan via an online trading platform.

[0111] The system for implementing the present invention provides an integrated approach to efficiently achieve asset formation goals. Users can input specific asset formation goals and associated financial information using a terminal. Specifically, this includes age, annual income, savings amount, and existing investment status.

[0112] Information entered on the terminal is sent to the server. The server processes the collected financial information using programming languages ​​such as Python. Furthermore, it uses external financial APIs to collect the latest market information such as interest rates and stock prices. This collection process often utilizes APIs such as the Bloomberg API and the Reuters API.

[0113] The server combines acquired external data and user data to generate an optimal asset building plan using AI models such as TENSORFLOW®. The generated plan is then converted into graphs and charts using visualization tools such as D3.js and presented to the user.

[0114] Furthermore, it integrates APIs for e-commerce services such as Stripe and PayPal, enabling automated execution of financial transactions based on asset building plans. This allows users to efficiently conduct financial transactions based on concrete and actionable plans.

[0115] For example, if a user chooses a plan to save a small portion of their monthly disposable income and invest the surplus in low-risk mutual funds, this system will automatically transfer the monthly savings to the investment account and purchase appropriate financial products. By using prompts such as, "Please suggest an investment plan that allows me to build wealth with minimal risk while slightly reducing my monthly expenses," more personalized suggestions can be obtained from the generating AI model.

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

[0117] Step 1:

[0118] The user enters their asset building goals and financial information on the terminal. The entered data includes the user's annual income, savings, and existing investment status, and the next processing steps are based on this information. The entered data is temporarily stored on the terminal and prepared to be sent to the server.

[0119] Step 2:

[0120] The terminal sends the collected user information to the server. The server receives this data and stores it in a database. The database uses a relational database management system (RDBMS), such as SQL, to effectively manage information for multiple users.

[0121] Step 3:

[0122] The server accesses financial APIs to retrieve external financial information. This information includes current interest rates, market trends, and stock prices. This data is obtained via the API using Python's request library and stored in a database.

[0123] Step 4:

[0124] The server combines user data and external financial information and inputs it into an AI model. The AI ​​model uses TensorFlow to generate an optimal asset building plan based on this data. The plan includes recommended savings amounts and investment percentages. The generated plan is temporarily stored in an internal data format.

[0125] Step 5:

[0126] The server uses visualization tools such as D3.js to create graphs and charts to visually represent the generated asset formation plan. The generated graphs are exported in HTML format and sent to the user's terminal.

[0127] Step 6:

[0128] The terminal displays visual information sent from the server in the browser. Based on this, the user can review their asset building plan. The visual information is provided in a user-interface format that can be manipulated.

[0129] Step 7:

[0130] Users input feedback on their asset building plan from their device. For example, they might input a request such as "I want to reduce risk" and send it to the server via their device.

[0131] Step 8:

[0132] The server receives user feedback and adjusts the asset building plan accordingly. It then reruns the AI ​​model, generates the adjusted plan, and saves it.

[0133] Step 9:

[0134] The server uses e-commerce tools to automate financial transactions based on a coordinated asset building plan. It utilizes APIs from Stripe and PayPal to execute the user's investment plan, resulting in efficient asset management.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention is a comprehensive system for users to effectively achieve their asset building goals, and in particular, by incorporating an emotion engine, it enables the provision of asset building plans that take into account the user's emotional state. The system mainly consists of a terminal, a server, and an emotion engine.

[0137] Through the terminal, users input their asset-building goals and provide financial information. This includes information such as the amount they want to achieve, the timeframe, their current asset status, income, and expenses. The terminal also provides an interface for acquiring data on the user's emotions. Here, the system can understand the user's current emotional state by analyzing their facial expressions and input content.

[0138] The server receives this information and first retrieves the latest financial information from external sources. This process takes into account market trends, interest rates, economic indicators, tax systems, etc. Based on the retrieved data and the information entered by the user, the server generates an asset building plan using artificial intelligence (AI). This AI incorporates emotional data analyzed by an emotion engine, enabling it to make suggestions tailored to the user's current psychological state. Specifically, it presents risk-reducing plans to users with high stress levels, and conversely, plans that include aggressive investments to highly motivated users.

[0139] The generated asset building plan is visualized by the server and sent to the terminal. The terminal not only displays the plan as graphs and charts to present it clearly to the user, but also dynamically adjusts the color scheme and design to suit their emotions.

[0140] As a concrete example, if a user sets a goal of "saving 5 million yen in 5 years," and the system's emotion engine recognizes that the user is feeling anxious, the server generates a low-risk savings plan and presents it using calming colors. Furthermore, if the user provides feedback on this plan, the server readjusts the plan based on that information. In this way, the present invention provides flexible and personalized support for asset building that responds to changes in emotions.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The user enters their asset building goals into the device. This includes detailed financial information such as the target asset amount, the planned age at which they will achieve it, and their current savings and income.

[0144] Step 2:

[0145] The terminal sends user input information to the server and analyzes the user's facial expressions and input content through an emotion engine to measure the user's emotional state. This information is also sent to the server.

[0146] Step 3:

[0147] The server retrieves the latest financial information from external sources. Specifically, it collects market trends, interest rates, tax systems, and other data via the internet and stores it in a database.

[0148] Step 4:

[0149] The server uses AI to generate an asset building plan based on the user's financial information and external data. In this process, it takes into account the user's emotional state, as analyzed by an emotion engine. For example, it proposes a low-risk plan to a user who is feeling anxious.

[0150] Step 5:

[0151] The server visually represents the generated asset building plan. This process incorporates graphs and charts, as well as color schemes and designs that respond to the user's emotions.

[0152] Step 6:

[0153] The terminal displays a visualized plan received from the server to the user. Based on this information, the user considers their own asset building strategy.

[0154] Step 7:

[0155] The user enters feedback on the presented plan into their device. If necessary, they can request adjustments to the plan.

[0156] Step 8:

[0157] The device sends user feedback to the server.

[0158] Step 9:

[0159] The server receives feedback and readjusts the plan. If necessary, it reviews the generation plan and repeats steps 4-6.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] In modern society, providing asset building plans that take into account the individual economic situation and emotional state of each user is a challenging task. Furthermore, providing proposals optimized for each user's specific circumstances, rather than generic suggestions, has been difficult with conventional methods. In particular, there are very few systems that consider the influence of user emotions on the acceptance and behavior of asset building plans. As a result, many users find it difficult to obtain the optimal strategy for their economic goals, potentially leading to failure in asset building.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for the user to input target asset figures, means for collecting the user's economic information, means for acquiring external financial information, and means for analyzing the user's emotional state. This makes it possible to propose a personalized asset formation plan that is tailored to the user's economic situation and emotional state.

[0165] A "user" refers to an individual or legal entity that uses the system to set their own economic goals and input and provide the necessary information.

[0166] "Asset figures" refer to the financial goals or amounts that a user aims to achieve within a specific period of time.

[0167] "Economic information" refers to general financial information, including a user's income, expenses, assets, and liabilities.

[0168] "External financial information" refers to data obtained from external sources regarding economic indicators, market trends, interest rates, and tax systems.

[0169] An "asset building plan" refers to a specific strategy or action plan proposed to achieve the user's financial goals.

[0170] "Emotional state" refers to the user's psychological and emotional condition, and by analyzing this, the system provides the underlying data for making personalized suggestions.

[0171] "Suggestions" refer to advice provided to users to support them in achieving their goals by presenting them with economic action guidelines and strategies generated by the system.

[0172] This invention is a comprehensive system for users to effectively achieve their asset building goals. The system comprises a terminal, a server, and an emotion analysis engine.

[0173] Users input asset planning goals and financial information via a terminal. The terminal has a built-in camera and input devices to capture facial expression data and text from the user. Based on this data, the terminal analyzes the user's emotional state and sends the information to the server.

[0174] The server uses intelligent technology to generate an asset building plan based on the user's economic and emotional data transmitted from the terminal. External financial information is also collected during this process, and APIs are used to maintain the integrity and timeliness of the information. The server visually structures the generated plan and presents it in an easy-to-understand format for the user. The suggestions are optimized based on the user's psychological state through emotional analysis. For example, a user experiencing stress will be offered a low-risk, stable savings plan.

[0175] For example, if a user enters "I want to save 5 million yen in 5 years," the system will present a concrete savings plan to achieve that goal. If sentiment analysis detects the user's anxiety, the server will suggest a more conservative investment strategy and present the plan in a calm color scheme.

[0176] Examples of prompts for a generative AI model are as follows:

[0177] "I want to save 5 million yen in 5 years. First, please tell me how much I need to save each month. My current income and expenses are as follows. To alleviate my emotional anxiety, please propose a low-risk plan."

[0178] This system allows users to access an optimal wealth-building plan tailored to their own financial situation and psychological state, making it more likely that they will achieve their wealth-building goals.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The user uses a terminal to input their asset-building goals and financial information. This input includes desired asset figures, target timeframe, income, expenses, and current asset status. The terminal records this information as digital data and prepares to send it to the server.

[0182] Step 2:

[0183] The device uses its built-in camera and sensors to capture the user's facial expressions. The collected visual data is passed to an emotion analysis engine to extract the user's emotional state. The emotional state data is generated as emotion tags (e.g., stress, high motivation) and sent to the server along with other input data.

[0184] Step 3:

[0185] The server receives user economic information and emotional state data sent from the terminal, while simultaneously accessing external financial databases to obtain the latest market trends, interest rates, and economic indicators. During this process, financial data is fetched via APIs, and statistical analysis is performed. The processed external data is then used to generate asset building plans.

[0186] Step 4:

[0187] The server uses collected user information and financial data as input to generate an asset building plan using a generative AI model. Leveraging data analysis and machine learning models, it designs the optimal plan to achieve the user's financial goals. This process includes optimization techniques that consider input emotional data and select a plan appropriate to the user's psychological state.

[0188] Step 5:

[0189] The generated asset building plan is sent from the server to the terminal. The terminal analyzes the received plan and converts it into a visually easy-to-understand format (graphs, charts) for the user. Furthermore, it presents the plan using a function that customizes the color scheme and design according to the user's emotional state. For example, it supports users who are feeling stressed by displaying the plan in calming colors.

[0190] Step 6:

[0191] Users can provide feedback on the presented asset building plan through their device. The device collects this feedback and sends it to a server. The server analyzes the feedback data and readjusts the plan as needed. This readjusted plan is then presented to the user again and optimized to better suit the user's needs.

[0192] (Application Example 2)

[0193] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0194] Traditional asset building systems offered asset building plans using a general approach without considering the user's emotional state. Therefore, they were unable to address situations where users sought different proposals based on their emotional state, making it difficult to achieve optimal asset building. Furthermore, there was a lack of means to provide advice linked to the user's psychological state by utilizing user purchase and payment data. This limited the effectiveness of user asset building.

[0195] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0196] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the asset building plan based on the user's emotional state, and means for performing emotional analysis based on the user's purchase and payment data and providing expenditure analysis and savings plans that are appropriate to the user's psychological state. As a result, the user can receive an asset building plan optimized for their own emotional state, enabling them to manage their expenditures and create savings plans that are suitable for their psychological state.

[0197] "User emotional state" refers to the psychological state of the user and includes data on emotions such as stress and motivation.

[0198] An "asset building plan" is a plan for users to effectively increase their assets, and includes specific investment and savings strategies.

[0199] "Purchase and payment data" refers to information about purchases and payments made by users, and is fundamental data for understanding spending trends.

[0200] "Sentiment analysis" is the process of analyzing data such as facial expressions and language to reveal a user's emotional state.

[0201] "Psychological state-based spending analysis" is a method for evaluating spending patterns and appropriate consumer behavior by taking into account the user's current psychological state.

[0202] A "savings plan" is a set of specific guidelines and policies for systematically saving money in order to achieve long-term wealth accumulation.

[0203] To implement this invention, the server receives user input and generates an asset building plan. The server acquires financial information from external sources and analyzes it in conjunction with the financial information provided by the user. Furthermore, it uses an emotion engine to analyze the user's emotional state, and an AI model proposes an optimal asset building plan to the user based on the emotional data.

[0204] The terminal functions as the user's input interface, enabling goal setting and financial information input. Furthermore, the terminal captures the user's facial expressions in real time and sends emotional data to a server to determine their emotional state. On the terminal, the generated asset building plan is visualized in graphs and charts, with the color scheme and design adjusted according to the user's emotional state.

[0205] The specific technologies used include camera devices and image processing libraries such as OpenCV for facial expression analysis. TensorFlow is used for AI model training and inference, and Pandas and NumPy are used for data analysis. These form the foundation for users to make optimal decisions based on daily accumulated data across various systems.

[0206] As a concrete example, if a user enters "I want to save 5 million yen in 5 years" into a smartphone app, and the emotion engine determines from the user's facial expression that they are in a stressed state, the server will generate a low-risk investment plan and display it on the device using a calming blue color scheme. At this time, the following prompt is entered into the generating AI model: "Based on the user's emotional state and monthly spending data, please generate suggestions to help them achieve their savings goal for the following month."

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The terminal provides a means for users to input their set asset target amount, as well as financial information such as income and expenses. This allows users to input their asset building goals and current status into the terminal. The terminal then formats this information into a database format and sends it to the server.

[0210] Step 2:

[0211] The server retrieves the latest market trends, interest rates, and economic indicators from external financial databases based on the financial information it receives. The server then integrates this information internally to create the foundational data for an asset building plan. Inputs are the user's financial information and external financial information, while output is integrated market data.

[0212] Step 3:

[0213] The device captures the user's facial expressions using a camera and performs analysis using an emotion engine. This quantifies the user's emotional state, and the data is sent to the server. The input is the user's facial expression data, and the output is the analyzed emotional state.

[0214] Step 4:

[0215] The server uses a generative AI model to generate asset building plans based on integrated market data and the user's emotional state. In this process, the AI ​​determines risk tolerance from emotional data and customizes the plan. The input is financial information and emotional state, and the output is a personalized asset building plan.

[0216] Step 5:

[0217] The server sends the generated asset building plan to the terminal, which then displays the plan visually. The display includes features that dynamically adjust the color scheme and design according to the user's emotional state. The user then uses this to consider their own asset building strategy. The input is a personalized asset building plan, and the output is a visualized plan screen.

[0218] Step 6:

[0219] The terminal receives feedback from the user and sends that information to the server. Based on the feedback, the server readjusts the asset building plan as needed. The input is the user's feedback, and the output is the adjusted asset building plan.

[0220] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0236] The system for implementing this invention is designed so that a user can input their asset building goals and receive the optimal plan for achieving those goals. This system mainly consists of two components: a terminal and a server.

[0237] On the terminal, the user enters specific asset-building goals, such as "save 20 million yen in 10 years." Simultaneously, they also enter financial information such as age, annual income, current savings, and existing investment status. This information is then sent to the server.

[0238] The server first researches external financial information, such as the latest interest rates and market trends, via the internet. This allows it to obtain the most up-to-date data necessary for the user to achieve their goals. Next, the server uses AI to generate an optimal asset building plan based on the information received from the user and the information obtained from external sources. This plan includes savings plans, investment strategies, and risk management. The server then converts the generated plan into a visually easy-to-understand format, such as graphs and charts, and sends it to the user's device.

[0239] The terminal displays information received from the server to the user. Based on this, the user can make decisions regarding their asset building. Furthermore, the user can provide feedback on the presented plan, for example, requesting modifications such as "I want to reduce the risk further." This feedback is sent back to the server, which then adjusts and optimizes the plan based on it.

[0240] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server will consider the user's current income and expenses, as well as their projected future expenses, and then present a specific plan such as "maintain monthly savings of 80,000 yen while investing a certain percentage in mutual funds." In this way, the present invention provides an environment in which users can continue to build assets based on a concrete action plan.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user enters their asset-building goals into the terminal. Specifically, they fill in financial data such as the target amount, desired age to achieve it, current savings, and annual income into the input form.

[0244] Step 2:

[0245] The terminal sends information entered by the user to the server. This information includes specific goals and details about the financial situation.

[0246] Step 3:

[0247] The server retrieves external financial information. This includes current interest rates, stock market trends, and inflation rates, which are collected and analyzed from the internet.

[0248] Step 4:

[0249] The server stores the user's financial information and external financial information in a database and prepares to generate an optimal asset building plan based on this information.

[0250] Step 5:

[0251] The server uses AI to generate an optimal asset building plan to help users achieve their goals. This plan includes recommendations for savings amounts, investment allocation, and risk management methods.

[0252] Step 6:

[0253] The server visualizes the generated asset building plan. Specifically, it creates an overview of the plan as graphs and charts, and presents it in a way that is easy for the user to understand.

[0254] Step 7:

[0255] The terminal displays visualization data received from the server to the user. The user then uses this to review their asset building strategy and take appropriate action if necessary.

[0256] Step 8:

[0257] Users input feedback about their asset building plan into the terminal. For example, they might input that they prefer a plan with reduced risk.

[0258] Step 9:

[0259] The device sends user feedback to the server.

[0260] Step 10:

[0261] The server incorporates user feedback and readjusts the plan. Steps 5-7 are repeated as needed to provide the user with an optimized plan.

[0262] (Example 1)

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

[0264] Developing an optimal asset building plan tailored to each individual user's financial situation and market conditions is complex and difficult, and there is a challenge in that it is difficult for ordinary users without specialized knowledge to easily obtain effective methods.

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

[0266] In this invention, the server includes means for collecting and receiving the user's financial information at a terminal, means for acquiring external financial information, means for generating an asset formation plan using a generation AI model, and means for visually converting the generated asset formation plan and presenting it at the terminal. This makes it possible for users to obtain a rational and specific asset formation plan based on market trends and their own financial situation, even without specialized knowledge.

[0267] A "user" is the entity that accesses the system and inputs their own financial information and asset building goals.

[0268] "Asset amount" refers to the specific financial target that the user wishes to achieve.

[0269] "Financial information" refers to data that shows an individual's economic status, including the user's age, annual income, current savings, and existing investment status.

[0270] A "terminal" is an interface device used by users to input information and receive suggestions from a server.

[0271] A "server" is a central processing unit that processes information from users and external data sources and generates asset formation plans using a generated AI model.

[0272] "External financial information" refers to financial data from external sources, such as the latest interest rates and market trends, obtained via the internet.

[0273] A "generative AI model" refers to an artificial intelligence algorithm that generates asset building plans using collected data.

[0274] An "asset building plan" is an actionable proposal that includes savings plans and investment strategies generated based on user input information and external financial information.

[0275] "Visual presentation" means displaying generated information on a device in the form of graphs, charts, or other formats, so that users can understand it intuitively.

[0276] "Feedback" refers to the opinions and suggestions for improvement that users provide regarding the asset building plan presented.

[0277] "Adjusting" means modifying existing asset building plans as needed, taking user feedback into consideration.

[0278] Users access the asset building support system using their own devices. These devices provide an interface for inputting financial information such as target asset amount, age, annual income, current savings, and existing investment status. This allows users to easily send the necessary information to the system.

[0279] The terminal is responsible for transmitting collected user information to the server. The server obtains external financial information via the internet, including the latest interest rates and market trends. This information is used as reference data to generate asset building plans.

[0280] The server is equipped with a generative AI model that generates an optimal asset building plan based on the user's financial information and external financial information. This AI model proposes savings plans and investment strategies tailored to the user's circumstances and has the flexibility to adjust the plan.

[0281] The generated asset building plan is visualized and sent to the device for presentation to the user. The user can evaluate their asset building strategy based on the presented graphs and charts and provide feedback as needed. This feedback is sent back to the server for further adjustments to the plan.

[0282] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server can use an AI model to generate and present a plan such as "maintaining a monthly savings of 80,000 yen while investing a certain percentage in mutual funds."

[0283] An example of a prompt for the generating AI model would be text such as, "I would like a low-risk plan to save 10 million yen by the age of 30." In this way, the user can obtain a specific and realistic asset building plan.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user accesses the system using a terminal and inputs financial information such as the goal of asset formation, age, annual income, current savings amount, and existing investment situation. The input information is organized as data in the terminal and formatted for transmission.

[0287] Step 2:

[0288] The terminal transmits the information input by the user to the server. This data includes the user's goals and financial status. The terminal encrypts the data during the transmission process to ensure it reaches the server safely.

[0289] Step 3:

[0290] The server analyzes the received financial information of the user and obtains external financial-related information via the Internet. This includes the latest interest rates and market trends, etc. This external information is obtained from the API and stored in the database for combination with the user information.

[0291] Step 4:

[0292] The server integrates the user's financial information and external financial-related information and generates an optimal asset formation plan using a generated AI model. The AI model evaluates the input data and presents the most suitable savings plan and investment strategy for the user. As a result, a specific action plan is generated from the data.

[0293] Step 5:

[0294] The generated asset formation plan is visualized within the server. Here, graphs and charts are created using data visualization tools. The visual display converts the information into a form that the user can easily understand.

[0295] Step 6:

[0296] The server sends a visualized plan to the terminal. The terminal displays the received information to the user, presenting a visual plan. The user then makes their own decisions based on this plan.

[0297] Step 7:

[0298] Users provide feedback on the presented plan through their device. For example, they may want to reduce risk or adjust investment ratios. User feedback is collected on the device and sent back to the server.

[0299] Step 8:

[0300] The server receives feedback from the user and uses a generated AI model to readjust the asset building plan. The readjusted plan is further visualized and presented to the user again. This allows for continuous plan improvement tailored to the user's needs.

[0301] (Application Example 1)

[0302] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0303] A challenge is that users often lack access to optimal plans for efficiently achieving their wealth-building goals. Furthermore, financial transactions are complex and cumbersome, and the numerous procedures required for wealth building can lead to a lack of progress. Therefore, it is necessary to provide means to simplify and automate the wealth-building process.

[0304] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for the user to input a target asset amount, means for collecting the user's financial information, means for obtaining external financial-related information, means for generating an asset formation plan, means for visually presenting the generated asset formation plan, and means for automating financial transactions for asset management through e-commerce transaction means. As a result, the user can effectively plan for asset formation, automatically reflect an executable plan in transactions, and efficiently achieve the goal.

[0306] The "means for the user to input a target asset amount" is an interface for the user to set a specific asset goal and provide the numerical value to the system.

[0307] The "means for collecting the user's financial information" is a function for obtaining financial data such as the user's age, annual income, savings amount, and existing investment status.

[0308] The "means for obtaining external financial-related information" is a function for obtaining external financial market information such as market trends and interest rates through the Internet.

[0309] The "means for generating an asset formation plan" is a function for creating a plan for goal achievement by AI using the user's financial information and external financial information.

[0310] The "means for visually presenting the generated asset formation plan" is a function for displaying the generated plan in graphs and charts so that the user can easily understand it.

[0311] The "means for automating financial transactions for asset management through e-commerce transaction means" is a function for automating the execution of investments and savings based on the user's asset formation plan through an online transaction platform.

[0312] The system for implementing the present invention provides an integrated approach to efficiently achieve asset formation goals. Users can input specific asset formation goals and associated financial information using a terminal. Specifically, this includes age, annual income, savings amount, and existing investment status.

[0313] Information entered on the terminal is sent to the server. The server processes the collected financial information using programming languages ​​such as Python. Furthermore, it uses external financial APIs to collect the latest market information such as interest rates and stock prices. This collection process often utilizes APIs such as the Bloomberg API and the Reuters API.

[0314] The server combines acquired external data with user data and generates an optimal asset building plan using AI models such as TensorFlow. The generated plan is then converted into graphs and charts using visualization tools such as D3.js and presented to the user.

[0315] Furthermore, it integrates APIs for e-commerce services such as Stripe and PayPal, enabling automated execution of financial transactions based on asset building plans. This allows users to efficiently conduct financial transactions based on concrete and actionable plans.

[0316] For example, if a user chooses a plan to save a small portion of their monthly disposable income and invest the surplus in low-risk mutual funds, this system will automatically transfer the monthly savings to the investment account and purchase appropriate financial products. By using prompts such as, "Please suggest an investment plan that allows me to build wealth with minimal risk while slightly reducing my monthly expenses," more personalized suggestions can be obtained from the generating AI model.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user enters their asset building goals and financial information on the terminal. The entered data includes the user's annual income, savings, and existing investment status, and the next processing steps are based on this information. The entered data is temporarily stored on the terminal and prepared to be sent to the server.

[0320] Step 2:

[0321] The terminal sends the collected user information to the server. The server receives this data and stores it in a database. The database uses a relational database management system (RDBMS), such as SQL, to effectively manage information for multiple users.

[0322] Step 3:

[0323] The server accesses financial APIs to retrieve external financial information. This information includes current interest rates, market trends, and stock prices. This data is obtained via the API using Python's request library and stored in a database.

[0324] Step 4:

[0325] The server combines user data and external financial information and inputs it into an AI model. The AI ​​model uses TensorFlow to generate an optimal asset building plan based on this data. The plan includes recommended savings amounts and investment percentages. The generated plan is temporarily stored in an internal data format.

[0326] Step 5:

[0327] The server uses visualization tools such as D3.js to create graphs and charts to visually represent the generated asset formation plan. The generated graphs are exported in HTML format and sent to the user's terminal.

[0328] Step 6:

[0329] The terminal displays visual information sent from the server in the browser. Based on this, the user can review their asset building plan. The visual information is provided in a user-interface format that can be manipulated.

[0330] Step 7:

[0331] Users input feedback on their asset building plan from their device. For example, they might input a request such as "I want to reduce risk" and send it to the server via their device.

[0332] Step 8:

[0333] The server receives user feedback and adjusts the asset building plan accordingly. It then reruns the AI ​​model, generates the adjusted plan, and saves it.

[0334] Step 9:

[0335] The server uses e-commerce tools to automate financial transactions based on a coordinated asset building plan. It utilizes APIs from Stripe and PayPal to execute the user's investment plan, resulting in efficient asset management.

[0336] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0337] This invention is a comprehensive system for users to effectively achieve their asset building goals, and in particular, by incorporating an emotion engine, it enables the provision of asset building plans that take into account the user's emotional state. The system mainly consists of a terminal, a server, and an emotion engine.

[0338] Through the terminal, users input their asset-building goals and provide financial information. This includes information such as the amount they want to achieve, the timeframe, their current asset status, income, and expenses. The terminal also provides an interface for acquiring data on the user's emotions. Here, the system can understand the user's current emotional state by analyzing their facial expressions and input content.

[0339] The server receives this information and first retrieves the latest financial information from external sources. This process takes into account market trends, interest rates, economic indicators, tax systems, etc. Based on the retrieved data and the information entered by the user, the server generates an asset building plan using artificial intelligence (AI). This AI incorporates emotional data analyzed by an emotion engine, enabling it to make suggestions tailored to the user's current psychological state. Specifically, it presents risk-reducing plans to users with high stress levels, and conversely, plans that include aggressive investments to highly motivated users.

[0340] The generated asset building plan is visualized by the server and sent to the terminal. The terminal not only displays the plan as graphs and charts to present it clearly to the user, but also dynamically adjusts the color scheme and design to suit their emotions.

[0341] As a concrete example, if a user sets a goal of "saving 5 million yen in 5 years," and the system's emotion engine recognizes that the user is feeling anxious, the server generates a low-risk savings plan and presents it using calming colors. Furthermore, if the user provides feedback on this plan, the server readjusts the plan based on that information. In this way, the present invention provides flexible and personalized support for asset building that responds to changes in emotions.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user enters their asset building goals into the device. This includes detailed financial information such as the target asset amount, the planned age at which they will achieve it, and their current savings and income.

[0345] Step 2:

[0346] The terminal sends user input information to the server and analyzes the user's facial expressions and input content through an emotion engine to measure the user's emotional state. This information is also sent to the server.

[0347] Step 3:

[0348] The server retrieves the latest financial information from external sources. Specifically, it collects market trends, interest rates, tax systems, and other data via the internet and stores it in a database.

[0349] Step 4:

[0350] The server uses AI to generate an asset building plan based on the user's financial information and external data. In this process, it takes into account the user's emotional state, as analyzed by an emotion engine. For example, it proposes a low-risk plan to a user who is feeling anxious.

[0351] Step 5:

[0352] The server visually represents the generated asset building plan. This process incorporates graphs and charts, as well as color schemes and designs that respond to the user's emotions.

[0353] Step 6:

[0354] The terminal displays a visualized plan received from the server to the user. Based on this information, the user considers their own asset building strategy.

[0355] Step 7:

[0356] The user enters feedback on the presented plan into their device. If necessary, they can request adjustments to the plan.

[0357] Step 8:

[0358] The device sends user feedback to the server.

[0359] Step 9:

[0360] The server receives feedback and readjusts the plan. If necessary, it reviews the generation plan and repeats steps 4-6.

[0361] (Example 2)

[0362] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0363] In modern society, providing asset building plans that take into account the individual economic situation and emotional state of each user is a challenging task. Furthermore, providing proposals optimized for each user's specific circumstances, rather than generic suggestions, has been difficult with conventional methods. In particular, there are very few systems that consider the influence of user emotions on the acceptance and behavior of asset building plans. As a result, many users find it difficult to obtain the optimal strategy for their economic goals, potentially leading to failure in asset building.

[0364] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0365] In this invention, the server includes means for the user to input target asset figures, means for collecting the user's economic information, means for acquiring external financial information, and means for analyzing the user's emotional state. This makes it possible to propose a personalized asset formation plan that is tailored to the user's economic situation and emotional state.

[0366] A "user" refers to an individual or legal entity that uses the system to set their own economic goals and input and provide the necessary information.

[0367] "Asset figures" refer to the financial goals or amounts that a user aims to achieve within a specific period of time.

[0368] "Economic information" refers to general financial information, including a user's income, expenses, assets, and liabilities.

[0369] "External financial information" refers to data obtained from external sources regarding economic indicators, market trends, interest rates, and tax systems.

[0370] An "asset building plan" refers to a specific strategy or action plan proposed to achieve the user's financial goals.

[0371] "Emotional state" refers to the user's psychological and emotional condition, and by analyzing this, the system provides the underlying data for making personalized suggestions.

[0372] "Suggestions" refer to advice provided to users to support them in achieving their goals by presenting them with economic action guidelines and strategies generated by the system.

[0373] This invention is a comprehensive system for users to effectively achieve their asset building goals. The system comprises a terminal, a server, and an emotion analysis engine.

[0374] Users input asset planning goals and financial information via a terminal. The terminal has a built-in camera and input devices to capture facial expression data and text from the user. Based on this data, the terminal analyzes the user's emotional state and sends the information to the server.

[0375] The server uses intelligent technology to generate an asset building plan based on the user's economic and emotional data transmitted from the terminal. External financial information is also collected during this process, and APIs are used to maintain the integrity and timeliness of the information. The server visually structures the generated plan and presents it in an easy-to-understand format for the user. The suggestions are optimized based on the user's psychological state through emotional analysis. For example, a user experiencing stress will be offered a low-risk, stable savings plan.

[0376] For example, if a user enters "I want to save 5 million yen in 5 years," the system will present a concrete savings plan to achieve that goal. If sentiment analysis detects the user's anxiety, the server will suggest a more conservative investment strategy and present the plan in a calm color scheme.

[0377] Examples of prompts for a generative AI model are as follows:

[0378] "I want to save 5 million yen in 5 years. First, please tell me how much I need to save each month. My current income and expenses are as follows. To alleviate my emotional anxiety, please propose a low-risk plan."

[0379] This system allows users to access an optimal wealth-building plan tailored to their own financial situation and psychological state, making it more likely that they will achieve their wealth-building goals.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The user uses a terminal to input their asset-building goals and financial information. This input includes desired asset figures, target timeframe, income, expenses, and current asset status. The terminal records this information as digital data and prepares to send it to the server.

[0383] Step 2:

[0384] The device uses its built-in camera and sensors to capture the user's facial expressions. The collected visual data is passed to an emotion analysis engine to extract the user's emotional state. The emotional state data is generated as emotion tags (e.g., stress, high motivation) and sent to the server along with other input data.

[0385] Step 3:

[0386] The server receives user economic information and emotional state data sent from the terminal, while simultaneously accessing external financial databases to obtain the latest market trends, interest rates, and economic indicators. During this process, financial data is fetched via APIs, and statistical analysis is performed. The processed external data is then used to generate asset building plans.

[0387] Step 4:

[0388] The server uses collected user information and financial data as input to generate an asset building plan using a generative AI model. Leveraging data analysis and machine learning models, it designs the optimal plan to achieve the user's financial goals. This process includes optimization techniques that consider input emotional data and select a plan appropriate to the user's psychological state.

[0389] Step 5:

[0390] The generated asset building plan is sent from the server to the terminal. The terminal analyzes the received plan and converts it into a visually easy-to-understand format (graphs, charts) for the user. Furthermore, it presents the plan using a function that customizes the color scheme and design according to the user's emotional state. For example, it supports users who are feeling stressed by displaying the plan in calming colors.

[0391] Step 6:

[0392] Users can provide feedback on the presented asset building plan through their device. The device collects this feedback and sends it to a server. The server analyzes the feedback data and readjusts the plan as needed. This readjusted plan is then presented to the user again and optimized to better suit the user's needs.

[0393] (Application Example 2)

[0394] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0395] Traditional asset building systems offered asset building plans using a general approach without considering the user's emotional state. Therefore, they were unable to address situations where users sought different proposals based on their emotional state, making it difficult to achieve optimal asset building. Furthermore, there was a lack of means to provide advice linked to the user's psychological state by utilizing user purchase and payment data. This limited the effectiveness of user asset building.

[0396] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0397] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the asset building plan based on the user's emotional state, and means for performing emotional analysis based on the user's purchase and payment data and providing expenditure analysis and savings plans that are appropriate to the user's psychological state. As a result, the user can receive an asset building plan optimized for their own emotional state, enabling them to manage their expenditures and create savings plans that are suitable for their psychological state.

[0398] "User emotional state" refers to the psychological state of the user and includes data on emotions such as stress and motivation.

[0399] An "asset building plan" is a plan for users to effectively increase their assets, and includes specific investment and savings strategies.

[0400] "Purchase and payment data" refers to information about purchases and payments made by users, and is fundamental data for understanding spending trends.

[0401] "Sentiment analysis" is the process of analyzing data such as facial expressions and language to reveal a user's emotional state.

[0402] "Psychological state-based spending analysis" is a method for evaluating spending patterns and appropriate consumer behavior by taking into account the user's current psychological state.

[0403] A "savings plan" is a set of specific guidelines and policies for systematically saving money in order to achieve long-term wealth accumulation.

[0404] To implement this invention, the server receives user input and generates an asset building plan. The server acquires financial information from external sources and analyzes it in conjunction with the financial information provided by the user. Furthermore, it uses an emotion engine to analyze the user's emotional state, and an AI model proposes an optimal asset building plan to the user based on the emotional data.

[0405] The terminal functions as the user's input interface, enabling goal setting and financial information input. Furthermore, the terminal captures the user's facial expressions in real time and sends emotional data to a server to determine their emotional state. On the terminal, the generated asset building plan is visualized in graphs and charts, with the color scheme and design adjusted according to the user's emotional state.

[0406] The specific technologies used include camera devices and image processing libraries such as OpenCV for facial expression analysis. TensorFlow is used for AI model training and inference, and Pandas and NumPy are used for data analysis. These form the foundation for users to make optimal decisions based on daily accumulated data across various systems.

[0407] As a concrete example, if a user enters "I want to save 5 million yen in 5 years" into a smartphone app, and the emotion engine determines from the user's facial expression that they are in a stressed state, the server will generate a low-risk investment plan and display it on the device using a calming blue color scheme. At this time, the following prompt is entered into the generating AI model: "Based on the user's emotional state and monthly spending data, please generate suggestions to help them achieve their savings goal for the following month."

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The terminal provides a means for users to input their set asset target amount, as well as financial information such as income and expenses. This allows users to input their asset building goals and current status into the terminal. The terminal then formats this information into a database format and sends it to the server.

[0411] Step 2:

[0412] The server retrieves the latest market trends, interest rates, and economic indicators from external financial databases based on the financial information it receives. The server then integrates this information internally to create the foundational data for an asset building plan. Inputs are the user's financial information and external financial information, while output is integrated market data.

[0413] Step 3:

[0414] The device captures the user's facial expressions using a camera and performs analysis using an emotion engine. This quantifies the user's emotional state, and the data is sent to the server. The input is the user's facial expression data, and the output is the analyzed emotional state.

[0415] Step 4:

[0416] The server uses a generative AI model to generate asset building plans based on integrated market data and the user's emotional state. In this process, the AI ​​determines risk tolerance from emotional data and customizes the plan. The input is financial information and emotional state, and the output is a personalized asset building plan.

[0417] Step 5:

[0418] The server sends the generated asset building plan to the terminal, which then displays the plan visually. The display includes features that dynamically adjust the color scheme and design according to the user's emotional state. The user then uses this to consider their own asset building strategy. The input is a personalized asset building plan, and the output is a visualized plan screen.

[0419] Step 6:

[0420] The terminal receives feedback from the user and sends that information to the server. Based on the feedback, the server readjusts the asset building plan as needed. The input is the user's feedback, and the output is the adjusted asset building plan.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0422] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0427] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0429] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0431] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0432] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0433] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0434] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0436] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0437] The system for implementing this invention is designed so that a user can input their asset building goals and receive the optimal plan for achieving those goals. This system mainly consists of two components: a terminal and a server.

[0438] On the terminal, the user enters specific asset-building goals, such as "save 20 million yen in 10 years." Simultaneously, they also enter financial information such as age, annual income, current savings, and existing investment status. This information is then sent to the server.

[0439] The server first researches external financial information, such as the latest interest rates and market trends, via the internet. This allows it to obtain the most up-to-date data necessary for the user to achieve their goals. Next, the server uses AI to generate an optimal asset building plan based on the information received from the user and the information obtained from external sources. This plan includes savings plans, investment strategies, and risk management. The server then converts the generated plan into a visually easy-to-understand format, such as graphs and charts, and sends it to the user's device.

[0440] The terminal displays information received from the server to the user. Based on this, the user can make decisions regarding their asset building. Furthermore, the user can provide feedback on the presented plan, for example, requesting modifications such as "I want to reduce the risk further." This feedback is sent back to the server, which then adjusts and optimizes the plan based on it.

[0441] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server will consider the user's current income and expenses, as well as their projected future expenses, and then present a specific plan such as "maintain monthly savings of 80,000 yen while investing a certain percentage in mutual funds." In this way, the present invention provides an environment in which users can continue to build assets based on a concrete action plan.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The user enters their asset-building goals into the terminal. Specifically, they fill in financial data such as the target amount, desired age to achieve it, current savings, and annual income into the input form.

[0445] Step 2:

[0446] The terminal sends information entered by the user to the server. This information includes specific goals and details about the financial situation.

[0447] Step 3:

[0448] The server retrieves external financial information. This includes current interest rates, stock market trends, and inflation rates, which are collected and analyzed from the internet.

[0449] Step 4:

[0450] The server stores the user's financial information and external financial information in a database and prepares to generate an optimal asset building plan based on this information.

[0451] Step 5:

[0452] The server uses AI to generate an optimal asset building plan to help users achieve their goals. This plan includes recommendations for savings amounts, investment allocation, and risk management methods.

[0453] Step 6:

[0454] The server visualizes the generated asset building plan. Specifically, it creates an overview of the plan as graphs and charts, and presents it in a way that is easy for the user to understand.

[0455] Step 7:

[0456] The terminal displays visualization data received from the server to the user. The user then uses this to review their asset building strategy and take appropriate action if necessary.

[0457] Step 8:

[0458] Users input feedback about their asset building plan into the terminal. For example, they might input that they prefer a plan with reduced risk.

[0459] Step 9:

[0460] The device sends user feedback to the server.

[0461] Step 10:

[0462] The server incorporates user feedback and readjusts the plan. Steps 5-7 are repeated as needed to provide the user with an optimized plan.

[0463] (Example 1)

[0464] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0465] Developing an optimal asset building plan tailored to each individual user's financial situation and market conditions is complex and difficult, and there is a challenge in that it is difficult for ordinary users without specialized knowledge to easily obtain effective methods.

[0466] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0467] In this invention, the server includes means for collecting and receiving the user's financial information at a terminal, means for acquiring external financial information, means for generating an asset formation plan using a generation AI model, and means for visually converting the generated asset formation plan and presenting it at the terminal. This makes it possible for users to obtain a rational and specific asset formation plan based on market trends and their own financial situation, even without specialized knowledge.

[0468] A "user" is the entity that accesses the system and inputs their own financial information and asset building goals.

[0469] "Asset amount" refers to the specific financial target that the user wishes to achieve.

[0470] "Financial information" refers to data that shows an individual's economic status, including the user's age, annual income, current savings, and existing investment status.

[0471] A "terminal" is an interface device used by users to input information and receive suggestions from a server.

[0472] A "server" is a central processing unit that processes information from users and external data sources and generates asset formation plans using a generated AI model.

[0473] "External financial information" refers to financial data from external sources, such as the latest interest rates and market trends, obtained via the internet.

[0474] A "generative AI model" refers to an artificial intelligence algorithm that generates asset building plans using collected data.

[0475] An "asset building plan" is an actionable proposal that includes savings plans and investment strategies generated based on user input information and external financial information.

[0476] "Visual presentation" means displaying generated information on a device in the form of graphs, charts, or other formats, so that users can understand it intuitively.

[0477] "Feedback" refers to the opinions and suggestions for improvement that users provide regarding the asset building plan presented.

[0478] "Adjusting" means modifying existing asset building plans as needed, taking user feedback into consideration.

[0479] Users access the asset building support system using their own devices. These devices provide an interface for inputting financial information such as target asset amount, age, annual income, current savings, and existing investment status. This allows users to easily send the necessary information to the system.

[0480] The terminal is responsible for transmitting collected user information to the server. The server obtains external financial information via the internet, including the latest interest rates and market trends. This information is used as reference data to generate asset building plans.

[0481] The server is equipped with a generative AI model that generates an optimal asset building plan based on the user's financial information and external financial information. This AI model proposes savings plans and investment strategies tailored to the user's circumstances and has the flexibility to adjust the plan.

[0482] The generated asset building plan is visualized and sent to the device for presentation to the user. The user can evaluate their asset building strategy based on the presented graphs and charts and provide feedback as needed. This feedback is sent back to the server for further adjustments to the plan.

[0483] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server can use an AI model to generate and present a plan such as "maintaining a monthly savings of 80,000 yen while investing a certain percentage in mutual funds."

[0484] An example of a prompt for the generating AI model would be text such as, "I would like a low-risk plan to save 10 million yen by the age of 30." In this way, the user can obtain a specific and realistic asset building plan.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] Users access the system using a terminal and input financial information such as their asset building goals, age, annual income, current savings, and existing investment status. The entered information is organized as data within the terminal and formatted in preparation for transmission.

[0488] Step 2:

[0489] The device sends the information entered by the user to the server. This data includes the user's goals and financial status. The device encrypts the data during transmission to ensure it reaches the server securely.

[0490] Step 3:

[0491] The server analyzes the user's financial information and retrieves external financial information via the internet. This includes the latest interest rates and market trends. This external information is obtained via API and stored in a database for combination with user information.

[0492] Step 4:

[0493] The server integrates the user's financial information with external financial information and generates an optimal asset building plan using a generative AI model. The AI ​​model evaluates the input data and presents the most suitable savings plan and investment strategy for the user. This generates a concrete action plan from the data.

[0494] Step 5:

[0495] The generated asset building plan is visualized on the server. Here, data visualization tools are used to create graphs and charts. The visual representation transforms the information into a format that users can easily understand.

[0496] Step 6:

[0497] The server sends a visualized plan to the terminal. The terminal displays the received information to the user, presenting a visual plan. The user then makes their own decisions based on this plan.

[0498] Step 7:

[0499] Users provide feedback on the presented plan through their device. For example, they may want to reduce risk or adjust investment ratios. User feedback is collected on the device and sent back to the server.

[0500] Step 8:

[0501] The server receives feedback from the user and uses a generated AI model to readjust the asset building plan. The readjusted plan is further visualized and presented to the user again. This allows for continuous plan improvement tailored to the user's needs.

[0502] (Application Example 1)

[0503] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0504] A challenge is that users often lack access to optimal plans for efficiently achieving their wealth-building goals. Furthermore, financial transactions are complex and cumbersome, and the numerous procedures required for wealth building can lead to a lack of progress. Therefore, it is necessary to provide means to simplify and automate the wealth-building process.

[0505] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0506] In this invention, the server includes means for the user to input a target asset amount, means for collecting the user's financial information, means for acquiring external financial information, means for generating an asset formation plan, means for visually presenting the generated asset formation plan, and means for automating financial transactions for asset management through electronic commerce means. This enables the user to effectively plan their asset formation, automatically reflect an actionable plan in transactions, and efficiently achieve their goals.

[0507] "A means for users to input their target asset amount" refers to an interface that allows users to set specific asset goals and provide those figures to the system.

[0508] "Means for collecting user financial information" refers to functions for obtaining financial data such as the user's age, annual income, savings, and existing investment status.

[0509] "Means of obtaining external financial information" refers to functions for obtaining external financial market information, such as market trends and interest rates, via the internet.

[0510] The "means for generating asset formation plans" refer to a function that uses AI to create a plan for achieving goals, based on the user's financial information and external financial information.

[0511] "Means of visually presenting the generated asset formation plan" refers to a function that displays the generated plan using graphs and charts to make it easier for the user to understand.

[0512] "Means of automating financial transactions for asset management through electronic commerce" refers to a function that automates the execution of investments and savings based on a user's asset formation plan via an online trading platform.

[0513] The system for implementing the present invention provides an integrated approach to efficiently achieve asset formation goals. Users can input specific asset formation goals and associated financial information using a terminal. Specifically, this includes age, annual income, savings amount, and existing investment status.

[0514] Information entered on the terminal is sent to the server. The server processes the collected financial information using programming languages ​​such as Python. Furthermore, it uses external financial APIs to collect the latest market information such as interest rates and stock prices. This collection process often utilizes APIs such as the Bloomberg API and the Reuters API.

[0515] The server combines acquired external data with user data and generates an optimal asset building plan using AI models such as TensorFlow. The generated plan is then converted into graphs and charts using visualization tools such as D3.js and presented to the user.

[0516] Furthermore, it integrates APIs for e-commerce services such as Stripe and PayPal, enabling automated execution of financial transactions based on asset building plans. This allows users to efficiently conduct financial transactions based on concrete and actionable plans.

[0517] For example, if a user chooses a plan to save a small portion of their monthly disposable income and invest the surplus in low-risk mutual funds, this system will automatically transfer the monthly savings to the investment account and purchase appropriate financial products. By using prompts such as, "Please suggest an investment plan that allows me to build wealth with minimal risk while slightly reducing my monthly expenses," more personalized suggestions can be obtained from the generating AI model.

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The user enters their asset building goals and financial information on the terminal. The entered data includes the user's annual income, savings, and existing investment status, and the next processing steps are based on this information. The entered data is temporarily stored on the terminal and prepared to be sent to the server.

[0521] Step 2:

[0522] The terminal sends the collected user information to the server. The server receives this data and stores it in a database. The database uses a relational database management system (RDBMS), such as SQL, to effectively manage information for multiple users.

[0523] Step 3:

[0524] The server accesses financial APIs to retrieve external financial information. This information includes current interest rates, market trends, and stock prices. This data is obtained via the API using Python's request library and stored in a database.

[0525] Step 4:

[0526] The server combines user data and external financial information and inputs it into an AI model. The AI ​​model uses TensorFlow to generate an optimal asset building plan based on this data. The plan includes recommended savings amounts and investment percentages. The generated plan is temporarily stored in an internal data format.

[0527] Step 5:

[0528] The server uses visualization tools such as D3.js to create graphs and charts to visually represent the generated asset formation plan. The generated graphs are exported in HTML format and sent to the user's terminal.

[0529] Step 6:

[0530] The terminal displays visual information sent from the server in the browser. Based on this, the user can review their asset building plan. The visual information is provided in a user-interface format that can be manipulated.

[0531] Step 7:

[0532] Users input feedback on their asset building plan from their device. For example, they might input a request such as "I want to reduce risk" and send it to the server via their device.

[0533] Step 8:

[0534] The server receives user feedback and adjusts the asset building plan accordingly. It then reruns the AI ​​model, generates the adjusted plan, and saves it.

[0535] Step 9:

[0536] The server uses e-commerce tools to automate financial transactions based on a coordinated asset building plan. It utilizes APIs from Stripe and PayPal to execute the user's investment plan, resulting in efficient asset management.

[0537] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0538] This invention is a comprehensive system for users to effectively achieve their asset building goals, and in particular, by incorporating an emotion engine, it enables the provision of asset building plans that take into account the user's emotional state. The system mainly consists of a terminal, a server, and an emotion engine.

[0539] Through the terminal, users input their asset-building goals and provide financial information. This includes information such as the amount they want to achieve, the timeframe, their current asset status, income, and expenses. The terminal also provides an interface for acquiring data on the user's emotions. Here, the system can understand the user's current emotional state by analyzing their facial expressions and input content.

[0540] The server receives this information and first retrieves the latest financial information from external sources. This process takes into account market trends, interest rates, economic indicators, tax systems, etc. Based on the retrieved data and the information entered by the user, the server generates an asset building plan using artificial intelligence (AI). This AI incorporates emotional data analyzed by an emotion engine, enabling it to make suggestions tailored to the user's current psychological state. Specifically, it presents risk-reducing plans to users with high stress levels, and conversely, plans that include aggressive investments to highly motivated users.

[0541] The generated asset building plan is visualized by the server and sent to the terminal. The terminal not only displays the plan as graphs and charts to present it clearly to the user, but also dynamically adjusts the color scheme and design to suit their emotions.

[0542] As a concrete example, if a user sets a goal of "saving 5 million yen in 5 years," and the system's emotion engine recognizes that the user is feeling anxious, the server generates a low-risk savings plan and presents it using calming colors. Furthermore, if the user provides feedback on this plan, the server readjusts the plan based on that information. In this way, the present invention provides flexible and personalized support for asset building that responds to changes in emotions.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The user enters their asset building goals into the device. This includes detailed financial information such as the target asset amount, the planned age at which they will achieve it, and their current savings and income.

[0546] Step 2:

[0547] The terminal sends user input information to the server and analyzes the user's facial expressions and input content through an emotion engine to measure the user's emotional state. This information is also sent to the server.

[0548] Step 3:

[0549] The server retrieves the latest financial information from external sources. Specifically, it collects market trends, interest rates, tax systems, and other data via the internet and stores it in a database.

[0550] Step 4:

[0551] The server uses AI to generate an asset building plan based on the user's financial information and external data. In this process, it takes into account the user's emotional state, as analyzed by an emotion engine. For example, it proposes a low-risk plan to a user who is feeling anxious.

[0552] Step 5:

[0553] The server visually represents the generated asset building plan. This process incorporates graphs and charts, as well as color schemes and designs that respond to the user's emotions.

[0554] Step 6:

[0555] The terminal displays a visualized plan received from the server to the user. Based on this information, the user considers their own asset building strategy.

[0556] Step 7:

[0557] The user enters feedback on the presented plan into their device. If necessary, they can request adjustments to the plan.

[0558] Step 8:

[0559] The device sends user feedback to the server.

[0560] Step 9:

[0561] The server receives feedback and readjusts the plan. If necessary, it reviews the generation plan and repeats steps 4-6.

[0562] (Example 2)

[0563] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] In modern society, providing asset building plans that take into account the individual economic situation and emotional state of each user is a challenging task. Furthermore, providing proposals optimized for each user's specific circumstances, rather than generic suggestions, has been difficult with conventional methods. In particular, there are very few systems that consider the influence of user emotions on the acceptance and behavior of asset building plans. As a result, many users find it difficult to obtain the optimal strategy for their economic goals, potentially leading to failure in asset building.

[0565] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0566] In this invention, the server includes means for the user to input target asset figures, means for collecting the user's economic information, means for acquiring external financial information, and means for analyzing the user's emotional state. This makes it possible to propose a personalized asset formation plan that is tailored to the user's economic situation and emotional state.

[0567] A "user" refers to an individual or legal entity that uses the system to set their own economic goals and input and provide the necessary information.

[0568] "Asset figures" refer to the financial goals or amounts that a user aims to achieve within a specific period of time.

[0569] "Economic information" refers to general financial information, including a user's income, expenses, assets, and liabilities.

[0570] "External financial information" refers to data obtained from external sources regarding economic indicators, market trends, interest rates, and tax systems.

[0571] An "asset building plan" refers to a specific strategy or action plan proposed to achieve the user's financial goals.

[0572] "Emotional state" refers to the user's psychological and emotional condition, and by analyzing this, the system provides the underlying data for making personalized suggestions.

[0573] "Suggestions" refer to advice provided to users to support them in achieving their goals by presenting them with economic action guidelines and strategies generated by the system.

[0574] This invention is a comprehensive system for users to effectively achieve their asset building goals. The system comprises a terminal, a server, and an emotion analysis engine.

[0575] Users input asset planning goals and financial information via a terminal. The terminal has a built-in camera and input devices to capture facial expression data and text from the user. Based on this data, the terminal analyzes the user's emotional state and sends the information to the server.

[0576] The server uses intelligent technology to generate an asset building plan based on the user's economic and emotional data transmitted from the terminal. External financial information is also collected during this process, and APIs are used to maintain the integrity and timeliness of the information. The server visually structures the generated plan and presents it in an easy-to-understand format for the user. The suggestions are optimized based on the user's psychological state through emotional analysis. For example, a user experiencing stress will be offered a low-risk, stable savings plan.

[0577] For example, if a user enters "I want to save 5 million yen in 5 years," the system will present a concrete savings plan to achieve that goal. If sentiment analysis detects the user's anxiety, the server will suggest a more conservative investment strategy and present the plan in a calm color scheme.

[0578] Examples of prompts for a generative AI model are as follows:

[0579] "I want to save 5 million yen in 5 years. First, please tell me how much I need to save each month. My current income and expenses are as follows. To alleviate my emotional anxiety, please propose a low-risk plan."

[0580] This system allows users to access an optimal wealth-building plan tailored to their own financial situation and psychological state, making it more likely that they will achieve their wealth-building goals.

[0581] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0582] Step 1:

[0583] The user uses a terminal to input their asset-building goals and financial information. This input includes desired asset figures, target timeframe, income, expenses, and current asset status. The terminal records this information as digital data and prepares to send it to the server.

[0584] Step 2:

[0585] The device uses its built-in camera and sensors to capture the user's facial expressions. The collected visual data is passed to an emotion analysis engine to extract the user's emotional state. The emotional state data is generated as emotion tags (e.g., stress, high motivation) and sent to the server along with other input data.

[0586] Step 3:

[0587] The server receives user economic information and emotional state data sent from the terminal, while simultaneously accessing external financial databases to obtain the latest market trends, interest rates, and economic indicators. During this process, financial data is fetched via APIs, and statistical analysis is performed. The processed external data is then used to generate asset building plans.

[0588] Step 4:

[0589] The server uses collected user information and financial data as input to generate an asset building plan using a generative AI model. Leveraging data analysis and machine learning models, it designs the optimal plan to achieve the user's financial goals. This process includes optimization techniques that consider input emotional data and select a plan appropriate to the user's psychological state.

[0590] Step 5:

[0591] The generated asset building plan is sent from the server to the terminal. The terminal analyzes the received plan and converts it into a visually easy-to-understand format (graphs, charts) for the user. Furthermore, it presents the plan using a function that customizes the color scheme and design according to the user's emotional state. For example, it supports users who are feeling stressed by displaying the plan in calming colors.

[0592] Step 6:

[0593] Users can provide feedback on the presented asset building plan through their device. The device collects this feedback and sends it to a server. The server analyzes the feedback data and readjusts the plan as needed. This readjusted plan is then presented to the user again and optimized to better suit the user's needs.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0596] Traditional asset building systems offered asset building plans using a general approach without considering the user's emotional state. Therefore, they were unable to address situations where users sought different proposals based on their emotional state, making it difficult to achieve optimal asset building. Furthermore, there was a lack of means to provide advice linked to the user's psychological state by utilizing user purchase and payment data. This limited the effectiveness of user asset building.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0598] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the asset building plan based on the user's emotional state, and means for performing emotional analysis based on the user's purchase and payment data and providing expenditure analysis and savings plans that are appropriate to the user's psychological state. As a result, the user can receive an asset building plan optimized for their own emotional state, enabling them to manage their expenditures and create savings plans that are suitable for their psychological state.

[0599] "User emotional state" refers to the psychological state of the user and includes data on emotions such as stress and motivation.

[0600] An "asset building plan" is a plan for users to effectively increase their assets, and includes specific investment and savings strategies.

[0601] "Purchase and payment data" refers to information about purchases and payments made by users, and is fundamental data for understanding spending trends.

[0602] "Sentiment analysis" is the process of analyzing data such as facial expressions and language to reveal a user's emotional state.

[0603] "Psychological state-based spending analysis" is a method for evaluating spending patterns and appropriate consumer behavior by taking into account the user's current psychological state.

[0604] A "savings plan" is a set of specific guidelines and policies for systematically saving money in order to achieve long-term wealth accumulation.

[0605] To implement this invention, the server receives user input and generates an asset building plan. The server acquires financial information from external sources and analyzes it in conjunction with the financial information provided by the user. Furthermore, it uses an emotion engine to analyze the user's emotional state, and an AI model proposes an optimal asset building plan to the user based on the emotional data.

[0606] The terminal functions as the user's input interface, enabling goal setting and financial information input. Furthermore, the terminal captures the user's facial expressions in real time and sends emotional data to a server to determine their emotional state. On the terminal, the generated asset building plan is visualized in graphs and charts, with the color scheme and design adjusted according to the user's emotional state.

[0607] The specific technologies used include camera devices and image processing libraries such as OpenCV for facial expression analysis. TensorFlow is used for AI model training and inference, and Pandas and NumPy are used for data analysis. These form the foundation for users to make optimal decisions based on daily accumulated data across various systems.

[0608] As a concrete example, if a user enters "I want to save 5 million yen in 5 years" into a smartphone app, and the emotion engine determines from the user's facial expression that they are in a stressed state, the server will generate a low-risk investment plan and display it on the device using a calming blue color scheme. At this time, the following prompt is entered into the generating AI model: "Based on the user's emotional state and monthly spending data, please generate suggestions to help them achieve their savings goal for the following month."

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The terminal provides a means for users to input their set asset target amount, as well as financial information such as income and expenses. This allows users to input their asset building goals and current status into the terminal. The terminal then formats this information into a database format and sends it to the server.

[0612] Step 2:

[0613] The server retrieves the latest market trends, interest rates, and economic indicators from external financial databases based on the financial information it receives. The server then integrates this information internally to create the foundational data for an asset building plan. Inputs are the user's financial information and external financial information, while output is integrated market data.

[0614] Step 3:

[0615] The device captures the user's facial expressions using a camera and performs analysis using an emotion engine. This quantifies the user's emotional state, and the data is sent to the server. The input is the user's facial expression data, and the output is the analyzed emotional state.

[0616] Step 4:

[0617] The server uses a generative AI model to generate asset building plans based on integrated market data and the user's emotional state. In this process, the AI ​​determines risk tolerance from emotional data and customizes the plan. The input is financial information and emotional state, and the output is a personalized asset building plan.

[0618] Step 5:

[0619] The server sends the generated asset building plan to the terminal, which then displays the plan visually. The display includes features that dynamically adjust the color scheme and design according to the user's emotional state. The user then uses this to consider their own asset building strategy. The input is a personalized asset building plan, and the output is a visualized plan screen.

[0620] Step 6:

[0621] The terminal receives feedback from the user and sends that information to the server. Based on the feedback, the server readjusts the asset building plan as needed. The input is the user's feedback, and the output is the adjusted asset building plan.

[0622] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0623] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0624] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0628] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0629] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0630] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0631] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0632] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0633] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0634] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0635] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0636] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0637] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0638] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] The system for implementing this invention is designed so that a user can input their asset building goals and receive the optimal plan for achieving those goals. This system mainly consists of two components: a terminal and a server.

[0640] On the terminal, the user enters specific asset-building goals, such as "save 20 million yen in 10 years." Simultaneously, they also enter financial information such as age, annual income, current savings, and existing investment status. This information is then sent to the server.

[0641] The server first researches external financial information, such as the latest interest rates and market trends, via the internet. This allows it to obtain the most up-to-date data necessary for the user to achieve their goals. Next, the server uses AI to generate an optimal asset building plan based on the information received from the user and the information obtained from external sources. This plan includes savings plans, investment strategies, and risk management. The server then converts the generated plan into a visually easy-to-understand format, such as graphs and charts, and sends it to the user's device.

[0642] The terminal displays information received from the server to the user. Based on this, the user can make decisions regarding their asset building. Furthermore, the user can provide feedback on the presented plan, for example, requesting modifications such as "I want to reduce the risk further." This feedback is sent back to the server, which then adjusts and optimizes the plan based on it.

[0643] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server will consider the user's current income and expenses, as well as their projected future expenses, and then present a specific plan such as "maintain monthly savings of 80,000 yen while investing a certain percentage in mutual funds." In this way, the present invention provides an environment in which users can continue to build assets based on a concrete action plan.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The user enters their asset-building goals into the terminal. Specifically, they fill in financial data such as the target amount, desired age to achieve it, current savings, and annual income into the input form.

[0647] Step 2:

[0648] The terminal sends information entered by the user to the server. This information includes specific goals and details about the financial situation.

[0649] Step 3:

[0650] The server retrieves external financial information. This includes current interest rates, stock market trends, and inflation rates, which are collected and analyzed from the internet.

[0651] Step 4:

[0652] The server stores the user's financial information and external financial information in a database and prepares to generate an optimal asset building plan based on this information.

[0653] Step 5:

[0654] The server uses AI to generate an optimal asset building plan to help users achieve their goals. This plan includes recommendations for savings amounts, investment allocation, and risk management methods.

[0655] Step 6:

[0656] The server visualizes the generated asset building plan. Specifically, it creates an overview of the plan as graphs and charts, and presents it in a way that is easy for the user to understand.

[0657] Step 7:

[0658] The terminal displays visualization data received from the server to the user. The user then uses this to review their asset building strategy and take appropriate action if necessary.

[0659] Step 8:

[0660] Users input feedback about their asset building plan into the terminal. For example, they might input that they prefer a plan with reduced risk.

[0661] Step 9:

[0662] The device sends user feedback to the server.

[0663] Step 10:

[0664] The server incorporates user feedback and readjusts the plan. Steps 5-7 are repeated as needed to provide the user with an optimized plan.

[0665] (Example 1)

[0666] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] Developing an optimal asset building plan tailored to each individual user's financial situation and market conditions is complex and difficult, and there is a challenge in that it is difficult for ordinary users without specialized knowledge to easily obtain effective methods.

[0668] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0669] In this invention, the server includes means for collecting and receiving the user's financial information at a terminal, means for acquiring external financial information, means for generating an asset formation plan using a generation AI model, and means for visually converting the generated asset formation plan and presenting it at the terminal. This makes it possible for users to obtain a rational and specific asset formation plan based on market trends and their own financial situation, even without specialized knowledge.

[0670] A "user" is the entity that accesses the system and inputs their own financial information and asset building goals.

[0671] "Asset amount" refers to the specific financial target that the user wishes to achieve.

[0672] "Financial information" refers to data that shows an individual's economic status, including the user's age, annual income, current savings, and existing investment status.

[0673] A "terminal" is an interface device used by users to input information and receive suggestions from a server.

[0674] A "server" is a central processing unit that processes information from users and external data sources and generates asset formation plans using a generated AI model.

[0675] "External financial information" refers to financial data from external sources, such as the latest interest rates and market trends, obtained via the internet.

[0676] A "generative AI model" refers to an artificial intelligence algorithm that generates asset building plans using collected data.

[0677] An "asset building plan" is an actionable proposal that includes savings plans and investment strategies generated based on user input information and external financial information.

[0678] "Visual presentation" means displaying generated information on a device in the form of graphs, charts, or other formats, so that users can understand it intuitively.

[0679] "Feedback" refers to the opinions and suggestions for improvement that users provide regarding the asset building plan presented.

[0680] "Adjusting" means modifying existing asset building plans as needed, taking user feedback into consideration.

[0681] Users access the asset building support system using their own devices. These devices provide an interface for inputting financial information such as target asset amount, age, annual income, current savings, and existing investment status. This allows users to easily send the necessary information to the system.

[0682] The terminal is responsible for transmitting collected user information to the server. The server obtains external financial information via the internet, including the latest interest rates and market trends. This information is used as reference data to generate asset building plans.

[0683] The server is equipped with a generative AI model that generates an optimal asset building plan based on the user's financial information and external financial information. This AI model proposes savings plans and investment strategies tailored to the user's circumstances and has the flexibility to adjust the plan.

[0684] The generated asset building plan is visualized and sent to the device for presentation to the user. The user can evaluate their asset building strategy based on the presented graphs and charts and provide feedback as needed. This feedback is sent back to the server for further adjustments to the plan.

[0685] For example, if a user in their 20s inputs into the system that they "want to save 10 million yen by the time they are 30 to buy a house in the future," the server can use an AI model to generate and present a plan such as "maintaining a monthly savings of 80,000 yen while investing a certain percentage in mutual funds."

[0686] An example of a prompt for the generating AI model would be text such as, "I would like a low-risk plan to save 10 million yen by the age of 30." In this way, the user can obtain a specific and realistic asset building plan.

[0687] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0688] Step 1:

[0689] Users access the system using a terminal and input financial information such as their asset building goals, age, annual income, current savings, and existing investment status. The entered information is organized as data within the terminal and formatted in preparation for transmission.

[0690] Step 2:

[0691] The device sends the information entered by the user to the server. This data includes the user's goals and financial status. The device encrypts the data during transmission to ensure it reaches the server securely.

[0692] Step 3:

[0693] The server analyzes the user's financial information and retrieves external financial information via the internet. This includes the latest interest rates and market trends. This external information is obtained via API and stored in a database for combination with user information.

[0694] Step 4:

[0695] The server integrates the user's financial information with external financial information and generates an optimal asset building plan using a generative AI model. The AI ​​model evaluates the input data and presents the most suitable savings plan and investment strategy for the user. This generates a concrete action plan from the data.

[0696] Step 5:

[0697] The generated asset building plan is visualized on the server. Here, data visualization tools are used to create graphs and charts. The visual representation transforms the information into a format that users can easily understand.

[0698] Step 6:

[0699] The server sends a visualized plan to the terminal. The terminal displays the received information to the user, presenting a visual plan. The user then makes their own decisions based on this plan.

[0700] Step 7:

[0701] Users provide feedback on the presented plan through their device. For example, they may want to reduce risk or adjust investment ratios. User feedback is collected on the device and sent back to the server.

[0702] Step 8:

[0703] The server receives feedback from the user and uses a generated AI model to readjust the asset building plan. The readjusted plan is further visualized and presented to the user again. This allows for continuous plan improvement tailored to the user's needs.

[0704] (Application Example 1)

[0705] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] A challenge is that users often lack access to optimal plans for efficiently achieving their wealth-building goals. Furthermore, financial transactions are complex and cumbersome, and the numerous procedures required for wealth building can lead to a lack of progress. Therefore, it is necessary to provide means to simplify and automate the wealth-building process.

[0707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0708] In this invention, the server includes means for the user to input a target asset amount, means for collecting the user's financial information, means for acquiring external financial information, means for generating an asset formation plan, means for visually presenting the generated asset formation plan, and means for automating financial transactions for asset management through electronic commerce means. This enables the user to effectively plan their asset formation, automatically reflect an actionable plan in transactions, and efficiently achieve their goals.

[0709] "A means for users to input their target asset amount" refers to an interface that allows users to set specific asset goals and provide those figures to the system.

[0710] "Means for collecting user financial information" refers to functions for obtaining financial data such as the user's age, annual income, savings, and existing investment status.

[0711] "Means of obtaining external financial information" refers to functions for obtaining external financial market information, such as market trends and interest rates, via the internet.

[0712] The "means for generating asset formation plans" refer to a function that uses AI to create a plan for achieving goals, based on the user's financial information and external financial information.

[0713] "Means of visually presenting the generated asset formation plan" refers to a function that displays the generated plan using graphs and charts to make it easier for the user to understand.

[0714] "Means of automating financial transactions for asset management through electronic commerce" refers to a function that automates the execution of investments and savings based on a user's asset formation plan via an online trading platform.

[0715] The system for implementing the present invention provides an integrated approach to efficiently achieve asset formation goals. Users can input specific asset formation goals and associated financial information using a terminal. Specifically, this includes age, annual income, savings amount, and existing investment status.

[0716] Information entered on the terminal is sent to the server. The server processes the collected financial information using programming languages ​​such as Python. Furthermore, it uses external financial APIs to collect the latest market information such as interest rates and stock prices. This collection process often utilizes APIs such as the Bloomberg API and the Reuters API.

[0717] The server combines acquired external data with user data and generates an optimal asset building plan using AI models such as TensorFlow. The generated plan is then converted into graphs and charts using visualization tools such as D3.js and presented to the user.

[0718] Furthermore, it integrates APIs for e-commerce services such as Stripe and PayPal, enabling automated execution of financial transactions based on asset building plans. This allows users to efficiently conduct financial transactions based on concrete and actionable plans.

[0719] For example, if a user chooses a plan to save a small portion of their monthly disposable income and invest the surplus in low-risk mutual funds, this system will automatically transfer the monthly savings to the investment account and purchase appropriate financial products. By using prompts such as, "Please suggest an investment plan that allows me to build wealth with minimal risk while slightly reducing my monthly expenses," more personalized suggestions can be obtained from the generating AI model.

[0720] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0721] Step 1:

[0722] The user enters their asset building goals and financial information on the terminal. The entered data includes the user's annual income, savings, and existing investment status, and the next processing steps are based on this information. The entered data is temporarily stored on the terminal and prepared to be sent to the server.

[0723] Step 2:

[0724] The terminal sends the collected user information to the server. The server receives this data and stores it in a database. The database uses a relational database management system (RDBMS), such as SQL, to effectively manage information for multiple users.

[0725] Step 3:

[0726] The server accesses financial APIs to retrieve external financial information. This information includes current interest rates, market trends, and stock prices. This data is obtained via the API using Python's request library and stored in a database.

[0727] Step 4:

[0728] The server combines user data and external financial information and inputs it into an AI model. The AI ​​model uses TensorFlow to generate an optimal asset building plan based on this data. The plan includes recommended savings amounts and investment percentages. The generated plan is temporarily stored in an internal data format.

[0729] Step 5:

[0730] The server uses visualization tools such as D3.js to create graphs and charts to visually represent the generated asset formation plan. The generated graphs are exported in HTML format and sent to the user's terminal.

[0731] Step 6:

[0732] The terminal displays visual information sent from the server in the browser. Based on this, the user can review their asset building plan. The visual information is provided in a user-interface format that can be manipulated.

[0733] Step 7:

[0734] Users input feedback on their asset building plan from their device. For example, they might input a request such as "I want to reduce risk" and send it to the server via their device.

[0735] Step 8:

[0736] The server receives user feedback and adjusts the asset building plan accordingly. It then reruns the AI ​​model, generates the adjusted plan, and saves it.

[0737] Step 9:

[0738] The server uses e-commerce tools to automate financial transactions based on a coordinated asset building plan. It utilizes APIs from Stripe and PayPal to execute the user's investment plan, resulting in efficient asset management.

[0739] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0740] This invention is a comprehensive system for users to effectively achieve their asset building goals, and in particular, by incorporating an emotion engine, it enables the provision of asset building plans that take into account the user's emotional state. The system mainly consists of a terminal, a server, and an emotion engine.

[0741] Through the terminal, users input their asset-building goals and provide financial information. This includes information such as the amount they want to achieve, the timeframe, their current asset status, income, and expenses. The terminal also provides an interface for acquiring data on the user's emotions. Here, the system can understand the user's current emotional state by analyzing their facial expressions and input content.

[0742] The server receives this information and first retrieves the latest financial information from external sources. This process takes into account market trends, interest rates, economic indicators, tax systems, etc. Based on the retrieved data and the information entered by the user, the server generates an asset building plan using artificial intelligence (AI). This AI incorporates emotional data analyzed by an emotion engine, enabling it to make suggestions tailored to the user's current psychological state. Specifically, it presents risk-reducing plans to users with high stress levels, and conversely, plans that include aggressive investments to highly motivated users.

[0743] The generated asset building plan is visualized by the server and sent to the terminal. The terminal not only displays the plan as graphs and charts to present it clearly to the user, but also dynamically adjusts the color scheme and design to suit their emotions.

[0744] As a concrete example, if a user sets a goal of "saving 5 million yen in 5 years," and the system's emotion engine recognizes that the user is feeling anxious, the server generates a low-risk savings plan and presents it using calming colors. Furthermore, if the user provides feedback on this plan, the server readjusts the plan based on that information. In this way, the present invention provides flexible and personalized support for asset building that responds to changes in emotions.

[0745] The following describes the processing flow.

[0746] Step 1:

[0747] The user enters their asset building goals into the device. This includes detailed financial information such as the target asset amount, the planned age at which they will achieve it, and their current savings and income.

[0748] Step 2:

[0749] The terminal sends user input information to the server and analyzes the user's facial expressions and input content through an emotion engine to measure the user's emotional state. This information is also sent to the server.

[0750] Step 3:

[0751] The server retrieves the latest financial information from external sources. Specifically, it collects market trends, interest rates, tax systems, and other data via the internet and stores it in a database.

[0752] Step 4:

[0753] The server uses AI to generate an asset building plan based on the user's financial information and external data. In this process, it takes into account the user's emotional state, as analyzed by an emotion engine. For example, it proposes a low-risk plan to a user who is feeling anxious.

[0754] Step 5:

[0755] The server visually represents the generated asset building plan. This process incorporates graphs and charts, as well as color schemes and designs that respond to the user's emotions.

[0756] Step 6:

[0757] The terminal displays a visualized plan received from the server to the user. Based on this information, the user considers their own asset building strategy.

[0758] Step 7:

[0759] The user enters feedback on the presented plan into their device. If necessary, they can request adjustments to the plan.

[0760] Step 8:

[0761] The device sends user feedback to the server.

[0762] Step 9:

[0763] The server receives feedback and readjusts the plan. If necessary, it reviews the generation plan and repeats steps 4-6.

[0764] (Example 2)

[0765] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0766] In modern society, providing asset building plans that take into account the individual economic situation and emotional state of each user is a challenging task. Furthermore, providing proposals optimized for each user's specific circumstances, rather than generic suggestions, has been difficult with conventional methods. In particular, there are very few systems that consider the influence of user emotions on the acceptance and behavior of asset building plans. As a result, many users find it difficult to obtain the optimal strategy for their economic goals, potentially leading to failure in asset building.

[0767] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0768] In this invention, the server includes means for the user to input target asset figures, means for collecting the user's economic information, means for acquiring external financial information, and means for analyzing the user's emotional state. This makes it possible to propose a personalized asset formation plan that is tailored to the user's economic situation and emotional state.

[0769] A "user" refers to an individual or legal entity that uses the system to set their own economic goals and input and provide the necessary information.

[0770] "Asset figures" refer to the financial goals or amounts that a user aims to achieve within a specific period of time.

[0771] "Economic information" refers to general financial information, including a user's income, expenses, assets, and liabilities.

[0772] "External financial information" refers to data obtained from external sources regarding economic indicators, market trends, interest rates, and tax systems.

[0773] An "asset building plan" refers to a specific strategy or action plan proposed to achieve the user's financial goals.

[0774] "Emotional state" refers to the user's psychological and emotional condition, and by analyzing this, the system provides the underlying data for making personalized suggestions.

[0775] "Suggestions" refer to advice provided to users to support them in achieving their goals by presenting them with economic action guidelines and strategies generated by the system.

[0776] This invention is a comprehensive system for users to effectively achieve their asset building goals. The system comprises a terminal, a server, and an emotion analysis engine.

[0777] Users input asset planning goals and financial information via a terminal. The terminal has a built-in camera and input devices to capture facial expression data and text from the user. Based on this data, the terminal analyzes the user's emotional state and sends the information to the server.

[0778] The server uses intelligent technology to generate an asset building plan based on the user's economic and emotional data transmitted from the terminal. External financial information is also collected during this process, and APIs are used to maintain the integrity and timeliness of the information. The server visually structures the generated plan and presents it in an easy-to-understand format for the user. The suggestions are optimized based on the user's psychological state through emotional analysis. For example, a user experiencing stress will be offered a low-risk, stable savings plan.

[0779] For example, if a user enters "I want to save 5 million yen in 5 years," the system will present a concrete savings plan to achieve that goal. If sentiment analysis detects the user's anxiety, the server will suggest a more conservative investment strategy and present the plan in a calm color scheme.

[0780] Examples of prompts for a generative AI model are as follows:

[0781] "I want to save 5 million yen in 5 years. First, please tell me how much I need to save each month. My current income and expenses are as follows. To alleviate my emotional anxiety, please propose a low-risk plan."

[0782] This system allows users to access an optimal wealth-building plan tailored to their own financial situation and psychological state, making it more likely that they will achieve their wealth-building goals.

[0783] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0784] Step 1:

[0785] The user uses a terminal to input their asset-building goals and financial information. This input includes desired asset figures, target timeframe, income, expenses, and current asset status. The terminal records this information as digital data and prepares to send it to the server.

[0786] Step 2:

[0787] The device uses its built-in camera and sensors to capture the user's facial expressions. The collected visual data is passed to an emotion analysis engine to extract the user's emotional state. The emotional state data is generated as emotion tags (e.g., stress, high motivation) and sent to the server along with other input data.

[0788] Step 3:

[0789] The server receives user economic information and emotional state data sent from the terminal, while simultaneously accessing external financial databases to obtain the latest market trends, interest rates, and economic indicators. During this process, financial data is fetched via APIs, and statistical analysis is performed. The processed external data is then used to generate asset building plans.

[0790] Step 4:

[0791] The server uses collected user information and financial data as input to generate an asset building plan using a generative AI model. Leveraging data analysis and machine learning models, it designs the optimal plan to achieve the user's financial goals. This process includes optimization techniques that consider input emotional data and select a plan appropriate to the user's psychological state.

[0792] Step 5:

[0793] The generated asset building plan is sent from the server to the terminal. The terminal analyzes the received plan and converts it into a visually easy-to-understand format (graphs, charts) for the user. Furthermore, it presents the plan using a function that customizes the color scheme and design according to the user's emotional state. For example, it supports users who are feeling stressed by displaying the plan in calming colors.

[0794] Step 6:

[0795] Users can provide feedback on the presented asset building plan through their device. The device collects this feedback and sends it to a server. The server analyzes the feedback data and readjusts the plan as needed. This readjusted plan is then presented to the user again and optimized to better suit the user's needs.

[0796] (Application Example 2)

[0797] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0798] Traditional asset building systems offered asset building plans using a general approach without considering the user's emotional state. Therefore, they were unable to address situations where users sought different proposals based on their emotional state, making it difficult to achieve optimal asset building. Furthermore, there was a lack of means to provide advice linked to the user's psychological state by utilizing user purchase and payment data. This limited the effectiveness of user asset building.

[0799] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0800] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the asset building plan based on the user's emotional state, and means for performing emotional analysis based on the user's purchase and payment data and providing expenditure analysis and savings plans that are appropriate to the user's psychological state. As a result, the user can receive an asset building plan optimized for their own emotional state, enabling them to manage their expenditures and create savings plans that are suitable for their psychological state.

[0801] "User emotional state" refers to the psychological state of the user and includes data on emotions such as stress and motivation.

[0802] An "asset building plan" is a plan for users to effectively increase their assets, and includes specific investment and savings strategies.

[0803] "Purchase and payment data" refers to information about purchases and payments made by users, and is fundamental data for understanding spending trends.

[0804] "Sentiment analysis" is the process of analyzing data such as facial expressions and language to reveal a user's emotional state.

[0805] "Psychological state-based spending analysis" is a method for evaluating spending patterns and appropriate consumer behavior by taking into account the user's current psychological state.

[0806] A "savings plan" is a set of specific guidelines and policies for systematically saving money in order to achieve long-term wealth accumulation.

[0807] To implement this invention, the server receives user input and generates an asset building plan. The server acquires financial information from external sources and analyzes it in conjunction with the financial information provided by the user. Furthermore, it uses an emotion engine to analyze the user's emotional state, and an AI model proposes an optimal asset building plan to the user based on the emotional data.

[0808] The terminal functions as the user's input interface, enabling goal setting and financial information input. Furthermore, the terminal captures the user's facial expressions in real time and sends emotional data to a server to determine their emotional state. On the terminal, the generated asset building plan is visualized in graphs and charts, with the color scheme and design adjusted according to the user's emotional state.

[0809] The specific technologies used include camera devices and image processing libraries such as OpenCV for facial expression analysis. TensorFlow is used for AI model training and inference, and Pandas and NumPy are used for data analysis. These form the foundation for users to make optimal decisions based on daily accumulated data across various systems.

[0810] As a concrete example, if a user enters "I want to save 5 million yen in 5 years" into a smartphone app, and the emotion engine determines from the user's facial expression that they are in a stressed state, the server will generate a low-risk investment plan and display it on the device using a calming blue color scheme. At this time, the following prompt is entered into the generating AI model: "Based on the user's emotional state and monthly spending data, please generate suggestions to help them achieve their savings goal for the following month."

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] The terminal provides a means for users to input their set asset target amount, as well as financial information such as income and expenses. This allows users to input their asset building goals and current status into the terminal. The terminal then formats this information into a database format and sends it to the server.

[0814] Step 2:

[0815] The server retrieves the latest market trends, interest rates, and economic indicators from external financial databases based on the financial information it receives. The server then integrates this information internally to create the foundational data for an asset building plan. Inputs are the user's financial information and external financial information, while output is integrated market data.

[0816] Step 3:

[0817] The device captures the user's facial expressions using a camera and performs analysis using an emotion engine. This quantifies the user's emotional state, and the data is sent to the server. The input is the user's facial expression data, and the output is the analyzed emotional state.

[0818] Step 4:

[0819] The server uses a generative AI model to generate asset building plans based on integrated market data and the user's emotional state. In this process, the AI ​​determines risk tolerance from emotional data and customizes the plan. The input is financial information and emotional state, and the output is a personalized asset building plan.

[0820] Step 5:

[0821] The server sends the generated asset building plan to the terminal, which then displays the plan visually. The display includes features that dynamically adjust the color scheme and design according to the user's emotional state. The user then uses this to consider their own asset building strategy. The input is a personalized asset building plan, and the output is a visualized plan screen.

[0822] Step 6:

[0823] The terminal receives feedback from the user and sends that information to the server. Based on the feedback, the server readjusts the asset building plan as needed. The input is the user's feedback, and the output is the adjusted asset building plan.

[0824] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0825] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0826] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0827] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0828] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0829] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0830] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0831] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0832] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0833] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0834] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0835] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0836] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0837] 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.

[0838] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0839] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0840] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0841] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0842] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0843] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0844] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0845] The following is further disclosed regarding the embodiments described above.

[0846] (Claim 1)

[0847] A means for the user to input their target asset amount,

[0848] Means for collecting users' financial information,

[0849] Means of obtaining external financial information,

[0850] A means for generating an asset formation plan based on the user's financial information and the external financial information,

[0851] A means of visually presenting the generated asset formation plan,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for receiving user feedback and adjusting the asset formation plan.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the means for generating the asset formation plan is to propose an optimized asset formation strategy using artificial intelligence.

[0857] "Example 1"

[0858] (Claim 1)

[0859] A means for the user to input their target asset amount,

[0860] A means of collecting and receiving users' financial information on a terminal,

[0861] A means of obtaining external financial information via a server,

[0862] A means for generating an asset formation plan using an AI model based on the user's financial information and the external financial information,

[0863] A means of visually converting the generated asset formation plan and presenting it on a terminal,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for receiving user feedback from a terminal and adjusting the asset formation plan on a server.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the means for generating the asset formation plan proposes an asset formation strategy optimized for the user's conditions using a generating AI model.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means for the user to input their target asset amount,

[0872] Means for collecting users' financial information,

[0873] Means of obtaining external financial information,

[0874] A means for generating an asset formation plan based on the user's financial information and the external financial information,

[0875] A means of visually presenting the generated asset formation plan,

[0876] A means of automating financial transactions for asset management through electronic commerce,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for receiving user feedback and adjusting the asset formation plan.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the means for generating the asset formation plan proposes an optimized asset formation strategy using artificial intelligence, and the electronic commerce means automatically generates instructions for executing financial transactions.

[0882] "Example 2 of combining an emotion engine"

[0883] (Claim 1)

[0884] A means for the user to input their target asset figures,

[0885] Means of collecting users' economic information,

[0886] Means of obtaining external financial information,

[0887] A means for generating an asset formation plan based on the user's economic information and the external financial information,

[0888] A means of analyzing the emotional state of users,

[0889] A means for visually presenting the generated asset formation plan,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, further comprising means for receiving user feedback and adjusting the asset formation plan.

[0893] (Claim 3)

[0894] The system according to claim 1, wherein the means for generating the asset formation plan proposes an optimized asset formation strategy using intelligent technology, and further adjusts the proposal based on the user's emotional state.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] A means for the user to input their target asset amount,

[0898] Means for collecting users' financial information,

[0899] Means of obtaining external financial information,

[0900] A means for generating an asset formation plan based on the user's financial information and the external financial information,

[0901] A means of visually presenting the generated asset formation plan,

[0902] A means of analyzing the user's emotional state,

[0903] A means of adjusting the asset formation plan based on the user's emotional state,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, comprising means for performing sentiment analysis based on user purchase and payment data and providing spending analysis and savings plans tailored to the user's psychological state.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the means for generating the asset formation plan proposes an optimized asset formation strategy using artificial intelligence and further dynamically changes the proposed content based on emotional data. [Explanation of symbols]

[0909] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the user to input their target asset amount, Means for collecting users' financial information, Means of obtaining external financial information, A means for generating an asset formation plan based on the user's financial information and the external financial information, A means of visually presenting the generated asset formation plan, A system that includes this.

2. The system according to claim 1, further comprising means for receiving user feedback and adjusting the asset formation plan.

3. The system according to claim 1, wherein the means for generating the asset formation plan is to propose an optimized asset formation strategy using artificial intelligence.

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