System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting data related to a living situation from a user; means for analyzing a tax saving potential of the user using the data; means for proposing a tax saving measure using a database in which latest tax system revision information and expert knowledge are aggregated; and means for providing the proposal to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many individuals and corporate employees find it difficult to quickly obtain accurate information on tax savings, making it difficult to find tax-saving methods that suit them. In particular, in an environment where tax reforms are frequent, it is necessary to provide appropriate tax-saving proposals based on the latest information, but doing this manually is inefficient and time-consuming. Furthermore, the procedures and paperwork required for tax savings are complicated, causing many users to feel overwhelmed. Given these factors, a system is needed that allows users to easily and quickly learn about optimal tax-saving strategies and apply them without hassle. [Means for solving the problem]

[0005] The present invention provides a system that collects data on a user's living situation and analyzes tax-saving potential based on that data. Specifically, the system includes a means for analyzing the user's data in real time using a database that aggregates the latest tax reform information and expert knowledge, and proposing optimal tax-saving strategies. The system also includes a means for generating and providing the user with a procedural guide and a list of required documents, allowing the user to easily complete tax-saving procedures. These means enable the user to efficiently collect complex information and complete procedures for tax savings.

[0006] "Living situation data" refers to information such as a user's income, family composition, place of residence, and expenses.

[0007] "Tax Saving Potential" is an indicator that shows how much tax a user could potentially save based on their current living situation.

[0008] "Latest Tax Reform Information" refers to information about the latest tax changes and amendments announced by the government and related organizations.

[0009] "Expert knowledge" refers to information that compiles the knowledge and advice of people with specialized knowledge and experience in tax matters, such as tax accountants and certified public accountants.

[0010] A "database" is a digital system that manages information in an organized manner and makes it easy to search and update.

[0011] "Tax saving strategies" refer to specific methods and techniques for minimizing tax payments.

[0012] A "Procedural Guide" is a document that details the procedural steps a user must take to apply a tax saving strategy.

[0013] The "List of Required Documents" is a list of documents required when applying tax-saving measures.

[0014] "Analysis" is the process of conducting detailed analysis of collected data and making decisions based on that analysis. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that allows users to input data about their own living situation, analyzes tax saving potential based on that data, and provides optimal tax saving plans to users. This system is equipped with various means for collecting and analyzing users' living situation data, and generating and providing tax saving plans based on the latest tax reform information and expert knowledge.

[0037] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[0038] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[0039] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate the optimal tax savings plan for the user, which is then sent to the user's device and displayed.

[0040] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[0041] [Specific example]

[0042] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[0043] 1. Annual income: 5 million yen

[0044] 2. Family: Married, two children

[0045] 3. Place of residence: Tokyo

[0046] 4. Expenses: Mortgage Deductible

[0047] The server analyzes the user's data in real time and generates tax-saving suggestions such as:

[0048] 1. Make the most of your mortgage deduction

[0049] 2. Application of special spouse deduction

[0050] 3. Optimizing education deductions

[0051] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[0052] This system allows users to quickly find out the tax-saving plan that best suits their living situation and allows them to proceed with the procedure efficiently, which is expected to increase the user's take-home pay.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[0056] Step 2:

[0057] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[0058] Step 3:

[0059] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[0060] Step 4:

[0061] The device displays a questionnaire screen about the user's living situation, and the user answers questions about income, family composition, place of residence, expenses, etc.

[0062] Step 5:

[0063] The user answers the survey and clicks the "Next" button to submit the response data.

[0064] Step 6:

[0065] The server receives the survey data sent by users in real time and stores it in a database, while an AI algorithm analyzes the data and calculates the tax saving potential.

[0066] Step 7:

[0067] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate the optimal tax saving plan for the user.

[0068] Step 8:

[0069] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[0070] Step 9:

[0071] The terminal displays the customized tax saving proposal received from the server to the user.

[0072] Step 10:

[0073] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[0074] Step 11:

[0075] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[0076] Step 12:

[0077] The server transmits the generated procedure guide and list of required documents to the terminal.

[0078] Step 13:

[0079] The terminal displays the procedure guide and required document list received from the server to the user.

[0080] Step 14:

[0081] Users follow the provided guide to carry out tax-saving procedures.

[0082] Example 1

[0083] 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."

[0084] Current systems and methods make it difficult for users to quickly obtain optimal tax-saving plans based on their own living circumstances, and they also face problems such as the time and effort required for procedures and collecting necessary documents.It is also difficult to keep up-to-date with tax reform information and expert knowledge, and there is a risk that the information provided to users will remain outdated.

[0085] 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.

[0086] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for generating and providing the user with a procedure guide and a list of required documents, thereby enabling the user to quickly obtain the optimal tax saving plan based on their living situation and to efficiently carry out the necessary procedures and collect the necessary documents.

[0087] "Lifestyle data" refers to information related to a user's daily life and financial situation, such as the user's income, family composition, place of residence, and expenses.

[0088] "Tax Saving Potential" refers to the extent to which users can qualify for deductions and tax benefits based on their living situation.

[0089] "Tax Reform Information" means information regarding the latest amendments and changes to tax laws and tax-related rules and regulations.

[0090] "Expert knowledge" refers to practical advice and recommendations based on the knowledge and experience of tax and accounting professionals.

[0091] "Procedural Guide" means a document that provides a detailed explanation and sequence of steps that a user must take to apply for a tax benefit.

[0092] "Required Document List" means the list of documents that a User must prepare in order to apply for a particular tax benefit.

[0093] "Database" refers to a system for efficiently storing and managing collected information and data.

[0094] A "generative algorithm" refers to a calculation method or program that analyzes collected data and automatically generates optimal tax-saving plans.

[0095] In the present invention, the following means are used to implement a system that collects and analyzes data on the user's living situation and provides optimal tax-saving ideas.

[0096] 1. Data collection methods:

[0097] Users access the system using a terminal and are required to register or log in. When registering, they enter personal information such as their name, email address, and password, which is then stored in a database by the server. When logging in, users enter their registered email address and password, which the server then verifies against the database.

[0098] 2. Enter living situation data:

[0099] Users answer a questionnaire about their living situation from their device. Questionnaire items include income, family composition, place of residence, expenses, etc., and the data is sent from the user's device to the server. The server saves this data in real time and stores it in a database.

[0100] 3. AI-based data analysis:

[0101] The server analyzes the received data using an AI algorithm. This algorithm uses, for example, Python's "scikit-learn" library. The AI ​​analyzes the user's lifestyle data and calculates tax savings potential. The analysis incorporates the latest tax reform information and expert knowledge.

[0102] 4. Generating optimal tax savings:

[0103] The server generates optimal tax-saving plans based on the results of AI analysis, the latest tax reform information, and expert knowledge. The generated tax-saving plans are sent from the server to the user's device and displayed.

[0104] 5. Generate procedure guide and required document list:

[0105] If the user selects the proposed tax saving plan, the server will generate a procedure guide and a list of required documents, which will be sent to the user's device so that the user can proceed with the procedure accordingly.

[0106] Specific examples

[0107] For example, a new user registers with the system and completes the following lifestyle questionnaire:

[0108] 1. Annual income: 5 million yen

[0109] 2. Family: Married, two children

[0110] 3. Place of residence: Tokyo

[0111] 4. Expenses: Mortgage Deductible

[0112] The server receives this information and analyzes it in real time, for example using Python's "scikit-learn" to generate optimal tax saving plans like the following:

[0113] 1. Make the most of your mortgage deduction

[0114] 2. Application of special spouse deduction

[0115] 3. Optimizing education deductions

[0116] The tax-saving plan is displayed on the user's device, and when the user selects "apply," a list of documents required for the procedure and specific steps are provided. For example, the loan balance certificate required for the mortgage deduction and documents for the education expense deduction are displayed.

[0117] Prompt Sentence Examples

[0118] Example prompt: "Please provide the optimal tax savings plan for the user whose annual income is 5 million yen, whose family consists of a spouse and two children, who lives in Tokyo, and who is eligible for a mortgage deduction."

[0119] This system allows users to quickly find out the tax-saving plan that best suits their living situation, and also allows them to efficiently complete the procedures and collect the necessary documents.

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

[0121] Step 1:

[0122] A user accesses the system using a terminal and is taken to a screen for new registration or login. In the case of new registration, the user enters personal information such as name, email address, and password, which the server receives and stores in a database. In the case of login, the server compares the entered authentication information with the data in the database, and if authentication is successful, the user is taken to the home screen.

[0123] Input: Personal information (name, email address, password) when registering, or authentication information (email address, password) when logging in

[0124] Data processing: Personal information is stored in a database, and authentication information is verified against the database.

[0125] Output: Notification of registration completion or transition to home screen

[0126] Step 2:

[0127] Users answer a questionnaire about their living situation from their device, including information on income, family composition, place of residence, expenses, etc., and the data entered by the user is sent from the device to the server.

[0128] Input: Living situation data (income, family composition, place of residence, expenses, etc.)

[0129] Data processing: Sending data from the device to the server

[0130] Output: Acknowledgement of data sent to the server

[0131] Step 3:

[0132] The server saves the received living situation data in real time and stores it in a database, while simultaneously checking the data for consistency and performing data cleansing as necessary.

[0133] Input: Living situation data

[0134] Data Processing: Real-time data storage and data cleansing

[0135] Output: Consistent living situation data stored in a database

[0136] Step 4:

[0137] The AI ​​algorithm installed on the server calculates the tax saving potential of users based on the lifestyle data stored in the database, using libraries such as Python's "scikit-learn."

[0138] Input: Consistent living situation data

[0139] Data processing: Analysis using AI algorithms and calculation of tax saving potential

[0140] Output: Tax saving potential calculation results

[0141] Step 5:

[0142] The server generates optimal tax-saving plans based on the analysis results, the latest tax reform information, and expert knowledge. The generated tax-saving plans are stored in a database and sent to the user's device.

[0143] Input: Tax saving potential calculation results, tax reform information, expert knowledge

[0144] Data processing: generating tax saving proposals

[0145] Output: Generated tax saving plans are saved in a database and sent to the user's device

[0146] Step 6:

[0147] The user checks the tax saving plan displayed on the terminal, selects "Apply" or "Not Apply", and the selection is sent from the terminal to the server.

[0148] Input: Generated tax savings plan, user selection

[0149] Data processing: Sending user-selected data

[0150] Output: User selections sent to the server

[0151] Step 7:

[0152] If the user selects "apply" for the tax saving plan, the server will generate a procedure guide and a list of required documents and send them to the user's device, allowing the user to proceed with the specific procedures.

[0153] Input: User's selection ("Apply")

[0154] Data processing: Procedural guide and required document list generation

[0155] Output: Procedure guide and required document list sent to user's device

[0156] This process allows users to quickly obtain the optimal tax-saving plan based on their living situation and to proceed with the process efficiently.

[0157] (Application example 1)

[0158] 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."

[0159] Conventional tax-saving proposal systems rely on analysis results based on user-entered lifestyle data, and are unable to provide real-time tax-saving proposals that take daily electronic payment information into account. This makes it difficult for users to immediately understand tax-saving potential and implement effective tax-saving strategies.

[0160] 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.

[0161] In this invention, the server includes means for collecting data on the user's living situation, means for analyzing the user's tax saving potential using the data, means for collecting and analyzing electronic payment data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for providing the proposals to the user, thereby enabling real-time analysis and proposal of tax saving potential using the user's daily electronic payment data.

[0162] "User" means an individual or corporation that uses the system to provide living situation data and electronic payment data and receive tax savings suggestions.

[0163] "Data regarding living conditions" refers to information that indicates the user's economic and living conditions, such as the user's income, family composition, place of residence, and expenses.

[0164] "Tax saving potential" refers to the possibility and extent of tax savings calculated based on data entered by the user.

[0165] "Electronic payment data" means information relating to the history and content of electronic payments made by a user.

[0166] "Tax Reform Information" refers to information about the latest changes and updates to the tax system announced by the government and related agencies.

[0167] "Expert knowledge" refers to the collection of tax-related knowledge and advice held by experts such as tax accountants and economists.

[0168] A "database" is a collection of information constructed to efficiently manage and use collected data.

[0169] "Tax Savings" are specific methods or strategies that users can use to reduce their tax payments.

[0170] "Server" means a computer system that receives, stores, and analyzes data sent by users and generates and provides tax-saving proposals.

[0171] The present invention is a system that collects and analyzes users' living conditions and electronic payment data to provide optimal tax-saving solutions. The system is configured as follows:

[0172] First, users access the system using their smartphones and enter data about their personal information and living situation, including income, family composition, place of residence, expenses, etc. This data is sent from the user's device to a server and stored in real time.

[0173] The server then collects the user's electronic payment data, which is information about the history and details of the user's purchases and payments. This data is then analyzed along with the user's lifestyle data.

[0174] The server uses an AI algorithm to analyze tax-saving potential based on this data. The AI ​​algorithm analyzes the user's data and combines it with tax reform information and expert knowledge to generate optimal tax-saving proposals. Common databases and machine learning libraries are used for data analysis and processing the AI ​​algorithm. Specifically, PostgreSQL is used for database management, and Python and Scikit-learn are used for data analysis.

[0175] The generated tax saving plan is sent from the server to the user's smartphone and displayed to the user. The user can review the displayed tax saving plan and choose whether or not to apply it. If the user chooses to apply it, the server generates a detailed procedural guide and a list of required documents and provides them to the user. This allows the user to go through the entire process efficiently.

[0176] Specific examples

[0177] For example, consider the case where User A registers and enters the following living situation data:

[0178] 1. Annual income: 5 million yen

[0179] 2. Family: Married, two children

[0180] 3. Place of residence: Tokyo

[0181] 4. Expenses: Mortgage Deductible

[0182] User A also provides the system with information about their daily electronic payments, including monthly mortgage payments, education expenses, etc. The server analyzes this data in real time and generates tax-saving suggestions such as:

[0183] 1. Make the most of your mortgage deduction

[0184] 2. Application of special spouse deduction

[0185] 3. Optimizing education deductions

[0186] The generated tax saving plan will be displayed on User A's smartphone, and when User A selects "Apply," a detailed guide to proceed with the procedure and a list of required documents will be provided, allowing User A to efficiently proceed with the tax saving procedure.

[0187] Examples of specific prompt sentences include the following:

[0188] "The user's annual income is 5 million yen, his family consists of a spouse and two children, he lives in Tokyo, and he is paying a mortgage. Please analyze the tax saving potential available under this situation and generate specific tax saving proposals."

[0189] This will enable real-time analysis and suggestions of tax saving potential using users' everyday electronic payment data.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] Users access the system using their smartphones and enter data about their personal information and living situation. This data includes income, family composition, place of residence, expenses, etc. The device then sends the entered data to the server. The input data is information that the user manually enters, and transmission to the server is complete.

[0193] Step 2:

[0194] The server saves the received living situation data in real time and stores it in a database. PostgreSQL is used as an example for managing this database. The input is the living situation data sent from the device, and the output is the data saved in the database. Data processing involves normalizing the raw data and storing it in an appropriate format.

[0195] Step 3:

[0196] Users provide their daily electronic payment information to the system. This includes credit card payments and electronic money payments on their smartphones. The terminal periodically sends this payment information to the server. The input is electronic payment data, and the output is the completion of transmission to the server. Data processing involves standardizing the format of the payment data and eliminating duplicate data.

[0197] Step 4:

[0198] The server saves the received electronic payment data in real time and stores it in a database. The input is the electronic payment data sent from the terminal, and the output is the data saved in the database. Data processing involves normalizing the data and standardizing the format, just like with the living situation data.

[0199] Step 5:

[0200] The server uses an AI algorithm to analyze tax saving potential based on living situation data and electronic payment data. Python and the Scikit-learn library are used to implement the AI ​​algorithm. The input is living situation data and electronic payment data, and the output is the tax saving potential analysis result. For data calculation, the data is input into a machine learning model to predict the tax saving potential.

[0201] Step 6:

[0202] The server uses a database that aggregates tax reform information and expert knowledge based on the analysis results to generate optimal tax-saving proposals. The input is the tax-saving potential analysis results, and the output is specific tax-saving proposals. Data processing combines the analysis results with the latest tax law data to generate tax-saving measures tailored to the user.

[0203] Step 7:

[0204] The server sends the generated tax-saving proposal to the user's smartphone and displays it. The input is the tax-saving proposal, and the output is the completion of sending it to the user's device and displaying it. Specifically, the proposal content is converted into an appropriate format and displayed on the user interface.

[0205] Step 8:

[0206] When the user confirms the tax saving plan and selects "Apply," the server generates a detailed procedure guide and a list of required documents and provides them to the user. The input is the user's selection information, and the output is the procedure guide and document list. Data processing involves generating specific procedure steps and a document list based on the user's selection.

[0207] 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.

[0208] This invention combines a system that allows users to input data about their own living situation, analyzes tax-saving potential based on that data, and provides optimal tax-saving plans to users with an emotion engine that recognizes the user's emotions. This system has the function of collecting and analyzing users' living situation data and emotion data, and providing users with optimal tax-saving plans that reduce stress based on the latest tax reform information and expert knowledge.

[0209] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[0210] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[0211] Furthermore, as users answer the questionnaire, the emotion engine recognizes and analyzes their emotions. The emotion engine analyzes facial expressions and tone of voice through the device's camera and microphone to determine the user's emotional state in real time. The emotion analysis results are sent to a server and stored in a database along with their lifestyle data.

[0212] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate optimal tax savings suggestions for the user. The results of the sentiment analysis are also taken into consideration, and tax savings suggestions that reduce the user's stress are prioritized. The generated tax savings suggestions are sent to the user's device and displayed.

[0213] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[0214] [Specific example]

[0215] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[0216] 1. Annual income: 5 million yen

[0217] 2. Family: Married, two children

[0218] 3. Place of residence: Tokyo

[0219] 4. Expenses: Mortgage Deductible

[0220] Furthermore, the emotion engine analyzes the user's facial expressions and tone of voice to detect when the user is stressed about tax. The server analyzes the user's data in real time and generates tax saving suggestions such as:

[0221] 1. Make the most of your mortgage deduction

[0222] 2. Application of special spouse deduction

[0223] 3. Optimizing education deductions

[0224] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[0225] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay while reducing tax-related stress.

[0226] The processing flow will be explained below.

[0227] Step 1:

[0228] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[0229] Step 2:

[0230] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[0231] Step 3:

[0232] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[0233] Step 4:

[0234] The device displays a questionnaire screen about the user's living situation, asking them to answer questions about income, family composition, place of residence, expenses, etc.

[0235] Step 5:

[0236] The user answers the survey and clicks the "Next" button to submit the response data.

[0237] Step 6:

[0238] The emotion engine analyzes the user's facial expressions and tone of voice and uses the device's camera and microphone to recognize their emotional state.

[0239] Step 7:

[0240] The device transmits the emotion data analyzed by the emotion engine to the server.

[0241] Step 8:

[0242] The server receives the survey data and sentiment data sent by users in real time and stores them in a database. At the same time, an AI algorithm analyzes the user data and calculates the tax saving potential.

[0243] Step 9:

[0244] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate optimal tax-saving plans for users, taking into account the results of sentiment analysis.

[0245] Step 10:

[0246] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[0247] Step 11:

[0248] The terminal displays the customized tax saving proposal received from the server to the user.

[0249] Step 12:

[0250] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[0251] Step 13:

[0252] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[0253] Step 14:

[0254] The server transmits the generated procedure guide and list of required documents to the terminal.

[0255] Step 15:

[0256] The terminal displays the procedure guide and required document list received from the server to the user.

[0257] Step 16:

[0258] Users follow the provided guide to carry out tax-saving procedures.

[0259] Step 17:

[0260] The terminal reports the progress of the process to the server, and the server provides the user with additional support information as needed.

[0261] Example 2

[0262] 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."

[0263] Conventional tax-saving suggestion systems simply collect data on users' lifestyles and suggest tax-saving strategies based on that data. However, many users often feel stressed about tax procedures, which makes it difficult for them to put appropriate advice into practice. In particular, the emotional stress users feel in the process of understanding tax information and procedures prevents them from implementing effective tax-saving strategies. This has continued to hinder users from optimizing their take-home pay.

[0264] 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.

[0265] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for recognizing and analyzing the user's emotional state, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge based on the data including the emotional state, and means for providing the proposals to the user. This makes it possible to propose optimal tax saving measures that take the user's emotional state into consideration, allowing the user to implement effective tax saving measures while reducing stress about taxes.

[0266] A "user" is an entity that uses the system to input living situation data and emotional data and receive tax-saving suggestions.

[0267] The "server" is an information processing device that receives, stores, and analyzes data sent by users, and generates and provides tax saving suggestions.

[0268] "Living situation data" refers to various information about a user's living environment, such as income, family composition, place of residence, and expenses.

[0269] "Emotional state data" refers to information about emotions analyzed from the user's facial expressions, tone of voice, etc.

[0270] "Tax saving potential" refers to the amount of tax reduction that can be obtained by applying various tax saving measures, calculated based on the user's living situation data.

[0271] "Latest Tax Reform Information" refers to official reform information relating to tax systems currently in effect and those scheduled to be implemented in the future.

[0272] "Expert knowledge" refers to information that compiles the specialized knowledge and experience regarding tax savings held by professionals such as tax accountants and accountants.

[0273] A "database" is a repository of information for systematically storing and managing data on living conditions, emotional state, tax reform information, expert knowledge, and so on.

[0274] "Tax Savings" refers to tax relief measures and offers available to users under the law.

[0275] "Emotion Engine" refers to software or hardware means for recognizing and analyzing a user's emotional state through the device's camera or microphone.

[0276] This invention is a system that allows users to input data about their living situation and emotional state, and provides optimal tax-saving plans based on that data. Specific embodiments for implementing this system will be described below.

[0277] Hardware and Software

[0278] To implement this system, the following hardware and software are required:

[0279] User device: A user data input and display device such as a computer, smartphone, tablet, etc. The device is equipped with a camera and microphone.

[0280] Server: A remote server that collects, stores, and analyzes data, working in conjunction with a database.

[0281] Database: NoSQL database (e.g. MongoDB) to store life situation data, emotional state data, tax reform information, and expert knowledge.

[0282] Emotion Engine: Software for analyzing emotional state data, using open source libraries (e.g., OpenCV, TensorFlow).

[0283] Generative AI model: A machine learning model that generates optimal tax savings plans based on user data. It uses TensorFlow and PyTorch.

[0284] System Operation

[0285] 1. Data Entry

[0286] Users access the system using a terminal and register or log in.

[0287] When registering for the first time, you enter your personal information (such as your name, address, and email address), and the server stores that information in a database.

[0288] When logging in, the server verifies the entered information and performs authentication.

[0289] 2. Collection of living situation data

[0290] Users go to a survey page on the system and enter information about their living situation, such as income, family composition, place of residence, and expenses.

[0291] The terminal transmits the input data to the server, which stores the data in a database.

[0292] 3. Collecting emotional state data

[0293] As users answer the survey, the device's camera and microphone capture their facial expressions and tone of voice.

[0294] The device sends the captured data to the emotion engine, which analyzes the data to determine the user's emotional state.

[0295] The results of the emotion analysis are sent to the server and stored in a database.

[0296] 4. Generate tax saving ideas

[0297] The server retrieves the user's life situation data and emotional state data from the database.

[0298] Based on the latest tax reform information and expert knowledge, a generative AI model is used to generate optimal tax saving plans for users.

[0299] Taking into account the user's emotional state, tax saving suggestions that reduce stress are prioritized.

[0300] 5. Providing tax-saving solutions and supporting procedures

[0301] The server sends the generated tax saving plan to the user's terminal and displays it for the user to check.

[0302] The user reviews the displayed tax savings suggestions and selects to apply them.

[0303] Once the selection is complete, the server generates a detailed procedure guide and a list of required documents and provides them to the user.

[0304] Specific examples

[0305] For example, if a user provides the following information:

[0306] Annual income: 5 million yen

[0307] Family: Married, two children

[0308] Place of residence: Tokyo

[0309] Expenses: Mortgage deduction

[0310] The emotion engine analyzes the user's stress about taxation, and the server generates the following tax-saving suggestions:

[0311] Make the most of your mortgage deduction

[0312] Application of special spouse deduction

[0313] Optimizing education deductions

[0314] These suggestions are displayed on the device, and once the user selects "apply," they are provided with a list of documents and instructions required for the procedure.

[0315] An example of a prompt is as follows:

[0316] "I earn 5 million yen a year and live in Tokyo with my family (spouse and two children). I receive a mortgage deduction. Please give me some tax advice in a stress-free way."

[0317] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay and reduce tax-related stress.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Step 1: Register or log in

[0320] input:

[0321] Users enter personal information such as their name, address, and email address. To log in, they enter their registered email address and password.

[0322] Operation:

[0323] The user accesses the system's home screen using a terminal. To register, select "New Registration", enter personal information, and press the "Register" button. To log in, select "Login", enter an email address and password, and press the "Login" button.

[0324] Data processing / calculation and its output:

[0325] The server receives the input information, saves it to the database in the case of a new registration, and sends a registration completion notification to the user when it is saved. In the case of a login, the server compares the input information with the database, outputs the authentication result, and redirects the user to the home screen.

[0326] Step 2: Answer the living situation questionnaire

[0327] input:

[0328] Users answer questionnaire questions such as income, family composition, place of residence, and expenses.

[0329] Operation:

[0330] The user goes to the survey page on the system, fills in the survey items one by one, and presses the "Submit" button.

[0331] Data processing / calculation and its output:

[0332] The terminal sends the entered survey data to the server, which then stores the received data in a database in real time. The server then analyzes the stored data, calculates the user's tax-saving potential, and saves the analysis results.

[0333] Step 3: Collect and analyze emotional state data

[0334] input:

[0335] Facial expressions and tone of voice while users are completing surveys.

[0336] Operation:

[0337] As users answer the survey, their device's camera and microphone capture their facial expressions and tone of voice, which are then sent to the emotion engine.

[0338] Data processing / calculation and its output:

[0339] The emotion engine analyzes the captured data and determines the user's emotional state. The analyzed emotional state data is sent from the device to a server, which stores the data in a database.

[0340] Step 4: Generate tax savings

[0341] input:

[0342] Living situation data, emotional state data, the latest tax reform information, and expert insights.

[0343] Operation:

[0344] The server retrieves the life situation data and emotional state data from the database.

[0345] Data processing / calculation and its output:

[0346] The server uses a generative AI model based on the latest tax reform information and expert knowledge to generate optimal tax savings plans for users. It prioritizes tax savings plans that reduce stress by taking into account the user's emotional state. The generated tax savings plans are stored on the server.

[0347] Step 5: Providing tax-saving solutions and supporting procedures

[0348] input:

[0349] Generated tax savings proposals.

[0350] Operation:

[0351] The server sends the generated tax saving plan to the user's terminal.

[0352] Data processing / calculation and its output:

[0353] The terminal displays the received tax saving plan to the user. The user checks the displayed tax saving plan and decides whether to apply it. When the user selects "apply," the server generates a detailed procedure guide and a list of required documents and sends them to the user. The user can then proceed with the procedure based on the received procedure guide and document list.

[0354] As described above, this system uses the user's living situation data and emotional state data to efficiently generate optimal tax-saving plans and help the user smoothly implement tax-saving measures.

[0355] (Application example 2)

[0356] 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."

[0357] Modern autonomous driving technology is rapidly evolving, but implementing tax-saving measures and optimizing fuel efficiency in operational management requires a great deal of effort. Furthermore, these tasks can be stressful and affect the performance of managers and drivers. Conventional systems are unable to adequately consider individual emotions, making it difficult to provide effective suggestions. Therefore, there is a need to develop a system that can recognize emotions in real time and propose appropriate tax-saving measures.

[0358] 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 data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, means for analyzing the user's emotional state using emotion recognition means and generating suggestions that prioritize stress reduction, and means for providing the suggestions to the user. This reduces the effort required for operation management, makes it possible to propose appropriate tax saving measures according to emotions in real time, and reduces stress for managers and drivers.

[0359] "User" refers to any individual or entity that uses the System.

[0360] "Living situation data" refers to information about a user's income, family composition, place of residence, expenses, etc.

[0361] "Tax savings potential" refers to the potential tax savings and benefits that users can obtain by saving on taxes.

[0362] "Latest tax reform information" refers to the latest information on currently implemented tax systems and their changes.

[0363] "Expert knowledge" refers to specialized knowledge and opinions regarding tax and accounting.

[0364] A "database" refers to a system for storing and managing various types of information.

[0365] "Emotion recognition means" refers to technology that uses sensors such as cameras and microphones to identify emotions from a user's facial expressions and voice.

[0366] "Server" refers to a computer system that stores, analyzes, and provides data.

[0367] "Means for providing suggestions" refers to the function for notifying users of analysis results and tax saving suggestions.

[0368] The "Vehicle Cost Optimization & Emotion Management Assistant" system, which is an application example of this invention, is implemented as follows.

[0369] The server collects data about the user's living situation and stores it in a database, including annual income, number of vehicles owned, vehicle type, annual mileage, expenses, etc. Based on this data, an AI algorithm is used to analyze the user's tax saving potential.

[0370] Next, the device (smartphone or in-car computer) analyzes the user's emotional state using a camera or microphone as an emotion recognition tool. This emotion recognition is performed using software such as OpenCV and DeepFace. The analyzed emotional data is sent to a server and stored in a database along with the user's living situation data.

[0371] The server uses a database that aggregates the latest tax reform information and expert knowledge based on lifestyle and emotional data to generate tax-saving suggestions, prioritizing suggestions that reduce stress. These suggestions are sent to the user's device in real time. Examples of suggestions include fuel-optimized routes and tax-saving suggestions based on the latest tax reform information.

[0372] If the user confirms the proposal and chooses to apply it, the server will generate and provide a detailed procedure guide and a list of required documents to the user, allowing the user to efficiently go through the complicated procedures.

[0373] For example, the following prompt sentence is input to the generative AI model:

[0374] "Mr. A, the manager of an autonomous vehicle, has an annual income of 5 million yen and owns two vehicles (a sedan and an SUV). He drives each vehicle 15,000 km per year, incurring expenses of 200,000 yen each. After analyzing Mr. A's emotions using DeepFace, we found that he is feeling stressed. Using this situation as input data, please run the following Python program, which will provide him with the optimal vehicle cost reduction plan."

[0375] In this way, the system can provide optimal tax-saving and operational management strategies in real time based on the user's living situation and emotional state, reducing stress for the user and improving work efficiency.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1:

[0378] Users access the system using a terminal and enter personal information and vehicle-related data. Specifically, they enter information such as annual income, number of vehicles owned, vehicle type, annual mileage, and expenses. This information is sent from the terminal to the server and stored in a database. Based on the entered data, the server creates a basic profile of the user. Input data: personal information, vehicle information; Output data: basic user profile.

[0379] Step 2:

[0380] The device's camera and microphone are used as emotion recognition tools to analyze the user's emotional state. Specifically, software such as OpenCV and DeepFace is used to identify emotions from facial expressions and voice. The analyzed emotion data is sent to a server and stored in a database along with living situation data. Input data: camera footage, audio data; output data: emotion analysis results.

[0381] Step 3:

[0382] The server uses an AI algorithm to analyze the user's tax-saving potential based on the submitted lifestyle and emotional data. It uses a database that compiles the latest tax reform information and expert knowledge to generate the most appropriate tax-saving proposals. Taking emotional data into consideration, proposals that reduce stress are prioritized. Input data: lifestyle data, emotional data, tax reform information. Output data: tax-saving potential analysis results.

[0383] Step 4:

[0384] The generated tax saving suggestions are notified to the user's device in real time. The device receives this notification and displays it to the user. The user reviews the suggestions and chooses whether to adopt them. Input data: tax saving suggestions, output data: notification to the user.

[0385] Step 5:

[0386] If the user chooses to adopt, the server generates a detailed procedure guide and a list of required documents and provides them to the user. Specifically, the server creates a step-by-step procedure guide and a list of required documents based on the relevant information and sends them to the terminal. The user can proceed with the procedure efficiently based on this information. Input data: user selection, Output data: procedure guide, list of required documents.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Second embodiment]

[0391] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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."

[0403] The present invention is a system that allows users to input data about their own living situation, analyzes tax saving potential based on that data, and provides optimal tax saving plans to users. This system is equipped with various means for collecting and analyzing users' living situation data, and generating and providing tax saving plans based on the latest tax reform information and expert knowledge.

[0404] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[0405] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[0406] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate the optimal tax savings plan for the user, which is then sent to the user's device and displayed.

[0407] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[0408] [Specific example]

[0409] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[0410] 1. Annual income: 5 million yen

[0411] 2. Family: Married, two children

[0412] 3. Place of residence: Tokyo

[0413] 4. Expenses: Mortgage Deductible

[0414] The server analyzes the user's data in real time and generates tax-saving suggestions such as:

[0415] 1. Make the most of your mortgage deduction

[0416] 2. Application of special spouse deduction

[0417] 3. Optimizing education deductions

[0418] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[0419] This system allows users to quickly find out the tax-saving plan that best suits their living situation and allows them to proceed with the procedure efficiently, which is expected to increase the user's take-home pay.

[0420] The processing flow will be explained below.

[0421] Step 1:

[0422] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[0423] Step 2:

[0424] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[0425] Step 3:

[0426] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[0427] Step 4:

[0428] The device displays a questionnaire screen about the user's living situation, and the user answers questions about income, family composition, place of residence, expenses, etc.

[0429] Step 5:

[0430] The user answers the survey and clicks the "Next" button to submit the response data.

[0431] Step 6:

[0432] The server receives the survey data sent by users in real time and stores it in a database, while an AI algorithm analyzes the data and calculates the tax saving potential.

[0433] Step 7:

[0434] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate the optimal tax saving plan for the user.

[0435] Step 8:

[0436] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[0437] Step 9:

[0438] The terminal displays the customized tax saving proposal received from the server to the user.

[0439] Step 10:

[0440] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[0441] Step 11:

[0442] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[0443] Step 12:

[0444] The server transmits the generated procedure guide and list of required documents to the terminal.

[0445] Step 13:

[0446] The terminal displays the procedure guide and required document list received from the server to the user.

[0447] Step 14:

[0448] Users follow the provided guide to carry out tax-saving procedures.

[0449] Example 1

[0450] 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."

[0451] Current systems and methods make it difficult for users to quickly obtain optimal tax-saving plans based on their own living circumstances, and they also face problems such as the time and effort required for procedures and collecting necessary documents.It is also difficult to keep up-to-date with tax reform information and expert knowledge, and there is a risk that the information provided to users will remain outdated.

[0452] 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.

[0453] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for generating and providing the user with a procedure guide and a list of required documents, thereby enabling the user to quickly obtain the optimal tax saving plan based on their living situation and to efficiently carry out the necessary procedures and collect the necessary documents.

[0454] "Lifestyle data" refers to information related to a user's daily life and financial situation, such as the user's income, family composition, place of residence, and expenses.

[0455] "Tax Saving Potential" refers to the extent to which users can qualify for deductions and tax benefits based on their living situation.

[0456] "Tax Reform Information" means information regarding the latest amendments and changes to tax laws and tax-related rules and regulations.

[0457] "Expert knowledge" refers to practical advice and recommendations based on the knowledge and experience of tax and accounting professionals.

[0458] "Procedural Guide" means a document that provides a detailed explanation and sequence of steps that a user must take to apply for a tax benefit.

[0459] "Required Document List" means the list of documents that a User must prepare in order to apply for a particular tax benefit.

[0460] "Database" refers to a system for efficiently storing and managing collected information and data.

[0461] A "generative algorithm" refers to a calculation method or program that analyzes collected data and automatically generates optimal tax-saving plans.

[0462] In the present invention, the following means are used to implement a system that collects and analyzes data on the user's living situation and provides optimal tax-saving ideas.

[0463] 1. Data collection methods:

[0464] Users access the system using a terminal and are required to register or log in. When registering, they enter personal information such as their name, email address, and password, which is then stored in a database by the server. When logging in, users enter their registered email address and password, which the server then verifies against the database.

[0465] 2. Enter living situation data:

[0466] Users answer a questionnaire about their living situation from their device. Questionnaire items include income, family composition, place of residence, expenses, etc., and the data is sent from the user's device to the server. The server saves this data in real time and stores it in a database.

[0467] 3. AI-based data analysis:

[0468] The server analyzes the received data using an AI algorithm. This algorithm uses, for example, Python's "scikit-learn" library. The AI ​​analyzes the user's lifestyle data and calculates tax savings potential. The analysis incorporates the latest tax reform information and expert knowledge.

[0469] 4. Generating optimal tax savings:

[0470] The server generates optimal tax-saving plans based on the results of AI analysis, the latest tax reform information, and expert knowledge. The generated tax-saving plans are sent from the server to the user's device and displayed.

[0471] 5. Generate procedure guide and required document list:

[0472] If the user selects the proposed tax saving plan, the server will generate a procedure guide and a list of required documents, which will be sent to the user's device so that the user can proceed with the procedure accordingly.

[0473] Specific examples

[0474] For example, a new user registers with the system and completes the following lifestyle questionnaire:

[0475] 1. Annual income: 5 million yen

[0476] 2. Family: Married, two children

[0477] 3. Place of residence: Tokyo

[0478] 4. Expenses: Mortgage Deductible

[0479] The server receives this information and analyzes it in real time, for example using Python's "scikit-learn" to generate optimal tax saving plans like the following:

[0480] 1. Make the most of your mortgage deduction

[0481] 2. Application of special spouse deduction

[0482] 3. Optimizing education deductions

[0483] The tax-saving plan is displayed on the user's device, and when the user selects "apply," a list of documents required for the procedure and specific steps are provided. For example, the loan balance certificate required for the mortgage deduction and documents for the education expense deduction are displayed.

[0484] Prompt Sentence Examples

[0485] Example prompt: "Please provide the optimal tax savings plan for the user whose annual income is 5 million yen, whose family consists of a spouse and two children, who lives in Tokyo, and who is eligible for a mortgage deduction."

[0486] This system allows users to quickly find out the tax-saving plan that best suits their living situation, and also allows them to efficiently complete the procedures and collect the necessary documents.

[0487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0488] Step 1:

[0489] A user accesses the system using a terminal and is taken to a screen for new registration or login. In the case of new registration, the user enters personal information such as name, email address, and password, which the server receives and stores in a database. In the case of login, the server compares the entered authentication information with the data in the database, and if authentication is successful, the user is taken to the home screen.

[0490] Input: Personal information (name, email address, password) when registering, or authentication information (email address, password) when logging in

[0491] Data processing: Personal information is stored in a database, and authentication information is verified against the database.

[0492] Output: Notification of registration completion or transition to home screen

[0493] Step 2:

[0494] Users answer a questionnaire about their living situation from their device, including information on income, family composition, place of residence, expenses, etc., and the data entered by the user is sent from the device to the server.

[0495] Input: Living situation data (income, family composition, place of residence, expenses, etc.)

[0496] Data processing: Sending data from the device to the server

[0497] Output: Acknowledgement of data sent to the server

[0498] Step 3:

[0499] The server saves the received living situation data in real time and stores it in a database, while simultaneously checking the data for consistency and performing data cleansing as necessary.

[0500] Input: Living situation data

[0501] Data Processing: Real-time data storage and data cleansing

[0502] Output: Consistent living situation data stored in a database

[0503] Step 4:

[0504] The AI ​​algorithm installed on the server calculates the tax saving potential of users based on the lifestyle data stored in the database, using libraries such as Python's "scikit-learn."

[0505] Input: Consistent living situation data

[0506] Data processing: Analysis using AI algorithms and calculation of tax saving potential

[0507] Output: Tax saving potential calculation results

[0508] Step 5:

[0509] The server generates optimal tax-saving plans based on the analysis results, the latest tax reform information, and expert knowledge. The generated tax-saving plans are stored in a database and sent to the user's device.

[0510] Input: Tax saving potential calculation results, tax reform information, expert knowledge

[0511] Data processing: generating tax saving proposals

[0512] Output: Generated tax saving plans are saved in a database and sent to the user's device

[0513] Step 6:

[0514] The user checks the tax saving plan displayed on the terminal, selects "Apply" or "Not Apply", and the selection is sent from the terminal to the server.

[0515] Input: Generated tax savings plan, user selection

[0516] Data processing: Sending user-selected data

[0517] Output: User selections sent to the server

[0518] Step 7:

[0519] If the user selects "apply" for the tax saving plan, the server will generate a procedure guide and a list of required documents and send them to the user's device, allowing the user to proceed with the specific procedures.

[0520] Input: User's selection ("Apply")

[0521] Data processing: Procedural guide and required document list generation

[0522] Output: Procedure guide and required document list sent to user's device

[0523] This process allows users to quickly obtain the optimal tax-saving plan based on their living situation and to proceed with the process efficiently.

[0524] (Application example 1)

[0525] 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."

[0526] Conventional tax-saving proposal systems rely on analysis results based on user-entered lifestyle data, and are unable to provide real-time tax-saving proposals that take daily electronic payment information into account. This makes it difficult for users to immediately understand tax-saving potential and implement effective tax-saving strategies.

[0527] 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.

[0528] In this invention, the server includes means for collecting data on the user's living situation, means for analyzing the user's tax saving potential using the data, means for collecting and analyzing electronic payment data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for providing the proposals to the user, thereby enabling real-time analysis and proposal of tax saving potential using the user's daily electronic payment data.

[0529] "User" means an individual or corporation that uses the system to provide living situation data and electronic payment data and receive tax savings suggestions.

[0530] "Data regarding living conditions" refers to information that indicates the user's economic and living conditions, such as the user's income, family composition, place of residence, and expenses.

[0531] "Tax saving potential" refers to the possibility and extent of tax savings calculated based on data entered by the user.

[0532] "Electronic payment data" means information relating to the history and content of electronic payments made by a user.

[0533] "Tax Reform Information" refers to information about the latest changes and updates to the tax system announced by the government and related agencies.

[0534] "Expert knowledge" refers to the collection of tax-related knowledge and advice held by experts such as tax accountants and economists.

[0535] A "database" is a collection of information constructed to efficiently manage and use collected data.

[0536] "Tax Savings" are specific methods or strategies that users can use to reduce their tax payments.

[0537] "Server" means a computer system that receives, stores, and analyzes data sent by users and generates and provides tax-saving proposals.

[0538] The present invention is a system that collects and analyzes users' living conditions and electronic payment data to provide optimal tax-saving solutions. The system is configured as follows:

[0539] First, users access the system using their smartphones and enter data about their personal information and living situation, including income, family composition, place of residence, expenses, etc. This data is sent from the user's device to a server and stored in real time.

[0540] The server then collects the user's electronic payment data, which is information about the history and details of the user's purchases and payments. This data is then analyzed along with the user's lifestyle data.

[0541] The server uses an AI algorithm to analyze tax-saving potential based on this data. The AI ​​algorithm analyzes the user's data and combines it with tax reform information and expert knowledge to generate optimal tax-saving proposals. Common databases and machine learning libraries are used for data analysis and processing the AI ​​algorithm. Specifically, PostgreSQL is used for database management, and Python and Scikit-learn are used for data analysis.

[0542] The generated tax saving plan is sent from the server to the user's smartphone and displayed to the user. The user can review the displayed tax saving plan and choose whether or not to apply it. If the user chooses to apply it, the server generates a detailed procedural guide and a list of required documents and provides them to the user. This allows the user to go through the entire process efficiently.

[0543] Specific examples

[0544] For example, consider the case where User A registers and enters the following living situation data:

[0545] 1. Annual income: 5 million yen

[0546] 2. Family: Married, two children

[0547] 3. Place of residence: Tokyo

[0548] 4. Expenses: Mortgage Deductible

[0549] User A also provides the system with information about their daily electronic payments, including monthly mortgage payments, education expenses, etc. The server analyzes this data in real time and generates tax-saving suggestions such as:

[0550] 1. Make the most of your mortgage deduction

[0551] 2. Application of special spouse deduction

[0552] 3. Optimizing education deductions

[0553] The generated tax saving plan will be displayed on User A's smartphone, and when User A selects "Apply," a detailed guide to proceed with the procedure and a list of required documents will be provided, allowing User A to efficiently proceed with the tax saving procedure.

[0554] Examples of specific prompt sentences include the following:

[0555] "The user's annual income is 5 million yen, his family consists of a spouse and two children, he lives in Tokyo, and he is paying a mortgage. Please analyze the tax saving potential available under this situation and generate specific tax saving proposals."

[0556] This will enable real-time analysis and suggestions of tax saving potential using users' everyday electronic payment data.

[0557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0558] Step 1:

[0559] Users access the system using their smartphones and enter data about their personal information and living situation. This data includes income, family composition, place of residence, expenses, etc. The device then sends the entered data to the server. The input data is information that the user manually enters, and transmission to the server is complete.

[0560] Step 2:

[0561] The server saves the received living situation data in real time and stores it in a database. PostgreSQL is used as an example for managing this database. The input is the living situation data sent from the device, and the output is the data saved in the database. Data processing involves normalizing the raw data and storing it in an appropriate format.

[0562] Step 3:

[0563] Users provide their daily electronic payment information to the system. This includes credit card payments and electronic money payments on their smartphones. The terminal periodically sends this payment information to the server. The input is electronic payment data, and the output is the completion of transmission to the server. Data processing involves standardizing the format of the payment data and eliminating duplicate data.

[0564] Step 4:

[0565] The server saves the received electronic payment data in real time and stores it in a database. The input is the electronic payment data sent from the terminal, and the output is the data saved in the database. Data processing involves normalizing the data and standardizing the format, just like with the living situation data.

[0566] Step 5:

[0567] The server uses an AI algorithm to analyze tax saving potential based on living situation data and electronic payment data. Python and the Scikit-learn library are used to implement the AI ​​algorithm. The input is living situation data and electronic payment data, and the output is the tax saving potential analysis result. For data calculation, the data is input into a machine learning model to predict the tax saving potential.

[0568] Step 6:

[0569] The server uses a database that aggregates tax reform information and expert knowledge based on the analysis results to generate optimal tax-saving proposals. The input is the tax-saving potential analysis results, and the output is specific tax-saving proposals. Data processing combines the analysis results with the latest tax law data to generate tax-saving measures tailored to the user.

[0570] Step 7:

[0571] The server sends the generated tax-saving proposal to the user's smartphone and displays it. The input is the tax-saving proposal, and the output is the completion of sending it to the user's device and displaying it. Specifically, the proposal content is converted into an appropriate format and displayed on the user interface.

[0572] Step 8:

[0573] When the user confirms the tax saving plan and selects "Apply," the server generates a detailed procedure guide and a list of required documents and provides them to the user. The input is the user's selection information, and the output is the procedure guide and document list. Data processing involves generating specific procedure steps and a document list based on the user's selection.

[0574] 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.

[0575] This invention combines a system that allows users to input data about their own living situation, analyzes tax-saving potential based on that data, and provides optimal tax-saving plans to users with an emotion engine that recognizes the user's emotions. This system has the function of collecting and analyzing users' living situation data and emotion data, and providing users with optimal tax-saving plans that reduce stress based on the latest tax reform information and expert knowledge.

[0576] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[0577] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[0578] Furthermore, as users answer the questionnaire, the emotion engine recognizes and analyzes their emotions. The emotion engine analyzes facial expressions and tone of voice through the device's camera and microphone to determine the user's emotional state in real time. The emotion analysis results are sent to a server and stored in a database along with their lifestyle data.

[0579] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate optimal tax savings suggestions for the user. The results of the sentiment analysis are also taken into consideration, and tax savings suggestions that reduce the user's stress are prioritized. The generated tax savings suggestions are sent to the user's device and displayed.

[0580] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[0581] [Specific example]

[0582] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[0583] 1. Annual income: 5 million yen

[0584] 2. Family: Married, two children

[0585] 3. Place of residence: Tokyo

[0586] 4. Expenses: Mortgage Deductible

[0587] Furthermore, the emotion engine analyzes the user's facial expressions and tone of voice to detect when the user is stressed about tax. The server analyzes the user's data in real time and generates tax saving suggestions such as:

[0588] 1. Make the most of your mortgage deduction

[0589] 2. Application of special spouse deduction

[0590] 3. Optimizing education deductions

[0591] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[0592] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay while reducing tax-related stress.

[0593] The processing flow will be explained below.

[0594] Step 1:

[0595] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[0596] Step 2:

[0597] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[0598] Step 3:

[0599] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[0600] Step 4:

[0601] The device displays a questionnaire screen about the user's living situation, asking them to answer questions about income, family composition, place of residence, expenses, etc.

[0602] Step 5:

[0603] The user answers the survey and clicks the "Next" button to submit the response data.

[0604] Step 6:

[0605] The emotion engine analyzes the user's facial expressions and tone of voice and uses the device's camera and microphone to recognize their emotional state.

[0606] Step 7:

[0607] The device transmits the emotion data analyzed by the emotion engine to the server.

[0608] Step 8:

[0609] The server receives the survey data and sentiment data sent by users in real time and stores them in a database. At the same time, an AI algorithm analyzes the user data and calculates the tax saving potential.

[0610] Step 9:

[0611] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate optimal tax-saving plans for users, taking into account the results of sentiment analysis.

[0612] Step 10:

[0613] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[0614] Step 11:

[0615] The terminal displays the customized tax saving proposal received from the server to the user.

[0616] Step 12:

[0617] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[0618] Step 13:

[0619] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[0620] Step 14:

[0621] The server transmits the generated procedure guide and list of required documents to the terminal.

[0622] Step 15:

[0623] The terminal displays the procedure guide and required document list received from the server to the user.

[0624] Step 16:

[0625] Users follow the provided guide to carry out tax-saving procedures.

[0626] Step 17:

[0627] The terminal reports the progress of the process to the server, and the server provides the user with additional support information as needed.

[0628] Example 2

[0629] 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."

[0630] Conventional tax-saving suggestion systems simply collect data on users' lifestyles and suggest tax-saving strategies based on that data. However, many users often feel stressed about tax procedures, which makes it difficult for them to put appropriate advice into practice. In particular, the emotional stress users feel in the process of understanding tax information and procedures prevents them from implementing effective tax-saving strategies. This has continued to hinder users from optimizing their take-home pay.

[0631] 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.

[0632] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for recognizing and analyzing the user's emotional state, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge based on the data including the emotional state, and means for providing the proposals to the user. This makes it possible to propose optimal tax saving measures that take the user's emotional state into consideration, allowing the user to implement effective tax saving measures while reducing stress about taxes.

[0633] A "user" is an entity that uses the system to input living situation data and emotional data and receive tax-saving suggestions.

[0634] The "server" is an information processing device that receives, stores, and analyzes data sent by users, and generates and provides tax saving suggestions.

[0635] "Living situation data" refers to various information about a user's living environment, such as income, family composition, place of residence, and expenses.

[0636] "Emotional state data" refers to information about emotions analyzed from the user's facial expressions, tone of voice, etc.

[0637] "Tax saving potential" refers to the amount of tax reduction that can be obtained by applying various tax saving measures, calculated based on the user's living situation data.

[0638] "Latest Tax Reform Information" refers to official reform information relating to tax systems currently in effect and those scheduled to be implemented in the future.

[0639] "Expert knowledge" refers to information that compiles the specialized knowledge and experience regarding tax savings held by professionals such as tax accountants and accountants.

[0640] A "database" is a repository of information for systematically storing and managing data on living conditions, emotional state, tax reform information, expert knowledge, and so on.

[0641] "Tax Savings" refers to tax relief measures and offers available to users under the law.

[0642] "Emotion Engine" refers to software or hardware means for recognizing and analyzing a user's emotional state through the device's camera or microphone.

[0643] This invention is a system that allows users to input data about their living situation and emotional state, and provides optimal tax-saving plans based on that data. Specific embodiments for implementing this system will be described below.

[0644] Hardware and Software

[0645] To implement this system, the following hardware and software are required:

[0646] User device: A user data input and display device such as a computer, smartphone, tablet, etc. The device is equipped with a camera and microphone.

[0647] Server: A remote server that collects, stores, and analyzes data, working in conjunction with a database.

[0648] Database: NoSQL database (e.g. MongoDB) to store life situation data, emotional state data, tax reform information, and expert knowledge.

[0649] Emotion Engine: Software for analyzing emotional state data, using open source libraries (e.g., OpenCV, TensorFlow).

[0650] Generative AI model: A machine learning model that generates optimal tax savings plans based on user data. It uses TensorFlow and PyTorch.

[0651] System Operation

[0652] 1. Data Entry

[0653] Users access the system using a terminal and register or log in.

[0654] When registering for the first time, you enter your personal information (such as your name, address, and email address), and the server stores that information in a database.

[0655] When logging in, the server verifies the entered information and performs authentication.

[0656] 2. Collection of living situation data

[0657] Users go to a survey page on the system and enter information about their living situation, such as income, family composition, place of residence, and expenses.

[0658] The terminal transmits the input data to the server, which stores the data in a database.

[0659] 3. Collecting emotional state data

[0660] As users answer the survey, the device's camera and microphone capture their facial expressions and tone of voice.

[0661] The device sends the captured data to the emotion engine, which analyzes the data to determine the user's emotional state.

[0662] The results of the emotion analysis are sent to the server and stored in a database.

[0663] 4. Generate tax saving ideas

[0664] The server retrieves the user's life situation data and emotional state data from the database.

[0665] Based on the latest tax reform information and expert knowledge, a generative AI model is used to generate optimal tax saving plans for users.

[0666] Taking into account the user's emotional state, tax saving suggestions that reduce stress are prioritized.

[0667] 5. Providing tax-saving solutions and supporting procedures

[0668] The server sends the generated tax saving plan to the user's terminal and displays it for the user to check.

[0669] The user reviews the displayed tax savings suggestions and selects to apply them.

[0670] Once the selection is complete, the server generates a detailed procedure guide and a list of required documents and provides them to the user.

[0671] Specific examples

[0672] For example, if a user provides the following information:

[0673] Annual income: 5 million yen

[0674] Family: Married, two children

[0675] Place of residence: Tokyo

[0676] Expenses: Mortgage deduction

[0677] The emotion engine analyzes the user's stress about taxation, and the server generates the following tax-saving suggestions:

[0678] Make the most of your mortgage deduction

[0679] Application of special spouse deduction

[0680] Optimizing education deductions

[0681] These suggestions are displayed on the device, and once the user selects "apply," they are provided with a list of documents and instructions required for the procedure.

[0682] An example of a prompt is as follows:

[0683] "I earn 5 million yen a year and live in Tokyo with my family (spouse and two children). I receive a mortgage deduction. Please give me some tax advice in a stress-free way."

[0684] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay and reduce tax-related stress.

[0685] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0686] Step 1: Register or log in

[0687] input:

[0688] Users enter personal information such as their name, address, and email address. To log in, they enter their registered email address and password.

[0689] Operation:

[0690] The user accesses the system's home screen using a terminal. To register, select "New Registration", enter personal information, and press the "Register" button. To log in, select "Login", enter an email address and password, and press the "Login" button.

[0691] Data processing / calculation and its output:

[0692] The server receives the input information, saves it to the database in the case of a new registration, and sends a registration completion notification to the user when it is saved. In the case of a login, the server compares the input information with the database, outputs the authentication result, and redirects the user to the home screen.

[0693] Step 2: Answer the living situation questionnaire

[0694] input:

[0695] Users answer questionnaire questions such as income, family composition, place of residence, and expenses.

[0696] Operation:

[0697] The user goes to the survey page on the system, fills in the survey items one by one, and presses the "Submit" button.

[0698] Data processing / calculation and its output:

[0699] The terminal sends the entered survey data to the server, which then stores the received data in a database in real time. The server then analyzes the stored data, calculates the user's tax-saving potential, and saves the analysis results.

[0700] Step 3: Collect and analyze emotional state data

[0701] input:

[0702] Facial expressions and tone of voice while users are completing surveys.

[0703] Operation:

[0704] As users answer the survey, their device's camera and microphone capture their facial expressions and tone of voice, which are then sent to the emotion engine.

[0705] Data processing / calculation and its output:

[0706] The emotion engine analyzes the captured data and determines the user's emotional state. The analyzed emotional state data is sent from the device to a server, which stores the data in a database.

[0707] Step 4: Generate tax savings

[0708] input:

[0709] Living situation data, emotional state data, the latest tax reform information, and expert insights.

[0710] Operation:

[0711] The server retrieves the life situation data and emotional state data from the database.

[0712] Data processing / calculation and its output:

[0713] The server uses a generative AI model based on the latest tax reform information and expert knowledge to generate optimal tax savings plans for users. It prioritizes tax savings plans that reduce stress by taking into account the user's emotional state. The generated tax savings plans are stored on the server.

[0714] Step 5: Providing tax-saving solutions and supporting procedures

[0715] input:

[0716] Generated tax savings proposals.

[0717] Operation:

[0718] The server sends the generated tax saving plan to the user's terminal.

[0719] Data processing / calculation and its output:

[0720] The terminal displays the received tax saving plan to the user. The user checks the displayed tax saving plan and decides whether to apply it. When the user selects "apply," the server generates a detailed procedure guide and a list of required documents and sends them to the user. The user can then proceed with the procedure based on the received procedure guide and document list.

[0721] As described above, this system uses the user's living situation data and emotional state data to efficiently generate optimal tax-saving plans and help the user smoothly implement tax-saving measures.

[0722] (Application example 2)

[0723] 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."

[0724] Modern autonomous driving technology is rapidly evolving, but implementing tax-saving measures and optimizing fuel efficiency in operational management requires a great deal of effort. Furthermore, these tasks can be stressful and affect the performance of managers and drivers. Conventional systems are unable to adequately consider individual emotions, making it difficult to provide effective suggestions. Therefore, there is a need to develop a system that can recognize emotions in real time and propose appropriate tax-saving measures.

[0725] 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 data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, means for analyzing the user's emotional state using emotion recognition means and generating suggestions that prioritize stress reduction, and means for providing the suggestions to the user. This reduces the effort required for operation management, makes it possible to propose appropriate tax saving measures according to emotions in real time, and reduces stress for managers and drivers.

[0726] "User" refers to any individual or entity that uses the System.

[0727] "Living situation data" refers to information about a user's income, family composition, place of residence, expenses, etc.

[0728] "Tax savings potential" refers to the potential tax savings and benefits that users can obtain by saving on taxes.

[0729] "Latest tax reform information" refers to the latest information on currently implemented tax systems and their changes.

[0730] "Expert knowledge" refers to specialized knowledge and opinions regarding tax and accounting.

[0731] A "database" refers to a system for storing and managing various types of information.

[0732] "Emotion recognition means" refers to technology that uses sensors such as cameras and microphones to identify emotions from a user's facial expressions and voice.

[0733] "Server" refers to a computer system that stores, analyzes, and provides data.

[0734] "Means for providing suggestions" refers to the function for notifying users of analysis results and tax saving suggestions.

[0735] The "Vehicle Cost Optimization & Emotion Management Assistant" system, which is an application example of this invention, is implemented as follows.

[0736] The server collects data about the user's living situation and stores it in a database, including annual income, number of vehicles owned, vehicle type, annual mileage, expenses, etc. Based on this data, an AI algorithm is used to analyze the user's tax saving potential.

[0737] Next, the device (smartphone or in-car computer) analyzes the user's emotional state using a camera or microphone as an emotion recognition tool. This emotion recognition is performed using software such as OpenCV and DeepFace. The analyzed emotional data is sent to a server and stored in a database along with the user's living situation data.

[0738] The server uses a database that aggregates the latest tax reform information and expert knowledge based on lifestyle and emotional data to generate tax-saving suggestions, prioritizing suggestions that reduce stress. These suggestions are sent to the user's device in real time. Examples of suggestions include fuel-optimized routes and tax-saving suggestions based on the latest tax reform information.

[0739] If the user confirms the proposal and chooses to apply it, the server will generate and provide a detailed procedure guide and a list of required documents to the user, allowing the user to efficiently go through the complicated procedures.

[0740] For example, the following prompt sentence is input to the generative AI model:

[0741] "Mr. A, the manager of an autonomous vehicle, has an annual income of 5 million yen and owns two vehicles (a sedan and an SUV). He drives each vehicle 15,000 km per year, incurring expenses of 200,000 yen each. After analyzing Mr. A's emotions using DeepFace, we found that he is feeling stressed. Using this situation as input data, please run the following Python program, which will provide him with the optimal vehicle cost reduction plan."

[0742] In this way, the system can provide optimal tax-saving and operational management strategies in real time based on the user's living situation and emotional state, reducing stress for the user and improving work efficiency.

[0743] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0744] Step 1:

[0745] Users access the system using a terminal and enter personal information and vehicle-related data. Specifically, they enter information such as annual income, number of vehicles owned, vehicle type, annual mileage, and expenses. This information is sent from the terminal to the server and stored in a database. Based on the entered data, the server creates a basic profile of the user. Input data: personal information, vehicle information; Output data: basic user profile.

[0746] Step 2:

[0747] The device's camera and microphone are used as emotion recognition tools to analyze the user's emotional state. Specifically, software such as OpenCV and DeepFace is used to identify emotions from facial expressions and voice. The analyzed emotion data is sent to a server and stored in a database along with living situation data. Input data: camera footage, audio data; output data: emotion analysis results.

[0748] Step 3:

[0749] The server uses an AI algorithm to analyze the user's tax-saving potential based on the submitted lifestyle and emotional data. It uses a database that compiles the latest tax reform information and expert knowledge to generate the most appropriate tax-saving proposals. Taking emotional data into consideration, proposals that reduce stress are prioritized. Input data: lifestyle data, emotional data, tax reform information. Output data: tax-saving potential analysis results.

[0750] Step 4:

[0751] The generated tax saving suggestions are notified to the user's device in real time. The device receives this notification and displays it to the user. The user reviews the suggestions and chooses whether to adopt them. Input data: tax saving suggestions, output data: notification to the user.

[0752] Step 5:

[0753] If the user chooses to adopt, the server generates a detailed procedure guide and a list of required documents and provides them to the user. Specifically, the server creates a step-by-step procedure guide and a list of required documents based on the relevant information and sends them to the terminal. The user can proceed with the procedure efficiently based on this information. Input data: user selection, Output data: procedure guide, list of required documents.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] [Third embodiment]

[0758] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0759] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0760] 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).

[0761] 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.

[0762] 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.

[0763] 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).

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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."

[0770] The present invention is a system that allows users to input data about their own living situation, analyzes tax saving potential based on that data, and provides optimal tax saving plans to users. This system is equipped with various means for collecting and analyzing users' living situation data, and generating and providing tax saving plans based on the latest tax reform information and expert knowledge.

[0771] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[0772] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[0773] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate the optimal tax savings plan for the user, which is then sent to the user's device and displayed.

[0774] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[0775] [Specific example]

[0776] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[0777] 1. Annual income: 5 million yen

[0778] 2. Family: Married, two children

[0779] 3. Place of residence: Tokyo

[0780] 4. Expenses: Mortgage Deductible

[0781] The server analyzes the user's data in real time and generates tax-saving suggestions such as:

[0782] 1. Make the most of your mortgage deduction

[0783] 2. Application of special spouse deduction

[0784] 3. Optimizing education deductions

[0785] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[0786] This system allows users to quickly find out the tax-saving plan that best suits their living situation and allows them to proceed with the procedure efficiently, which is expected to increase the user's take-home pay.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[0790] Step 2:

[0791] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[0792] Step 3:

[0793] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[0794] Step 4:

[0795] The device displays a questionnaire screen about the user's living situation, and the user answers questions about income, family composition, place of residence, expenses, etc.

[0796] Step 5:

[0797] The user answers the survey and clicks the "Next" button to submit the response data.

[0798] Step 6:

[0799] The server receives the survey data sent by users in real time and stores it in a database, while an AI algorithm analyzes the data and calculates the tax saving potential.

[0800] Step 7:

[0801] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate the optimal tax saving plan for the user.

[0802] Step 8:

[0803] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[0804] Step 9:

[0805] The terminal displays the customized tax saving proposal received from the server to the user.

[0806] Step 10:

[0807] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[0808] Step 11:

[0809] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[0810] Step 12:

[0811] The server transmits the generated procedure guide and list of required documents to the terminal.

[0812] Step 13:

[0813] The terminal displays the procedure guide and required document list received from the server to the user.

[0814] Step 14:

[0815] Users follow the provided guide to carry out tax-saving procedures.

[0816] Example 1

[0817] 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."

[0818] Current systems and methods make it difficult for users to quickly obtain optimal tax-saving plans based on their own living circumstances, and they also face problems such as the time and effort required for procedures and collecting necessary documents.It is also difficult to keep up-to-date with tax reform information and expert knowledge, and there is a risk that the information provided to users will remain outdated.

[0819] 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.

[0820] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for generating and providing the user with a procedure guide and a list of required documents, thereby enabling the user to quickly obtain the optimal tax saving plan based on their living situation and to efficiently carry out the necessary procedures and collect the necessary documents.

[0821] "Lifestyle data" refers to information related to a user's daily life and financial situation, such as the user's income, family composition, place of residence, and expenses.

[0822] "Tax Saving Potential" refers to the extent to which users can qualify for deductions and tax benefits based on their living situation.

[0823] "Tax Reform Information" means information regarding the latest amendments and changes to tax laws and tax-related rules and regulations.

[0824] "Expert knowledge" refers to practical advice and recommendations based on the knowledge and experience of tax and accounting professionals.

[0825] "Procedural Guide" means a document that provides a detailed explanation and sequence of steps that a user must take to apply for a tax benefit.

[0826] "Required Document List" means the list of documents that a User must prepare in order to apply for a particular tax benefit.

[0827] "Database" refers to a system for efficiently storing and managing collected information and data.

[0828] A "generative algorithm" refers to a calculation method or program that analyzes collected data and automatically generates optimal tax-saving plans.

[0829] In the present invention, the following means are used to implement a system that collects and analyzes data on the user's living situation and provides optimal tax-saving ideas.

[0830] 1. Data collection methods:

[0831] Users access the system using a terminal and are required to register or log in. When registering, they enter personal information such as their name, email address, and password, which is then stored in a database by the server. When logging in, users enter their registered email address and password, which the server then verifies against the database.

[0832] 2. Enter living situation data:

[0833] Users answer a questionnaire about their living situation from their device. Questionnaire items include income, family composition, place of residence, expenses, etc., and the data is sent from the user's device to the server. The server saves this data in real time and stores it in a database.

[0834] 3. AI-based data analysis:

[0835] The server analyzes the received data using an AI algorithm. This algorithm uses, for example, Python's "scikit-learn" library. The AI ​​analyzes the user's lifestyle data and calculates tax savings potential. The analysis incorporates the latest tax reform information and expert knowledge.

[0836] 4. Generating optimal tax savings:

[0837] The server generates optimal tax-saving plans based on the results of AI analysis, the latest tax reform information, and expert knowledge. The generated tax-saving plans are sent from the server to the user's device and displayed.

[0838] 5. Generate procedure guide and required document list:

[0839] If the user selects the proposed tax saving plan, the server will generate a procedure guide and a list of required documents, which will be sent to the user's device so that the user can proceed with the procedure accordingly.

[0840] Specific examples

[0841] For example, a new user registers with the system and completes the following lifestyle questionnaire:

[0842] 1. Annual income: 5 million yen

[0843] 2. Family: Married, two children

[0844] 3. Place of residence: Tokyo

[0845] 4. Expenses: Mortgage Deductible

[0846] The server receives this information and analyzes it in real time, for example using Python's "scikit-learn" to generate optimal tax saving plans like the following:

[0847] 1. Make the most of your mortgage deduction

[0848] 2. Application of special spouse deduction

[0849] 3. Optimizing education deductions

[0850] The tax-saving plan is displayed on the user's device, and when the user selects "apply," a list of documents required for the procedure and specific steps are provided. For example, the loan balance certificate required for the mortgage deduction and documents for the education expense deduction are displayed.

[0851] Prompt Sentence Examples

[0852] Example prompt: "Please provide the optimal tax savings plan for the user whose annual income is 5 million yen, whose family consists of a spouse and two children, who lives in Tokyo, and who is eligible for a mortgage deduction."

[0853] This system allows users to quickly find out the tax-saving plan that best suits their living situation, and also allows them to efficiently complete the procedures and collect the necessary documents.

[0854] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0855] Step 1:

[0856] A user accesses the system using a terminal and is taken to a screen for new registration or login. In the case of new registration, the user enters personal information such as name, email address, and password, which the server receives and stores in a database. In the case of login, the server compares the entered authentication information with the data in the database, and if authentication is successful, the user is taken to the home screen.

[0857] Input: Personal information (name, email address, password) when registering, or authentication information (email address, password) when logging in

[0858] Data processing: Personal information is stored in a database, and authentication information is verified against the database.

[0859] Output: Notification of registration completion or transition to home screen

[0860] Step 2:

[0861] Users answer a questionnaire about their living situation from their device, including information on income, family composition, place of residence, expenses, etc., and the data entered by the user is sent from the device to the server.

[0862] Input: Living situation data (income, family composition, place of residence, expenses, etc.)

[0863] Data processing: Sending data from the device to the server

[0864] Output: Acknowledgement of data sent to the server

[0865] Step 3:

[0866] The server saves the received living situation data in real time and stores it in a database, while simultaneously checking the data for consistency and performing data cleansing as necessary.

[0867] Input: Living situation data

[0868] Data Processing: Real-time data storage and data cleansing

[0869] Output: Consistent living situation data stored in a database

[0870] Step 4:

[0871] The AI ​​algorithm installed on the server calculates the tax saving potential of users based on the lifestyle data stored in the database, using libraries such as Python's "scikit-learn."

[0872] Input: Consistent living situation data

[0873] Data processing: Analysis using AI algorithms and calculation of tax saving potential

[0874] Output: Tax saving potential calculation results

[0875] Step 5:

[0876] The server generates optimal tax-saving plans based on the analysis results, the latest tax reform information, and expert knowledge. The generated tax-saving plans are stored in a database and sent to the user's device.

[0877] Input: Tax saving potential calculation results, tax reform information, expert knowledge

[0878] Data processing: generating tax saving proposals

[0879] Output: Generated tax saving plans are saved in a database and sent to the user's device

[0880] Step 6:

[0881] The user checks the tax saving plan displayed on the terminal, selects "Apply" or "Not Apply", and the selection is sent from the terminal to the server.

[0882] Input: Generated tax savings plan, user selection

[0883] Data processing: Sending user-selected data

[0884] Output: User selections sent to the server

[0885] Step 7:

[0886] If the user selects "apply" for the tax saving plan, the server will generate a procedure guide and a list of required documents and send them to the user's device, allowing the user to proceed with the specific procedures.

[0887] Input: User's selection ("Apply")

[0888] Data processing: Procedural guide and required document list generation

[0889] Output: Procedure guide and required document list sent to user's device

[0890] This process allows users to quickly obtain the optimal tax-saving plan based on their living situation and to proceed with the process efficiently.

[0891] (Application example 1)

[0892] 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."

[0893] Conventional tax-saving proposal systems rely on analysis results based on user-entered lifestyle data, and are unable to provide real-time tax-saving proposals that take daily electronic payment information into account. This makes it difficult for users to immediately understand tax-saving potential and implement effective tax-saving strategies.

[0894] 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.

[0895] In this invention, the server includes means for collecting data on the user's living situation, means for analyzing the user's tax saving potential using the data, means for collecting and analyzing electronic payment data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for providing the proposals to the user, thereby enabling real-time analysis and proposal of tax saving potential using the user's daily electronic payment data.

[0896] "User" means an individual or corporation that uses the system to provide living situation data and electronic payment data and receive tax savings suggestions.

[0897] "Data regarding living conditions" refers to information that indicates the user's economic and living conditions, such as the user's income, family composition, place of residence, and expenses.

[0898] "Tax saving potential" refers to the possibility and extent of tax savings calculated based on data entered by the user.

[0899] "Electronic payment data" means information relating to the history and content of electronic payments made by a user.

[0900] "Tax Reform Information" refers to information about the latest changes and updates to the tax system announced by the government and related agencies.

[0901] "Expert knowledge" refers to the collection of tax-related knowledge and advice held by experts such as tax accountants and economists.

[0902] A "database" is a collection of information constructed to efficiently manage and use collected data.

[0903] "Tax Savings" are specific methods or strategies that users can use to reduce their tax payments.

[0904] "Server" means a computer system that receives, stores, and analyzes data sent by users and generates and provides tax-saving proposals.

[0905] The present invention is a system that collects and analyzes users' living conditions and electronic payment data to provide optimal tax-saving solutions. The system is configured as follows:

[0906] First, users access the system using their smartphones and enter data about their personal information and living situation, including income, family composition, place of residence, expenses, etc. This data is sent from the user's device to a server and stored in real time.

[0907] The server then collects the user's electronic payment data, which is information about the history and details of the user's purchases and payments. This data is then analyzed along with the user's lifestyle data.

[0908] The server uses an AI algorithm to analyze tax-saving potential based on this data. The AI ​​algorithm analyzes the user's data and combines it with tax reform information and expert knowledge to generate optimal tax-saving proposals. Common databases and machine learning libraries are used for data analysis and processing the AI ​​algorithm. Specifically, PostgreSQL is used for database management, and Python and Scikit-learn are used for data analysis.

[0909] The generated tax saving plan is sent from the server to the user's smartphone and displayed to the user. The user can review the displayed tax saving plan and choose whether or not to apply it. If the user chooses to apply it, the server generates a detailed procedural guide and a list of required documents and provides them to the user. This allows the user to go through the entire process efficiently.

[0910] Specific examples

[0911] For example, consider the case where User A registers and enters the following living situation data:

[0912] 1. Annual income: 5 million yen

[0913] 2. Family: Married, two children

[0914] 3. Place of residence: Tokyo

[0915] 4. Expenses: Mortgage Deductible

[0916] User A also provides the system with information about their daily electronic payments, including monthly mortgage payments, education expenses, etc. The server analyzes this data in real time and generates tax-saving suggestions such as:

[0917] 1. Make the most of your mortgage deduction

[0918] 2. Application of special spouse deduction

[0919] 3. Optimizing education deductions

[0920] The generated tax saving plan will be displayed on User A's smartphone, and when User A selects "Apply," a detailed guide to proceed with the procedure and a list of required documents will be provided, allowing User A to efficiently proceed with the tax saving procedure.

[0921] Examples of specific prompt sentences include the following:

[0922] "The user's annual income is 5 million yen, his family consists of a spouse and two children, he lives in Tokyo, and he is paying a mortgage. Please analyze the tax saving potential available under this situation and generate specific tax saving proposals."

[0923] This will enable real-time analysis and suggestions of tax saving potential using users' everyday electronic payment data.

[0924] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0925] Step 1:

[0926] Users access the system using their smartphones and enter data about their personal information and living situation. This data includes income, family composition, place of residence, expenses, etc. The device then sends the entered data to the server. The input data is information that the user manually enters, and transmission to the server is complete.

[0927] Step 2:

[0928] The server saves the received living situation data in real time and stores it in a database. PostgreSQL is used as an example for managing this database. The input is the living situation data sent from the device, and the output is the data saved in the database. Data processing involves normalizing the raw data and storing it in an appropriate format.

[0929] Step 3:

[0930] Users provide their daily electronic payment information to the system. This includes credit card payments and electronic money payments on their smartphones. The terminal periodically sends this payment information to the server. The input is electronic payment data, and the output is the completion of transmission to the server. Data processing involves standardizing the format of the payment data and eliminating duplicate data.

[0931] Step 4:

[0932] The server saves the received electronic payment data in real time and stores it in a database. The input is the electronic payment data sent from the terminal, and the output is the data saved in the database. Data processing involves normalizing the data and standardizing the format, just like with the living situation data.

[0933] Step 5:

[0934] The server uses an AI algorithm to analyze tax saving potential based on living situation data and electronic payment data. Python and the Scikit-learn library are used to implement the AI ​​algorithm. The input is living situation data and electronic payment data, and the output is the tax saving potential analysis result. For data calculation, the data is input into a machine learning model to predict the tax saving potential.

[0935] Step 6:

[0936] The server uses a database that aggregates tax reform information and expert knowledge based on the analysis results to generate optimal tax-saving proposals. The input is the tax-saving potential analysis results, and the output is specific tax-saving proposals. Data processing combines the analysis results with the latest tax law data to generate tax-saving measures tailored to the user.

[0937] Step 7:

[0938] The server sends the generated tax-saving proposal to the user's smartphone and displays it. The input is the tax-saving proposal, and the output is the completion of sending it to the user's device and displaying it. Specifically, the proposal content is converted into an appropriate format and displayed on the user interface.

[0939] Step 8:

[0940] When the user confirms the tax saving plan and selects "Apply," the server generates a detailed procedure guide and a list of required documents and provides them to the user. The input is the user's selection information, and the output is the procedure guide and document list. Data processing involves generating specific procedure steps and a document list based on the user's selection.

[0941] 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.

[0942] This invention combines a system that allows users to input data about their own living situation, analyzes tax-saving potential based on that data, and provides optimal tax-saving plans to users with an emotion engine that recognizes the user's emotions. This system has the function of collecting and analyzing users' living situation data and emotion data, and providing users with optimal tax-saving plans that reduce stress based on the latest tax reform information and expert knowledge.

[0943] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[0944] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[0945] Furthermore, as users answer the questionnaire, the emotion engine recognizes and analyzes their emotions. The emotion engine analyzes facial expressions and tone of voice through the device's camera and microphone to determine the user's emotional state in real time. The emotion analysis results are sent to a server and stored in a database along with their lifestyle data.

[0946] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate optimal tax savings suggestions for the user. The results of the sentiment analysis are also taken into consideration, and tax savings suggestions that reduce the user's stress are prioritized. The generated tax savings suggestions are sent to the user's device and displayed.

[0947] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[0948] [Specific example]

[0949] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[0950] 1. Annual income: 5 million yen

[0951] 2. Family: Married, two children

[0952] 3. Place of residence: Tokyo

[0953] 4. Expenses: Mortgage Deductible

[0954] Furthermore, the emotion engine analyzes the user's facial expressions and tone of voice to detect when the user is stressed about tax. The server analyzes the user's data in real time and generates tax saving suggestions such as:

[0955] 1. Make the most of your mortgage deduction

[0956] 2. Application of special spouse deduction

[0957] 3. Optimizing education deductions

[0958] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[0959] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay while reducing tax-related stress.

[0960] The processing flow will be explained below.

[0961] Step 1:

[0962] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[0963] Step 2:

[0964] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[0965] Step 3:

[0966] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[0967] Step 4:

[0968] The device displays a questionnaire screen about the user's living situation, asking them to answer questions about income, family composition, place of residence, expenses, etc.

[0969] Step 5:

[0970] The user answers the survey and clicks the "Next" button to submit the response data.

[0971] Step 6:

[0972] The emotion engine analyzes the user's facial expressions and tone of voice and uses the device's camera and microphone to recognize their emotional state.

[0973] Step 7:

[0974] The device transmits the emotion data analyzed by the emotion engine to the server.

[0975] Step 8:

[0976] The server receives the survey data and sentiment data sent by users in real time and stores them in a database. At the same time, an AI algorithm analyzes the user data and calculates the tax saving potential.

[0977] Step 9:

[0978] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate optimal tax-saving plans for users, taking into account the results of sentiment analysis.

[0979] Step 10:

[0980] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[0981] Step 11:

[0982] The terminal displays the customized tax saving proposal received from the server to the user.

[0983] Step 12:

[0984] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[0985] Step 13:

[0986] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[0987] Step 14:

[0988] The server transmits the generated procedure guide and list of required documents to the terminal.

[0989] Step 15:

[0990] The terminal displays the procedure guide and required document list received from the server to the user.

[0991] Step 16:

[0992] Users follow the provided guide to carry out tax-saving procedures.

[0993] Step 17:

[0994] The terminal reports the progress of the process to the server, and the server provides the user with additional support information as needed.

[0995] Example 2

[0996] 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."

[0997] Conventional tax-saving suggestion systems simply collect data on users' lifestyles and suggest tax-saving strategies based on that data. However, many users often feel stressed about tax procedures, which makes it difficult for them to put appropriate advice into practice. In particular, the emotional stress users feel in the process of understanding tax information and procedures prevents them from implementing effective tax-saving strategies. This has continued to hinder users from optimizing their take-home pay.

[0998] 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.

[0999] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for recognizing and analyzing the user's emotional state, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge based on the data including the emotional state, and means for providing the proposals to the user. This makes it possible to propose optimal tax saving measures that take the user's emotional state into consideration, allowing the user to implement effective tax saving measures while reducing stress about taxes.

[1000] A "user" is an entity that uses the system to input living situation data and emotional data and receive tax-saving suggestions.

[1001] The "server" is an information processing device that receives, stores, and analyzes data sent by users, and generates and provides tax saving suggestions.

[1002] "Living situation data" refers to various information about a user's living environment, such as income, family composition, place of residence, and expenses.

[1003] "Emotional state data" refers to information about emotions analyzed from the user's facial expressions, tone of voice, etc.

[1004] "Tax saving potential" refers to the amount of tax reduction that can be obtained by applying various tax saving measures, calculated based on the user's living situation data.

[1005] "Latest Tax Reform Information" refers to official reform information relating to tax systems currently in effect and those scheduled to be implemented in the future.

[1006] "Expert knowledge" refers to information that compiles the specialized knowledge and experience regarding tax savings held by professionals such as tax accountants and accountants.

[1007] A "database" is a repository of information for systematically storing and managing data on living conditions, emotional state, tax reform information, expert knowledge, and so on.

[1008] "Tax Savings" refers to tax relief measures and offers available to users under the law.

[1009] "Emotion Engine" refers to software or hardware means for recognizing and analyzing a user's emotional state through the device's camera or microphone.

[1010] This invention is a system that allows users to input data about their living situation and emotional state, and provides optimal tax-saving plans based on that data. Specific embodiments for implementing this system will be described below.

[1011] Hardware and Software

[1012] To implement this system, the following hardware and software are required:

[1013] User device: A user data input and display device such as a computer, smartphone, tablet, etc. The device is equipped with a camera and microphone.

[1014] Server: A remote server that collects, stores, and analyzes data, working in conjunction with a database.

[1015] Database: NoSQL database (e.g. MongoDB) to store life situation data, emotional state data, tax reform information, and expert knowledge.

[1016] Emotion Engine: Software for analyzing emotional state data, using open source libraries (e.g., OpenCV, TensorFlow).

[1017] Generative AI model: A machine learning model that generates optimal tax savings plans based on user data. It uses TensorFlow and PyTorch.

[1018] System Operation

[1019] 1. Data Entry

[1020] Users access the system using a terminal and register or log in.

[1021] When registering for the first time, you enter your personal information (such as your name, address, and email address), and the server stores that information in a database.

[1022] When logging in, the server verifies the entered information and performs authentication.

[1023] 2. Collection of living situation data

[1024] Users go to a survey page on the system and enter information about their living situation, such as income, family composition, place of residence, and expenses.

[1025] The terminal transmits the input data to the server, which stores the data in a database.

[1026] 3. Collecting emotional state data

[1027] As users answer the survey, the device's camera and microphone capture their facial expressions and tone of voice.

[1028] The device sends the captured data to the emotion engine, which analyzes the data to determine the user's emotional state.

[1029] The results of the emotion analysis are sent to the server and stored in a database.

[1030] 4. Generate tax saving ideas

[1031] The server retrieves the user's life situation data and emotional state data from the database.

[1032] Based on the latest tax reform information and expert knowledge, a generative AI model is used to generate optimal tax saving plans for users.

[1033] Taking into account the user's emotional state, tax saving suggestions that reduce stress are prioritized.

[1034] 5. Providing tax-saving solutions and supporting procedures

[1035] The server sends the generated tax saving plan to the user's terminal and displays it for the user to check.

[1036] The user reviews the displayed tax savings suggestions and selects to apply them.

[1037] Once the selection is complete, the server generates a detailed procedure guide and a list of required documents and provides them to the user.

[1038] Specific examples

[1039] For example, if a user provides the following information:

[1040] Annual income: 5 million yen

[1041] Family: Married, two children

[1042] Place of residence: Tokyo

[1043] Expenses: Mortgage deduction

[1044] The emotion engine analyzes the user's stress about taxation, and the server generates the following tax-saving suggestions:

[1045] Make the most of your mortgage deduction

[1046] Application of special spouse deduction

[1047] Optimizing education deductions

[1048] These suggestions are displayed on the device, and once the user selects "apply," they are provided with a list of documents and instructions required for the procedure.

[1049] An example of a prompt is as follows:

[1050] "I earn 5 million yen a year and live in Tokyo with my family (spouse and two children). I receive a mortgage deduction. Please give me some tax advice in a stress-free way."

[1051] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay and reduce tax-related stress.

[1052] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1053] Step 1: Register or log in

[1054] input:

[1055] Users enter personal information such as their name, address, and email address. To log in, they enter their registered email address and password.

[1056] Operation:

[1057] The user accesses the system's home screen using a terminal. To register, select "New Registration", enter personal information, and press the "Register" button. To log in, select "Login", enter an email address and password, and press the "Login" button.

[1058] Data processing / calculation and its output:

[1059] The server receives the input information, saves it to the database in the case of a new registration, and sends a registration completion notification to the user when it is saved. In the case of a login, the server compares the input information with the database, outputs the authentication result, and redirects the user to the home screen.

[1060] Step 2: Answer the living situation questionnaire

[1061] input:

[1062] Users answer questionnaire questions such as income, family composition, place of residence, and expenses.

[1063] Operation:

[1064] The user goes to the survey page on the system, fills in the survey items one by one, and presses the "Submit" button.

[1065] Data processing / calculation and its output:

[1066] The terminal sends the entered survey data to the server, which then stores the received data in a database in real time. The server then analyzes the stored data, calculates the user's tax-saving potential, and saves the analysis results.

[1067] Step 3: Collect and analyze emotional state data

[1068] input:

[1069] Facial expressions and tone of voice while users are completing surveys.

[1070] Operation:

[1071] As users answer the survey, their device's camera and microphone capture their facial expressions and tone of voice, which are then sent to the emotion engine.

[1072] Data processing / calculation and its output:

[1073] The emotion engine analyzes the captured data and determines the user's emotional state. The analyzed emotional state data is sent from the device to a server, which stores the data in a database.

[1074] Step 4: Generate tax savings

[1075] input:

[1076] Living situation data, emotional state data, the latest tax reform information, and expert insights.

[1077] Operation:

[1078] The server retrieves the life situation data and emotional state data from the database.

[1079] Data processing / calculation and its output:

[1080] The server uses a generative AI model based on the latest tax reform information and expert knowledge to generate optimal tax savings plans for users. It prioritizes tax savings plans that reduce stress by taking into account the user's emotional state. The generated tax savings plans are stored on the server.

[1081] Step 5: Providing tax-saving solutions and supporting procedures

[1082] input:

[1083] Generated tax savings proposals.

[1084] Operation:

[1085] The server sends the generated tax saving plan to the user's terminal.

[1086] Data processing / calculation and its output:

[1087] The terminal displays the received tax saving plan to the user. The user checks the displayed tax saving plan and decides whether to apply it. When the user selects "apply," the server generates a detailed procedure guide and a list of required documents and sends them to the user. The user can then proceed with the procedure based on the received procedure guide and document list.

[1088] As described above, this system uses the user's living situation data and emotional state data to efficiently generate optimal tax-saving plans and help the user smoothly implement tax-saving measures.

[1089] (Application example 2)

[1090] 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."

[1091] Modern autonomous driving technology is rapidly evolving, but implementing tax-saving measures and optimizing fuel efficiency in operational management requires a great deal of effort. Furthermore, these tasks can be stressful and affect the performance of managers and drivers. Conventional systems are unable to adequately consider individual emotions, making it difficult to provide effective suggestions. Therefore, there is a need to develop a system that can recognize emotions in real time and propose appropriate tax-saving measures.

[1092] 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 data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, means for analyzing the user's emotional state using emotion recognition means and generating suggestions that prioritize stress reduction, and means for providing the suggestions to the user. This reduces the effort required for operation management, makes it possible to propose appropriate tax saving measures according to emotions in real time, and reduces stress for managers and drivers.

[1093] "User" refers to any individual or entity that uses the System.

[1094] "Living situation data" refers to information about a user's income, family composition, place of residence, expenses, etc.

[1095] "Tax savings potential" refers to the potential tax savings and benefits that users can obtain by saving on taxes.

[1096] "Latest tax reform information" refers to the latest information on currently implemented tax systems and their changes.

[1097] "Expert knowledge" refers to specialized knowledge and opinions regarding tax and accounting.

[1098] A "database" refers to a system for storing and managing various types of information.

[1099] "Emotion recognition means" refers to technology that uses sensors such as cameras and microphones to identify emotions from a user's facial expressions and voice.

[1100] "Server" refers to a computer system that stores, analyzes, and provides data.

[1101] "Means for providing suggestions" refers to the function for notifying users of analysis results and tax saving suggestions.

[1102] The "Vehicle Cost Optimization & Emotion Management Assistant" system, which is an application example of this invention, is implemented as follows.

[1103] The server collects data about the user's living situation and stores it in a database, including annual income, number of vehicles owned, vehicle type, annual mileage, expenses, etc. Based on this data, an AI algorithm is used to analyze the user's tax saving potential.

[1104] Next, the device (smartphone or in-car computer) analyzes the user's emotional state using a camera or microphone as an emotion recognition tool. This emotion recognition is performed using software such as OpenCV and DeepFace. The analyzed emotional data is sent to a server and stored in a database along with the user's living situation data.

[1105] The server uses a database that aggregates the latest tax reform information and expert knowledge based on lifestyle and emotional data to generate tax-saving suggestions, prioritizing suggestions that reduce stress. These suggestions are sent to the user's device in real time. Examples of suggestions include fuel-optimized routes and tax-saving suggestions based on the latest tax reform information.

[1106] If the user confirms the proposal and chooses to apply it, the server will generate and provide a detailed procedure guide and a list of required documents to the user, allowing the user to efficiently go through the complicated procedures.

[1107] For example, the following prompt sentence is input to the generative AI model:

[1108] "Mr. A, the manager of an autonomous vehicle, has an annual income of 5 million yen and owns two vehicles (a sedan and an SUV). He drives each vehicle 15,000 km per year, incurring expenses of 200,000 yen each. After analyzing Mr. A's emotions using DeepFace, we found that he is feeling stressed. Using this situation as input data, please run the following Python program, which will provide him with the optimal vehicle cost reduction plan."

[1109] In this way, the system can provide optimal tax-saving and operational management strategies in real time based on the user's living situation and emotional state, reducing stress for the user and improving work efficiency.

[1110] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1111] Step 1:

[1112] Users access the system using a terminal and enter personal information and vehicle-related data. Specifically, they enter information such as annual income, number of vehicles owned, vehicle type, annual mileage, and expenses. This information is sent from the terminal to the server and stored in a database. Based on the entered data, the server creates a basic profile of the user. Input data: personal information, vehicle information; Output data: basic user profile.

[1113] Step 2:

[1114] The device's camera and microphone are used as emotion recognition tools to analyze the user's emotional state. Specifically, software such as OpenCV and DeepFace is used to identify emotions from facial expressions and voice. The analyzed emotion data is sent to a server and stored in a database along with living situation data. Input data: camera footage, audio data; output data: emotion analysis results.

[1115] Step 3:

[1116] The server uses an AI algorithm to analyze the user's tax-saving potential based on the submitted lifestyle and emotional data. It uses a database that compiles the latest tax reform information and expert knowledge to generate the most appropriate tax-saving proposals. Taking emotional data into consideration, proposals that reduce stress are prioritized. Input data: lifestyle data, emotional data, tax reform information. Output data: tax-saving potential analysis results.

[1117] Step 4:

[1118] The generated tax saving suggestions are notified to the user's device in real time. The device receives this notification and displays it to the user. The user reviews the suggestions and chooses whether to adopt them. Input data: tax saving suggestions, output data: notification to the user.

[1119] Step 5:

[1120] If the user chooses to adopt, the server generates a detailed procedure guide and a list of required documents and provides them to the user. Specifically, the server creates a step-by-step procedure guide and a list of required documents based on the relevant information and sends them to the terminal. The user can proceed with the procedure efficiently based on this information. Input data: user selection, Output data: procedure guide, list of required documents.

[1121] 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.

[1122] 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.

[1123] 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.

[1124] [Fourth embodiment]

[1125] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1126] 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.

[1127] 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).

[1128] 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.

[1129] 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.

[1130] 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).

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] 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.

[1137] 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."

[1138] The present invention is a system that allows users to input data about their own living situation, analyzes tax saving potential based on that data, and provides optimal tax saving plans to users. This system is equipped with various means for collecting and analyzing users' living situation data, and generating and providing tax saving plans based on the latest tax reform information and expert knowledge.

[1139] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[1140] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[1141] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate the optimal tax savings plan for the user, which is then sent to the user's device and displayed.

[1142] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[1143] [Specific example]

[1144] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[1145] 1. Annual income: 5 million yen

[1146] 2. Family: Married, two children

[1147] 3. Place of residence: Tokyo

[1148] 4. Expenses: Mortgage Deductible

[1149] The server analyzes the user's data in real time and generates tax-saving suggestions such as:

[1150] 1. Make the most of your mortgage deduction

[1151] 2. Application of special spouse deduction

[1152] 3. Optimizing education deductions

[1153] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[1154] This system allows users to quickly find out the tax-saving plan that best suits their living situation and allows them to proceed with the procedure efficiently, which is expected to increase the user's take-home pay.

[1155] The processing flow will be explained below.

[1156] Step 1:

[1157] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[1158] Step 2:

[1159] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[1160] Step 3:

[1161] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[1162] Step 4:

[1163] The device displays a questionnaire screen about the user's living situation, and the user answers questions about income, family composition, place of residence, expenses, etc.

[1164] Step 5:

[1165] The user answers the survey and clicks the "Next" button to submit the response data.

[1166] Step 6:

[1167] The server receives the survey data sent by users in real time and stores it in a database, while an AI algorithm analyzes the data and calculates the tax saving potential.

[1168] Step 7:

[1169] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate the optimal tax saving plan for the user.

[1170] Step 8:

[1171] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[1172] Step 9:

[1173] The terminal displays the customized tax saving proposal received from the server to the user.

[1174] Step 10:

[1175] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[1176] Step 11:

[1177] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[1178] Step 12:

[1179] The server transmits the generated procedure guide and list of required documents to the terminal.

[1180] Step 13:

[1181] The terminal displays the procedure guide and required document list received from the server to the user.

[1182] Step 14:

[1183] Users follow the provided guide to carry out tax-saving procedures.

[1184] Example 1

[1185] 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."

[1186] Current systems and methods make it difficult for users to quickly obtain optimal tax-saving plans based on their own living circumstances, and they also face problems such as the time and effort required for procedures and collecting necessary documents.It is also difficult to keep up-to-date with tax reform information and expert knowledge, and there is a risk that the information provided to users will remain outdated.

[1187] 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.

[1188] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for generating and providing the user with a procedure guide and a list of required documents, thereby enabling the user to quickly obtain the optimal tax saving plan based on their living situation and to efficiently carry out the necessary procedures and collect the necessary documents.

[1189] "Lifestyle data" refers to information related to a user's daily life and financial situation, such as the user's income, family composition, place of residence, and expenses.

[1190] "Tax Saving Potential" refers to the extent to which users can qualify for deductions and tax benefits based on their living situation.

[1191] "Tax Reform Information" means information regarding the latest amendments and changes to tax laws and tax-related rules and regulations.

[1192] "Expert knowledge" refers to practical advice and recommendations based on the knowledge and experience of tax and accounting professionals.

[1193] "Procedural Guide" means a document that provides a detailed explanation and sequence of steps that a user must take to apply for a tax benefit.

[1194] "Required Document List" means the list of documents that a User must prepare in order to apply for a particular tax benefit.

[1195] "Database" refers to a system for efficiently storing and managing collected information and data.

[1196] A "generative algorithm" refers to a calculation method or program that analyzes collected data and automatically generates optimal tax-saving plans.

[1197] In the present invention, the following means are used to implement a system that collects and analyzes data on the user's living situation and provides optimal tax-saving ideas.

[1198] 1. Data collection methods:

[1199] Users access the system using a terminal and are required to register or log in. When registering, they enter personal information such as their name, email address, and password, which is then stored in a database by the server. When logging in, users enter their registered email address and password, which the server then verifies against the database.

[1200] 2. Enter living situation data:

[1201] Users answer a questionnaire about their living situation from their device. Questionnaire items include income, family composition, place of residence, expenses, etc., and the data is sent from the user's device to the server. The server saves this data in real time and stores it in a database.

[1202] 3. AI-based data analysis:

[1203] The server analyzes the received data using an AI algorithm. This algorithm uses, for example, Python's "scikit-learn" library. The AI ​​analyzes the user's lifestyle data and calculates tax savings potential. The analysis incorporates the latest tax reform information and expert knowledge.

[1204] 4. Generating optimal tax savings:

[1205] The server generates optimal tax-saving plans based on the results of AI analysis, the latest tax reform information, and expert knowledge. The generated tax-saving plans are sent from the server to the user's device and displayed.

[1206] 5. Generate procedure guide and required document list:

[1207] If the user selects the proposed tax saving plan, the server will generate a procedure guide and a list of required documents, which will be sent to the user's device so that the user can proceed with the procedure accordingly.

[1208] Specific examples

[1209] For example, a new user registers with the system and completes the following lifestyle questionnaire:

[1210] 1. Annual income: 5 million yen

[1211] 2. Family: Married, two children

[1212] 3. Place of residence: Tokyo

[1213] 4. Expenses: Mortgage Deductible

[1214] The server receives this information and analyzes it in real time, for example using Python's "scikit-learn" to generate optimal tax saving plans like the following:

[1215] 1. Make the most of your mortgage deduction

[1216] 2. Application of special spouse deduction

[1217] 3. Optimizing education deductions

[1218] The tax-saving plan is displayed on the user's device, and when the user selects "apply," a list of documents required for the procedure and specific steps are provided. For example, the loan balance certificate required for the mortgage deduction and documents for the education expense deduction are displayed.

[1219] Prompt Sentence Examples

[1220] Example prompt: "Please provide the optimal tax savings plan for the user whose annual income is 5 million yen, whose family consists of a spouse and two children, who lives in Tokyo, and who is eligible for a mortgage deduction."

[1221] This system allows users to quickly find out the tax-saving plan that best suits their living situation, and also allows them to efficiently complete the procedures and collect the necessary documents.

[1222] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] A user accesses the system using a terminal and is taken to a screen for new registration or login. In the case of new registration, the user enters personal information such as name, email address, and password, which the server receives and stores in a database. In the case of login, the server compares the entered authentication information with the data in the database, and if authentication is successful, the user is taken to the home screen.

[1225] Input: Personal information (name, email address, password) when registering, or authentication information (email address, password) when logging in

[1226] Data processing: Personal information is stored in a database, and authentication information is verified against the database.

[1227] Output: Notification of registration completion or transition to home screen

[1228] Step 2:

[1229] Users answer a questionnaire about their living situation from their device, including information on income, family composition, place of residence, expenses, etc., and the data entered by the user is sent from the device to the server.

[1230] Input: Living situation data (income, family composition, place of residence, expenses, etc.)

[1231] Data processing: Sending data from the device to the server

[1232] Output: Acknowledgement of data sent to the server

[1233] Step 3:

[1234] The server saves the received living situation data in real time and stores it in a database, while simultaneously checking the data for consistency and performing data cleansing as necessary.

[1235] Input: Living situation data

[1236] Data Processing: Real-time data storage and data cleansing

[1237] Output: Consistent living situation data stored in a database

[1238] Step 4:

[1239] The AI ​​algorithm installed on the server calculates the tax saving potential of users based on the lifestyle data stored in the database, using libraries such as Python's "scikit-learn."

[1240] Input: Consistent living situation data

[1241] Data processing: Analysis using AI algorithms and calculation of tax saving potential

[1242] Output: Tax saving potential calculation results

[1243] Step 5:

[1244] The server generates optimal tax-saving plans based on the analysis results, the latest tax reform information, and expert knowledge. The generated tax-saving plans are stored in a database and sent to the user's device.

[1245] Input: Tax saving potential calculation results, tax reform information, expert knowledge

[1246] Data processing: generating tax saving proposals

[1247] Output: Generated tax saving plans are saved in a database and sent to the user's device

[1248] Step 6:

[1249] The user checks the tax saving plan displayed on the terminal, selects "Apply" or "Not Apply", and the selection is sent from the terminal to the server.

[1250] Input: Generated tax savings plan, user selection

[1251] Data processing: Sending user-selected data

[1252] Output: User selections sent to the server

[1253] Step 7:

[1254] If the user selects "apply" for the tax saving plan, the server will generate a procedure guide and a list of required documents and send them to the user's device, allowing the user to proceed with the specific procedures.

[1255] Input: User's selection ("Apply")

[1256] Data processing: Procedural guide and required document list generation

[1257] Output: Procedure guide and required document list sent to user's device

[1258] This process allows users to quickly obtain the optimal tax-saving plan based on their living situation and to proceed with the process efficiently.

[1259] (Application example 1)

[1260] 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."

[1261] Conventional tax-saving proposal systems rely on analysis results based on user-entered lifestyle data, and are unable to provide real-time tax-saving proposals that take daily electronic payment information into account. This makes it difficult for users to immediately understand tax-saving potential and implement effective tax-saving strategies.

[1262] 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.

[1263] In this invention, the server includes means for collecting data on the user's living situation, means for analyzing the user's tax saving potential using the data, means for collecting and analyzing electronic payment data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, and means for providing the proposals to the user, thereby enabling real-time analysis and proposal of tax saving potential using the user's daily electronic payment data.

[1264] "User" means an individual or corporation that uses the system to provide living situation data and electronic payment data and receive tax savings suggestions.

[1265] "Data regarding living conditions" refers to information that indicates the user's economic and living conditions, such as the user's income, family composition, place of residence, and expenses.

[1266] "Tax saving potential" refers to the possibility and extent of tax savings calculated based on data entered by the user.

[1267] "Electronic payment data" means information relating to the history and content of electronic payments made by a user.

[1268] "Tax Reform Information" refers to information about the latest changes and updates to the tax system announced by the government and related agencies.

[1269] "Expert knowledge" refers to the collection of tax-related knowledge and advice held by experts such as tax accountants and economists.

[1270] A "database" is a collection of information constructed to efficiently manage and use collected data.

[1271] "Tax Savings" are specific methods or strategies that users can use to reduce their tax payments.

[1272] "Server" means a computer system that receives, stores, and analyzes data sent by users and generates and provides tax-saving proposals.

[1273] The present invention is a system that collects and analyzes users' living conditions and electronic payment data to provide optimal tax-saving solutions. The system is configured as follows:

[1274] First, users access the system using their smartphones and enter data about their personal information and living situation, including income, family composition, place of residence, expenses, etc. This data is sent from the user's device to a server and stored in real time.

[1275] The server then collects the user's electronic payment data, which is information about the history and details of the user's purchases and payments. This data is then analyzed along with the user's lifestyle data.

[1276] The server uses an AI algorithm to analyze tax-saving potential based on this data. The AI ​​algorithm analyzes the user's data and combines it with tax reform information and expert knowledge to generate optimal tax-saving proposals. Common databases and machine learning libraries are used for data analysis and processing the AI ​​algorithm. Specifically, PostgreSQL is used for database management, and Python and Scikit-learn are used for data analysis.

[1277] The generated tax saving plan is sent from the server to the user's smartphone and displayed to the user. The user can review the displayed tax saving plan and choose whether or not to apply it. If the user chooses to apply it, the server generates a detailed procedural guide and a list of required documents and provides them to the user. This allows the user to go through the entire process efficiently.

[1278] Specific examples

[1279] For example, consider the case where User A registers and enters the following living situation data:

[1280] 1. Annual income: 5 million yen

[1281] 2. Family: Married, two children

[1282] 3. Place of residence: Tokyo

[1283] 4. Expenses: Mortgage Deductible

[1284] User A also provides the system with information about their daily electronic payments, including monthly mortgage payments, education expenses, etc. The server analyzes this data in real time and generates tax-saving suggestions such as:

[1285] 1. Make the most of your mortgage deduction

[1286] 2. Application of special spouse deduction

[1287] 3. Optimizing education deductions

[1288] The generated tax saving plan will be displayed on User A's smartphone, and when User A selects "Apply," a detailed guide to proceed with the procedure and a list of required documents will be provided, allowing User A to efficiently proceed with the tax saving procedure.

[1289] Examples of specific prompt sentences include the following:

[1290] "The user's annual income is 5 million yen, his family consists of a spouse and two children, he lives in Tokyo, and he is paying a mortgage. Please analyze the tax saving potential available under this situation and generate specific tax saving proposals."

[1291] This will enable real-time analysis and suggestions of tax saving potential using users' everyday electronic payment data.

[1292] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1293] Step 1:

[1294] Users access the system using their smartphones and enter data about their personal information and living situation. This data includes income, family composition, place of residence, expenses, etc. The device then sends the entered data to the server. The input data is information that the user manually enters, and transmission to the server is complete.

[1295] Step 2:

[1296] The server saves the received living situation data in real time and stores it in a database. PostgreSQL is used as an example for managing this database. The input is the living situation data sent from the device, and the output is the data saved in the database. Data processing involves normalizing the raw data and storing it in an appropriate format.

[1297] Step 3:

[1298] Users provide their daily electronic payment information to the system. This includes credit card payments and electronic money payments on their smartphones. The terminal periodically sends this payment information to the server. The input is electronic payment data, and the output is the completion of transmission to the server. Data processing involves standardizing the format of the payment data and eliminating duplicate data.

[1299] Step 4:

[1300] The server saves the received electronic payment data in real time and stores it in a database. The input is the electronic payment data sent from the terminal, and the output is the data saved in the database. Data processing involves normalizing the data and standardizing the format, just like with the living situation data.

[1301] Step 5:

[1302] The server uses an AI algorithm to analyze tax saving potential based on living situation data and electronic payment data. Python and the Scikit-learn library are used to implement the AI ​​algorithm. The input is living situation data and electronic payment data, and the output is the tax saving potential analysis result. For data calculation, the data is input into a machine learning model to predict the tax saving potential.

[1303] Step 6:

[1304] The server uses a database that aggregates tax reform information and expert knowledge based on the analysis results to generate optimal tax-saving proposals. The input is the tax-saving potential analysis results, and the output is specific tax-saving proposals. Data processing combines the analysis results with the latest tax law data to generate tax-saving measures tailored to the user.

[1305] Step 7:

[1306] The server sends the generated tax-saving proposal to the user's smartphone and displays it. The input is the tax-saving proposal, and the output is the completion of sending it to the user's device and displaying it. Specifically, the proposal content is converted into an appropriate format and displayed on the user interface.

[1307] Step 8:

[1308] When the user confirms the tax saving plan and selects "Apply," the server generates a detailed procedure guide and a list of required documents and provides them to the user. The input is the user's selection information, and the output is the procedure guide and document list. Data processing involves generating specific procedure steps and a document list based on the user's selection.

[1309] 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.

[1310] This invention combines a system that allows users to input data about their own living situation, analyzes tax-saving potential based on that data, and provides optimal tax-saving plans to users with an emotion engine that recognizes the user's emotions. This system has the function of collecting and analyzing users' living situation data and emotion data, and providing users with optimal tax-saving plans that reduce stress based on the latest tax reform information and expert knowledge.

[1311] First, the user accesses the system using a terminal and enters the necessary personal information. If registering for a new account, the server stores the information in a database after the personal information is entered and notifies the user that registration is complete. If logging in to an existing account, the server compares the entered information with the database and performs authentication.

[1312] Next, users answer a questionnaire about their living situation, including information about their income, family composition, place of residence, and expenses. This information is then sent from the user's device to the server, which saves the data in real time and stores it in a database. At the same time, an AI algorithm analyzes the user's information and calculates their tax savings potential.

[1313] Furthermore, as users answer the questionnaire, the emotion engine recognizes and analyzes their emotions. The emotion engine analyzes facial expressions and tone of voice through the device's camera and microphone to determine the user's emotional state in real time. The emotion analysis results are sent to a server and stored in a database along with their lifestyle data.

[1314] Once the analysis is complete, the server then uses a database of expert knowledge and the latest tax reform information to generate optimal tax savings suggestions for the user. The results of the sentiment analysis are also taken into consideration, and tax savings suggestions that reduce the user's stress are prioritized. The generated tax savings suggestions are sent to the user's device and displayed.

[1315] The user checks the displayed tax saving plans and selects whether or not to apply them. After the user's choice is entered, the server generates a detailed procedure guide and a list of required documents and provides them to the user, allowing the user to efficiently go through the complicated procedures and gather information.

[1316] [Specific example]

[1317] For example, suppose a user registers and answers a lifestyle questionnaire. The user provides the following information:

[1318] 1. Annual income: 5 million yen

[1319] 2. Family: Married, two children

[1320] 3. Place of residence: Tokyo

[1321] 4. Expenses: Mortgage Deductible

[1322] Furthermore, the emotion engine analyzes the user's facial expressions and tone of voice to detect when the user is stressed about tax. The server analyzes the user's data in real time and generates tax saving suggestions such as:

[1323] 1. Make the most of your mortgage deduction

[1324] 2. Application of special spouse deduction

[1325] 3. Optimizing education deductions

[1326] These suggestions are displayed on the user's device, and once the user selects "apply," they are provided with a list of documents required for application and specific instructions, including details on the documents required for mortgage deductions and the application process for education expenses deductions.

[1327] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay while reducing tax-related stress.

[1328] The processing flow will be explained below.

[1329] Step 1:

[1330] The device accesses the TaxOptimizer website or mobile app and presents the user with an initial registration or login screen.

[1331] Step 2:

[1332] If the user is registering, they enter the required personal information (such as name, email address, and password) and click the Register button. If they are logging in, they enter their existing account information and click the Log In button.

[1333] Step 3:

[1334] The server receives the information sent by the user and stores it in a database if it is a new registration. If it is a login, the entered information is compared with the database for authentication. If the authentication is successful, the server sends a success notification to the device.

[1335] Step 4:

[1336] The device displays a questionnaire screen about the user's living situation, asking them to answer questions about income, family composition, place of residence, expenses, etc.

[1337] Step 5:

[1338] The user answers the survey and clicks the "Next" button to submit the response data.

[1339] Step 6:

[1340] The emotion engine analyzes the user's facial expressions and tone of voice and uses the device's camera and microphone to recognize their emotional state.

[1341] Step 7:

[1342] The device transmits the emotion data analyzed by the emotion engine to the server.

[1343] Step 8:

[1344] The server receives the survey data and sentiment data sent by users in real time and stores them in a database. At the same time, an AI algorithm analyzes the user data and calculates the tax saving potential.

[1345] Step 9:

[1346] Based on the analysis results, the server uses a database of expert knowledge and the latest tax reform information to generate optimal tax-saving plans for users, taking into account the results of sentiment analysis.

[1347] Step 10:

[1348] The server creates the generated tax saving plan as an information packet and transmits it to the terminal.

[1349] Step 11:

[1350] The terminal displays the customized tax saving proposal received from the server to the user.

[1351] Step 12:

[1352] The user can review the proposed tax savings and choose whether to apply them. If so, click the "Apply" button.

[1353] Step 13:

[1354] The server receives the user's selection and generates a detailed procedural guide and a list of required documents.

[1355] Step 14:

[1356] The server transmits the generated procedure guide and list of required documents to the terminal.

[1357] Step 15:

[1358] The terminal displays the procedure guide and required document list received from the server to the user.

[1359] Step 16:

[1360] Users follow the provided guide to carry out tax-saving procedures.

[1361] Step 17:

[1362] The terminal reports the progress of the process to the server, and the server provides the user with additional support information as needed.

[1363] Example 2

[1364] 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."

[1365] Conventional tax-saving suggestion systems simply collect data on users' lifestyles and suggest tax-saving strategies based on that data. However, many users often feel stressed about tax procedures, which makes it difficult for them to put appropriate advice into practice. In particular, the emotional stress users feel in the process of understanding tax information and procedures prevents them from implementing effective tax-saving strategies. This has continued to hinder users from optimizing their take-home pay.

[1366] 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.

[1367] In this invention, the server includes means for collecting data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for recognizing and analyzing the user's emotional state, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge based on the data including the emotional state, and means for providing the proposals to the user. This makes it possible to propose optimal tax saving measures that take the user's emotional state into consideration, allowing the user to implement effective tax saving measures while reducing stress about taxes.

[1368] A "user" is an entity that uses the system to input living situation data and emotional data and receive tax-saving suggestions.

[1369] The "server" is an information processing device that receives, stores, and analyzes data sent by users, and generates and provides tax saving suggestions.

[1370] "Living situation data" refers to various information about a user's living environment, such as income, family composition, place of residence, and expenses.

[1371] "Emotional state data" refers to information about emotions analyzed from the user's facial expressions, tone of voice, etc.

[1372] "Tax saving potential" refers to the amount of tax reduction that can be obtained by applying various tax saving measures, calculated based on the user's living situation data.

[1373] "Latest Tax Reform Information" refers to official reform information relating to tax systems currently in effect and those scheduled to be implemented in the future.

[1374] "Expert knowledge" refers to information that compiles the specialized knowledge and experience regarding tax savings held by professionals such as tax accountants and accountants.

[1375] A "database" is a repository of information for systematically storing and managing data on living conditions, emotional state, tax reform information, expert knowledge, and so on.

[1376] "Tax Savings" refers to tax relief measures and offers available to users under the law.

[1377] "Emotion Engine" refers to software or hardware means for recognizing and analyzing a user's emotional state through the device's camera or microphone.

[1378] This invention is a system that allows users to input data about their living situation and emotional state, and provides optimal tax-saving plans based on that data. Specific embodiments for implementing this system will be described below.

[1379] Hardware and Software

[1380] To implement this system, the following hardware and software are required:

[1381] User device: A user data input and display device such as a computer, smartphone, tablet, etc. The device is equipped with a camera and microphone.

[1382] Server: A remote server that collects, stores, and analyzes data, working in conjunction with a database.

[1383] Database: NoSQL database (e.g. MongoDB) to store life situation data, emotional state data, tax reform information, and expert knowledge.

[1384] Emotion Engine: Software for analyzing emotional state data, using open source libraries (e.g., OpenCV, TensorFlow).

[1385] Generative AI model: A machine learning model that generates optimal tax savings plans based on user data. It uses TensorFlow and PyTorch.

[1386] System Operation

[1387] 1. Data Entry

[1388] Users access the system using a terminal and register or log in.

[1389] When registering for the first time, you enter your personal information (such as your name, address, and email address), and the server stores that information in a database.

[1390] When logging in, the server verifies the entered information and performs authentication.

[1391] 2. Collection of living situation data

[1392] Users go to a survey page on the system and enter information about their living situation, such as income, family composition, place of residence, and expenses.

[1393] The terminal transmits the input data to the server, which stores the data in a database.

[1394] 3. Collecting emotional state data

[1395] As users answer the survey, the device's camera and microphone capture their facial expressions and tone of voice.

[1396] The device sends the captured data to the emotion engine, which analyzes the data to determine the user's emotional state.

[1397] The results of the emotion analysis are sent to the server and stored in a database.

[1398] 4. Generate tax saving ideas

[1399] The server retrieves the user's life situation data and emotional state data from the database.

[1400] Based on the latest tax reform information and expert knowledge, a generative AI model is used to generate optimal tax saving plans for users.

[1401] Taking into account the user's emotional state, tax saving suggestions that reduce stress are prioritized.

[1402] 5. Providing tax-saving solutions and supporting procedures

[1403] The server sends the generated tax saving plan to the user's terminal and displays it for the user to check.

[1404] The user reviews the displayed tax savings suggestions and selects to apply them.

[1405] Once the selection is complete, the server generates a detailed procedure guide and a list of required documents and provides them to the user.

[1406] Specific examples

[1407] For example, if a user provides the following information:

[1408] Annual income: 5 million yen

[1409] Family: Married, two children

[1410] Place of residence: Tokyo

[1411] Expenses: Mortgage deduction

[1412] The emotion engine analyzes the user's stress about taxation, and the server generates the following tax-saving suggestions:

[1413] Make the most of your mortgage deduction

[1414] Application of special spouse deduction

[1415] Optimizing education deductions

[1416] These suggestions are displayed on the device, and once the user selects "apply," they are provided with a list of documents and instructions required for the procedure.

[1417] An example of a prompt is as follows:

[1418] "I earn 5 million yen a year and live in Tokyo with my family (spouse and two children). I receive a mortgage deduction. Please give me some tax advice in a stress-free way."

[1419] This system allows users to quickly find the tax-saving plan that best suits their lifestyle and emotional state, and allows them to proceed with the process efficiently, which is expected to increase users' take-home pay and reduce tax-related stress.

[1420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1421] Step 1: Register or log in

[1422] input:

[1423] Users enter personal information such as their name, address, and email address. To log in, they enter their registered email address and password.

[1424] Operation:

[1425] The user accesses the system's home screen using a terminal. To register, select "New Registration", enter personal information, and press the "Register" button. To log in, select "Login", enter an email address and password, and press the "Login" button.

[1426] Data processing / calculation and its output:

[1427] The server receives the input information, saves it to the database in the case of a new registration, and sends a registration completion notification to the user when it is saved. In the case of a login, the server compares the input information with the database, outputs the authentication result, and redirects the user to the home screen.

[1428] Step 2: Answer the living situation questionnaire

[1429] input:

[1430] Users answer questionnaire questions such as income, family composition, place of residence, and expenses.

[1431] Operation:

[1432] The user goes to the survey page on the system, fills in the survey items one by one, and presses the "Submit" button.

[1433] Data processing / calculation and its output:

[1434] The terminal sends the entered survey data to the server, which then stores the received data in a database in real time. The server then analyzes the stored data, calculates the user's tax-saving potential, and saves the analysis results.

[1435] Step 3: Collect and analyze emotional state data

[1436] input:

[1437] Facial expressions and tone of voice while users are completing surveys.

[1438] Operation:

[1439] As users answer the survey, their device's camera and microphone capture their facial expressions and tone of voice, which are then sent to the emotion engine.

[1440] Data processing / calculation and its output:

[1441] The emotion engine analyzes the captured data and determines the user's emotional state. The analyzed emotional state data is sent from the device to a server, which stores the data in a database.

[1442] Step 4: Generate tax savings

[1443] input:

[1444] Living situation data, emotional state data, the latest tax reform information, and expert insights.

[1445] Operation:

[1446] The server retrieves the life situation data and emotional state data from the database.

[1447] Data processing / calculation and its output:

[1448] The server uses a generative AI model based on the latest tax reform information and expert knowledge to generate optimal tax savings plans for users. It prioritizes tax savings plans that reduce stress by taking into account the user's emotional state. The generated tax savings plans are stored on the server.

[1449] Step 5: Providing tax-saving solutions and supporting procedures

[1450] input:

[1451] Generated tax savings proposals.

[1452] Operation:

[1453] The server sends the generated tax saving plan to the user's terminal.

[1454] Data processing / calculation and its output:

[1455] The terminal displays the received tax saving plan to the user. The user checks the displayed tax saving plan and decides whether to apply it. When the user selects "apply," the server generates a detailed procedure guide and a list of required documents and sends them to the user. The user can then proceed with the procedure based on the received procedure guide and document list.

[1456] As described above, this system uses the user's living situation data and emotional state data to efficiently generate optimal tax-saving plans and help the user smoothly implement tax-saving measures.

[1457] (Application example 2)

[1458] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1459] Modern autonomous driving technology is rapidly evolving, but implementing tax-saving measures and optimizing fuel efficiency in operational management requires a great deal of effort. Furthermore, these tasks can be stressful and affect the performance of managers and drivers. Conventional systems are unable to adequately consider individual emotions, making it difficult to provide effective suggestions. Therefore, there is a need to develop a system that can recognize emotions in real time and propose appropriate tax-saving measures.

[1460] 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 data on the user's living situation from the user, means for analyzing the user's tax saving potential using the data, means for proposing tax saving measures using a database that compiles the latest tax reform information and expert knowledge, means for analyzing the user's emotional state using emotion recognition means and generating suggestions that prioritize stress reduction, and means for providing the suggestions to the user. This reduces the effort required for operation management, makes it possible to propose appropriate tax saving measures according to emotions in real time, and reduces stress for managers and drivers.

[1461] "User" refers to any individual or entity that uses the System.

[1462] "Living situation data" refers to information about a user's income, family composition, place of residence, expenses, etc.

[1463] "Tax savings potential" refers to the potential tax savings and benefits that users can obtain by saving on taxes.

[1464] "Latest tax reform information" refers to the latest information on currently implemented tax systems and their changes.

[1465] "Expert knowledge" refers to specialized knowledge and opinions regarding tax and accounting.

[1466] A "database" refers to a system for storing and managing various types of information.

[1467] "Emotion recognition means" refers to technology that uses sensors such as cameras and microphones to identify emotions from a user's facial expressions and voice.

[1468] "Server" refers to a computer system that stores, analyzes, and provides data.

[1469] "Means for providing suggestions" refers to the function for notifying users of analysis results and tax saving suggestions.

[1470] The "Vehicle Cost Optimization & Emotion Management Assistant" system, which is an application example of this invention, is implemented as follows.

[1471] The server collects data about the user's living situation and stores it in a database, including annual income, number of vehicles owned, vehicle type, annual mileage, expenses, etc. Based on this data, an AI algorithm is used to analyze the user's tax saving potential.

[1472] Next, the device (smartphone or in-car computer) analyzes the user's emotional state using a camera or microphone as an emotion recognition tool. This emotion recognition is performed using software such as OpenCV and DeepFace. The analyzed emotional data is sent to a server and stored in a database along with the user's living situation data.

[1473] The server uses a database that aggregates the latest tax reform information and expert knowledge based on lifestyle and emotional data to generate tax-saving suggestions, prioritizing suggestions that reduce stress. These suggestions are sent to the user's device in real time. Examples of suggestions include fuel-optimized routes and tax-saving suggestions based on the latest tax reform information.

[1474] If the user confirms the proposal and chooses to apply it, the server will generate and provide a detailed procedure guide and a list of required documents to the user, allowing the user to efficiently go through the complicated procedures.

[1475] For example, the following prompt sentence is input to the generative AI model:

[1476] "Mr. A, the manager of an autonomous vehicle, has an annual income of 5 million yen and owns two vehicles (a sedan and an SUV). He drives each vehicle 15,000 km per year, incurring expenses of 200,000 yen each. After analyzing Mr. A's emotions using DeepFace, we found that he is feeling stressed. Using this situation as input data, please run the following Python program, which will provide him with the optimal vehicle cost reduction plan."

[1477] In this way, the system can provide optimal tax-saving and operational management strategies in real time based on the user's living situation and emotional state, reducing stress for the user and improving work efficiency.

[1478] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1479] Step 1:

[1480] Users access the system using a terminal and enter personal information and vehicle-related data. Specifically, they enter information such as annual income, number of vehicles owned, vehicle type, annual mileage, and expenses. This information is sent from the terminal to the server and stored in a database. Based on the entered data, the server creates a basic profile of the user. Input data: personal information, vehicle information; Output data: basic user profile.

[1481] Step 2:

[1482] The device's camera and microphone are used as emotion recognition tools to analyze the user's emotional state. Specifically, software such as OpenCV and DeepFace is used to identify emotions from facial expressions and voice. The analyzed emotion data is sent to a server and stored in a database along with living situation data. Input data: camera footage, audio data; output data: emotion analysis results.

[1483] Step 3:

[1484] The server uses an AI algorithm to analyze the user's tax-saving potential based on the submitted lifestyle and emotional data. It uses a database that compiles the latest tax reform information and expert knowledge to generate the most appropriate tax-saving proposals. Taking emotional data into consideration, proposals that reduce stress are prioritized. Input data: lifestyle data, emotional data, tax reform information. Output data: tax-saving potential analysis results.

[1485] Step 4:

[1486] The generated tax saving suggestions are notified to the user's device in real time. The device receives this notification and displays it to the user. The user reviews the suggestions and chooses whether to adopt them. Input data: tax saving suggestions, output data: notification to the user.

[1487] Step 5:

[1488] If the user chooses to adopt, the server generates a detailed procedure guide and a list of required documents and provides them to the user. Specifically, the server creates a step-by-step procedure guide and a list of required documents based on the relevant information and sends them to the terminal. The user can proceed with the procedure efficiently based on this information. Input data: user selection, Output data: procedure guide, list of required documents.

[1489] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1490] 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.

[1491] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1492] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1493] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1494] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1495] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1496] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1497] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1498] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1499] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1500] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1501] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1502] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1503] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1504] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1505] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1506] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1507] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1508] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1509] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1510] The following is further disclosed regarding the above embodiment.

[1511] (Claim 1)

[1512] a means of collecting data from users about their living conditions;

[1513] means for analyzing the tax saving potential of a user using the data;

[1514] A means to propose tax-saving measures using a database that compiles the latest tax reform information and expert knowledge,

[1515] means for providing the suggestions to a user;

[1516] A system including:

[1517] (Claim 2)

[1518] 10. The system of claim 1, wherein the system stores and analyzes user-entered living situation data in real time.

[1519] (Claim 3)

[1520] The system of claim 1, wherein the system generates and provides a procedure guide and a list of required documents to the user.

[1521] "Example 1"

[1522] (Claim 1)

[1523] a means of collecting data from users about their living conditions;

[1524] means for analyzing the tax saving potential of a user using the data;

[1525] A means to propose tax-saving measures using a database that compiles the latest tax reform information and expert knowledge,

[1526] means for providing the suggestions to a user;

[1527] A means for generating and providing a procedure guide and a list of required documents to the user;

[1528] A system including:

[1529] (Claim 2)

[1530] 10. The system of claim 1, wherein the system stores and analyzes user-entered living situation data in real time.

[1531] (Claim 3)

[1532] The system of claim 1, which uses a generation algorithm to analyze user data and automatically generate optimal tax saving plans.

[1533] "Application Example 1"

[1534] New Claims

[1535] (Claim 1)

[1536] a means of collecting data from users about their living conditions;

[1537] means for analyzing the tax saving potential of a user using the data;

[1538] means for collecting and analyzing electronic payment data;

[1539] A means to propose tax-saving measures using a database that compiles the latest tax reform information and expert knowledge,

[1540] means for providing the suggestions to a user;

[1541] A system including:

[1542] (Claim 2)

[1543] 10. The system of claim 1, wherein the system stores and analyzes user-entered living situation data and electronic payment data in real time.

[1544] (Claim 3)

[1545] The system of claim 1, wherein the system generates and provides a procedure guide and a list of required documents to the user.

[1546] "Example 2: Combining Emotion Engines"

[1547] Claims

[1548] (Claim 1)

[1549] a means of collecting data from users about their living conditions;

[1550] means for analyzing the tax saving potential of a user using the data;

[1551] means for recognizing and analyzing the emotional state of a user;

[1552] A method to propose tax-saving measures using a database that compiles the latest tax reform information and expert knowledge based on data including emotional state, and

[1553] means for providing the suggestions to a user;

[1554] A system including:

[1555] (Claim 2)

[1556] 10. The system of claim 1, wherein the system stores and analyzes the user-entered life situation data and emotional state data in real time.

[1557] (Claim 3)

[1558] The system of claim 1, wherein the system generates and provides a procedure guide and a list of required documents to the user.

[1559] "Application example 2 when combining emotion engines"

[1560] (Claim 1)

[1561] a means of collecting data from users about their living conditions;

[1562] means for analyzing the tax saving potential of a user using the data;

[1563] A means to propose tax-saving measures using a database that compiles the latest tax reform information and expert knowledge,

[1564] means for analyzing a user's emotional state using emotion recognition means and for preferentially generating stress-reducing suggestions;

[1565] means for providing the suggestions to a user;

[1566] A system including:

[1567] (Claim 2)

[1568] 10. The system of claim 1, wherein the system stores and analyzes the life situation data and emotion data input by the user in real time.

[1569] (Claim 3)

[1570] The system of claim 1, wherein the system generates and provides a procedure guide and a list of required documents to the user. [Explanation of symbols]

[1571] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of collecting data from users about their living conditions; means for analyzing the tax saving potential of a user using the data; A means to propose tax-saving measures using a database that compiles the latest tax reform information and expert knowledge, means for providing the suggestions to a user; A system including:

2. 10. The system of claim 1, wherein the system stores and analyzes user-entered life situation data in real time.

3. The system of claim 1 , wherein a procedure guide and a required document list are generated and provided to the user.

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