System

Abstract

A system is provided.SOLUTION: This system includes a means for collecting personal information and asset information of a user, a means for analyzing the collected personal information and asset information, a means for automatically generating an optimum plan for asset formation, an inheritance countermeasure and a tax saving countermeasure based on an analysis result, and a means for visually displaying the generated plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern retirement planning, it is extremely difficult for elderly households to take appropriate measures for asset formation, inheritance planning, and tax savings. In particular, knowledge of complex laws and tax systems is required, and understanding and implementing these takes a great deal of time and effort. Furthermore, specialized knowledge is essential to create specific plans tailored to individual circumstances. There is a need for concrete support that can solve these problems and enable elderly households to retire with peace of mind. [Means for solving the problem]

[0005] This invention collects a user's personal information and asset information and analyzes the collected information. Based on the analysis results, the system automatically generates and visually displays optimal plans for asset formation, inheritance planning, and tax savings. Furthermore, by learning the latest legal amendments in real time and reflecting this in the plan, users can always obtain optimal plans based on the latest information. In addition, by storing extracted numerical and text information in a database and using it for data analysis, it is possible to provide highly accurate plans. With such a system, elderly households can easily plan their retirement appropriately and live with peace of mind.

[0006] "User" means an individual who uses the system to provide their personal information and asset information.

[0007] "Personal information" refers to personal information about the user, and specifically includes name, age, address, family composition, and the like.

[0008] "Asset information" refers to information about a user's financial situation, specifically including income, expenditure, savings, real estate, financial investments, and the like.

[0009] "Means of collection" refers to the methods and techniques used to obtain personal information and asset information from users.

[0010] "Means of analysis" refers to methods and technologies for analyzing collected personal information and asset information and generating optimal plans based on the results.

[0011] "Means for automatic generation" refers to methods and technologies for automatically creating optimal plans for asset formation, inheritance planning, and tax savings based on analyzed data.

[0012] "Visual display means" refers to methods and techniques for displaying the generated plan in a format that is easy for the user to understand.

[0013] "Legal reform information" refers to information on the latest legal changes and tax reforms related to asset formation, inheritance planning, and tax saving strategies.

[0014] "Database" refers to an information system for effectively storing and managing collected and analyzed personal and asset information.

[0015] "Data analytics" refers to the process of conducting statistical or algorithmic analysis based on collected information. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them. Specific embodiments of this system and the operation of its program are described below.

[0038] Data collection

[0039] User Action:

[0040] A user uses a rich client terminal to upload documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, real estate ownership status, etc.

[0041] Terminal operation:

[0042] The device scans the provided document and generates image data, which is then sent to the server.

[0043] Data Extraction

[0044] Server Operation:

[0045] The server uses image recognition software to extract numerical and textual information from the image data sent to it. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[0046] Server Operation:

[0047] The extracted numerical and text information is stored in a database on the server.

[0048] Plan Generation

[0049] Server Operation:

[0050] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[0051] Server Operation:

[0052] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, which means that the database is continuously updated and plans are generated based on that information.

[0053] Visual image generation and presentation

[0054] Server Operation:

[0055] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and forecasts of future asset values. Specific examples of lifestyle planning are provided based on specific simulation results and statistical data.

[0056] Server Operation:

[0057] The server transmits the generated plan and visual image to the terminal.

[0058] Terminal operation:

[0059] The terminal presents the generated plan and visual images to the user, and the information is displayed in a format that is intuitively easy for the user to understand.

[0060] User Action:

[0061] Users can check the plans and visual images presented and provide feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[0062] Specific examples

[0063] Example 1: Creating a wealth plan

[0064] 1. Users upload documents related to their income, expenses, and investment status.

[0065] 2. The device scans the document and sends the image data to the server.

[0066] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[0067] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase by 1.5 times in 10 years.

[0068] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0069] 6. The terminal presents this to the user, who confirms the plan.

[0070] Example 2: Creating an estate planning plan

[0071] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[0072] 2. The device sends this information in text form to the server.

[0073] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0074] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0075] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[0076] This allows users to easily understand the plan that best suits their situation, even if they do not have a deep knowledge of complex laws and tax systems, and to plan their post-retirement life with peace of mind.

[0077] The above is a specific embodiment for carrying out the present invention.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] User Action:

[0081] A user logs into a rich client terminal and uploads a document containing personal and asset information to the terminal.

[0082] Step 2:

[0083] Terminal operation:

[0084] The device scans the uploaded documents and converts them into image data.

[0085] Step 3:

[0086] Terminal operation:

[0087] The terminal transmits the converted image data to the server.

[0088] Step 4:

[0089] Server Operation:

[0090] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[0091] Step 5:

[0092] Server Operation:

[0093] The server stores the extracted numerical and text information in a database.

[0094] Step 6:

[0095] Server Operation:

[0096] The server analyzes the stored data and selects the algorithm to apply, such as an asset formation algorithm or an inheritance planning algorithm.

[0097] Step 7:

[0098] Server Operation:

[0099] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0100] Step 8:

[0101] Server Operation:

[0102] The server uses the selected algorithm to analyze the data and generate an optimal plan. For example, it generates a specific prediction such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[0103] Step 9:

[0104] Server Operation:

[0105] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[0106] Step 10:

[0107] Server Operation:

[0108] The server sends the generated plan and visual image to the terminal.

[0109] Step 11:

[0110] Terminal operation:

[0111] The terminal presents the plan and visual image received from the server to the user.

[0112] Step 12:

[0113] User Action:

[0114] The user reviews the presented plans and visual images and provides further details and feedback as needed.

[0115] This allows users to obtain specific plans for their own asset formation, inheritance planning, and tax savings, allowing them to plan their retirement with peace of mind.

[0116] Example 1

[0117] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0118] Conventional asset formation, inheritance planning, and tax saving planning systems require users to input accurate and detailed information, making operation cumbersome. Furthermore, due to frequent legal changes, it is difficult to reflect the latest legal changes, and as a result, generated plans may not comply with the latest laws and regulations. Furthermore, generated plans are often not presented in a format that is intuitively easy for users to understand.

[0119] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0120] In this invention, the server includes means for users to upload information, means for a terminal to scan documents and generate image data, means for the server to extract numerical and textual information from the image data, means for the server to store the extracted information in a database, means for the server to analyze the data using an algorithm and generate optimal plans for asset formation, inheritance planning, and tax savings, means for the server to convert the generated plans into visual images, and means for the terminal to present the generated plans and visual images to the user. This makes it possible for users to easily provide detailed information and to receive intuitively understandable plans that reflect the latest legal amendments.

[0121] A "user" is an entity that uses the system to provide personal information and asset information.

[0122] "Means for uploading information" is a function that allows users to send their own personal information and asset information to the system.

[0123] A "terminal" is a computer device operated by a user, and is a device that has functions such as scanning documents and displaying data.

[0124] "Means for scanning documents and generating image data" refers to the function of converting physical documents into digital image data.

[0125] A "server" is a computer system that processes, stores, analyzes data, and communicates with other devices.

[0126] "Means for extracting numerical and textual information from image data" refers to technology that identifies and extracts text data from digital images.

[0127] "Means for storing information in a database" refers to a system for appropriately structuring extracted information and storing it for a long period of time.

[0128] An "algorithm" is a set of computational steps for analyzing data or solving problems.

[0129] "A means of analyzing data and generating optimal plans for asset formation, inheritance planning, and tax savings" is a function that automatically creates various plans using mathematical models and analytical methods based on stored data.

[0130] "Means for converting the generated plan into a visual image" refers to a technique for converting the analysis results into a format that is visually easy to understand (e.g., graphs or charts).

[0131] The "means for presenting plans and visual images" is a function for displaying the generated plans and visual information to the user.

[0132] "Latest legal reform information" refers to the latest information on regulations and changes to laws related to asset formation, inheritance planning, and tax savings.

[0133] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them.

[0134] Hardware and Software Configuration

[0135] User operation and information provision

[0136] Users use a rich client device to upload documents containing their personal and financial information. This requires a computer, a scanner, and an internet connection. Specifically, documents provide information such as annual income, monthly expenses, bank balance, and real estate ownership.

[0137] Document scanning and image data generation

[0138] The device scans the document provided by the user and generates high-resolution image data. This step uses a scanner and image processing software (e.g., Adobe Acrobat). The generated image data is then sent to the server via a secure protocol.

[0139] Information extraction from image data

[0140] The server uses image recognition software (e.g., OCR software) to extract numerical and textual information from the image data sent. For example, OCR software extracts specific information such as "annual income of 5 million yen" and "monthly expenses of 300,000 yen" as text data.

[0141] Database storage

[0142] The extracted numerical and textual information is stored in a database on the server. The database system (e.g., MySQL (registered trademark) or PostgreSQL) is built with high availability and security in mind. Annual income and expenditure information is stored, linked to the user ID.

[0143] Data analysis and plan generation

[0144] The server analyzes the stored data by applying specific algorithms (e.g., regression analysis, machine learning models, etc.). The analysis targets the user's annual income, expenses, investment status, etc. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[0145] Reflecting the latest legal amendments

[0146] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, continuously updating the database so that the plans generated based on them comply with the latest laws and regulations.

[0147] Visual image generation

[0148] Based on the generated plan, the server graphs the progress of income and expenditures and the forecast of future asset value. This visual image is provided in a format that is intuitively easy for users to understand. Specifically, a graph drawing tool (e.g., D3.js, Chart.js) is used.

[0149] Presentation of plans and visual images

[0150] The server sends the generated plan and visual image to the terminal, which then presents it to the user, displaying the information in a format that is intuitively easy for the user to understand. For example, graphs and simulation results are displayed in an easy-to-understand manner.

[0151] User Feedback

[0152] Users can check the plans and visual images presented and provide feedback as needed. For example, they can send questions to the server such as, "What specific procedures are required for this tax-saving measure?" The system will then be improved based on the feedback.

[0153] Examples of prompt statements

[0154] Below are some examples of specific prompts to input into a generative AI model:

[0155] Prompt statement:

[0156] Generate an optimal asset formation plan based on the user's personal and asset information. First, provide information such as the user's annual income, monthly expenses, deposit balance, and real estate ownership status, and analyze this to predict future asset value. Next, propose a specific plan that takes into account tax savings and inheritance planning, and generate and display a visual image.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Specific explanation of processing steps

[0159] Step 1: User uploads information

[0160] Input: Personal and financial information such as the user's annual income, monthly expenses, bank balance, and real estate ownership status is provided as documents.

[0161] What happens: Users upload these documents to the system using rich client devices, which have scanning and digital form-filling capabilities.

[0162] Output: The device receives this information as digital data.

[0163] Step 2: The device scans the document and generates image data.

[0164] Input: Documents uploaded by users.

[0165] Specific operation: The device uses a high-resolution scanner to scan the provided document and generate image data. Image processing software (e.g., Adobe Acrobat) is used.

[0166] Output: The generated image data is saved to the device.

[0167] Step 3: The device sends the image data to the server

[0168] Input: scanned image data.

[0169] Specific operation: The device sends image data to the server via a secure protocol (e.g., HTTPS).

[0170] Output: The transmitted image data is saved on the server.

[0171] Step 4: The server extracts numerical and textual information from the image data.

[0172] Input: Image data stored on the server.

[0173] Specific operation: The server uses OCR software to extract numerical and textual information from the image data. For example, it uses text detection to extract specific information such as "annual income of 5 million yen" or "monthly expenses of 300,000 yen."

[0174] Output: The extracted numerical and character information is generated as text data.

[0175] Step 5: The server stores the extracted information in a database

[0176] Input: Extracted numeric and textual information.

[0177] Specific operation: The server stores this information in a database system (e.g., MySQL, PostgreSQL). Each piece of information is structured and linked to the user ID.

[0178] Output: Structured data stored in a database.

[0179] Step 6: The server uses algorithms to analyze the data and generate a plan

[0180] Input: User's personal and financial information stored in a database.

[0181] Specific operation: The server applies data analysis algorithms (e.g., regression analysis, machine learning models) to calculate optimal plans for asset formation, inheritance planning, and tax savings based on various information. For example, it predicts future assets based on the user's annual income, expenses, and investment status.

[0182] Output: The generated optimal plan.

[0183] Step 7: The server updates with the latest legal information

[0184] Input: Latest legal change information and generated plan.

[0185] How it works: The server learns the latest legal changes in real time and reflects them in the analysis algorithm, so the generated plans comply with the latest laws and regulations.

[0186] Output: An updated plan that reflects the latest legal changes.

[0187] Step 8: The server converts the generated plan into a visual image

[0188] Input: The generated plan.

[0189] Specific operation: The server uses a visual image generation tool (e.g., D3.js, Chart.js) to convert the analysis results into a visually understandable format, such as graphing the progress of income and expenditure or future asset value.

[0190] Output: Graphs and charts as visual images.

[0191] Step 9: The server sends the generated plan and visual image to the device.

[0192] Input: Generated plans and visual images.

[0193] Specific operation: The server sends this information to the device via a secure protocol (e.g., HTTPS).

[0194] Output: Plans and visual images sent to the device.

[0195] Step 10: The device presents the user with a plan and visual images

[0196] Input: Plan and visual images sent from the server.

[0197] Specific operation: The device presents the plan and visual image to the user through a display function (e.g., web browser, dedicated app). The plan and visual image are displayed on the screen in a way that allows the user to intuitively understand it.

[0198] Output: Plans and visual images presented to the user.

[0199] Step 11: User provides feedback

[0200] Input: Plan and visual image displayed on the terminal.

[0201] Specific operation: The user checks the plan and provides feedback as needed (e.g., "What specific steps are required to implement this tax-saving measure?"). The feedback is sent to the server via the device.

[0202] Output: User feedback sent to the server.

[0203] (Application example 1)

[0204] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] Existing asset management, inheritance planning, and tax-saving systems often require users to manage their own information and have specialized knowledge in order to be effective. Furthermore, there is a lack of practical support, as there are limited ways for users to automatically implement asset formation and tax-saving strategies. Therefore, there is a need for a system that allows users to easily implement optimal asset formation and tax-saving strategies, and also allows for integrated electronic payments based on those plans.

[0206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0207] In this invention, the server includes means for collecting personal information and asset information of a user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, means for supporting electronic payments based on the generated plans, and means for providing interactive feedback. This allows users to put optimal asset management and tax savings plans into practice without specialized knowledge. Furthermore, the integration of electronic payments based on the plans provides practical support for asset formation and tax savings, enabling comprehensive asset management.

[0208] "User's personal information" refers to information that identifies an individual or indicates an individual's attributes, and includes, for example, name, age, sex, address, occupation, income, and the like.

[0209] "Asset information" refers to information about financial assets, real estate, and other assets owned by a user, including, for example, bank account balances, appraised values ​​of stocks and investment trusts, appraised values ​​of real estate, and the like.

[0210] "Means for collecting" refers to a method or mechanism for obtaining personal information and asset information from a user and transmitting it to a server.

[0211] "Means for analysis" refers to a method or mechanism for performing data analysis based on collected personal information and asset information and deriving useful results.

[0212] "Means for automatically generating optimal plans" refers to a method or mechanism that automatically creates asset formation, inheritance planning, and tax saving plans that are most suitable for each user based on the results of data analysis.

[0213] "Visual display means" refers to a method or mechanism for visually presenting the generated plan to a user, such as a graph or chart.

[0214] "Means for supporting electronic payments" refers to a method or mechanism for a user to make the electronic payments necessary to manage his or her assets based on the generated plan.

[0215] "Means for providing interactive feedback" refers to a method or mechanism for responding to questions or feedback from a user in real time and providing additional information or advice.

[0216] The system for implementing this invention collects and analyzes a user's personal information and asset information, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. It also includes functions to support electronic payments based on the generated plans and provide interactive feedback.

[0217] Program processing and hardware / software used

[0218] Data collection

[0219] The device scans documents provided by the user (such as annual income, monthly expenses, bank balance, and real estate ownership status) and generates image data. This image data is then sent to a cloud server. The hardware used includes a scanner and a camera.

[0220] Data Extraction

[0221] The server uses image recognition software called "pytesseract" to extract necessary numerical and text information from the transmitted image data. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[0222] Data storage and analysis

[0223] The extracted numerical and textual information is stored in a secure database on the server, which then applies specific algorithms to analyze the data.

[0224] Plan Generation

[0225] The server generates an optimal asset formation plan based on the user's annual income, expenses, and investment status. It also learns the latest legal reform information in real time and reflects it in the analysis results. Based on this analysis result, it provides the user with the optimal asset formation plan.

[0226] Visual image generation and presentation

[0227] Based on the generated plan, the server uses matplotlib to graph the progress of income and expenditures and the forecast of future asset value. This visual image is sent to the terminal and presented to the user in an easy-to-understand format.

[0228] Support for electronic payments

[0229] Based on the generated plan, the server supports electronic payments for users to carry out asset management and tax saving measures. The server has the function of automatically transferring funds and issuing instructions for investment.

[0230] Interactive Feedback

[0231] Users can ask questions about the generated plans and visual images, and the server will respond in real time via chatbots or other means, providing additional information and advice.

[0232] Examples of concrete examples and prompts

[0233] For example, based on data provided by a user with an annual income of 5 million yen and monthly expenses of 300,000 yen, the server generates the following asset formation plan:

[0234] It is predicted that by investing 200,000 yen per month in low-risk investments, assets will increase by 1.5 times in 10 years.

[0235] Based on the generated plan and its visual image, electronic payment support is provided to users for asset management.

[0236] Example prompt sentence:

[0237] "Upload your annual income and monthly expenses. We'll then predict your future asset growth and display it as a graph."

[0238] The above is a specific embodiment for carrying out the present invention. This system allows users to appropriately manage assets and take tax-saving measures without requiring specialized knowledge.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1:

[0241] The user uploads documents containing their personal and financial information to the terminal, including annual income, monthly expenses, bank balance, real estate ownership status, etc. The user's input is captured in the terminal as image data of the document and sent to the server.

[0242] Step 2:

[0243] The device scans the uploaded document and generates image data, which serves as input data to be sent to the server. The output of the device is a high-resolution scanned image file.

[0244] Step 3:

[0245] The server uses the image recognition software "pytesseract" to extract text information from the transmitted image data. Specifically, it converts the image data to grayscale and applies a text recognition algorithm. The server's input is the image data, and its output is the extracted information as text. For example, specific numerical information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted.

[0246] Step 4:

[0247] The server stores the extracted numerical and textual information in a secure database. The stored data is used for subsequent data analysis. The input to the server is the extracted textual information, and the output is storage in the database.

[0248] Step 5:

[0249] The server applies specific algorithms to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. This analysis automatically generates optimal asset formation, inheritance planning, and tax-saving plans. The server's input is numerical information stored in a database, and its output is a specific plan.

[0250] Step 6:

[0251] The server learns the latest legal amendments in real time and reflects them in the analysis algorithm. This ensures that the generated plans are always based on the latest laws and regulations. The server's input is legal amendments, and the output is the latest plan based on that information.

[0252] Step 7:

[0253] The server uses "matplotlib" to generate visual images based on the generated plan. Specifically, it graphs the progress of income and expenditures and the forecast of future asset value. The server's input is the generated plan information, and its output is visual graphs and charts.

[0254] Step 8:

[0255] The server sends the generated visual image and plan to the terminal. The terminal displays them in a format that is easy for the user to understand. This allows the user to visually confirm their own asset formation plan. The input to the terminal is the visual image and plan sent from the server, and the output is the display to the user.

[0256] Step 9:

[0257] Based on the generated plan, the terminal supports electronic payments, automatically executing specific fund transfer and investment instructions. The input of the terminal is the specific plan instructions, and the output is the actual payment transaction.

[0258] Step 10:

[0259] The user checks the generated plan and visual image and provides feedback as needed. For example, a question might be, "What exactly will this tax saving measure do?" The user's input is the feedback or question, and the server responds by providing an interactive response. The server's input is the user's feedback, and its output is the answer or additional information.

[0260] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0261] This invention is a system that combines a system that collects a user's personal information and asset information, analyzes it, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system and the operation of its program are described below.

[0262] Data collection

[0263] User Action:

[0264] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[0265] Terminal operation:

[0266] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[0267] Data Extraction

[0268] Server Operation:

[0269] The server uses image recognition software to extract numerical and textual information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[0270] Server Operation:

[0271] The extracted numerical and text information is stored in a database.

[0272] Plan Generation

[0273] Server Operation:

[0274] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal asset formation, inheritance planning, and tax saving plans.

[0275] Server Operation:

[0276] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0277] Manipulating the Emotion Engine

[0278] Terminal operation:

[0279] The emotion engine installed in the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc.

[0280] Server Operation:

[0281] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the server presents a more detailed explanation in polite language.

[0282] Visual image generation and presentation

[0283] Server Operation:

[0284] Based on the plan generated by the server, a visual image is generated, such as graphing income and expenditure trends and future asset values. Specific examples of lifestyle planning are also provided based on specific simulation results and statistical data.

[0285] Server Operation:

[0286] The server transmits the generated plan and visual image to the terminal.

[0287] Terminal operation:

[0288] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[0289] User Action:

[0290] Users can review the plans and visual images presented and, if necessary, request more detailed information or provide feedback. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[0291] Specific examples

[0292] Example 1: Creating a wealth plan

[0293] 1. Users upload documents related to their income, expenses, and investment status.

[0294] 2. The device scans the document and sends the image data to the server.

[0295] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[0296] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[0297] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0298] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[0299] Example 2: Creating an estate planning plan

[0300] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[0301] 2. The device sends this information in text form to the server.

[0302] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0303] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0304] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[0305] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[0306] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[0307] The above is a specific embodiment for carrying out the present invention.

[0308] The processing flow will be explained below.

[0309] Data collection and extraction

[0310] Step 1:

[0311] User Action:

[0312] A user logs in to a rich client terminal and uploads documents containing personal and financial information to the terminal, including annual income, monthly expenses, savings balance, real estate ownership status, etc.

[0313] Step 2:

[0314] Terminal operation:

[0315] The device scans the uploaded document and converts it into image data.

[0316] Step 3:

[0317] Terminal operation:

[0318] The terminal transmits the converted image data to the server.

[0319] Step 4:

[0320] Server Operation:

[0321] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[0322] Step 5:

[0323] Server Operation:

[0324] The extracted numerical and text information is stored in a database.

[0325] Plan generation and emotion engine operation

[0326] Step 6:

[0327] Server Operation:

[0328] The server analyzes the stored data and applies selected algorithms, including algorithms that generate asset formation, inheritance planning, and tax savings plans based on the user's annual income, expenses, and investment status.

[0329] Step 7:

[0330] Server Operation:

[0331] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0332] Step 8:

[0333] Server Operation:

[0334] The server uses the selected algorithm to automatically generate optimal asset formation, inheritance planning, and tax saving plans based on the analysis results. For example, it makes specific predictions such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[0335] Step 9:

[0336] Terminal operation:

[0337] The device's built-in emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies them as "relief," "anxiety," "interest," etc.

[0338] Visual image generation and presentation

[0339] Step 10:

[0340] Server Operation:

[0341] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[0342] Step 11:

[0343] Server Operation:

[0344] The server transmits the generated plan and visual image to the terminal.

[0345] Step 12:

[0346] Terminal operation:

[0347] The device receives the plan and visual images from the server and presents them to the user. The emotion engine then monitors the user's real-time reactions and adjusts the displayed content as needed. For example, if the user is recognized as "anxious," the level of detail and wording of the explanation will be changed.

[0348] Step 13:

[0349] User Action:

[0350] The user reviews the presented plan and visual image and gives feedback, for example, by asking specific questions such as, "What specific procedures are required for this tax-saving measure?"

[0351] Specific examples

[0352] Example 1: Creating a wealth plan

[0353] Step 1:

[0354] Users upload documents related to their income, expenses, and investment status.

[0355] Step 2:

[0356] The device scans the document and sends the image data to the server.

[0357] Step 3:

[0358] The server uses image recognition software to extract numbers (annual income, expenses, investment amounts, etc.).

[0359] Step 4:

[0360] The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[0361] Step 5:

[0362] The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0363] Step 6:

[0364] The device presents this to the user, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[0365] Example 2: Creating an estate planning plan

[0366] Step 1:

[0367] Users provide information about their family structure, real estate holdings, and financial assets.

[0368] Step 2:

[0369] The terminal sends this information to the server in text form.

[0370] Step 3:

[0371] The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0372] Step 4:

[0373] The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0374] Step 5:

[0375] The server transmits the generated plan to the terminal, which then presents it to the user.

[0376] Step 6:

[0377] The emotion engine analyzes the user's reaction, and if it recognizes that the user feels "at ease," the detailed explanation of the plan is omitted and the system moves on to the next step.

[0378] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[0379] Example 2

[0380] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] Conventional asset formation, inheritance planning, and tax-saving systems offer the ability to automatically generate and display plans based on the user's personal and asset information. However, these systems do not take the user's emotional state into account, which often leaves users feeling anxious or makes the plans difficult to understand. It is also difficult to reflect changes due to legal amendments in real time, making it difficult to provide plans based on the latest information. Furthermore, there are limitations to properly analyzing collected numerical and text information and presenting optimal plans. A method to solve these issues is needed.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0383] In this invention, the server includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plan based on the user's emotional state, means for learning the latest legal amendment information in real time and reflecting it in the plan, and means for storing the extracted numerical and text information in a database and performing data analysis. This makes it possible to propose personalized plans based on the user's emotions and always provide optimal plans based on the latest legal amendment information.

[0384] "User's personal information" refers to information for identifying an individual, such as the user's name, age, sex, and occupation.

[0385] "Asset information" refers to information about financial assets and physical assets owned by a user, such as cash, deposits, real estate, stocks, and bonds.

[0386] "Means for collection" refers to the method and device for acquiring personal information and asset information from users and incorporating it into the system.

[0387] "Means for analysis" refers to algorithms and programs that perform data analysis based on collected data and make predictions about the user's financial situation and future prospects.

[0388] "Means for automatic generation" refers to algorithms and programs that automatically generate optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[0389] "Visual display means" refers to devices and software for presenting the generated plan to a user in an easy-to-view format through visual elements such as graphs and charts.

[0390] "Means for recognizing in real time" refers to technology and devices that detect a user's emotions in real time and analyze that information.

[0391] "Adjusting means" refers to software and algorithms for appropriately modifying the content and presentation of the plan based on the user's emotional state as recognized in real time.

[0392] "Legal Change Information" refers to information on the latest laws and regulations, including in particular changes to tax laws and inheritance laws.

[0393] "Means of learning in real time" refers to the data acquisition mechanism and learning algorithms that instantly acquire the latest legal amendment information and reflect it in the system.

[0394] "Means for storing data in a database" refers to a database system and related software for efficiently storing and managing collected numerical and textual information.

[0395] This invention is a system that collects and analyzes a user's personal and asset information to automatically generate and visually display optimal asset formation, inheritance planning, and tax savings plans. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, a more personalized experience is provided. A specific embodiment of this system and the operation of its program are described below.

[0396] Data collection

[0397] User Action:

[0398] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[0399] Terminal operation:

[0400] The terminal scans the provided document, converts it into image data, and sends the data to the server. Specifically, the terminal controls a scanner device to scan the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[0401] Data Extraction

[0402] Server Operation:

[0403] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenses of 300,000 yen." The server starts Tesseract OCR, passes the image data as input, and the OCR extracts the text data, which is then saved as structured data (e.g., JSON format).

[0404] Server Operation:

[0405] Store the extracted numeric and text information in a database (e.g., MySQL or PostgreSQL). The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[0406] Plan Generation

[0407] Server Operation:

[0408] The server applies a specific algorithm (e.g., a machine learning model) to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings. The server loads a machine learning model (e.g., scikit-learn or TENSORFLOW (registered trademark)) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[0409] Server Operation:

[0410] The server updates the database with the latest legal amendment information in real time and reflects it in the analysis algorithm. The server periodically obtains the latest legal amendment information from an external API (e.g., a government tax law information service) and updates the database. The obtained information is input into the analysis algorithm.

[0411] Manipulating the Emotion Engine

[0412] Terminal operation:

[0413] An emotion engine (for example, Microsoft® Azure® Emotion API) installed on the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while they are looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc. The device collects real-time data through the camera and microphone and sends it to the emotion engine to obtain the analysis results.

[0414] Server Operation:

[0415] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the explanation is presented in more detail and in more polite language. The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results. The display content and presentation format are changed.

[0416] Visual image generation and presentation

[0417] Server Operation:

[0418] Based on the generated plan, the server generates visual images, such as graphs of income and expenditure trends and future asset value. Specifically, the server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize income and expenditure trends, and the generated graphs are saved as image files or dynamically generated in HTML format.

[0419] Server Operation:

[0420] The server sends the generated plan and visual image to the terminal, and the server packages the generated data and graph and sends it to the terminal as an HTTP response.

[0421] Terminal operation:

[0422] The device presents the generated plan and visual images to the user. Furthermore, an emotion engine monitors the user's real-time reactions and adjusts the displayed content as necessary. The device displays the generated plan and visual images through a web browser or dedicated client application, and new user reaction data is acquired and reflected in the displayed content.

[0423] User Action:

[0424] The user checks the presented plan and visual image, and requests more detailed information or gives feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax saving measure?" The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[0425] Specific examples

[0426] Example 1: Creating a wealth plan

[0427] 1. Users upload documents related to their income, expenses, and investment status.

[0428] 2. The device scans the document and sends the image data to the server.

[0429] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[0430] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[0431] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0432] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[0433] Example 2: Creating an estate planning plan

[0434] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[0435] 2. The device sends this information in text form to the server.

[0436] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0437] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0438] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[0439] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[0440] Examples of prompt statements

[0441] Prompt statement:

[0442] "If you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, what will your asset value be in 10 years?"

[0443] This system makes it easy for users to understand the plan that is best suited to their situation, allowing them to plan for their future assets with peace of mind.

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] System program processing flow

[0446] Step 1: User input

[0447] User Action:

[0448] A user logs into a rich client terminal and uploads a document containing their personal and financial information.

[0449] input:

[0450] Users select and upload documents containing information such as annual income, monthly expenses, bank balance, and real estate ownership status.

[0451] output:

[0452] The device generates the digital data that has been scanned and uploaded.

[0453] Specific behavior:

[0454] The user uses the file selection dialog on the device to select a PDF or image document and clicks the upload button, generating digital data.

[0455] Step 2: Data conversion

[0456] Terminal operation:

[0457] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[0458] input:

[0459] Document data provided by the user.

[0460] output:

[0461] High resolution image data sent to the server.

[0462] Specific behavior:

[0463] The terminal controls the scanner device, scans the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[0464] Step 3: Extracting information from image data

[0465] Server Operation:

[0466] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data.

[0467] input:

[0468] The image data sent.

[0469] output:

[0470] Extracted numerical and textual information (structured data such as JSON format).

[0471] Specific behavior:

[0472] The server runs Tesseract OCR and passes the image data as input, which extracts the text data and stores it as structured data.

[0473] Step 4: Save your data

[0474] Server Operation:

[0475] Store the extracted numerical and text information in a database (e.g., MySQL or PostgreSQL).

[0476] input:

[0477] Structured data extracted by OCR.

[0478] output:

[0479] The data to be inserted into the database.

[0480] Specific behavior:

[0481] The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[0482] Step 5: Analyze the data

[0483] Server Operation:

[0484] The server analyzes the stored data using specific algorithms (for example, machine learning models) and automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[0485] input:

[0486] User data stored in a database.

[0487] output:

[0488] Analysis results and auto-generated plans.

[0489] Specific behavior:

[0490] The server loads a machine learning model (e.g., scikit-learn or TensorFlow) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[0491] Step 6: Update your legal information

[0492] Server Operation:

[0493] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0494] input:

[0495] Up-to-date legal information retrieved from an external API.

[0496] output:

[0497] Updated database information and revised analysis algorithms.

[0498] Specific behavior:

[0499] The server periodically retrieves the latest legal change information from an external API (e.g., a government tax law information service) and updates the database. The retrieved information is then input into an analysis algorithm.

[0500] Step 7: User sentiment analysis

[0501] Terminal operation:

[0502] The device's built-in emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's facial expressions and voice data in real time to recognize their emotional state.

[0503] input:

[0504] The user's facial expression data and voice data.

[0505] output:

[0506] Perceived emotional state (e.g., relief, anxiety, interest).

[0507] Specific behavior:

[0508] The device collects real-time data through the camera and microphone, sends it to the emotion engine, and obtains analysis results.

[0509] Step 8: Feedback of emotional data

[0510] Server Operation:

[0511] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state.

[0512] input:

[0513] Emotion data sent from the emotion engine.

[0514] output:

[0515] Adjusted plan content and presentation.

[0516] Specific behavior:

[0517] The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results, changing the display content and presentation format.

[0518] Step 9: Creating a visual image

[0519] Server Operation:

[0520] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[0521] input:

[0522] Auto-generated plan.

[0523] output:

[0524] Graphs and charts generated as visual images.

[0525] Specific behavior:

[0526] The server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize the progress of income and expenditures. The generated graphs are saved as image files or dynamically generated in HTML format.

[0527] Step 10: Present a visual image

[0528] Server Operation:

[0529] The server transmits the generated plan and visual image to the terminal.

[0530] input:

[0531] Generated visual images and plan data.

[0532] output:

[0533] Data sent to the device.

[0534] Specific behavior:

[0535] The server packages the generated data and graphs and sends them to the terminal as an HTTP response.

[0536] Step 11: Present to the user

[0537] Terminal operation:

[0538] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[0539] input:

[0540] Plans and visual images sent from the server.

[0541] output:

[0542] Plans and visual images presented to users.

[0543] Specific behavior:

[0544] The device displays the generated plan and visual image through a web browser or a dedicated client application. The user's reaction data is newly acquired and reflected in the displayed content.

[0545] Step 12: User Feedback

[0546] User Action:

[0547] The user checks the presented plan and visual images, and requests further information or provides feedback as necessary.

[0548] input:

[0549] User feedback and questions.

[0550] output:

[0551] User feedback and question data sent to the server.

[0552] Specific behavior:

[0553] The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[0554] (Application example 2)

[0555] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0556] Conventional asset formation, inheritance planning, and tax saving plan generation systems do not take into account the user's emotional state, which can make the proposed plans difficult for users to understand, potentially causing anxiety and stress. In addition, the inability to provide appropriate information based on the user's emotions has led to a problem of reduced user satisfaction.

[0557] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting personal information and asset information of the user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, and means for recognizing the user's emotions in real time and adjusting the content and display format of the plans according to the emotions. This makes it possible to provide personalized plans according to the user's emotional state.

[0558] "Personal information" refers to information for identifying a user, as well as personal data such as age, occupation, income, and expenses.

[0559] "Asset information" refers to information about the assets held by a user, such as cash, real estate, investments, and debts.

[0560] "Means of collection" refers to the hardware and software mechanisms used to capture user-provided data into the system.

[0561] The "analysis means" refers to the algorithms and servers that process the collected personal and asset information and perform calculations and evaluations based on that data.

[0562] The "means of automatic generation" is a software mechanism that automatically creates optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[0563] "Visual display means" refers to a mechanism for displaying the generated plans and data on a monitor or display as graphs or text in a format that is easy for the user to understand.

[0564] "Means for recognizing emotions in real time" refers to software and hardware that analyzes the user's facial expressions and voice data and identifies their emotional state in real time.

[0565] "Adjustment means" refers to the system's functionality for changing the content and display format of the generated plan in response to the recognized emotion.

[0566] This invention is a system that collects personal information and asset information of a user, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. This system includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plans according to those emotions.

[0567] System Configuration

[0568] 1. Data Collection and Analysis

[0569] Users upload their personal and asset information to the device via a smartphone application. This collects information such as annual income, monthly expenses, bank balance, and real estate ownership status. Documents are scanned using the device's built-in scanner and camera and converted into image data. This data is then sent to the server.

[0570] 2. Image Recognition and Data Extraction

[0571] The server uses OCRTesseract to extract numerical and text information from the image data. For example, specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted. The extracted numerical and text information is stored in a database.

[0572] 3. Plan Generation

[0573] The server analyzes the stored data using a generative AI model. A specific algorithm is applied to predict future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, an optimal asset formation, inheritance planning, and tax saving plan is automatically generated. The system also learns the latest legal changes in real time and reflects them in the plan.

[0574] 4. Emotion recognition

[0575] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice data in real time. This emotion data is sent to a server and classified into the user's emotional state, such as "relief," "anxiety," or "interest."

[0576] 5. Plan adjustment and visual image creation

[0577] The server receives the emotional data and adjusts the content and display format of the plan based on the user's emotional state. For example, if the server detects that the user is feeling anxious, it will provide a more detailed explanation of the plan and use more polite language. Based on the analysis results, it will graph the progress of income and expenditures and future asset value, and also provide specific examples of lifestyle plans.

[0578] Specific examples of programs

[0579] When a user uploads a document about their annual income and expenses, the document is scanned and converted into image data. Numerical information is then extracted using OCR and sent to a server. The server analyzes the data and creates an asset formation plan using a generative AI model. The generated plan is presented to the user as a visual image, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," more detailed explanations and different investment options are displayed.

[0580] Prompt Sentence Examples

[0581] Please calculate the specific amount you could save in a year based on a scenario where your annual income is 5 million yen and your monthly expenses are 300,000 yen. Also, if that scenario makes users feel uneasy, please suggest how you should change the explanation or offer.

[0582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0583] Step 1:

[0584] Users upload personal and asset information to a device via a smartphone application. The input data includes personal data such as annual income, monthly expenses, bank balance, and real estate ownership status. This data is captured as an image using a camera or scanner installed on the device. The output is the captured image data.

[0585] Step 2:

[0586] The terminal transmits the captured image data to the server. The input data is the image data, and the output is the image data transmitted to the server.

[0587] Step 3:

[0588] The server receives the image data and uses OCR to extract text information from the image. The input data is image data, and OCR processing is performed as data processing. The output is information such as annual income, expenses, and real estate appraisal value as text data.

[0589] Step 4:

[0590] The server saves the extracted text information in a database. The input data is text information, which is written to the database as data calculation. The output is user information stored in the database.

[0591] Step 5:

[0592] Based on the data stored on the server, a generative AI model is used to automatically generate plans for asset formation, inheritance planning, and tax savings. The input data is user information obtained from a database, and data analysis and generation are performed based on an algorithm. The output is the generated plan.

[0593] Step 6:

[0594] The server learns the latest legal amendments in real time and reflects them in the generated plan. The input data is legal amendments and the generated plan, and the plan is updated through data calculations. The output is a plan that reflects the latest legal amendments.

[0595] Step 7:

[0596] The device's emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. The input data is the user's facial expression images and voice data, and emotion analysis is performed. The output is emotion data classified as "relief," "anxiety," "interest," etc.

[0597] Step 8:

[0598] The server receives the emotional data and adjusts the content and display format of the plan based on the user's emotional state. The input data is the emotional data and the generated plan, and the content and display format of the plan are adjusted as data processing. The output is a plan adjusted according to the user's emotions.

[0599] Step 9:

[0600] The server generates a visual image of the adjusted plan and graphs the progress of income and expenditures and future asset value. The input data is the adjusted plan, and visualization is performed as a data calculation. The output is a graphed plan.

[0601] Step 10:

[0602] The device presents the generated visual image to the user and monitors the user's reaction in real time. The input data is a graphed plan, and the output is the plan presented to the user and the real-time monitoring results of the emotion engine.

[0603] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0604] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0605] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0606] [Second embodiment]

[0607] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0608] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0609] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0610] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0611] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0612] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0613] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0614] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0615] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0616] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0617] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0618] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0619] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them. Specific embodiments of this system and the operation of its program are described below.

[0620] Data collection

[0621] User Action:

[0622] A user uses a rich client terminal to upload documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, real estate ownership status, etc.

[0623] Terminal operation:

[0624] The device scans the provided document and generates image data, which is then sent to the server.

[0625] Data Extraction

[0626] Server Operation:

[0627] The server uses image recognition software to extract numerical and textual information from the image data sent to it. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[0628] Server Operation:

[0629] The extracted numerical and text information is stored in a database on the server.

[0630] Plan Generation

[0631] Server Operation:

[0632] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[0633] Server Operation:

[0634] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, which means that the database is continuously updated and plans are generated based on that information.

[0635] Visual image generation and presentation

[0636] Server Operation:

[0637] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and forecasts of future asset values. Specific examples of lifestyle planning are provided based on specific simulation results and statistical data.

[0638] Server Operation:

[0639] The server transmits the generated plan and visual image to the terminal.

[0640] Terminal operation:

[0641] The terminal presents the generated plan and visual images to the user, and the information is displayed in a format that is intuitively easy for the user to understand.

[0642] User Action:

[0643] Users can check the plans and visual images presented and provide feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[0644] Specific examples

[0645] Example 1: Creating a wealth plan

[0646] 1. Users upload documents related to their income, expenses, and investment status.

[0647] 2. The device scans the document and sends the image data to the server.

[0648] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[0649] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase by 1.5 times in 10 years.

[0650] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0651] 6. The terminal presents this to the user, who confirms the plan.

[0652] Example 2: Creating an estate planning plan

[0653] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[0654] 2. The device sends this information in text form to the server.

[0655] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0656] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0657] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[0658] This allows users to easily understand the plan that best suits their situation, even if they do not have a deep knowledge of complex laws and tax systems, and to plan their post-retirement life with peace of mind.

[0659] The above is a specific embodiment for carrying out the present invention.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] User Action:

[0663] A user logs into a rich client terminal and uploads a document containing personal and asset information to the terminal.

[0664] Step 2:

[0665] Terminal operation:

[0666] The device scans the uploaded documents and converts them into image data.

[0667] Step 3:

[0668] Terminal operation:

[0669] The terminal transmits the converted image data to the server.

[0670] Step 4:

[0671] Server Operation:

[0672] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[0673] Step 5:

[0674] Server Operation:

[0675] The server stores the extracted numerical and text information in a database.

[0676] Step 6:

[0677] Server Operation:

[0678] The server analyzes the stored data and selects the algorithm to apply, such as an asset formation algorithm or an inheritance planning algorithm.

[0679] Step 7:

[0680] Server Operation:

[0681] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0682] Step 8:

[0683] Server Operation:

[0684] The server uses the selected algorithm to analyze the data and generate an optimal plan. For example, it generates a specific prediction such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[0685] Step 9:

[0686] Server Operation:

[0687] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[0688] Step 10:

[0689] Server Operation:

[0690] The server sends the generated plan and visual image to the terminal.

[0691] Step 11:

[0692] Terminal operation:

[0693] The terminal presents the plan and visual image received from the server to the user.

[0694] Step 12:

[0695] User Action:

[0696] The user reviews the presented plans and visual images and provides further details and feedback as needed.

[0697] This allows users to obtain specific plans for their own asset formation, inheritance planning, and tax savings, allowing them to plan their retirement with peace of mind.

[0698] Example 1

[0699] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] Conventional asset formation, inheritance planning, and tax saving planning systems require users to input accurate and detailed information, making operation cumbersome. Furthermore, due to frequent legal changes, it is difficult to reflect the latest legal changes, and as a result, generated plans may not comply with the latest laws and regulations. Furthermore, generated plans are often not presented in a format that is intuitively easy for users to understand.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0702] In this invention, the server includes means for users to upload information, means for a terminal to scan documents and generate image data, means for the server to extract numerical and textual information from the image data, means for the server to store the extracted information in a database, means for the server to analyze the data using an algorithm and generate optimal plans for asset formation, inheritance planning, and tax savings, means for the server to convert the generated plans into visual images, and means for the terminal to present the generated plans and visual images to the user. This makes it possible for users to easily provide detailed information and to receive intuitively understandable plans that reflect the latest legal amendments.

[0703] A "user" is an entity that uses the system to provide personal information and asset information.

[0704] "Means for uploading information" is a function that allows users to send their own personal information and asset information to the system.

[0705] A "terminal" is a computer device operated by a user, and is a device that has functions such as scanning documents and displaying data.

[0706] "Means for scanning documents and generating image data" refers to the function of converting physical documents into digital image data.

[0707] A "server" is a computer system that processes, stores, analyzes data, and communicates with other devices.

[0708] "Means for extracting numerical and textual information from image data" refers to technology that identifies and extracts text data from digital images.

[0709] "Means for storing information in a database" refers to a system for appropriately structuring extracted information and storing it for a long period of time.

[0710] An "algorithm" is a set of computational steps for analyzing data or solving problems.

[0711] "A means of analyzing data and generating optimal plans for asset formation, inheritance planning, and tax savings" is a function that automatically creates various plans using mathematical models and analytical methods based on stored data.

[0712] "Means for converting the generated plan into a visual image" refers to a technique for converting the analysis results into a format that is visually easy to understand (e.g., graphs or charts).

[0713] The "means for presenting plans and visual images" is a function for displaying the generated plans and visual information to the user.

[0714] "Latest legal reform information" refers to the latest information on regulations and changes to laws related to asset formation, inheritance planning, and tax savings.

[0715] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them.

[0716] Hardware and Software Configuration

[0717] User operation and information provision

[0718] Users use a rich client device to upload documents containing their personal and financial information. This requires a computer, a scanner, and an internet connection. Specifically, documents provide information such as annual income, monthly expenses, bank balance, and real estate ownership.

[0719] Document scanning and image data generation

[0720] The device scans the document provided by the user and generates high-resolution image data. This step uses a scanner and image processing software (e.g., Adobe Acrobat). The generated image data is then sent to the server via a secure protocol.

[0721] Information extraction from image data

[0722] The server uses image recognition software (e.g., OCR software) to extract numerical and textual information from the image data sent. For example, OCR software extracts specific information such as "annual income of 5 million yen" and "monthly expenses of 300,000 yen" as text data.

[0723] Database storage

[0724] The extracted numerical and textual information is stored in a database on the server. The database system (e.g., MySQL or PostgreSQL) is built with high availability and security in mind. Annual income and expenditure information is stored linked to the user ID.

[0725] Data analysis and plan generation

[0726] The server analyzes the stored data by applying specific algorithms (e.g., regression analysis, machine learning models, etc.). The analysis targets the user's annual income, expenses, investment status, etc. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[0727] Reflecting the latest legal amendments

[0728] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, continuously updating the database so that the plans generated based on them comply with the latest laws and regulations.

[0729] Visual image generation

[0730] Based on the generated plan, the server graphs the progress of income and expenditures and the forecast of future asset value. This visual image is provided in a format that is intuitively easy for users to understand. Specifically, a graph drawing tool (e.g., D3.js, Chart.js) is used.

[0731] Presentation of plans and visual images

[0732] The server sends the generated plan and visual image to the terminal, which then presents it to the user, displaying the information in a format that is intuitively easy for the user to understand. For example, graphs and simulation results are displayed in an easy-to-understand manner.

[0733] User Feedback

[0734] Users can check the plans and visual images presented and provide feedback as needed. For example, they can send questions to the server such as, "What specific procedures are required for this tax-saving measure?" The system will then be improved based on the feedback.

[0735] Examples of prompt statements

[0736] Below are some examples of specific prompts to input into a generative AI model:

[0737] Prompt statement:

[0738] Generate an optimal asset formation plan based on the user's personal and asset information. First, provide information such as the user's annual income, monthly expenses, deposit balance, and real estate ownership status, and analyze this to predict future asset value. Next, propose a specific plan that takes into account tax savings and inheritance planning, and generate and display a visual image.

[0739] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0740] Specific explanation of processing steps

[0741] Step 1: User uploads information

[0742] Input: Personal and financial information such as the user's annual income, monthly expenses, bank balance, and real estate ownership status is provided as documents.

[0743] What happens: Users upload these documents to the system using rich client devices, which have scanning and digital form-filling capabilities.

[0744] Output: The device receives this information as digital data.

[0745] Step 2: The device scans the document and generates image data.

[0746] Input: Documents uploaded by users.

[0747] Specific operation: The device uses a high-resolution scanner to scan the provided document and generate image data. Image processing software (e.g., Adobe Acrobat) is used.

[0748] Output: The generated image data is saved to the device.

[0749] Step 3: The device sends the image data to the server

[0750] Input: scanned image data.

[0751] Specific operation: The device sends image data to the server via a secure protocol (e.g., HTTPS).

[0752] Output: The transmitted image data is saved on the server.

[0753] Step 4: The server extracts numerical and textual information from the image data.

[0754] Input: Image data stored on the server.

[0755] Specific operation: The server uses OCR software to extract numerical and textual information from the image data. For example, it uses text detection to extract specific information such as "annual income of 5 million yen" or "monthly expenses of 300,000 yen."

[0756] Output: The extracted numerical and character information is generated as text data.

[0757] Step 5: The server stores the extracted information in a database

[0758] Input: Extracted numeric and textual information.

[0759] Specific operation: The server stores this information in a database system (e.g., MySQL, PostgreSQL). Each piece of information is structured and linked to the user ID.

[0760] Output: Structured data stored in a database.

[0761] Step 6: The server uses algorithms to analyze the data and generate a plan

[0762] Input: User's personal and financial information stored in a database.

[0763] Specific operation: The server applies data analysis algorithms (e.g., regression analysis, machine learning models) to calculate optimal plans for asset formation, inheritance planning, and tax savings based on various information. For example, it predicts future assets based on the user's annual income, expenses, and investment status.

[0764] Output: The generated optimal plan.

[0765] Step 7: The server updates with the latest legal information

[0766] Input: Latest legal change information and generated plan.

[0767] How it works: The server learns the latest legal changes in real time and reflects them in the analysis algorithm, so the generated plans comply with the latest laws and regulations.

[0768] Output: An updated plan that reflects the latest legal changes.

[0769] Step 8: The server converts the generated plan into a visual image

[0770] Input: The generated plan.

[0771] Specific operation: The server uses a visual image generation tool (e.g., D3.js, Chart.js) to convert the analysis results into a visually understandable format, such as graphing the progress of income and expenditure or future asset value.

[0772] Output: Graphs and charts as visual images.

[0773] Step 9: The server sends the generated plan and visual image to the device.

[0774] Input: Generated plans and visual images.

[0775] Specific operation: The server sends this information to the device via a secure protocol (e.g., HTTPS).

[0776] Output: Plans and visual images sent to the device.

[0777] Step 10: The device presents the user with a plan and visual images

[0778] Input: Plan and visual images sent from the server.

[0779] Specific operation: The device presents the plan and visual image to the user through a display function (e.g., web browser, dedicated app). The plan and visual image are displayed on the screen in a way that allows the user to intuitively understand it.

[0780] Output: Plans and visual images presented to the user.

[0781] Step 11: User provides feedback

[0782] Input: Plan and visual image displayed on the terminal.

[0783] Specific operation: The user checks the plan and provides feedback as needed (e.g., "What specific steps are required to implement this tax-saving measure?"). The feedback is sent to the server via the device.

[0784] Output: User feedback sent to the server.

[0785] (Application example 1)

[0786] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0787] Existing asset management, inheritance planning, and tax-saving systems often require users to manage their own information and have specialized knowledge in order to be effective. Furthermore, there is a lack of practical support, as there are limited ways for users to automatically implement asset formation and tax-saving strategies. Therefore, there is a need for a system that allows users to easily implement optimal asset formation and tax-saving strategies, and also allows for integrated electronic payments based on those plans.

[0788] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0789] In this invention, the server includes means for collecting personal information and asset information of a user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, means for supporting electronic payments based on the generated plans, and means for providing interactive feedback. This allows users to put optimal asset management and tax savings plans into practice without specialized knowledge. Furthermore, the integration of electronic payments based on the plans provides practical support for asset formation and tax savings, enabling comprehensive asset management.

[0790] "User's personal information" refers to information that identifies an individual or indicates an individual's attributes, and includes, for example, name, age, sex, address, occupation, income, and the like.

[0791] "Asset information" refers to information about financial assets, real estate, and other assets owned by a user, including, for example, bank account balances, appraised values ​​of stocks and investment trusts, appraised values ​​of real estate, and the like.

[0792] "Means for collecting" refers to a method or mechanism for obtaining personal information and asset information from a user and transmitting it to a server.

[0793] "Means for analysis" refers to a method or mechanism for performing data analysis based on collected personal information and asset information and deriving useful results.

[0794] "Means for automatically generating optimal plans" refers to a method or mechanism that automatically creates asset formation, inheritance planning, and tax saving plans that are most suitable for each user based on the results of data analysis.

[0795] "Visual display means" refers to a method or mechanism for visually presenting the generated plan to a user, such as a graph or chart.

[0796] "Means for supporting electronic payments" refers to a method or mechanism for a user to make the electronic payments necessary to manage his or her assets based on the generated plan.

[0797] "Means for providing interactive feedback" refers to a method or mechanism for responding to questions or feedback from a user in real time and providing additional information or advice.

[0798] The system for implementing this invention collects and analyzes a user's personal information and asset information, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. It also includes functions to support electronic payments based on the generated plans and provide interactive feedback.

[0799] Program processing and hardware / software used

[0800] Data collection

[0801] The device scans documents provided by the user (such as annual income, monthly expenses, bank balance, and real estate ownership status) and generates image data. This image data is then sent to a cloud server. The hardware used includes a scanner and a camera.

[0802] Data Extraction

[0803] The server uses image recognition software called "pytesseract" to extract necessary numerical and text information from the transmitted image data. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[0804] Data storage and analysis

[0805] The extracted numerical and textual information is stored in a secure database on the server, which then applies specific algorithms to analyze the data.

[0806] Plan Generation

[0807] The server generates an optimal asset formation plan based on the user's annual income, expenses, and investment status. It also learns the latest legal reform information in real time and reflects it in the analysis results. Based on this analysis result, it provides the user with the optimal asset formation plan.

[0808] Visual image generation and presentation

[0809] Based on the generated plan, the server uses matplotlib to graph the progress of income and expenditures and the forecast of future asset value. This visual image is sent to the terminal and presented to the user in an easy-to-understand format.

[0810] Support for electronic payments

[0811] Based on the generated plan, the server supports electronic payments for users to carry out asset management and tax saving measures. The server has the function of automatically transferring funds and issuing instructions for investment.

[0812] Interactive Feedback

[0813] Users can ask questions about the generated plans and visual images, and the server will respond in real time via chatbots or other means, providing additional information and advice.

[0814] Examples of concrete examples and prompts

[0815] For example, based on data provided by a user with an annual income of 5 million yen and monthly expenses of 300,000 yen, the server generates the following asset formation plan:

[0816] It is predicted that by investing 200,000 yen per month in low-risk investments, assets will increase by 1.5 times in 10 years.

[0817] Based on the generated plan and its visual image, electronic payment support is provided to users for asset management.

[0818] Example prompt sentence:

[0819] "Upload your annual income and monthly expenses. We'll then predict your future asset growth and display it as a graph."

[0820] The above is a specific embodiment for carrying out the present invention. This system allows users to appropriately manage assets and take tax-saving measures without requiring specialized knowledge.

[0821] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0822] Step 1:

[0823] The user uploads documents containing their personal and financial information to the terminal, including annual income, monthly expenses, bank balance, real estate ownership status, etc. The user's input is captured in the terminal as image data of the document and sent to the server.

[0824] Step 2:

[0825] The device scans the uploaded document and generates image data, which serves as input data to be sent to the server. The output of the device is a high-resolution scanned image file.

[0826] Step 3:

[0827] The server uses the image recognition software "pytesseract" to extract text information from the transmitted image data. Specifically, it converts the image data to grayscale and applies a text recognition algorithm. The server's input is the image data, and its output is the extracted information as text. For example, specific numerical information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted.

[0828] Step 4:

[0829] The server stores the extracted numerical and textual information in a secure database. The stored data is used for subsequent data analysis. The input to the server is the extracted textual information, and the output is storage in the database.

[0830] Step 5:

[0831] The server applies specific algorithms to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. This analysis automatically generates optimal asset formation, inheritance planning, and tax-saving plans. The server's input is numerical information stored in a database, and its output is a specific plan.

[0832] Step 6:

[0833] The server learns the latest legal amendments in real time and reflects them in the analysis algorithm. This ensures that the generated plans are always based on the latest laws and regulations. The server's input is legal amendments, and the output is the latest plan based on that information.

[0834] Step 7:

[0835] The server uses "matplotlib" to generate visual images based on the generated plan. Specifically, it graphs the progress of income and expenditures and the forecast of future asset value. The server's input is the generated plan information, and its output is visual graphs and charts.

[0836] Step 8:

[0837] The server sends the generated visual image and plan to the terminal. The terminal displays them in a format that is easy for the user to understand. This allows the user to visually confirm their own asset formation plan. The input to the terminal is the visual image and plan sent from the server, and the output is the display to the user.

[0838] Step 9:

[0839] Based on the generated plan, the terminal supports electronic payments, automatically executing specific fund transfer and investment instructions. The input of the terminal is the specific plan instructions, and the output is the actual payment transaction.

[0840] Step 10:

[0841] The user checks the generated plan and visual image and provides feedback as needed. For example, a question might be, "What exactly will this tax saving measure do?" The user's input is the feedback or question, and the server responds by providing an interactive response. The server's input is the user's feedback, and its output is the answer or additional information.

[0842] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0843] This invention is a system that combines a system that collects a user's personal information and asset information, analyzes it, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system and the operation of its program are described below.

[0844] Data collection

[0845] User Action:

[0846] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[0847] Terminal operation:

[0848] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[0849] Data Extraction

[0850] Server Operation:

[0851] The server uses image recognition software to extract numerical and textual information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[0852] Server Operation:

[0853] The extracted numerical and text information is stored in a database.

[0854] Plan Generation

[0855] Server Operation:

[0856] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal asset formation, inheritance planning, and tax saving plans.

[0857] Server Operation:

[0858] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0859] Manipulating the Emotion Engine

[0860] Terminal operation:

[0861] The emotion engine installed in the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc.

[0862] Server Operation:

[0863] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the server presents a more detailed explanation in polite language.

[0864] Visual image generation and presentation

[0865] Server Operation:

[0866] Based on the plan generated by the server, a visual image is generated, such as graphing income and expenditure trends and future asset values. Specific examples of lifestyle planning are also provided based on specific simulation results and statistical data.

[0867] Server Operation:

[0868] The server transmits the generated plan and visual image to the terminal.

[0869] Terminal operation:

[0870] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[0871] User Action:

[0872] Users can review the plans and visual images presented and, if necessary, request more detailed information or provide feedback. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[0873] Specific examples

[0874] Example 1: Creating a wealth plan

[0875] 1. Users upload documents related to their income, expenses, and investment status.

[0876] 2. The device scans the document and sends the image data to the server.

[0877] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[0878] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[0879] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0880] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[0881] Example 2: Creating an estate planning plan

[0882] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[0883] 2. The device sends this information in text form to the server.

[0884] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0885] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0886] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[0887] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[0888] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[0889] The above is a specific embodiment for carrying out the present invention.

[0890] The processing flow will be explained below.

[0891] Data collection and extraction

[0892] Step 1:

[0893] User Action:

[0894] A user logs in to a rich client terminal and uploads documents containing personal and financial information to the terminal, including annual income, monthly expenses, savings balance, real estate ownership status, etc.

[0895] Step 2:

[0896] Terminal operation:

[0897] The device scans the uploaded document and converts it into image data.

[0898] Step 3:

[0899] Terminal operation:

[0900] The terminal transmits the converted image data to the server.

[0901] Step 4:

[0902] Server Operation:

[0903] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[0904] Step 5:

[0905] Server Operation:

[0906] The extracted numerical and text information is stored in a database.

[0907] Plan generation and emotion engine operation

[0908] Step 6:

[0909] Server Operation:

[0910] The server analyzes the stored data and applies selected algorithms, including algorithms that generate asset formation, inheritance planning, and tax savings plans based on the user's annual income, expenses, and investment status.

[0911] Step 7:

[0912] Server Operation:

[0913] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[0914] Step 8:

[0915] Server Operation:

[0916] The server uses the selected algorithm to automatically generate optimal asset formation, inheritance planning, and tax saving plans based on the analysis results. For example, it makes specific predictions such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[0917] Step 9:

[0918] Terminal operation:

[0919] The device's built-in emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies them as "relief," "anxiety," "interest," etc.

[0920] Visual image generation and presentation

[0921] Step 10:

[0922] Server Operation:

[0923] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[0924] Step 11:

[0925] Server Operation:

[0926] The server transmits the generated plan and visual image to the terminal.

[0927] Step 12:

[0928] Terminal operation:

[0929] The device receives the plan and visual images from the server and presents them to the user. The emotion engine then monitors the user's real-time reactions and adjusts the displayed content as needed. For example, if the user is recognized as "anxious," the level of detail and wording of the explanation will be changed.

[0930] Step 13:

[0931] User Action:

[0932] The user reviews the presented plan and visual image and gives feedback, for example, by asking specific questions such as, "What specific procedures are required for this tax-saving measure?"

[0933] Specific examples

[0934] Example 1: Creating a wealth plan

[0935] Step 1:

[0936] Users upload documents related to their income, expenses, and investment status.

[0937] Step 2:

[0938] The device scans the document and sends the image data to the server.

[0939] Step 3:

[0940] The server uses image recognition software to extract numbers (annual income, expenses, investment amounts, etc.).

[0941] Step 4:

[0942] The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[0943] Step 5:

[0944] The server graphs the plan and generates a visual image of the progress of income and expenditure.

[0945] Step 6:

[0946] The device presents this to the user, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[0947] Example 2: Creating an estate planning plan

[0948] Step 1:

[0949] Users provide information about their family structure, real estate holdings, and financial assets.

[0950] Step 2:

[0951] The terminal sends this information to the server in text form.

[0952] Step 3:

[0953] The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[0954] Step 4:

[0955] The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[0956] Step 5:

[0957] The server transmits the generated plan to the terminal, which then presents it to the user.

[0958] Step 6:

[0959] The emotion engine analyzes the user's reaction, and if it recognizes that the user feels "at ease," the detailed explanation of the plan is omitted and the system moves on to the next step.

[0960] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[0961] Example 2

[0962] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0963] Conventional asset formation, inheritance planning, and tax-saving systems offer the ability to automatically generate and display plans based on the user's personal and asset information. However, these systems do not take the user's emotional state into account, which often leaves users feeling anxious or makes the plans difficult to understand. It is also difficult to reflect changes due to legal amendments in real time, making it difficult to provide plans based on the latest information. Furthermore, there are limitations to properly analyzing collected numerical and text information and presenting optimal plans. A method to solve these issues is needed.

[0964] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0965] In this invention, the server includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plan based on the user's emotional state, means for learning the latest legal amendment information in real time and reflecting it in the plan, and means for storing the extracted numerical and text information in a database and performing data analysis. This makes it possible to propose personalized plans based on the user's emotions and always provide optimal plans based on the latest legal amendment information.

[0966] "User's personal information" refers to information for identifying an individual, such as the user's name, age, sex, and occupation.

[0967] "Asset information" refers to information about financial assets and physical assets owned by a user, such as cash, deposits, real estate, stocks, and bonds.

[0968] "Means for collection" refers to the method and device for acquiring personal information and asset information from users and incorporating it into the system.

[0969] "Means for analysis" refers to algorithms and programs that perform data analysis based on collected data and make predictions about the user's financial situation and future prospects.

[0970] "Means for automatic generation" refers to algorithms and programs that automatically generate optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[0971] "Visual display means" refers to devices and software for presenting the generated plan to a user in an easy-to-view format through visual elements such as graphs and charts.

[0972] "Means for recognizing in real time" refers to technology and devices that detect a user's emotions in real time and analyze that information.

[0973] "Adjusting means" refers to software and algorithms for appropriately modifying the content and presentation of the plan based on the user's emotional state as recognized in real time.

[0974] "Legal Change Information" refers to information on the latest laws and regulations, including in particular changes to tax laws and inheritance laws.

[0975] "Means of learning in real time" refers to the data acquisition mechanism and learning algorithms that instantly acquire the latest legal amendment information and reflect it in the system.

[0976] "Means for storing data in a database" refers to a database system and related software for efficiently storing and managing collected numerical and textual information.

[0977] This invention is a system that collects and analyzes a user's personal and asset information to automatically generate and visually display optimal asset formation, inheritance planning, and tax savings plans. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, a more personalized experience is provided. A specific embodiment of this system and the operation of its program are described below.

[0978] Data collection

[0979] User Action:

[0980] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[0981] Terminal operation:

[0982] The terminal scans the provided document, converts it into image data, and sends the data to the server. Specifically, the terminal controls a scanner device to scan the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[0983] Data Extraction

[0984] Server Operation:

[0985] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenses of 300,000 yen." The server starts Tesseract OCR, passes the image data as input, and the OCR extracts the text data, which is then saved as structured data (e.g., JSON format).

[0986] Server Operation:

[0987] Store the extracted numeric and text information in a database (e.g., MySQL or PostgreSQL). The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[0988] Plan Generation

[0989] Server Operation:

[0990] The server applies a specific algorithm (e.g., a machine learning model) to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings. The server loads a machine learning model (e.g., scikit-learn or TensorFlow) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[0991] Server Operation:

[0992] The server updates the database with the latest legal amendment information in real time and reflects it in the analysis algorithm. The server periodically obtains the latest legal amendment information from an external API (e.g., a government tax law information service) and updates the database. The obtained information is input into the analysis algorithm.

[0993] Manipulating the Emotion Engine

[0994] Terminal operation:

[0995] An emotion engine (for example, Microsoft Azure Emotion API) installed on the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while they are looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc. The device collects real-time data through the camera and microphone and sends it to the emotion engine to obtain the analysis results.

[0996] Server Operation:

[0997] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the explanation is presented in more detail and in more polite language. The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results. The display content and presentation format are changed.

[0998] Visual image generation and presentation

[0999] Server Operation:

[1000] Based on the generated plan, the server generates visual images, such as graphs of income and expenditure trends and future asset value. Specifically, the server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize income and expenditure trends, and the generated graphs are saved as image files or dynamically generated in HTML format.

[1001] Server Operation:

[1002] The server sends the generated plan and visual image to the terminal, and the server packages the generated data and graph and sends it to the terminal as an HTTP response.

[1003] Terminal operation:

[1004] The device presents the generated plan and visual images to the user. Furthermore, an emotion engine monitors the user's real-time reactions and adjusts the displayed content as necessary. The device displays the generated plan and visual images through a web browser or dedicated client application, and new user reaction data is acquired and reflected in the displayed content.

[1005] User Action:

[1006] The user checks the presented plan and visual image, and requests more detailed information or gives feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax saving measure?" The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[1007] Specific examples

[1008] Example 1: Creating a wealth plan

[1009] 1. Users upload documents related to their income, expenses, and investment status.

[1010] 2. The device scans the document and sends the image data to the server.

[1011] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[1012] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[1013] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[1014] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[1015] Example 2: Creating an estate planning plan

[1016] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[1017] 2. The device sends this information in text form to the server.

[1018] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[1019] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[1020] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[1021] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[1022] Examples of prompt statements

[1023] Prompt statement:

[1024] "If you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, what will your asset value be in 10 years?"

[1025] This system makes it easy for users to understand the plan that is best suited to their situation, allowing them to plan for their future assets with peace of mind.

[1026] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1027] System program processing flow

[1028] Step 1: User input

[1029] User Action:

[1030] A user logs into a rich client terminal and uploads a document containing their personal and financial information.

[1031] input:

[1032] Users select and upload documents containing information such as annual income, monthly expenses, bank balance, and real estate ownership status.

[1033] output:

[1034] The device generates the digital data that has been scanned and uploaded.

[1035] Specific behavior:

[1036] The user uses the file selection dialog on the device to select a PDF or image document and clicks the upload button, generating digital data.

[1037] Step 2: Data conversion

[1038] Terminal operation:

[1039] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[1040] input:

[1041] Document data provided by the user.

[1042] output:

[1043] High resolution image data sent to the server.

[1044] Specific behavior:

[1045] The terminal controls the scanner device, scans the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[1046] Step 3: Extracting information from image data

[1047] Server Operation:

[1048] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data.

[1049] input:

[1050] The image data sent.

[1051] output:

[1052] Extracted numerical and textual information (structured data such as JSON format).

[1053] Specific behavior:

[1054] The server runs Tesseract OCR and passes the image data as input, which extracts the text data and stores it as structured data.

[1055] Step 4: Save your data

[1056] Server Operation:

[1057] Store the extracted numerical and text information in a database (e.g., MySQL or PostgreSQL).

[1058] input:

[1059] Structured data extracted by OCR.

[1060] output:

[1061] The data to be inserted into the database.

[1062] Specific behavior:

[1063] The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[1064] Step 5: Analyze the data

[1065] Server Operation:

[1066] The server analyzes the stored data using specific algorithms (for example, machine learning models) and automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[1067] input:

[1068] User data stored in a database.

[1069] output:

[1070] Analysis results and auto-generated plans.

[1071] Specific behavior:

[1072] The server loads a machine learning model (e.g., scikit-learn or TensorFlow) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[1073] Step 6: Update your legal information

[1074] Server Operation:

[1075] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[1076] input:

[1077] Up-to-date legal information retrieved from an external API.

[1078] output:

[1079] Updated database information and revised analysis algorithms.

[1080] Specific behavior:

[1081] The server periodically retrieves the latest legal change information from an external API (e.g., a government tax law information service) and updates the database. The retrieved information is then input into an analysis algorithm.

[1082] Step 7: User sentiment analysis

[1083] Terminal operation:

[1084] The device's built-in emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's facial expressions and voice data in real time to recognize their emotional state.

[1085] input:

[1086] The user's facial expression data and voice data.

[1087] output:

[1088] Perceived emotional state (e.g., relief, anxiety, interest).

[1089] Specific behavior:

[1090] The device collects real-time data through the camera and microphone, sends it to the emotion engine, and obtains analysis results.

[1091] Step 8: Feedback of emotional data

[1092] Server Operation:

[1093] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state.

[1094] input:

[1095] Emotion data sent from the emotion engine.

[1096] output:

[1097] Adjusted plan content and presentation.

[1098] Specific behavior:

[1099] The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results, changing the display content and presentation format.

[1100] Step 9: Creating a visual image

[1101] Server Operation:

[1102] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[1103] input:

[1104] Auto-generated plan.

[1105] output:

[1106] Graphs and charts generated as visual images.

[1107] Specific behavior:

[1108] The server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize the progress of income and expenditures. The generated graphs are saved as image files or dynamically generated in HTML format.

[1109] Step 10: Present a visual image

[1110] Server Operation:

[1111] The server transmits the generated plan and visual image to the terminal.

[1112] input:

[1113] Generated visual images and plan data.

[1114] output:

[1115] Data sent to the device.

[1116] Specific behavior:

[1117] The server packages the generated data and graphs and sends them to the terminal as an HTTP response.

[1118] Step 11: Present to the user

[1119] Terminal operation:

[1120] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[1121] input:

[1122] Plans and visual images sent from the server.

[1123] output:

[1124] Plans and visual images presented to users.

[1125] Specific behavior:

[1126] The device displays the generated plan and visual image through a web browser or a dedicated client application. The user's reaction data is newly acquired and reflected in the displayed content.

[1127] Step 12: User Feedback

[1128] User Action:

[1129] The user checks the presented plan and visual images, and requests further information or provides feedback as necessary.

[1130] input:

[1131] User feedback and questions.

[1132] output:

[1133] User feedback and question data sent to the server.

[1134] Specific behavior:

[1135] The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[1136] (Application example 2)

[1137] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1138] Conventional asset formation, inheritance planning, and tax saving plan generation systems do not take into account the user's emotional state, which can make the proposed plans difficult for users to understand, potentially causing anxiety and stress. In addition, the inability to provide appropriate information based on the user's emotions has led to a problem of reduced user satisfaction.

[1139] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting personal information and asset information of the user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, and means for recognizing the user's emotions in real time and adjusting the content and display format of the plans according to the emotions. This makes it possible to provide personalized plans according to the user's emotional state.

[1140] "Personal information" refers to information for identifying a user, as well as personal data such as age, occupation, income, and expenses.

[1141] "Asset information" refers to information about the assets held by a user, such as cash, real estate, investments, and debts.

[1142] "Means of collection" refers to the hardware and software mechanisms used to capture user-provided data into the system.

[1143] The "analysis means" refers to the algorithms and servers that process the collected personal and asset information and perform calculations and evaluations based on that data.

[1144] The "means of automatic generation" is a software mechanism that automatically creates optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[1145] "Visual display means" refers to a mechanism for displaying the generated plans and data on a monitor or display as graphs or text in a format that is easy for the user to understand.

[1146] "Means for recognizing emotions in real time" refers to software and hardware that analyzes the user's facial expressions and voice data and identifies their emotional state in real time.

[1147] "Adjustment means" refers to the system's functionality for changing the content and display format of the generated plan in response to the recognized emotion.

[1148] This invention is a system that collects personal information and asset information of a user, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. This system includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plans according to those emotions.

[1149] System Configuration

[1150] 1. Data Collection and Analysis

[1151] Users upload their personal and asset information to the device via a smartphone application. This collects information such as annual income, monthly expenses, bank balance, and real estate ownership status. Documents are scanned using the device's built-in scanner and camera and converted into image data. This data is then sent to the server.

[1152] 2. Image Recognition and Data Extraction

[1153] The server uses OCRTesseract to extract numerical and text information from the image data. For example, specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted. The extracted numerical and text information is stored in a database.

[1154] 3. Plan Generation

[1155] The server analyzes the stored data using a generative AI model. A specific algorithm is applied to predict future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, an optimal asset formation, inheritance planning, and tax saving plan is automatically generated. The system also learns the latest legal changes in real time and reflects them in the plan.

[1156] 4. Emotion recognition

[1157] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice data in real time. This emotion data is sent to a server and classified into the user's emotional state, such as "relief," "anxiety," or "interest."

[1158] 5. Plan adjustment and visual image creation

[1159] The server receives the emotional data and adjusts the content and display format of the plan based on the user's emotional state. For example, if the server detects that the user is feeling anxious, it will provide a more detailed explanation of the plan and use more polite language. Based on the analysis results, it will graph the progress of income and expenditures and future asset value, and also provide specific examples of lifestyle plans.

[1160] Specific examples of programs

[1161] When a user uploads a document about their annual income and expenses, the document is scanned and converted into image data. Numerical information is then extracted using OCR and sent to a server. The server analyzes the data and creates an asset formation plan using a generative AI model. The generated plan is presented to the user as a visual image, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," more detailed explanations and different investment options are displayed.

[1162] Prompt Sentence Examples

[1163] Please calculate the specific amount you could save in a year based on a scenario where your annual income is 5 million yen and your monthly expenses are 300,000 yen. Also, if that scenario makes users feel uneasy, please suggest how you should change the explanation or offer.

[1164] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1165] Step 1:

[1166] Users upload personal and asset information to a device via a smartphone application. The input data includes personal data such as annual income, monthly expenses, bank balance, and real estate ownership status. This data is captured as an image using a camera or scanner installed on the device. The output is the captured image data.

[1167] Step 2:

[1168] The terminal transmits the captured image data to the server. The input data is the image data, and the output is the image data transmitted to the server.

[1169] Step 3:

[1170] The server receives the image data and uses OCR to extract text information from the image. The input data is image data, and OCR processing is performed as data processing. The output is information such as annual income, expenses, and real estate appraisal value as text data.

[1171] Step 4:

[1172] The server saves the extracted text information in a database. The input data is text information, which is written to the database as data calculation. The output is user information stored in the database.

[1173] Step 5:

[1174] Based on the data stored on the server, a generative AI model is used to automatically generate plans for asset formation, inheritance planning, and tax savings. The input data is user information obtained from a database, and data analysis and generation are performed based on an algorithm. The output is the generated plan.

[1175] Step 6:

[1176] The server learns the latest legal amendments in real time and reflects them in the generated plan. The input data is legal amendments and the generated plan, and the plan is updated through data calculations. The output is a plan that reflects the latest legal amendments.

[1177] Step 7:

[1178] The device's emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. The input data is the user's facial expression images and voice data, and emotion analysis is performed. The output is emotion data classified as "relief," "anxiety," "interest," etc.

[1179] Step 8:

[1180] The server receives the emotional data and adjusts the content and display format of the plan based on the user's emotional state. The input data is the emotional data and the generated plan, and the content and display format of the plan are adjusted as data processing. The output is a plan adjusted according to the user's emotions.

[1181] Step 9:

[1182] The server generates a visual image of the adjusted plan and graphs the progress of income and expenditures and future asset value. The input data is the adjusted plan, and visualization is performed as a data calculation. The output is a graphed plan.

[1183] Step 10:

[1184] The device presents the generated visual image to the user and monitors the user's reaction in real time. The input data is a graphed plan, and the output is the plan presented to the user and the real-time monitoring results of the emotion engine.

[1185] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1186] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1187] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1188] [Third embodiment]

[1189] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1190] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1191] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1192] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1193] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1195] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1196] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1197] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1198] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1199] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1200] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1201] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them. Specific embodiments of this system and the operation of its program are described below.

[1202] Data collection

[1203] User Action:

[1204] A user uses a rich client terminal to upload documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, real estate ownership status, etc.

[1205] Terminal operation:

[1206] The device scans the provided document and generates image data, which is then sent to the server.

[1207] Data Extraction

[1208] Server Operation:

[1209] The server uses image recognition software to extract numerical and textual information from the image data sent to it. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[1210] Server Operation:

[1211] The extracted numerical and text information is stored in a database on the server.

[1212] Plan Generation

[1213] Server Operation:

[1214] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[1215] Server Operation:

[1216] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, which means that the database is continuously updated and plans are generated based on that information.

[1217] Visual image generation and presentation

[1218] Server Operation:

[1219] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and forecasts of future asset values. Specific examples of lifestyle planning are provided based on specific simulation results and statistical data.

[1220] Server Operation:

[1221] The server transmits the generated plan and visual image to the terminal.

[1222] Terminal operation:

[1223] The terminal presents the generated plan and visual images to the user, and the information is displayed in a format that is intuitively easy for the user to understand.

[1224] User Action:

[1225] Users can check the plans and visual images presented and provide feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[1226] Specific examples

[1227] Example 1: Creating a wealth plan

[1228] 1. Users upload documents related to their income, expenses, and investment status.

[1229] 2. The device scans the document and sends the image data to the server.

[1230] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[1231] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase by 1.5 times in 10 years.

[1232] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[1233] 6. The terminal presents this to the user, who confirms the plan.

[1234] Example 2: Creating an estate planning plan

[1235] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[1236] 2. The device sends this information in text form to the server.

[1237] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[1238] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[1239] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[1240] This allows users to easily understand the plan that best suits their situation, even if they do not have a deep knowledge of complex laws and tax systems, and to plan their post-retirement life with peace of mind.

[1241] The above is a specific embodiment for carrying out the present invention.

[1242] The processing flow will be explained below.

[1243] Step 1:

[1244] User Action:

[1245] A user logs into a rich client terminal and uploads a document containing personal and asset information to the terminal.

[1246] Step 2:

[1247] Terminal operation:

[1248] The device scans the uploaded documents and converts them into image data.

[1249] Step 3:

[1250] Terminal operation:

[1251] The terminal transmits the converted image data to the server.

[1252] Step 4:

[1253] Server Operation:

[1254] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[1255] Step 5:

[1256] Server Operation:

[1257] The server stores the extracted numerical and text information in a database.

[1258] Step 6:

[1259] Server Operation:

[1260] The server analyzes the stored data and selects the algorithm to apply, such as an asset formation algorithm or an inheritance planning algorithm.

[1261] Step 7:

[1262] Server Operation:

[1263] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[1264] Step 8:

[1265] Server Operation:

[1266] The server uses the selected algorithm to analyze the data and generate an optimal plan. For example, it generates a specific prediction such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[1267] Step 9:

[1268] Server Operation:

[1269] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[1270] Step 10:

[1271] Server Operation:

[1272] The server sends the generated plan and visual image to the terminal.

[1273] Step 11:

[1274] Terminal operation:

[1275] The terminal presents the plan and visual image received from the server to the user.

[1276] Step 12:

[1277] User Action:

[1278] The user reviews the presented plans and visual images and provides further details and feedback as needed.

[1279] This allows users to obtain specific plans for their own asset formation, inheritance planning, and tax savings, allowing them to plan their retirement with peace of mind.

[1280] Example 1

[1281] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1282] Conventional asset formation, inheritance planning, and tax saving planning systems require users to input accurate and detailed information, making operation cumbersome. Furthermore, due to frequent legal changes, it is difficult to reflect the latest legal changes, and as a result, generated plans may not comply with the latest laws and regulations. Furthermore, generated plans are often not presented in a format that is intuitively easy for users to understand.

[1283] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1284] In this invention, the server includes means for users to upload information, means for a terminal to scan documents and generate image data, means for the server to extract numerical and textual information from the image data, means for the server to store the extracted information in a database, means for the server to analyze the data using an algorithm and generate optimal plans for asset formation, inheritance planning, and tax savings, means for the server to convert the generated plans into visual images, and means for the terminal to present the generated plans and visual images to the user. This makes it possible for users to easily provide detailed information and to receive intuitively understandable plans that reflect the latest legal amendments.

[1285] A "user" is an entity that uses the system to provide personal information and asset information.

[1286] "Means for uploading information" is a function that allows users to send their own personal information and asset information to the system.

[1287] A "terminal" is a computer device operated by a user, and is a device that has functions such as scanning documents and displaying data.

[1288] "Means for scanning documents and generating image data" refers to the function of converting physical documents into digital image data.

[1289] A "server" is a computer system that processes, stores, analyzes data, and communicates with other devices.

[1290] "Means for extracting numerical and textual information from image data" refers to technology that identifies and extracts text data from digital images.

[1291] "Means for storing information in a database" refers to a system for appropriately structuring extracted information and storing it for a long period of time.

[1292] An "algorithm" is a set of computational steps for analyzing data or solving problems.

[1293] "A means of analyzing data and generating optimal plans for asset formation, inheritance planning, and tax savings" is a function that automatically creates various plans using mathematical models and analytical methods based on stored data.

[1294] "Means for converting the generated plan into a visual image" refers to a technique for converting the analysis results into a format that is visually easy to understand (e.g., graphs or charts).

[1295] The "means for presenting plans and visual images" is a function for displaying the generated plans and visual information to the user.

[1296] "Latest legal reform information" refers to the latest information on regulations and changes to laws related to asset formation, inheritance planning, and tax savings.

[1297] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them.

[1298] Hardware and Software Configuration

[1299] User operation and information provision

[1300] Users use a rich client device to upload documents containing their personal and financial information. This requires a computer, a scanner, and an internet connection. Specifically, documents provide information such as annual income, monthly expenses, bank balance, and real estate ownership.

[1301] Document scanning and image data generation

[1302] The device scans the document provided by the user and generates high-resolution image data. This step uses a scanner and image processing software (e.g., Adobe Acrobat). The generated image data is then sent to the server via a secure protocol.

[1303] Information extraction from image data

[1304] The server uses image recognition software (e.g., OCR software) to extract numerical and textual information from the image data sent. For example, OCR software extracts specific information such as "annual income of 5 million yen" and "monthly expenses of 300,000 yen" as text data.

[1305] Database storage

[1306] The extracted numerical and textual information is stored in a database on the server. The database system (e.g., MySQL or PostgreSQL) is built with high availability and security in mind. Annual income and expenditure information is stored linked to the user ID.

[1307] Data analysis and plan generation

[1308] The server analyzes the stored data by applying specific algorithms (e.g., regression analysis, machine learning models, etc.). The analysis targets the user's annual income, expenses, investment status, etc. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[1309] Reflecting the latest legal amendments

[1310] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, continuously updating the database so that the plans generated based on them comply with the latest laws and regulations.

[1311] Visual image generation

[1312] Based on the generated plan, the server graphs the progress of income and expenditures and the forecast of future asset value. This visual image is provided in a format that is intuitively easy for users to understand. Specifically, a graph drawing tool (e.g., D3.js, Chart.js) is used.

[1313] Presentation of plans and visual images

[1314] The server sends the generated plan and visual image to the terminal, which then presents it to the user, displaying the information in a format that is intuitively easy for the user to understand. For example, graphs and simulation results are displayed in an easy-to-understand manner.

[1315] User Feedback

[1316] Users can check the plans and visual images presented and provide feedback as needed. For example, they can send questions to the server such as, "What specific procedures are required for this tax-saving measure?" The system will then be improved based on the feedback.

[1317] Examples of prompt statements

[1318] Below are some examples of specific prompts to input into a generative AI model:

[1319] Prompt statement:

[1320] Generate an optimal asset formation plan based on the user's personal and asset information. First, provide information such as the user's annual income, monthly expenses, deposit balance, and real estate ownership status, and analyze this to predict future asset value. Next, propose a specific plan that takes into account tax savings and inheritance planning, and generate and display a visual image.

[1321] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1322] Specific explanation of processing steps

[1323] Step 1: User uploads information

[1324] Input: Personal and financial information such as the user's annual income, monthly expenses, bank balance, and real estate ownership status is provided as documents.

[1325] What happens: Users upload these documents to the system using rich client devices, which have scanning and digital form-filling capabilities.

[1326] Output: The device receives this information as digital data.

[1327] Step 2: The device scans the document and generates image data.

[1328] Input: Documents uploaded by users.

[1329] Specific operation: The device uses a high-resolution scanner to scan the provided document and generate image data. Image processing software (e.g., Adobe Acrobat) is used.

[1330] Output: The generated image data is saved to the device.

[1331] Step 3: The device sends the image data to the server

[1332] Input: scanned image data.

[1333] Specific operation: The device sends image data to the server via a secure protocol (e.g., HTTPS).

[1334] Output: The transmitted image data is saved on the server.

[1335] Step 4: The server extracts numerical and textual information from the image data.

[1336] Input: Image data stored on the server.

[1337] Specific operation: The server uses OCR software to extract numerical and textual information from the image data. For example, it uses text detection to extract specific information such as "annual income of 5 million yen" or "monthly expenses of 300,000 yen."

[1338] Output: The extracted numerical and character information is generated as text data.

[1339] Step 5: The server stores the extracted information in a database

[1340] Input: Extracted numeric and textual information.

[1341] Specific operation: The server stores this information in a database system (e.g., MySQL, PostgreSQL). Each piece of information is structured and linked to the user ID.

[1342] Output: Structured data stored in a database.

[1343] Step 6: The server uses algorithms to analyze the data and generate a plan

[1344] Input: User's personal and financial information stored in a database.

[1345] Specific operation: The server applies data analysis algorithms (e.g., regression analysis, machine learning models) to calculate optimal plans for asset formation, inheritance planning, and tax savings based on various information. For example, it predicts future assets based on the user's annual income, expenses, and investment status.

[1346] Output: The generated optimal plan.

[1347] Step 7: The server updates with the latest legal information

[1348] Input: Latest legal change information and generated plan.

[1349] How it works: The server learns the latest legal changes in real time and reflects them in the analysis algorithm, so the generated plans comply with the latest laws and regulations.

[1350] Output: An updated plan that reflects the latest legal changes.

[1351] Step 8: The server converts the generated plan into a visual image

[1352] Input: The generated plan.

[1353] Specific operation: The server uses a visual image generation tool (e.g., D3.js, Chart.js) to convert the analysis results into a visually understandable format, such as graphing the progress of income and expenditure or future asset value.

[1354] Output: Graphs and charts as visual images.

[1355] Step 9: The server sends the generated plan and visual image to the device.

[1356] Input: Generated plans and visual images.

[1357] Specific operation: The server sends this information to the device via a secure protocol (e.g., HTTPS).

[1358] Output: Plans and visual images sent to the device.

[1359] Step 10: The device presents the user with a plan and visual images

[1360] Input: Plan and visual images sent from the server.

[1361] Specific operation: The device presents the plan and visual image to the user through a display function (e.g., web browser, dedicated app). The plan and visual image are displayed on the screen in a way that allows the user to intuitively understand it.

[1362] Output: Plans and visual images presented to the user.

[1363] Step 11: User provides feedback

[1364] Input: Plan and visual image displayed on the terminal.

[1365] Specific operation: The user checks the plan and provides feedback as needed (e.g., "What specific steps are required to implement this tax-saving measure?"). The feedback is sent to the server via the device.

[1366] Output: User feedback sent to the server.

[1367] (Application example 1)

[1368] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1369] Existing asset management, inheritance planning, and tax-saving systems often require users to manage their own information and have specialized knowledge in order to be effective. Furthermore, there is a lack of practical support, as there are limited ways for users to automatically implement asset formation and tax-saving strategies. Therefore, there is a need for a system that allows users to easily implement optimal asset formation and tax-saving strategies, and also allows for integrated electronic payments based on those plans.

[1370] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1371] In this invention, the server includes means for collecting personal information and asset information of a user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, means for supporting electronic payments based on the generated plans, and means for providing interactive feedback. This allows users to put optimal asset management and tax savings plans into practice without specialized knowledge. Furthermore, the integration of electronic payments based on the plans provides practical support for asset formation and tax savings, enabling comprehensive asset management.

[1372] "User's personal information" refers to information that identifies an individual or indicates an individual's attributes, and includes, for example, name, age, sex, address, occupation, income, and the like.

[1373] "Asset information" refers to information about financial assets, real estate, and other assets owned by a user, including, for example, bank account balances, appraised values ​​of stocks and investment trusts, appraised values ​​of real estate, and the like.

[1374] "Means for collecting" refers to a method or mechanism for obtaining personal information and asset information from a user and transmitting it to a server.

[1375] "Means for analysis" refers to a method or mechanism for performing data analysis based on collected personal information and asset information and deriving useful results.

[1376] "Means for automatically generating optimal plans" refers to a method or mechanism that automatically creates asset formation, inheritance planning, and tax saving plans that are most suitable for each user based on the results of data analysis.

[1377] "Visual display means" refers to a method or mechanism for visually presenting the generated plan to a user, such as a graph or chart.

[1378] "Means for supporting electronic payments" refers to a method or mechanism for a user to make the electronic payments necessary to manage his or her assets based on the generated plan.

[1379] "Means for providing interactive feedback" refers to a method or mechanism for responding to questions or feedback from a user in real time and providing additional information or advice.

[1380] The system for implementing this invention collects and analyzes a user's personal information and asset information, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. It also includes functions to support electronic payments based on the generated plans and provide interactive feedback.

[1381] Program processing and hardware / software used

[1382] Data collection

[1383] The device scans documents provided by the user (such as annual income, monthly expenses, bank balance, and real estate ownership status) and generates image data. This image data is then sent to a cloud server. The hardware used includes a scanner and a camera.

[1384] Data Extraction

[1385] The server uses image recognition software called "pytesseract" to extract necessary numerical and text information from the transmitted image data. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[1386] Data storage and analysis

[1387] The extracted numerical and textual information is stored in a secure database on the server, which then applies specific algorithms to analyze the data.

[1388] Plan Generation

[1389] The server generates an optimal asset formation plan based on the user's annual income, expenses, and investment status. It also learns the latest legal reform information in real time and reflects it in the analysis results. Based on this analysis result, it provides the user with the optimal asset formation plan.

[1390] Visual image generation and presentation

[1391] Based on the generated plan, the server uses matplotlib to graph the progress of income and expenditures and the forecast of future asset value. This visual image is sent to the terminal and presented to the user in an easy-to-understand format.

[1392] Support for electronic payments

[1393] Based on the generated plan, the server supports electronic payments for users to carry out asset management and tax saving measures. The server has the function of automatically transferring funds and issuing instructions for investment.

[1394] Interactive Feedback

[1395] Users can ask questions about the generated plans and visual images, and the server will respond in real time via chatbots or other means, providing additional information and advice.

[1396] Examples of concrete examples and prompts

[1397] For example, based on data provided by a user with an annual income of 5 million yen and monthly expenses of 300,000 yen, the server generates the following asset formation plan:

[1398] It is predicted that by investing 200,000 yen per month in low-risk investments, assets will increase by 1.5 times in 10 years.

[1399] Based on the generated plan and its visual image, electronic payment support is provided to users for asset management.

[1400] Example prompt sentence:

[1401] "Upload your annual income and monthly expenses. We'll then predict your future asset growth and display it as a graph."

[1402] The above is a specific embodiment for carrying out the present invention. This system allows users to appropriately manage assets and take tax-saving measures without requiring specialized knowledge.

[1403] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1404] Step 1:

[1405] The user uploads documents containing their personal and financial information to the terminal, including annual income, monthly expenses, bank balance, real estate ownership status, etc. The user's input is captured in the terminal as image data of the document and sent to the server.

[1406] Step 2:

[1407] The device scans the uploaded document and generates image data, which serves as input data to be sent to the server. The output of the device is a high-resolution scanned image file.

[1408] Step 3:

[1409] The server uses the image recognition software "pytesseract" to extract text information from the transmitted image data. Specifically, it converts the image data to grayscale and applies a text recognition algorithm. The server's input is the image data, and its output is the extracted information as text. For example, specific numerical information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted.

[1410] Step 4:

[1411] The server stores the extracted numerical and textual information in a secure database. The stored data is used for subsequent data analysis. The input to the server is the extracted textual information, and the output is storage in the database.

[1412] Step 5:

[1413] The server applies specific algorithms to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. This analysis automatically generates optimal asset formation, inheritance planning, and tax-saving plans. The server's input is numerical information stored in a database, and its output is a specific plan.

[1414] Step 6:

[1415] The server learns the latest legal amendments in real time and reflects them in the analysis algorithm. This ensures that the generated plans are always based on the latest laws and regulations. The server's input is legal amendments, and the output is the latest plan based on that information.

[1416] Step 7:

[1417] The server uses "matplotlib" to generate visual images based on the generated plan. Specifically, it graphs the progress of income and expenditures and the forecast of future asset value. The server's input is the generated plan information, and its output is visual graphs and charts.

[1418] Step 8:

[1419] The server sends the generated visual image and plan to the terminal. The terminal displays them in a format that is easy for the user to understand. This allows the user to visually confirm their own asset formation plan. The input to the terminal is the visual image and plan sent from the server, and the output is the display to the user.

[1420] Step 9:

[1421] Based on the generated plan, the terminal supports electronic payments, automatically executing specific fund transfer and investment instructions. The input of the terminal is the specific plan instructions, and the output is the actual payment transaction.

[1422] Step 10:

[1423] The user checks the generated plan and visual image and provides feedback as needed. For example, a question might be, "What exactly will this tax saving measure do?" The user's input is the feedback or question, and the server responds by providing an interactive response. The server's input is the user's feedback, and its output is the answer or additional information.

[1424] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1425] This invention is a system that combines a system that collects a user's personal information and asset information, analyzes it, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system and the operation of its program are described below.

[1426] Data collection

[1427] User Action:

[1428] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[1429] Terminal operation:

[1430] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[1431] Data Extraction

[1432] Server Operation:

[1433] The server uses image recognition software to extract numerical and textual information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[1434] Server Operation:

[1435] The extracted numerical and text information is stored in a database.

[1436] Plan Generation

[1437] Server Operation:

[1438] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal asset formation, inheritance planning, and tax saving plans.

[1439] Server Operation:

[1440] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[1441] Manipulating the Emotion Engine

[1442] Terminal operation:

[1443] The emotion engine installed in the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc.

[1444] Server Operation:

[1445] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the server presents a more detailed explanation in polite language.

[1446] Visual image generation and presentation

[1447] Server Operation:

[1448] Based on the plan generated by the server, a visual image is generated, such as graphing income and expenditure trends and future asset values. Specific examples of lifestyle planning are also provided based on specific simulation results and statistical data.

[1449] Server Operation:

[1450] The server transmits the generated plan and visual image to the terminal.

[1451] Terminal operation:

[1452] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[1453] User Action:

[1454] Users can review the plans and visual images presented and, if necessary, request more detailed information or provide feedback. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[1455] Specific examples

[1456] Example 1: Creating a wealth plan

[1457] 1. Users upload documents related to their income, expenses, and investment status.

[1458] 2. The device scans the document and sends the image data to the server.

[1459] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[1460] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[1461] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[1462] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[1463] Example 2: Creating an estate planning plan

[1464] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[1465] 2. The device sends this information in text form to the server.

[1466] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[1467] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[1468] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[1469] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[1470] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[1471] The above is a specific embodiment for carrying out the present invention.

[1472] The processing flow will be explained below.

[1473] Data collection and extraction

[1474] Step 1:

[1475] User Action:

[1476] A user logs in to a rich client terminal and uploads documents containing personal and financial information to the terminal, including annual income, monthly expenses, savings balance, real estate ownership status, etc.

[1477] Step 2:

[1478] Terminal operation:

[1479] The device scans the uploaded document and converts it into image data.

[1480] Step 3:

[1481] Terminal operation:

[1482] The terminal transmits the converted image data to the server.

[1483] Step 4:

[1484] Server Operation:

[1485] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[1486] Step 5:

[1487] Server Operation:

[1488] The extracted numerical and text information is stored in a database.

[1489] Plan generation and emotion engine operation

[1490] Step 6:

[1491] Server Operation:

[1492] The server analyzes the stored data and applies selected algorithms, including algorithms that generate asset formation, inheritance planning, and tax savings plans based on the user's annual income, expenses, and investment status.

[1493] Step 7:

[1494] Server Operation:

[1495] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[1496] Step 8:

[1497] Server Operation:

[1498] The server uses the selected algorithm to automatically generate optimal asset formation, inheritance planning, and tax saving plans based on the analysis results. For example, it makes specific predictions such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[1499] Step 9:

[1500] Terminal operation:

[1501] The device's built-in emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies them as "relief," "anxiety," "interest," etc.

[1502] Visual image generation and presentation

[1503] Step 10:

[1504] Server Operation:

[1505] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[1506] Step 11:

[1507] Server Operation:

[1508] The server transmits the generated plan and visual image to the terminal.

[1509] Step 12:

[1510] Terminal operation:

[1511] The device receives the plan and visual images from the server and presents them to the user. The emotion engine then monitors the user's real-time reactions and adjusts the displayed content as needed. For example, if the user is recognized as "anxious," the level of detail and wording of the explanation will be changed.

[1512] Step 13:

[1513] User Action:

[1514] The user reviews the presented plan and visual image and gives feedback, for example, by asking specific questions such as, "What specific procedures are required for this tax-saving measure?"

[1515] Specific examples

[1516] Example 1: Creating a wealth plan

[1517] Step 1:

[1518] Users upload documents related to their income, expenses, and investment status.

[1519] Step 2:

[1520] The device scans the document and sends the image data to the server.

[1521] Step 3:

[1522] The server uses image recognition software to extract numbers (annual income, expenses, investment amounts, etc.).

[1523] Step 4:

[1524] The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[1525] Step 5:

[1526] The server graphs the plan and generates a visual image of the progress of income and expenditure.

[1527] Step 6:

[1528] The device presents this to the user, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[1529] Example 2: Creating an estate planning plan

[1530] Step 1:

[1531] Users provide information about their family structure, real estate holdings, and financial assets.

[1532] Step 2:

[1533] The terminal sends this information to the server in text form.

[1534] Step 3:

[1535] The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[1536] Step 4:

[1537] The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[1538] Step 5:

[1539] The server transmits the generated plan to the terminal, which then presents it to the user.

[1540] Step 6:

[1541] The emotion engine analyzes the user's reaction, and if it recognizes that the user feels "at ease," the detailed explanation of the plan is omitted and the system moves on to the next step.

[1542] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[1543] Example 2

[1544] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1545] Conventional asset formation, inheritance planning, and tax-saving systems offer the ability to automatically generate and display plans based on the user's personal and asset information. However, these systems do not take the user's emotional state into account, which often leaves users feeling anxious or makes the plans difficult to understand. It is also difficult to reflect changes due to legal amendments in real time, making it difficult to provide plans based on the latest information. Furthermore, there are limitations to properly analyzing collected numerical and text information and presenting optimal plans. A method to solve these issues is needed.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1547] In this invention, the server includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plan based on the user's emotional state, means for learning the latest legal amendment information in real time and reflecting it in the plan, and means for storing the extracted numerical and text information in a database and performing data analysis. This makes it possible to propose personalized plans based on the user's emotions and always provide optimal plans based on the latest legal amendment information.

[1548] "User's personal information" refers to information for identifying an individual, such as the user's name, age, sex, and occupation.

[1549] "Asset information" refers to information about financial assets and physical assets owned by a user, such as cash, deposits, real estate, stocks, and bonds.

[1550] "Means for collection" refers to the method and device for acquiring personal information and asset information from users and incorporating it into the system.

[1551] "Means for analysis" refers to algorithms and programs that perform data analysis based on collected data and make predictions about the user's financial situation and future prospects.

[1552] "Means for automatic generation" refers to algorithms and programs that automatically generate optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[1553] "Visual display means" refers to devices and software for presenting the generated plan to a user in an easy-to-view format through visual elements such as graphs and charts.

[1554] "Means for recognizing in real time" refers to technology and devices that detect a user's emotions in real time and analyze that information.

[1555] "Adjusting means" refers to software and algorithms for appropriately modifying the content and presentation of the plan based on the user's emotional state as recognized in real time.

[1556] "Legal Change Information" refers to information on the latest laws and regulations, including in particular changes to tax laws and inheritance laws.

[1557] "Means of learning in real time" refers to the data acquisition mechanism and learning algorithms that instantly acquire the latest legal amendment information and reflect it in the system.

[1558] "Means for storing data in a database" refers to a database system and related software for efficiently storing and managing collected numerical and textual information.

[1559] This invention is a system that collects and analyzes a user's personal and asset information to automatically generate and visually display optimal asset formation, inheritance planning, and tax savings plans. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, a more personalized experience is provided. A specific embodiment of this system and the operation of its program are described below.

[1560] Data collection

[1561] User Action:

[1562] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[1563] Terminal operation:

[1564] The terminal scans the provided document, converts it into image data, and sends the data to the server. Specifically, the terminal controls a scanner device to scan the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[1565] Data Extraction

[1566] Server Operation:

[1567] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenses of 300,000 yen." The server starts Tesseract OCR, passes the image data as input, and the OCR extracts the text data, which is then saved as structured data (e.g., JSON format).

[1568] Server Operation:

[1569] Store the extracted numeric and text information in a database (e.g., MySQL or PostgreSQL). The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[1570] Plan Generation

[1571] Server Operation:

[1572] The server applies a specific algorithm (e.g., a machine learning model) to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings. The server loads a machine learning model (e.g., scikit-learn or TensorFlow) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[1573] Server Operation:

[1574] The server updates the database with the latest legal amendment information in real time and reflects it in the analysis algorithm. The server periodically obtains the latest legal amendment information from an external API (e.g., a government tax law information service) and updates the database. The obtained information is input into the analysis algorithm.

[1575] Manipulating the Emotion Engine

[1576] Terminal operation:

[1577] An emotion engine (for example, Microsoft Azure Emotion API) installed on the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while they are looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc. The device collects real-time data through the camera and microphone and sends it to the emotion engine to obtain the analysis results.

[1578] Server Operation:

[1579] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the explanation is presented in more detail and in more polite language. The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results. The display content and presentation format are changed.

[1580] Visual image generation and presentation

[1581] Server Operation:

[1582] Based on the generated plan, the server generates visual images, such as graphs of income and expenditure trends and future asset value. Specifically, the server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize income and expenditure trends, and the generated graphs are saved as image files or dynamically generated in HTML format.

[1583] Server Operation:

[1584] The server sends the generated plan and visual image to the terminal, and the server packages the generated data and graph and sends it to the terminal as an HTTP response.

[1585] Terminal operation:

[1586] The device presents the generated plan and visual images to the user. Furthermore, an emotion engine monitors the user's real-time reactions and adjusts the displayed content as necessary. The device displays the generated plan and visual images through a web browser or dedicated client application, and new user reaction data is acquired and reflected in the displayed content.

[1587] User Action:

[1588] The user checks the presented plan and visual image, and requests more detailed information or gives feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax saving measure?" The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[1589] Specific examples

[1590] Example 1: Creating a wealth plan

[1591] 1. Users upload documents related to their income, expenses, and investment status.

[1592] 2. The device scans the document and sends the image data to the server.

[1593] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[1594] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[1595] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[1596] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[1597] Example 2: Creating an estate planning plan

[1598] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[1599] 2. The device sends this information in text form to the server.

[1600] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[1601] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[1602] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[1603] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[1604] Examples of prompt statements

[1605] Prompt statement:

[1606] "If you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, what will your asset value be in 10 years?"

[1607] This system makes it easy for users to understand the plan that is best suited to their situation, allowing them to plan for their future assets with peace of mind.

[1608] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1609] System program processing flow

[1610] Step 1: User input

[1611] User Action:

[1612] A user logs into a rich client terminal and uploads a document containing their personal and financial information.

[1613] input:

[1614] Users select and upload documents containing information such as annual income, monthly expenses, bank balance, and real estate ownership status.

[1615] output:

[1616] The device generates the digital data that has been scanned and uploaded.

[1617] Specific behavior:

[1618] The user uses the file selection dialog on the device to select a PDF or image document and clicks the upload button, generating digital data.

[1619] Step 2: Data conversion

[1620] Terminal operation:

[1621] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[1622] input:

[1623] Document data provided by the user.

[1624] output:

[1625] High resolution image data sent to the server.

[1626] Specific behavior:

[1627] The terminal controls the scanner device, scans the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[1628] Step 3: Extracting information from image data

[1629] Server Operation:

[1630] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data.

[1631] input:

[1632] The image data sent.

[1633] output:

[1634] Extracted numerical and textual information (structured data such as JSON format).

[1635] Specific behavior:

[1636] The server runs Tesseract OCR and passes the image data as input, which extracts the text data and stores it as structured data.

[1637] Step 4: Save your data

[1638] Server Operation:

[1639] Store the extracted numerical and text information in a database (e.g., MySQL or PostgreSQL).

[1640] input:

[1641] Structured data extracted by OCR.

[1642] output:

[1643] The data to be inserted into the database.

[1644] Specific behavior:

[1645] The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[1646] Step 5: Analyze the data

[1647] Server Operation:

[1648] The server analyzes the stored data using specific algorithms (for example, machine learning models) and automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[1649] input:

[1650] User data stored in a database.

[1651] output:

[1652] Analysis results and auto-generated plans.

[1653] Specific behavior:

[1654] The server loads a machine learning model (e.g., scikit-learn or TensorFlow) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[1655] Step 6: Update your legal information

[1656] Server Operation:

[1657] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[1658] input:

[1659] Up-to-date legal information retrieved from an external API.

[1660] output:

[1661] Updated database information and revised analysis algorithms.

[1662] Specific behavior:

[1663] The server periodically retrieves the latest legal change information from an external API (e.g., a government tax law information service) and updates the database. The retrieved information is then input into an analysis algorithm.

[1664] Step 7: User sentiment analysis

[1665] Terminal operation:

[1666] The device's built-in emotion engine (for example, Microsoft Azure Emotion API) analyzes the user's facial expressions and voice data in real time to recognize their emotional state.

[1667] input:

[1668] The user's facial expression data and voice data.

[1669] output:

[1670] Perceived emotional state (e.g., relief, anxiety, interest).

[1671] Specific behavior:

[1672] The device collects real-time data through the camera and microphone, sends it to the emotion engine, and obtains analysis results.

[1673] Step 8: Feedback of emotional data

[1674] Server Operation:

[1675] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state.

[1676] input:

[1677] Emotion data sent from the emotion engine.

[1678] output:

[1679] Adjusted plan content and presentation.

[1680] Specific behavior:

[1681] The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results, changing the display content and presentation format.

[1682] Step 9: Creating a visual image

[1683] Server Operation:

[1684] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[1685] input:

[1686] Auto-generated plan.

[1687] output:

[1688] Graphs and charts generated as visual images.

[1689] Specific behavior:

[1690] The server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize the progress of income and expenditures. The generated graphs are saved as image files or dynamically generated in HTML format.

[1691] Step 10: Present a visual image

[1692] Server Operation:

[1693] The server transmits the generated plan and visual image to the terminal.

[1694] input:

[1695] Generated visual images and plan data.

[1696] output:

[1697] Data sent to the device.

[1698] Specific behavior:

[1699] The server packages the generated data and graphs and sends them to the terminal as an HTTP response.

[1700] Step 11: Present to the user

[1701] Terminal operation:

[1702] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[1703] input:

[1704] Plans and visual images sent from the server.

[1705] output:

[1706] Plans and visual images presented to users.

[1707] Specific behavior:

[1708] The device displays the generated plan and visual image through a web browser or a dedicated client application. The user's reaction data is newly acquired and reflected in the displayed content.

[1709] Step 12: User Feedback

[1710] User Action:

[1711] The user checks the presented plan and visual images, and requests further information or provides feedback as necessary.

[1712] input:

[1713] User feedback and questions.

[1714] output:

[1715] User feedback and question data sent to the server.

[1716] Specific behavior:

[1717] The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[1718] (Application example 2)

[1719] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1720] Conventional asset formation, inheritance planning, and tax saving plan generation systems do not take into account the user's emotional state, which can make the proposed plans difficult for users to understand, potentially causing anxiety and stress. In addition, the inability to provide appropriate information based on the user's emotions has led to a problem of reduced user satisfaction.

[1721] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting personal information and asset information of the user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, and means for recognizing the user's emotions in real time and adjusting the content and display format of the plans according to the emotions. This makes it possible to provide personalized plans according to the user's emotional state.

[1722] "Personal information" refers to information for identifying a user, as well as personal data such as age, occupation, income, and expenses.

[1723] "Asset information" refers to information about the assets held by a user, such as cash, real estate, investments, and debts.

[1724] "Means of collection" refers to the hardware and software mechanisms used to capture user-provided data into the system.

[1725] The "analysis means" refers to the algorithms and servers that process the collected personal and asset information and perform calculations and evaluations based on that data.

[1726] The "means of automatic generation" is a software mechanism that automatically creates optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[1727] "Visual display means" refers to a mechanism for displaying the generated plans and data on a monitor or display as graphs or text in a format that is easy for the user to understand.

[1728] "Means for recognizing emotions in real time" refers to software and hardware that analyzes the user's facial expressions and voice data and identifies their emotional state in real time.

[1729] "Adjustment means" refers to the system's functionality for changing the content and display format of the generated plan in response to the recognized emotion.

[1730] This invention is a system that collects personal information and asset information of a user, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. This system includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plans according to those emotions.

[1731] System Configuration

[1732] 1. Data Collection and Analysis

[1733] Users upload their personal and asset information to the device via a smartphone application. This collects information such as annual income, monthly expenses, bank balance, and real estate ownership status. Documents are scanned using the device's built-in scanner and camera and converted into image data. This data is then sent to the server.

[1734] 2. Image Recognition and Data Extraction

[1735] The server uses OCRTesseract to extract numerical and text information from the image data. For example, specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted. The extracted numerical and text information is stored in a database.

[1736] 3. Plan Generation

[1737] The server analyzes the stored data using a generative AI model. A specific algorithm is applied to predict future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, an optimal asset formation, inheritance planning, and tax saving plan is automatically generated. The system also learns the latest legal changes in real time and reflects them in the plan.

[1738] 4. Emotion recognition

[1739] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice data in real time. This emotion data is sent to a server and classified into the user's emotional state, such as "relief," "anxiety," or "interest."

[1740] 5. Plan adjustment and visual image creation

[1741] The server receives the emotional data and adjusts the content and display format of the plan based on the user's emotional state. For example, if the server detects that the user is feeling anxious, it will provide a more detailed explanation of the plan and use more polite language. Based on the analysis results, it will graph the progress of income and expenditures and future asset value, and also provide specific examples of lifestyle plans.

[1742] Specific examples of programs

[1743] When a user uploads a document about their annual income and expenses, the document is scanned and converted into image data. Numerical information is then extracted using OCR and sent to a server. The server analyzes the data and creates an asset formation plan using a generative AI model. The generated plan is presented to the user as a visual image, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," more detailed explanations and different investment options are displayed.

[1744] Prompt Sentence Examples

[1745] Please calculate the specific amount you could save in a year based on a scenario where your annual income is 5 million yen and your monthly expenses are 300,000 yen. Also, if that scenario makes users feel uneasy, please suggest how you should change the explanation or offer.

[1746] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1747] Step 1:

[1748] Users upload personal and asset information to a device via a smartphone application. The input data includes personal data such as annual income, monthly expenses, bank balance, and real estate ownership status. This data is captured as an image using a camera or scanner installed on the device. The output is the captured image data.

[1749] Step 2:

[1750] The terminal transmits the captured image data to the server. The input data is the image data, and the output is the image data transmitted to the server.

[1751] Step 3:

[1752] The server receives the image data and uses OCR to extract text information from the image. The input data is image data, and OCR processing is performed as data processing. The output is information such as annual income, expenses, and real estate appraisal value as text data.

[1753] Step 4:

[1754] The server saves the extracted text information in a database. The input data is text information, which is written to the database as data calculation. The output is user information stored in the database.

[1755] Step 5:

[1756] Based on the data stored on the server, a generative AI model is used to automatically generate plans for asset formation, inheritance planning, and tax savings. The input data is user information obtained from a database, and data analysis and generation are performed based on an algorithm. The output is the generated plan.

[1757] Step 6:

[1758] The server learns the latest legal amendments in real time and reflects them in the generated plan. The input data is legal amendments and the generated plan, and the plan is updated through data calculations. The output is a plan that reflects the latest legal amendments.

[1759] Step 7:

[1760] The device's emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. The input data is the user's facial expression images and voice data, and emotion analysis is performed. The output is emotion data classified as "relief," "anxiety," "interest," etc.

[1761] Step 8:

[1762] The server receives the emotional data and adjusts the content and display format of the plan based on the user's emotional state. The input data is the emotional data and the generated plan, and the content and display format of the plan are adjusted as data processing. The output is a plan adjusted according to the user's emotions.

[1763] Step 9:

[1764] The server generates a visual image of the adjusted plan and graphs the progress of income and expenditures and future asset value. The input data is the adjusted plan, and visualization is performed as a data calculation. The output is a graphed plan.

[1765] Step 10:

[1766] The device presents the generated visual image to the user and monitors the user's reaction in real time. The input data is a graphed plan, and the output is the plan presented to the user and the real-time monitoring results of the emotion engine.

[1767] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1768] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1769] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1770] [Fourth embodiment]

[1771] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1772] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1773] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1774] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1775] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1776] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1777] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1778] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1779] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1780] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1781] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1782] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1783] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1784] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them. Specific embodiments of this system and the operation of its program are described below.

[1785] Data collection

[1786] User Action:

[1787] A user uses a rich client terminal to upload documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, real estate ownership status, etc.

[1788] Terminal operation:

[1789] The device scans the provided document and generates image data, which is then sent to the server.

[1790] Data Extraction

[1791] Server Operation:

[1792] The server uses image recognition software to extract numerical and textual information from the image data sent to it. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[1793] Server Operation:

[1794] The extracted numerical and text information is stored in a database on the server.

[1795] Plan Generation

[1796] Server Operation:

[1797] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[1798] Server Operation:

[1799] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, which means that the database is continuously updated and plans are generated based on that information.

[1800] Visual image generation and presentation

[1801] Server Operation:

[1802] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and forecasts of future asset values. Specific examples of lifestyle planning are provided based on specific simulation results and statistical data.

[1803] Server Operation:

[1804] The server transmits the generated plan and visual image to the terminal.

[1805] Terminal operation:

[1806] The terminal presents the generated plan and visual images to the user, and the information is displayed in a format that is intuitively easy for the user to understand.

[1807] User Action:

[1808] Users can check the plans and visual images presented and provide feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[1809] Specific examples

[1810] Example 1: Creating a wealth plan

[1811] 1. Users upload documents related to their income, expenses, and investment status.

[1812] 2. The device scans the document and sends the image data to the server.

[1813] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[1814] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase by 1.5 times in 10 years.

[1815] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[1816] 6. The terminal presents this to the user, who confirms the plan.

[1817] Example 2: Creating an estate planning plan

[1818] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[1819] 2. The device sends this information in text form to the server.

[1820] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[1821] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[1822] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[1823] This allows users to easily understand the plan that best suits their situation, even if they do not have a deep knowledge of complex laws and tax systems, and to plan their post-retirement life with peace of mind.

[1824] The above is a specific embodiment for carrying out the present invention.

[1825] The processing flow will be explained below.

[1826] Step 1:

[1827] User Action:

[1828] A user logs into a rich client terminal and uploads a document containing personal and asset information to the terminal.

[1829] Step 2:

[1830] Terminal operation:

[1831] The device scans the uploaded documents and converts them into image data.

[1832] Step 3:

[1833] Terminal operation:

[1834] The terminal transmits the converted image data to the server.

[1835] Step 4:

[1836] Server Operation:

[1837] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[1838] Step 5:

[1839] Server Operation:

[1840] The server stores the extracted numerical and text information in a database.

[1841] Step 6:

[1842] Server Operation:

[1843] The server analyzes the stored data and selects the algorithm to apply, such as an asset formation algorithm or an inheritance planning algorithm.

[1844] Step 7:

[1845] Server Operation:

[1846] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[1847] Step 8:

[1848] Server Operation:

[1849] The server uses the selected algorithm to analyze the data and generate an optimal plan. For example, it generates a specific prediction such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[1850] Step 9:

[1851] Server Operation:

[1852] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[1853] Step 10:

[1854] Server Operation:

[1855] The server sends the generated plan and visual image to the terminal.

[1856] Step 11:

[1857] Terminal operation:

[1858] The terminal presents the plan and visual image received from the server to the user.

[1859] Step 12:

[1860] User Action:

[1861] The user reviews the presented plans and visual images and provides further details and feedback as needed.

[1862] This allows users to obtain specific plans for their own asset formation, inheritance planning, and tax savings, allowing them to plan their retirement with peace of mind.

[1863] Example 1

[1864] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1865] Conventional asset formation, inheritance planning, and tax saving planning systems require users to input accurate and detailed information, making operation cumbersome. Furthermore, due to frequent legal changes, it is difficult to reflect the latest legal changes, and as a result, generated plans may not comply with the latest laws and regulations. Furthermore, generated plans are often not presented in a format that is intuitively easy for users to understand.

[1866] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1867] In this invention, the server includes means for users to upload information, means for a terminal to scan documents and generate image data, means for the server to extract numerical and textual information from the image data, means for the server to store the extracted information in a database, means for the server to analyze the data using an algorithm and generate optimal plans for asset formation, inheritance planning, and tax savings, means for the server to convert the generated plans into visual images, and means for the terminal to present the generated plans and visual images to the user. This makes it possible for users to easily provide detailed information and to receive intuitively understandable plans that reflect the latest legal amendments.

[1868] A "user" is an entity that uses the system to provide personal information and asset information.

[1869] "Means for uploading information" is a function that allows users to send their own personal information and asset information to the system.

[1870] A "terminal" is a computer device operated by a user, and is a device that has functions such as scanning documents and displaying data.

[1871] "Means for scanning documents and generating image data" refers to the function of converting physical documents into digital image data.

[1872] A "server" is a computer system that processes, stores, analyzes data, and communicates with other devices.

[1873] "Means for extracting numerical and textual information from image data" refers to technology that identifies and extracts text data from digital images.

[1874] "Means for storing information in a database" refers to a system for appropriately structuring extracted information and storing it for a long period of time.

[1875] An "algorithm" is a set of computational steps for analyzing data or solving problems.

[1876] "A means of analyzing data and generating optimal plans for asset formation, inheritance planning, and tax savings" is a function that automatically creates various plans using mathematical models and analytical methods based on stored data.

[1877] "Means for converting the generated plan into a visual image" refers to a technique for converting the analysis results into a format that is visually easy to understand (e.g., graphs or charts).

[1878] The "means for presenting plans and visual images" is a function for displaying the generated plans and visual information to the user.

[1879] "Latest legal reform information" refers to the latest information on regulations and changes to laws related to asset formation, inheritance planning, and tax savings.

[1880] This invention is a system that collects personal information and asset information of users, analyzes the collected information, automatically generates optimal plans for asset formation, inheritance planning, and tax savings, and visually displays them.

[1881] Hardware and Software Configuration

[1882] User operation and information provision

[1883] Users use a rich client device to upload documents containing their personal and financial information. This requires a computer, a scanner, and an internet connection. Specifically, documents provide information such as annual income, monthly expenses, bank balance, and real estate ownership.

[1884] Document scanning and image data generation

[1885] The device scans the document provided by the user and generates high-resolution image data. This step uses a scanner and image processing software (e.g., Adobe Acrobat). The generated image data is then sent to the server via a secure protocol.

[1886] Information extraction from image data

[1887] The server uses image recognition software (e.g., OCR software) to extract numerical and textual information from the image data sent. For example, OCR software extracts specific information such as "annual income of 5 million yen" and "monthly expenses of 300,000 yen" as text data.

[1888] Database storage

[1889] The extracted numerical and textual information is stored in a database on the server. The database system (e.g., MySQL or PostgreSQL) is built with high availability and security in mind. Annual income and expenditure information is stored linked to the user ID.

[1890] Data analysis and plan generation

[1891] The server analyzes the stored data by applying specific algorithms (e.g., regression analysis, machine learning models, etc.). The analysis targets the user's annual income, expenses, investment status, etc. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings.

[1892] Reflecting the latest legal amendments

[1893] The server learns the latest legal changes in real time and reflects them in the analysis algorithm, continuously updating the database so that the plans generated based on them comply with the latest laws and regulations.

[1894] Visual image generation

[1895] Based on the generated plan, the server graphs the progress of income and expenditures and the forecast of future asset value. This visual image is provided in a format that is intuitively easy for users to understand. Specifically, a graph drawing tool (e.g., D3.js, Chart.js) is used.

[1896] Presentation of plans and visual images

[1897] The server sends the generated plan and visual image to the terminal, which then presents it to the user, displaying the information in a format that is intuitively easy for the user to understand. For example, graphs and simulation results are displayed in an easy-to-understand manner.

[1898] User Feedback

[1899] Users can check the plans and visual images presented and provide feedback as needed. For example, they can send questions to the server such as, "What specific procedures are required for this tax-saving measure?" The system will then be improved based on the feedback.

[1900] Examples of prompt statements

[1901] Below are some examples of specific prompts to input into a generative AI model:

[1902] Prompt statement:

[1903] Generate an optimal asset formation plan based on the user's personal and asset information. First, provide information such as the user's annual income, monthly expenses, deposit balance, and real estate ownership status, and analyze this to predict future asset value. Next, propose a specific plan that takes into account tax savings and inheritance planning, and generate and display a visual image.

[1904] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1905] Specific explanation of processing steps

[1906] Step 1: User uploads information

[1907] Input: Personal and financial information such as the user's annual income, monthly expenses, bank balance, and real estate ownership status is provided as documents.

[1908] What happens: Users upload these documents to the system using rich client devices, which have scanning and digital form-filling capabilities.

[1909] Output: The device receives this information as digital data.

[1910] Step 2: The device scans the document and generates image data.

[1911] Input: Documents uploaded by users.

[1912] Specific operation: The device uses a high-resolution scanner to scan the provided document and generate image data. Image processing software (e.g., Adobe Acrobat) is used.

[1913] Output: The generated image data is saved to the device.

[1914] Step 3: The device sends the image data to the server

[1915] Input: scanned image data.

[1916] Specific operation: The device sends image data to the server via a secure protocol (e.g., HTTPS).

[1917] Output: The transmitted image data is saved on the server.

[1918] Step 4: The server extracts numerical and textual information from the image data.

[1919] Input: Image data stored on the server.

[1920] Specific operation: The server uses OCR software to extract numerical and textual information from the image data. For example, it uses text detection to extract specific information such as "annual income of 5 million yen" or "monthly expenses of 300,000 yen."

[1921] Output: The extracted numerical and character information is generated as text data.

[1922] Step 5: The server stores the extracted information in a database

[1923] Input: Extracted numeric and textual information.

[1924] Specific operation: The server stores this information in a database system (e.g., MySQL, PostgreSQL). Each piece of information is structured and linked to the user ID.

[1925] Output: Structured data stored in a database.

[1926] Step 6: The server uses algorithms to analyze the data and generate a plan

[1927] Input: User's personal and financial information stored in a database.

[1928] Specific operation: The server applies data analysis algorithms (e.g., regression analysis, machine learning models) to calculate optimal plans for asset formation, inheritance planning, and tax savings based on various information. For example, it predicts future assets based on the user's annual income, expenses, and investment status.

[1929] Output: The generated optimal plan.

[1930] Step 7: The server updates with the latest legal information

[1931] Input: Latest legal change information and generated plan.

[1932] How it works: The server learns the latest legal changes in real time and reflects them in the analysis algorithm, so the generated plans comply with the latest laws and regulations.

[1933] Output: An updated plan that reflects the latest legal changes.

[1934] Step 8: The server converts the generated plan into a visual image

[1935] Input: The generated plan.

[1936] Specific operation: The server uses a visual image generation tool (e.g., D3.js, Chart.js) to convert the analysis results into a visually understandable format, such as graphing the progress of income and expenditure or future asset value.

[1937] Output: Graphs and charts as visual images.

[1938] Step 9: The server sends the generated plan and visual image to the device.

[1939] Input: Generated plans and visual images.

[1940] Specific operation: The server sends this information to the device via a secure protocol (e.g., HTTPS).

[1941] Output: Plans and visual images sent to the device.

[1942] Step 10: The device presents the user with a plan and visual images

[1943] Input: Plan and visual images sent from the server.

[1944] Specific operation: The device presents the plan and visual image to the user through a display function (e.g., web browser, dedicated app). The plan and visual image are displayed on the screen in a way that allows the user to intuitively understand it.

[1945] Output: Plans and visual images presented to the user.

[1946] Step 11: User provides feedback

[1947] Input: Plan and visual image displayed on the terminal.

[1948] Specific operation: The user checks the plan and provides feedback as needed (e.g., "What specific steps are required to implement this tax-saving measure?"). The feedback is sent to the server via the device.

[1949] Output: User feedback sent to the server.

[1950] (Application example 1)

[1951] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1952] Existing asset management, inheritance planning, and tax-saving systems often require users to manage their own information and have specialized knowledge in order to be effective. Furthermore, there is a lack of practical support, as there are limited ways for users to automatically implement asset formation and tax-saving strategies. Therefore, there is a need for a system that allows users to easily implement optimal asset formation and tax-saving strategies, and also allows for integrated electronic payments based on those plans.

[1953] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1954] In this invention, the server includes means for collecting personal information and asset information of a user, means for analyzing the collected personal information and asset information, means for automatically generating optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results, means for visually displaying the generated plans, means for supporting electronic payments based on the generated plans, and means for providing interactive feedback. This allows users to put optimal asset management and tax savings plans into practice without specialized knowledge. Furthermore, the integration of electronic payments based on the plans provides practical support for asset formation and tax savings, enabling comprehensive asset management.

[1955] "User's personal information" refers to information that identifies an individual or indicates an individual's attributes, and includes, for example, name, age, sex, address, occupation, income, and the like.

[1956] "Asset information" refers to information about financial assets, real estate, and other assets owned by a user, including, for example, bank account balances, appraised values ​​of stocks and investment trusts, appraised values ​​of real estate, and the like.

[1957] "Means for collecting" refers to a method or mechanism for obtaining personal information and asset information from a user and transmitting it to a server.

[1958] "Means for analysis" refers to a method or mechanism for performing data analysis based on collected personal information and asset information and deriving useful results.

[1959] "Means for automatically generating optimal plans" refers to a method or mechanism that automatically creates asset formation, inheritance planning, and tax saving plans that are most suitable for each user based on the results of data analysis.

[1960] "Visual display means" refers to a method or mechanism for visually presenting the generated plan to a user, such as a graph or chart.

[1961] "Means for supporting electronic payments" refers to a method or mechanism for a user to make the electronic payments necessary to manage his or her assets based on the generated plan.

[1962] "Means for providing interactive feedback" refers to a method or mechanism for responding to questions or feedback from a user in real time and providing additional information or advice.

[1963] The system for implementing this invention collects and analyzes a user's personal information and asset information, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them. It also includes functions to support electronic payments based on the generated plans and provide interactive feedback.

[1964] Program processing and hardware / software used

[1965] Data collection

[1966] The device scans documents provided by the user (such as annual income, monthly expenses, bank balance, and real estate ownership status) and generates image data. This image data is then sent to a cloud server. The hardware used includes a scanner and a camera.

[1967] Data Extraction

[1968] The server uses image recognition software called "pytesseract" to extract necessary numerical and text information from the transmitted image data. For example, it can recognize and extract specific information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" from the image data.

[1969] Data storage and analysis

[1970] The extracted numerical and textual information is stored in a secure database on the server, which then applies specific algorithms to analyze the data.

[1971] Plan Generation

[1972] The server generates an optimal asset formation plan based on the user's annual income, expenses, and investment status. It also learns the latest legal reform information in real time and reflects it in the analysis results. Based on this analysis result, it provides the user with the optimal asset formation plan.

[1973] Visual image generation and presentation

[1974] Based on the generated plan, the server uses matplotlib to graph the progress of income and expenditures and the forecast of future asset value. This visual image is sent to the terminal and presented to the user in an easy-to-understand format.

[1975] Support for electronic payments

[1976] Based on the generated plan, the server supports electronic payments for users to carry out asset management and tax saving measures. The server has the function of automatically transferring funds and issuing instructions for investment.

[1977] Interactive Feedback

[1978] Users can ask questions about the generated plans and visual images, and the server will respond in real time via chatbots or other means, providing additional information and advice.

[1979] Examples of concrete examples and prompts

[1980] For example, based on data provided by a user with an annual income of 5 million yen and monthly expenses of 300,000 yen, the server generates the following asset formation plan:

[1981] It is predicted that by investing 200,000 yen per month in low-risk investments, assets will increase by 1.5 times in 10 years.

[1982] Based on the generated plan and its visual image, electronic payment support is provided to users for asset management.

[1983] Example prompt sentence:

[1984] "Upload your annual income and monthly expenses. We'll then predict your future asset growth and display it as a graph."

[1985] The above is a specific embodiment for carrying out the present invention. This system allows users to appropriately manage assets and take tax-saving measures without requiring specialized knowledge.

[1986] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1987] Step 1:

[1988] The user uploads documents containing their personal and financial information to the terminal, including annual income, monthly expenses, bank balance, real estate ownership status, etc. The user's input is captured in the terminal as image data of the document and sent to the server.

[1989] Step 2:

[1990] The device scans the uploaded document and generates image data, which serves as input data to be sent to the server. The output of the device is a high-resolution scanned image file.

[1991] Step 3:

[1992] The server uses the image recognition software "pytesseract" to extract text information from the transmitted image data. Specifically, it converts the image data to grayscale and applies a text recognition algorithm. The server's input is the image data, and its output is the extracted information as text. For example, specific numerical information such as "annual income of 5 million yen" and "monthly expenditure of 300,000 yen" is extracted.

[1993] Step 4:

[1994] The server stores the extracted numerical and textual information in a secure database. The stored data is used for subsequent data analysis. The input to the server is the extracted textual information, and the output is storage in the database.

[1995] Step 5:

[1996] The server applies specific algorithms to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. This analysis automatically generates optimal asset formation, inheritance planning, and tax-saving plans. The server's input is numerical information stored in a database, and its output is a specific plan.

[1997] Step 6:

[1998] The server learns the latest legal amendments in real time and reflects them in the analysis algorithm. This ensures that the generated plans are always based on the latest laws and regulations. The server's input is legal amendments, and the output is the latest plan based on that information.

[1999] Step 7:

[2000] The server uses "matplotlib" to generate visual images based on the generated plan. Specifically, it graphs the progress of income and expenditures and the forecast of future asset value. The server's input is the generated plan information, and its output is visual graphs and charts.

[2001] Step 8:

[2002] The server sends the generated visual image and plan to the terminal. The terminal displays them in a format that is easy for the user to understand. This allows the user to visually confirm their own asset formation plan. The input to the terminal is the visual image and plan sent from the server, and the output is the display to the user.

[2003] Step 9:

[2004] Based on the generated plan, the terminal supports electronic payments, automatically executing specific fund transfer and investment instructions. The input of the terminal is the specific plan instructions, and the output is the actual payment transaction.

[2005] Step 10:

[2006] The user checks the generated plan and visual image and provides feedback as needed. For example, a question might be, "What exactly will this tax saving measure do?" The user's input is the feedback or question, and the server responds by providing an interactive response. The server's input is the user's feedback, and its output is the answer or additional information.

[2007] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2008] This invention is a system that combines a system that collects a user's personal information and asset information, analyzes it, automatically generates optimal asset formation, inheritance planning, and tax saving plans, and visually displays them, with an emotion engine that recognizes the user's emotions. Specific embodiments of this system and the operation of its program are described below.

[2009] Data collection

[2010] User Action:

[2011] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[2012] Terminal operation:

[2013] The terminal scans the provided document, converts it into image data, and transmits the data to a server.

[2014] Data Extraction

[2015] Server Operation:

[2016] The server uses image recognition software to extract numerical and textual information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[2017] Server Operation:

[2018] The extracted numerical and text information is stored in a database.

[2019] Plan Generation

[2020] Server Operation:

[2021] The server applies a specific algorithm to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal asset formation, inheritance planning, and tax saving plans.

[2022] Server Operation:

[2023] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[2024] Manipulating the Emotion Engine

[2025] Terminal operation:

[2026] The emotion engine installed in the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc.

[2027] Server Operation:

[2028] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the server presents a more detailed explanation in polite language.

[2029] Visual image generation and presentation

[2030] Server Operation:

[2031] Based on the plan generated by the server, a visual image is generated, such as graphing income and expenditure trends and future asset values. Specific examples of lifestyle planning are also provided based on specific simulation results and statistical data.

[2032] Server Operation:

[2033] The server transmits the generated plan and visual image to the terminal.

[2034] Terminal operation:

[2035] The device presents the generated plan and visual images to the user, and an emotion engine monitors the user's real-time reactions and adjusts the display content as needed.

[2036] User Action:

[2037] Users can review the plans and visual images presented and, if necessary, request more detailed information or provide feedback. For example, they can ask questions such as, "What specific procedures are required for this tax-saving measure?"

[2038] Specific examples

[2039] Example 1: Creating a wealth plan

[2040] 1. Users upload documents related to their income, expenses, and investment status.

[2041] 2. The device scans the document and sends the image data to the server.

[2042] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[2043] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[2044] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[2045] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[2046] Example 2: Creating an estate planning plan

[2047] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[2048] 2. The device sends this information in text form to the server.

[2049] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[2050] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[2051] 5. The server sends the generated plan to the terminal, which then presents it to the user.

[2052] 6. The emotion engine analyzes the user's reaction, and if it determines that the user feels "safe," it skips the detailed explanation of the plan and moves on to the next step.

[2053] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[2054] The above is a specific embodiment for carrying out the present invention.

[2055] The processing flow will be explained below.

[2056] Data collection and extraction

[2057] Step 1:

[2058] User Action:

[2059] A user logs in to a rich client terminal and uploads documents containing personal and financial information to the terminal, including annual income, monthly expenses, savings balance, real estate ownership status, etc.

[2060] Step 2:

[2061] Terminal operation:

[2062] The device scans the uploaded document and converts it into image data.

[2063] Step 3:

[2064] Terminal operation:

[2065] The terminal transmits the converted image data to the server.

[2066] Step 4:

[2067] Server Operation:

[2068] The server uses image recognition software to extract numerical and textual information from the image data sent. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenditure of 300,000 yen."

[2069] Step 5:

[2070] Server Operation:

[2071] The extracted numerical and text information is stored in a database.

[2072] Plan generation and emotion engine operation

[2073] Step 6:

[2074] Server Operation:

[2075] The server analyzes the stored data and applies selected algorithms, including algorithms that generate asset formation, inheritance planning, and tax savings plans based on the user's annual income, expenses, and investment status.

[2076] Step 7:

[2077] Server Operation:

[2078] The server updates the database with the latest legal amendments in real time and reflects them in the analysis algorithm.

[2079] Step 8:

[2080] Server Operation:

[2081] The server uses the selected algorithm to automatically generate optimal asset formation, inheritance planning, and tax saving plans based on the analysis results. For example, it makes specific predictions such as, "If you have an annual income of 5 million yen, investing 200,000 yen per month in low-risk investments will increase your assets by 1.5 times in 10 years."

[2082] Step 9:

[2083] Terminal operation:

[2084] The device's built-in emotion engine analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while looking at the screen and classifies them as "relief," "anxiety," "interest," etc.

[2085] Visual image generation and presentation

[2086] Step 10:

[2087] Server Operation:

[2088] Based on the plan generated by the server, a visual image is generated, such as graphing trends in income and expenditure and future asset value.

[2089] Step 11:

[2090] Server Operation:

[2091] The server transmits the generated plan and visual image to the terminal.

[2092] Step 12:

[2093] Terminal operation:

[2094] The device receives the plan and visual images from the server and presents them to the user. The emotion engine then monitors the user's real-time reactions and adjusts the displayed content as needed. For example, if the user is recognized as "anxious," the level of detail and wording of the explanation will be changed.

[2095] Step 13:

[2096] User Action:

[2097] The user reviews the presented plan and visual image and gives feedback, for example, by asking specific questions such as, "What specific procedures are required for this tax-saving measure?"

[2098] Specific examples

[2099] Example 1: Creating a wealth plan

[2100] Step 1:

[2101] Users upload documents related to their income, expenses, and investment status.

[2102] Step 2:

[2103] The device scans the document and sends the image data to the server.

[2104] Step 3:

[2105] The server uses image recognition software to extract numbers (annual income, expenses, investment amounts, etc.).

[2106] Step 4:

[2107] The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[2108] Step 5:

[2109] The server graphs the plan and generates a visual image of the progress of income and expenditure.

[2110] Step 6:

[2111] The device presents this to the user, and an emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[2112] Example 2: Creating an estate planning plan

[2113] Step 1:

[2114] Users provide information about their family structure, real estate holdings, and financial assets.

[2115] Step 2:

[2116] The terminal sends this information to the server in text form.

[2117] Step 3:

[2118] The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[2119] Step 4:

[2120] The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your spouse and distributing your financial assets to your children can be a tax-saving measure.

[2121] Step 5:

[2122] The server transmits the generated plan to the terminal, which then presents it to the user.

[2123] Step 6:

[2124] The emotion engine analyzes the user's reaction, and if it recognizes that the user feels "at ease," the detailed explanation of the plan is omitted and the system moves on to the next step.

[2125] In this way, by recognizing the user's emotions in real time and adjusting the content and display format accordingly, it is possible to provide a more personalized plan that is easy for the user to understand. This system allows users to easily understand the plan that is best suited to their situation and to plan their retirement with peace of mind.

[2126] Example 2

[2127] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2128] Conventional asset formation, inheritance planning, and tax-saving systems offer the ability to automatically generate and display plans based on the user's personal and asset information. However, these systems do not take the user's emotional state into account, which often leaves users feeling anxious or makes the plans difficult to understand. It is also difficult to reflect changes due to legal amendments in real time, making it difficult to provide plans based on the latest information. Furthermore, there are limitations to properly analyzing collected numerical and text information and presenting optimal plans. A method to solve these issues is needed.

[2129] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2130] In this invention, the server includes means for recognizing the user's emotions in real time and adjusting the content and display format of the plan based on the user's emotional state, means for learning the latest legal amendment information in real time and reflecting it in the plan, and means for storing the extracted numerical and text information in a database and performing data analysis. This makes it possible to propose personalized plans based on the user's emotions and always provide optimal plans based on the latest legal amendment information.

[2131] "User's personal information" refers to information for identifying an individual, such as the user's name, age, sex, and occupation.

[2132] "Asset information" refers to information about financial assets and physical assets owned by a user, such as cash, deposits, real estate, stocks, and bonds.

[2133] "Means for collection" refers to the method and device for acquiring personal information and asset information from users and incorporating it into the system.

[2134] "Means for analysis" refers to algorithms and programs that perform data analysis based on collected data and make predictions about the user's financial situation and future prospects.

[2135] "Means for automatic generation" refers to algorithms and programs that automatically generate optimal plans for asset formation, inheritance planning, and tax savings based on the analysis results.

[2136] "Visual display means" refers to devices and software for presenting the generated plan to a user in an easy-to-view format through visual elements such as graphs and charts.

[2137] "Means for recognizing in real time" refers to technology and devices that detect a user's emotions in real time and analyze that information.

[2138] "Adjusting means" refers to software and algorithms for appropriately modifying the content and presentation of the plan based on the user's emotional state as recognized in real time.

[2139] "Legal Change Information" refers to information on the latest laws and regulations, including in particular changes to tax laws and inheritance laws.

[2140] "Means of learning in real time" refers to the data acquisition mechanism and learning algorithms that instantly acquire the latest legal amendment information and reflect it in the system.

[2141] "Means for storing data in a database" refers to a database system and related software for efficiently storing and managing collected numerical and textual information.

[2142] This invention is a system that collects and analyzes a user's personal and asset information to automatically generate and visually display optimal asset formation, inheritance planning, and tax savings plans. Furthermore, by combining this with an emotion engine that recognizes the user's emotions, a more personalized experience is provided. A specific embodiment of this system and the operation of its program are described below.

[2143] Data collection

[2144] User Action:

[2145] A user logs in to a rich client terminal and uploads documents containing his or her personal and financial information, such as annual income, monthly expenses, bank balance, and real estate ownership status.

[2146] Terminal operation:

[2147] The terminal scans the provided document, converts it into image data, and sends the data to the server. Specifically, the terminal controls a scanner device to scan the document, generates high-resolution image data, and sends it to the server via an HTTP request.

[2148] Data Extraction

[2149] Server Operation:

[2150] The server uses image recognition software (e.g., Tesseract OCR) to extract numerical and text information from the transmitted image data. For example, from an image of a household account book, it can recognize "annual income of 5 million yen" and "monthly expenses of 300,000 yen." The server starts Tesseract OCR, passes the image data as input, and the OCR extracts the text data, which is then saved as structured data (e.g., JSON format).

[2151] Server Operation:

[2152] Store the extracted numeric and text information in a database (e.g., MySQL or PostgreSQL). The server organizes the extracted data and generates and executes SQL queries to insert it into the corresponding database fields.

[2153] Plan Generation

[2154] Server Operation:

[2155] The server applies a specific algorithm (e.g., a machine learning model) to the stored data and performs analysis. For example, it predicts future asset formation based on the user's annual income, expenses, and investment status. Based on the results of this analysis, it automatically generates optimal plans for asset formation, inheritance planning, and tax savings. The server loads a machine learning model (e.g., scikit-learn or TensorFlow) and runs the prediction algorithm using user data retrieved from the database. Based on the predicted data, it generates a proposed plan.

[2156] Server Operation:

[2157] The server updates the database with the latest legal amendment information in real time and reflects it in the analysis algorithm. The server periodically obtains the latest legal amendment information from an external API (e.g., a government tax law information service) and updates the database. The obtained information is input into the analysis algorithm.

[2158] Manipulating the Emotion Engine

[2159] Terminal operation:

[2160] An emotion engine (for example, Microsoft Azure Emotion API) installed on the device analyzes the user's facial expressions and voice data in real time to recognize their emotional state. For example, it analyzes the user's facial expressions and tone of voice while they are looking at the screen and classifies the user's emotions as "relief," "anxiety," "interest," etc. The device collects real-time data through the camera and microphone and sends it to the emotion engine to obtain the analysis results.

[2161] Server Operation:

[2162] The server receives the emotion data sent from the emotion engine and adjusts the content and display format of the plan based on the user's emotional state. For example, if the user is recognized as "anxious," the explanation is presented in more detail and in more polite language. The server receives the emotion data and dynamically adjusts the user's display settings, including the analysis results. The display content and presentation format are changed.

[2163] Visual image generation and presentation

[2164] Server Operation:

[2165] Based on the generated plan, the server generates visual images, such as graphs of income and expenditure trends and future asset value. Specifically, the server uses graph drawing libraries such as Python's matplotlib or D3.js to visualize income and expenditure trends, and the generated graphs are saved as image files or dynamically generated in HTML format.

[2166] Server Operation:

[2167] The server sends the generated plan and visual image to the terminal, and the server packages the generated data and graph and sends it to the terminal as an HTTP response.

[2168] Terminal operation:

[2169] The device presents the generated plan and visual images to the user. Furthermore, an emotion engine monitors the user's real-time reactions and adjusts the displayed content as necessary. The device displays the generated plan and visual images through a web browser or dedicated client application, and new user reaction data is acquired and reflected in the displayed content.

[2170] User Action:

[2171] The user checks the presented plan and visual image, and requests more detailed information or gives feedback as needed. For example, they can ask questions such as, "What specific procedures are required for this tax saving measure?" The user uses the interactive UI to enter their question or feedback and clicks the submit button.

[2172] Specific examples

[2173] Example 1: Creating a wealth plan

[2174] 1. Users upload documents related to their income, expenses, and investment status.

[2175] 2. The device scans the document and sends the image data to the server.

[2176] 3. The server uses image recognition software to extract numerical values ​​(annual income, expenses, investment amounts, etc.).

[2177] 4. The server analyzes the extracted data and generates a specific asset formation plan. For example, it predicts that if you have an annual income of 5 million yen and invest 200,000 yen per month in low-risk investments, your assets will increase 1.5 times in 10 years.

[2178] 5. The server graphs the plan and generates a visual image of the progress of income and expenditure.

[2179] 6. The device presents this to the user, and the emotion engine analyzes the user's reaction. If the user is recognized as "anxious," additional explanations or different investment options are displayed.

[2180] Example 2: Creating an estate planning plan

[2181] 1. The user provides information about their family structure, real estate holdings, and financial assets.

[2182] 2. The device sends this information in text form to the server.

[2183] 3. The server generates the optimal inheritance planning plan based on family composition, real estate appraisal value, and total financial assets.

[2184] 4. The server makes specific proposals based on the latest inheritance tax laws. For example, it performs a simulation showing how bequeathing your home to your sp...

Claims

1. A means for collecting personal information and asset information of a user; means for analyzing the collected personal information and asset information; Based on the analysis results, a means to automatically generate optimal plans for asset formation, inheritance planning, and tax savings, a means for visually displaying the generated plan; A system including:

2. 2. The system according to claim 1, which learns the latest legal amendment information in real time and reflects it in the plan.

3. 2. The system according to claim 1, wherein the extracted numerical values ​​and text information are stored in a database and data analysis is performed.

Images