system

Abstract

The system according to this embodiment aims to efficiently generate tax-saving plans and prepare application forms based on income and expenditure data. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and an application unit. The analysis unit analyzes income and expenditure data. The generation unit generates a tax-saving plan based on the income and expenditure data analyzed by the analysis unit. The application unit generates an application form based on the tax-saving plan generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time and effort to generate a tax-saving plan and create an application form based on income and expenditure data, and it is difficult to perform them efficiently.

[0005] The system according to the embodiment aims to efficiently generate a tax-saving plan and create an application form based on income and expenditure data.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and an application unit. The analysis unit analyzes income and expenditure data. The generation unit generates a tax-saving plan based on the income and expenditure data analyzed by the analysis unit. The application unit generates an application form based on the tax-saving plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate tax-saving plans and prepare application forms based on income and expenditure data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The tax-saving support system according to an embodiment of the present invention is a system that uses an AI agent to generate a tax-saving plan based on income and expenditure information and handles various applications. This tax-saving support system autonomously learns various tax-saving methods and constantly updates with the latest information. Next, the tax-saving support system collaborates with banks and other institutions to analyze an individual's income and expenditure data. This income and expenditure data includes income information such as salary and various payment information. The AI ​​agent presents a tax-saving plan tailored to the income and expenditure situation and generates a simulation of the tax-saving effect when the plan is implemented. It also presents the procedures necessary for the tax-saving plan and generates application forms. The AI ​​agent also handles tax returns and year-end adjustments. For example, regarding the calculation method for medical expense deductions, the AI ​​agent calculates the deduction amount by subtracting the amount reimbursed by private medical insurance, etc., from the total medical expenses actually paid, and then subtracting 100,000 yen from that amount. If the total income is 2 million yen or less, 5% of the total income is deducted instead of 100,000 yen. Medical expenses eligible for deduction include childbirth expenses and hospitalization expenses. The AI ​​agent generates medical expense deduction application forms based on this information. The AI ​​agent also learns about tax-saving measures available to individuals, such as salaried employees, and presents the optimal tax-saving plan based on income and expenditure information. For example, it suggests tax-saving methods such as dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, mortgage interest deductions, and hometown tax donations. The AI ​​agent generates application forms for implementing these tax-saving methods and presents the necessary procedures. Furthermore, the AI ​​agent performs tax-saving effect simulations based on income and expenditure information, calculating the effect of each tax-saving plan. For example, it simulates the net amount received after making a hometown tax donation or after applying medical expense deductions. This allows users to select the optimal tax-saving plan. This system makes complex tax-saving procedures easy for users, increasing their net income. Because the AI ​​agent learns the latest tax-saving information and presents the optimal tax-saving plan based on income and expenditure information, users can implement the most effective tax-saving measures in real time. Additionally, because the AI ​​agent generates application forms and presents the necessary procedures, filing tax returns and year-end adjustments are also easy.This allows the tax-saving support system to easily perform tax-saving procedures for users.

[0029] The tax-saving support system according to this embodiment comprises an analysis unit, a generation unit, and an application unit. The analysis unit analyzes income and expenditure data. Income and expenditure data includes, but is not limited to, income information such as salary, bonuses, and investment returns, and expenditure information such as rent, utilities, and food expenses. The analysis unit statistically analyzes the income and expenditure data and evaluates the balance between income and expenditure. The analysis unit can also perform trend analysis to grasp the fluctuation patterns of income and expenditure. Furthermore, the analysis unit can perform anomaly detection to identify abnormal expenditures and income. For example, the analysis unit treats the income and expenditure data as time-series data and detects abnormal fluctuations. The generation unit generates a tax-saving plan based on the income and expenditure data analyzed by the analysis unit. The tax-saving plan includes, but is not limited to, dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, housing loan deductions, and hometown tax donations. The generation unit proposes an optimal tax-saving plan based on the income and expenditure data. Furthermore, the generation unit can generate tax-saving effect simulations and calculate the effects of each tax-saving plan. For example, the generation unit simulates the net amount received when making a hometown tax donation or when applying for medical expense deductions. The application unit generates application forms based on the tax-saving plans generated by the generation unit. Application forms include, but are not limited to, tax return forms, year-end adjustment documents, medical expense deduction application forms, and dependent deduction application forms. The application unit can also, for example, present the procedures required for a tax-saving plan and generate application forms. The application unit can also generate application forms for tax returns and year-end adjustments. As a result, the tax-saving support system according to this embodiment allows users to easily perform tax-saving procedures.

[0030] The analysis department conducts a detailed analysis of income and expenditure data. This data includes, but is not limited to, income information such as salary, bonuses, and investment returns, and expenditure information such as rent, utilities, and food expenses. Specifically, the analysis department uses statistical methods to analyze the income and expenditure data and evaluate the balance between income and expenses. For example, it can grasp the increase or decrease in income and the trends in expenditures, and calculate the monthly balance of income and expenses. The analysis department can also perform trend analysis to understand patterns of fluctuations in income and expenses. This allows it to identify seasonal fluctuations in income and expenses and the impact of specific events. Furthermore, the analysis department can use anomaly detection algorithms to identify abnormal expenditures and incomes. For example, it can treat income and expenditure data as time-series data and detect abnormal fluctuations. This allows for the early detection of unexpected large expenditures or fluctuations in income, enabling appropriate responses. Based on these analysis results, the analysis department can provide users with suggestions for improving their income and expenses and points to pay attention to. For example, it can provide advice on reducing unnecessary expenses and suggestions on increasing income. The analysis department can also predict future income and expenses based on past data and provide information for long-term financial planning. This allows users to efficiently manage their assets while maintaining a balance between income and expenses.

[0031] The generation unit generates the optimal tax-saving plan for the user based on the income and expenditure data analyzed by the analysis unit. These tax-saving plans include, but are not limited to, dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, mortgage interest deductions, and hometown tax donations. Specifically, the generation unit analyzes income and expenditure data in detail and proposes the optimal tax-saving plan tailored to the user's income and expenditure situation. For example, if a user's medical expenses exceed a certain amount, they can achieve tax savings by applying medical expense deductions. Also, if a user has a mortgage, they can reduce their tax burden by applying mortgage interest deductions. The generation unit simulates these tax-saving plans and calculates the tax-saving effect of each plan. For example, by simulating the net amount received when making hometown tax donations or when applying medical expense deductions, it can present the user with concrete tax-saving effects. Furthermore, the generation unit can also provide customized tax-saving plans tailored to the user's lifestyle and future plans. For example, it can propose long-term tax-saving plans that consider children's education expenses and retirement living expenses. This allows the generation unit to support users in efficiently minimizing taxes and making the most of their assets.

[0032] The application unit automatically generates the necessary application documents based on the tax-saving plan generated by the generation unit. These documents include, but are not limited to, tax returns, year-end tax adjustment documents, medical expense deduction applications, and dependent deduction applications. Specifically, the application unit automatically extracts the necessary information and generates the application documents based on the user's income and expense data and tax-saving plan. For example, if applying for a medical expense deduction, it generates a medical expense deduction application based on medical expense details and receipts. Similarly, if applying for a dependent deduction, it generates a dependent deduction application based on information about dependents. The application unit provides these application documents to the user and guides them through the necessary procedures. For example, it generates a tax return and notifies the user of the submission method and deadline. It also generates year-end tax adjustment documents and guides the user through the procedures for submitting them to their company. Furthermore, the application unit can also provide necessary support when the user submits the application documents. For example, it provides information on how to fill out the application documents and where to submit them, helping the user complete the process smoothly. This allows the application department to easily perform tax-saving procedures and maximize the tax-saving effect.

[0033] The analysis department can analyze income and expenditure data and propose tax-saving plans tailored to the user's financial situation. For example, the analysis department can statistically analyze income and expenditure data to evaluate the balance between income and expenses. For instance, it can analyze the difference between months with high and low income and propose tax-saving plans. The analysis department can also perform trend analysis to understand patterns of income and expenditure fluctuations. For example, it can predict future income and expenditures based on income and expenditure data from the past several years and propose tax-saving plans. Furthermore, the analysis department can perform anomaly detection to identify unusual expenses and income. For example, if there are unusually high expenses in a particular month, the analysis department can identify the cause and propose tax-saving plans. In this way, by presenting tax-saving plans tailored to the user's financial situation, the department can provide the user with the most suitable tax-saving method.

[0034] The generation unit can generate tax-saving effect simulations based on income and expenditure data. For example, the generation unit can simulate the effects of each tax-saving plan based on income and expenditure data. For instance, the generation unit can simulate the net amount received when making a hometown tax donation. It can also simulate the net amount received when applying medical expense deductions. Furthermore, the generation unit can simulate the effects of combining multiple tax-saving plans based on income and expenditure data. For example, it can simulate the net amount received when combining medical expense deductions and hometown tax donations. This allows users to check the effects of tax-saving plans in advance by generating tax-saving effect simulations.

[0035] The application unit can present the necessary procedures for a tax-saving plan and generate application forms. For example, the application unit can present the necessary procedures for a tax-saving plan. For example, the application unit can present the necessary documents and procedures for applying for a medical expense deduction. The application unit can also present the necessary documents and procedures for applying for a dependent deduction. Furthermore, the application unit can generate application forms based on the tax-saving plan. For example, the application unit can generate an application form for a medical expense deduction. The application unit can also generate an application form for a dependent deduction. This allows users to easily perform tax-saving procedures by presenting the necessary procedures for a tax-saving plan and generating application forms.

[0036] The application unit can generate application forms for tax returns and year-end tax adjustments. For example, the application unit can generate tax return application forms. For example, the application unit can provide instructions on how to fill out and submit tax return forms. The application unit can also generate application forms for year-end tax adjustments. For example, the application unit can provide instructions on how to fill out and submit year-end tax adjustment documents. This allows users to easily complete tax procedures by generating application forms for tax returns and year-end tax adjustments.

[0037] The analysis unit can select the optimal analysis method by referring to the user's past income and expense history when analyzing income and expense data. For example, the analysis unit can extract specific spending patterns from the user's past income and expense history and select an analysis method based on those patterns. For example, the analysis unit can extract specific spending patterns based on past income and expense data and select the optimal analysis method based on those patterns. The analysis unit can also select an analysis method that takes into account seasonal spending trends based on the user's past income and expense history. For example, the analysis unit can select the optimal analysis method by taking into account seasonal spending trends based on past income and expense data. Furthermore, the analysis unit can refer to the user's past income and expense history and select an analysis method based on specific events (e.g., bonus payment period). For example, the analysis unit can select an analysis method specifically for bonus payment periods based on past income and expense data. In this way, the optimal analysis method can be selected by referring to the user's past income and expense history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis department can input past revenue and expenditure data into a generating AI and have the AI ​​select the optimal analysis method.

[0038] The analysis unit can filter income and expenditure data based on the user's lifestyle and spending patterns. For example, the analysis unit can filter relevant spending items based on the user's lifestyle (e.g., family structure, type of housing). For example, the analysis unit can filter spending items for the entire family based on family structure. The analysis unit can also filter out abnormal spending items based on the user's spending patterns (e.g., monthly spending trends). For example, the analysis unit can analyze monthly spending trends and filter out abnormal spending items. Furthermore, the analysis unit can prioritize the analysis of specific spending items (e.g., education expenses, medical expenses) considering the user's lifestyle and spending patterns. For example, the analysis unit can prioritize the analysis of education expenses and medical expenses and present a tax-saving plan. This allows for more accurate analysis results by filtering based on the user's lifestyle and spending patterns. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the user's lifestyle and spending patterns into a generating AI and have the generating AI perform the filtering.

[0039] The analysis department can prioritize the analysis of highly relevant data by considering the user's geographical location when analyzing income and expenditure data. For example, the analysis department can prioritize the analysis of region-specific expenditure items (e.g., local taxes, public utility charges) based on the user's place of residence. For example, the analysis department can prioritize the analysis of local taxes and public utility charges based on the user's place of residence. The analysis department can also analyze the usage of nearby stores and services based on the user's geographical location. For example, the analysis department can analyze the usage of nearby stores and services based on the user's geographical location and present a tax-saving plan. Furthermore, the analysis department can compare and analyze regional expenditure trends by considering the user's geographical location. For example, the analysis department can compare and analyze regional expenditure trends based on the user's geographical location and present a tax-saving plan. This allows for the priority analysis of region-specific expenditure items by considering the user's geographical location. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the generating AI and have the AI ​​perform priority analysis of highly relevant data.

[0040] The analysis department can analyze users' social media activity and obtain relevant data when analyzing income and expenditure data. For example, the analysis department can obtain expenditure data related to specific events (e.g., travel, shopping) from users' social media activity. For example, the analysis department can analyze users' social media activity and obtain expenditure data related to travel and shopping. The analysis department can also analyze the usage of specific brands or stores based on users' social media activity. For example, the analysis department can analyze the usage of specific brands or stores based on users' social media activity and propose tax-saving plans. Furthermore, the analysis department can refer to users' social media activity to analyze spending trends over a specific period. For example, the analysis department can refer to users' social media activity to analyze spending trends over a specific period and propose tax-saving plans. In this way, relevant expenditure data can be obtained by analyzing users' social media activity. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data on users' social media activity into a generating AI and have the generating AI perform the acquisition of relevant data.

[0041] The generation unit can adjust the level of detail in a tax-saving plan based on the importance of the income and expense data when generating the plan. For example, the generation unit can generate a detailed tax-saving plan based on the importance of the income and expense data. For example, the generation unit can generate a tax-saving plan that includes a detailed explanation based on the importance of the income and expense data. The generation unit can also generate a simplified tax-saving plan based on the importance of the income and expense data. For example, the generation unit can generate a tax-saving plan that includes a concise explanation based on the importance of the income and expense data. Furthermore, the generation unit can generate a tax-saving plan that focuses on specific items, taking into account the importance of the income and expense data. For example, the generation unit can generate a tax-saving plan that focuses on specific items based on the importance of the income and expense data. This allows the generation unit to provide the user with the optimal tax-saving plan by adjusting the level of detail based on the importance of the income and expense data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the income and expense data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0042] The generation unit can apply different generation algorithms depending on the category of income and expenditure data when generating tax-saving plans. For example, the generation unit generates the optimal tax-saving plan depending on the category of income and expenditure data (e.g., medical expenses, education expenses). For example, the generation unit generates a tax-saving plan for medical expense deductions based on income and expenditure data related to medical expenses. The generation unit can also generate a tax-saving plan for education expense deductions based on income and expenditure data related to education expenses. Furthermore, the generation unit can apply different generation algorithms based on the category of income and expenditure data to present the optimal tax-saving plan. For example, the generation unit applies an algorithm to generate a tax-saving plan for medical expense deductions based on income and expenditure data related to medical expenses. The generation unit can also apply an algorithm to generate a tax-saving plan for education expense deductions based on income and expenditure data related to education expenses. This allows the optimal tax-saving plan to be provided by applying different generation algorithms depending on the category of income and expenditure data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input categories of income and expenditure data into the generation AI and have the generation AI execute different generation algorithms.

[0043] The generation unit can determine the priority of tax-saving plans based on the submission timing of income and expense data when generating tax-saving plans. For example, the generation unit can prioritize presenting tax-saving plans that need to be submitted as soon as possible based on the submission timing of income and expense data. For example, the generation unit can prioritize presenting tax-saving plans with approaching submission deadlines based on the submission timing of income and expense data. The generation unit can also postpone tax-saving plans with distant submission deadlines, taking into account the submission timing of income and expense data. For example, the generation unit can postpone tax-saving plans with distant submission deadlines based on the submission timing of income and expense data. By determining the priority of plans based on the submission timing of income and expense data, the generation unit can provide tax-saving plans at the optimal time for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the submission timing of income and expense data into the generation AI and have the generation AI perform the determination of plan priorities.

[0044] The generation unit can adjust the order of tax-saving plans based on the relevance of income and expense data when generating tax-saving plans. For example, the generation unit can present the most relevant tax-saving plan first based on the relevance of income and expense data. The generation unit can also postpone less relevant tax-saving plans, taking into account the relevance of income and expense data. For example, the generation unit postpones less relevant tax-saving plans based on the relevance of income and expense data. Furthermore, the generation unit can prioritize presenting highly relevant tax-saving plans based on the relevance of income and expense data. For example, the generation unit prioritizes presenting highly relevant tax-saving plans based on the relevance of income and expense data. This allows the system to provide the user with the optimal tax-saving plan by adjusting the order of plans based on the relevance of income and expense data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of income and expense data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0045] The application unit can adjust the content of an application form based on the level of detail of the income and expenditure data when generating the application form. For example, the application unit can generate a detailed application form depending on the level of detail of the income and expenditure data. For example, the application unit can generate an application form that includes a detailed explanation based on the level of detail of the income and expenditure data. The application unit can also generate a simplified application form based on the level of detail of the income and expenditure data. For example, the application unit can generate an application form that includes a concise explanation based on the level of detail of the income and expenditure data. Furthermore, the application unit can generate an application form that focuses on specific items, taking into account the level of detail of the income and expenditure data. For example, the application unit can generate an application form that focuses on specific items based on the level of detail of the income and expenditure data. This allows the application unit to provide the user with the most suitable application form by adjusting the content of the application form based on the level of detail of the income and expenditure data. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input the level of detail of the income and expenditure data into the generation AI and have the generation AI perform the adjustment of the application form content.

[0046] The application unit can apply different application generation algorithms depending on the category of income and expenditure data when generating an application form. For example, the application unit generates the optimal application form depending on the category of income and expenditure data (e.g., medical expenses, education expenses). For example, the application unit generates an application form for medical expense deductions based on income and expenditure data related to medical expenses. The application unit can also generate an application form for education expense deductions based on income and expenditure data related to education expenses. Furthermore, the application unit can apply different application generation algorithms based on the category of income and expenditure data to present the optimal application form. For example, the application unit applies an algorithm to generate an application form for medical expense deductions based on income and expenditure data related to medical expenses. The application unit can also apply an algorithm to generate an application form for education expense deductions based on income and expenditure data related to education expenses. This allows the application unit to provide the optimal application form by applying different application generation algorithms depending on the category of income and expenditure data. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application department can input the categories of income and expenditure data into the generation AI and have the generation AI execute different application form generation algorithms.

[0047] The application department can adjust the order of application forms based on the submission timing of the income and expenditure data when generating applications. For example, the application department can prioritize generating applications that need to be submitted as soon as possible based on the submission timing of the income and expenditure data. For example, the application department can prioritize generating applications with approaching submission deadlines based on the submission timing of the income and expenditure data. The application department can also postpone applications with later submission deadlines, taking into account the submission timing of the income and expenditure data. For example, the application department can postpone applications with later submission deadlines based on the submission timing of the income and expenditure data. By adjusting the order of application forms based on the submission timing of the income and expenditure data, applications can be provided to users at the optimal time. Some or all of the above processing in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the submission timing of the income and expenditure data into the generation AI and have the generation AI perform the adjustment of the order of application forms.

[0048] The application unit can adjust the content of application forms based on the relevance of income and expenditure data when generating them. For example, the application unit can generate the most relevant application forms first based on the relevance of income and expenditure data. The application unit can also postpone less relevant application forms, taking into account the relevance of income and expenditure data. For example, the application unit can postpone less relevant application forms based on the relevance of income and expenditure data. Furthermore, the application unit can prioritize the generation of highly relevant application forms based on the relevance of income and expenditure data. For example, the application unit can prioritize the generation of highly relevant application forms based on the relevance of income and expenditure data. This allows the application unit to provide the user with the most suitable application form by adjusting the content of the application forms based on the relevance of income and expenditure data. Some or all of the above processing in the application unit may be performed using AI, for example, or not. For example, the application unit can input the relevance of income and expenditure data into a generation AI and have the generation AI perform the adjustment of the application form content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analytics department can adjust the analysis of income and expenditure data based on the user's health status. For example, if a user provides the results of a health checkup, the analytics department can take those results into account to predict medical expenses and suggest an appropriate tax-saving plan. Furthermore, if a user has a specific illness, the department can predict the cost of treatment and suggest applying for medical expense deductions. It can also analyze the user's activities for maintaining health (e.g., gym memberships, purchases of health foods) and adjust the tax-saving plan accordingly. This allows for the provision of an optimal tax-saving plan tailored to the user's health condition.

[0051] The generation unit can adjust tax-saving plans based on the user's life events. For example, if a user gets married, the generation unit can present an optimal tax-saving plan that takes into account the changes in income and expenses associated with marriage. Similarly, if a user has children, the generation unit can adjust the tax-saving plan to account for expenses related to children (e.g., education costs, medical expenses). Furthermore, if a user purchases a home, the generation unit can present a tax-saving plan that applies the mortgage interest deduction. This allows the system to provide optimal tax-saving plans tailored to the user's life events.

[0052] The application system can adjust the content of the application form based on the user's occupation. For example, if the user is a freelancer, the system can generate an application form that takes into account freelance-specific expenses (e.g., office rent, communication costs). If the user is a salaried employee, the system can also generate an application form that applies the salary income deduction. Furthermore, if the user is self-employed, the system can generate an application form that takes into account expenses related to business income. This allows the system to provide the most suitable application form for each user's occupation.

[0053] The analytics department can adjust the analysis method of income and expenditure data based on the user's hobbies and preferences. For example, if a user enjoys traveling, the analytics department can propose a tax-saving plan that takes travel-related expenses into account. Similarly, if a user enjoys sports, the tax-saving plan can be adjusted to take sports-related expenses (e.g., gym membership fees, sports equipment purchases) into account. Furthermore, if a user enjoys music or movies, the department can analyze related expenses and propose the most suitable tax-saving plan. This allows for the provision of optimal tax-saving plans tailored to the user's hobbies and preferences.

[0054] The generation unit can adjust tax-saving plans based on the user's future goals. For example, if a user wants to save for future education expenses, the generation unit can present a tax-saving plan that applies education expense deductions. Similarly, if a user aims to purchase a home in the future, the generation unit can present a tax-saving plan that applies mortgage interest deductions. Furthermore, if a user is planning for future retirement, the generation unit can present a tax-saving plan that applies retirement allowance deductions. This allows the system to provide the optimal tax-saving plan tailored to the user's future goals.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The analysis department analyzes the income and expenditure data. This data includes income information such as salary, bonuses, and investment returns, as well as expenditure information such as rent, utilities, and food expenses. The analysis department statistically analyzes the income and expenditure data to evaluate the balance between income and expenses. They also perform trend analysis to understand the fluctuation patterns of income and expenses, detect anomalies, and identify abnormal expenses and income. For example, the income and expenditure data can be treated as time-series data to detect abnormal fluctuations. Step 2: The generation unit generates tax-saving plans based on the income and expenditure data analyzed by the analysis unit. These tax-saving plans include dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, mortgage interest deductions, and hometown tax donations. The generation unit proposes the optimal tax-saving plan based on the income and expenditure data and generates a tax-saving effect simulation to calculate the effect of each tax-saving plan. For example, it simulates the net amount received when making hometown tax donations or when applying medical expense deductions. Step 3: The application unit generates application forms based on the tax-saving plan generated by the generation unit. These application forms include tax return forms, year-end adjustment documents, medical expense deduction application forms, and dependent deduction application forms. The application unit presents the necessary procedures for the tax-saving plan and generates the application forms. It can also generate tax return and year-end adjustment application forms.

[0057] (Example of form 2) The tax-saving support system according to an embodiment of the present invention is a system that uses an AI agent to generate a tax-saving plan based on income and expenditure information and handles various applications. This tax-saving support system autonomously learns various tax-saving methods and constantly updates with the latest information. Next, the tax-saving support system collaborates with banks and other institutions to analyze an individual's income and expenditure data. This income and expenditure data includes income information such as salary and various payment information. The AI ​​agent presents a tax-saving plan tailored to the income and expenditure situation and generates a simulation of the tax-saving effect when the plan is implemented. It also presents the procedures necessary for the tax-saving plan and generates application forms. The AI ​​agent also handles tax returns and year-end adjustments. For example, regarding the calculation method for medical expense deductions, the AI ​​agent calculates the deduction amount by subtracting the amount reimbursed by private medical insurance, etc., from the total medical expenses actually paid, and then subtracting 100,000 yen from that amount. If the total income is 2 million yen or less, 5% of the total income is deducted instead of 100,000 yen. Medical expenses eligible for deduction include childbirth expenses and hospitalization expenses. The AI ​​agent generates medical expense deduction application forms based on this information. The AI ​​agent also learns about tax-saving measures available to individuals, such as salaried employees, and presents the optimal tax-saving plan based on income and expenditure information. For example, it suggests tax-saving methods such as dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, mortgage interest deductions, and hometown tax donations. The AI ​​agent generates application forms for implementing these tax-saving methods and presents the necessary procedures. Furthermore, the AI ​​agent performs tax-saving effect simulations based on income and expenditure information, calculating the effect of each tax-saving plan. For example, it simulates the net amount received after making a hometown tax donation or after applying medical expense deductions. This allows users to select the optimal tax-saving plan. This system makes complex tax-saving procedures easy for users, increasing their net income. Because the AI ​​agent learns the latest tax-saving information and presents the optimal tax-saving plan based on income and expenditure information, users can implement the most effective tax-saving measures in real time. Additionally, because the AI ​​agent generates application forms and presents the necessary procedures, filing tax returns and year-end adjustments are also easy.This allows the tax-saving support system to easily perform tax-saving procedures for users.

[0058] The tax-saving support system according to this embodiment comprises an analysis unit, a generation unit, and an application unit. The analysis unit analyzes income and expenditure data. Income and expenditure data includes, but is not limited to, income information such as salary, bonuses, and investment returns, and expenditure information such as rent, utilities, and food expenses. The analysis unit statistically analyzes the income and expenditure data and evaluates the balance between income and expenditure. The analysis unit can also perform trend analysis to grasp the fluctuation patterns of income and expenditure. Furthermore, the analysis unit can perform anomaly detection to identify abnormal expenditures and income. For example, the analysis unit treats the income and expenditure data as time-series data and detects abnormal fluctuations. The generation unit generates a tax-saving plan based on the income and expenditure data analyzed by the analysis unit. The tax-saving plan includes, but is not limited to, dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, housing loan deductions, and hometown tax donations. The generation unit proposes an optimal tax-saving plan based on the income and expenditure data. Furthermore, the generation unit can generate tax-saving effect simulations and calculate the effects of each tax-saving plan. For example, the generation unit simulates the net amount received when making a hometown tax donation or when applying for medical expense deductions. The application unit generates application forms based on the tax-saving plans generated by the generation unit. Application forms include, but are not limited to, tax return forms, year-end adjustment documents, medical expense deduction application forms, and dependent deduction application forms. The application unit can also, for example, present the procedures required for a tax-saving plan and generate application forms. The application unit can also generate application forms for tax returns and year-end adjustments. As a result, the tax-saving support system according to this embodiment allows users to easily perform tax-saving procedures.

[0059] The analysis department conducts a detailed analysis of income and expenditure data. This data includes, but is not limited to, income information such as salary, bonuses, and investment returns, and expenditure information such as rent, utilities, and food expenses. Specifically, the analysis department uses statistical methods to analyze the income and expenditure data and evaluate the balance between income and expenses. For example, it can grasp the increase or decrease in income and the trends in expenditures, and calculate the monthly balance of income and expenses. The analysis department can also perform trend analysis to understand patterns of fluctuations in income and expenses. This allows it to identify seasonal fluctuations in income and expenses and the impact of specific events. Furthermore, the analysis department can use anomaly detection algorithms to identify abnormal expenditures and incomes. For example, it can treat income and expenditure data as time-series data and detect abnormal fluctuations. This allows for the early detection of unexpected large expenditures or fluctuations in income, enabling appropriate responses. Based on these analysis results, the analysis department can provide users with suggestions for improving their income and expenses and points to pay attention to. For example, it can provide advice on reducing unnecessary expenses and suggestions on increasing income. The analysis department can also predict future income and expenses based on past data and provide information for long-term financial planning. This allows users to efficiently manage their assets while maintaining a balance between income and expenses.

[0060] The generation unit generates the optimal tax-saving plan for the user based on the income and expenditure data analyzed by the analysis unit. These tax-saving plans include, but are not limited to, dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, mortgage interest deductions, and hometown tax donations. Specifically, the generation unit analyzes income and expenditure data in detail and proposes the optimal tax-saving plan tailored to the user's income and expenditure situation. For example, if a user's medical expenses exceed a certain amount, they can achieve tax savings by applying medical expense deductions. Also, if a user has a mortgage, they can reduce their tax burden by applying mortgage interest deductions. The generation unit simulates these tax-saving plans and calculates the tax-saving effect of each plan. For example, by simulating the net amount received when making hometown tax donations or when applying medical expense deductions, it can present the user with concrete tax-saving effects. Furthermore, the generation unit can also provide customized tax-saving plans tailored to the user's lifestyle and future plans. For example, it can propose long-term tax-saving plans that consider children's education expenses and retirement living expenses. This allows the generation unit to support users in efficiently minimizing taxes and making the most of their assets.

[0061] The application unit automatically generates the necessary application documents based on the tax-saving plan generated by the generation unit. These documents include, but are not limited to, tax returns, year-end tax adjustment documents, medical expense deduction applications, and dependent deduction applications. Specifically, the application unit automatically extracts the necessary information and generates the application documents based on the user's income and expense data and tax-saving plan. For example, if applying for a medical expense deduction, it generates a medical expense deduction application based on medical expense details and receipts. Similarly, if applying for a dependent deduction, it generates a dependent deduction application based on information about dependents. The application unit provides these application documents to the user and guides them through the necessary procedures. For example, it generates a tax return and notifies the user of the submission method and deadline. It also generates year-end tax adjustment documents and guides the user through the procedures for submitting them to their company. Furthermore, the application unit can also provide necessary support when the user submits the application documents. For example, it provides information on how to fill out the application documents and where to submit them, helping the user complete the process smoothly. This allows the application department to easily perform tax-saving procedures and maximize the tax-saving effect.

[0062] The analysis department can analyze income and expenditure data and propose tax-saving plans tailored to the user's financial situation. For example, the analysis department can statistically analyze income and expenditure data to evaluate the balance between income and expenses. For instance, it can analyze the difference between months with high and low income and propose tax-saving plans. The analysis department can also perform trend analysis to understand patterns of income and expenditure fluctuations. For example, it can predict future income and expenditures based on income and expenditure data from the past several years and propose tax-saving plans. Furthermore, the analysis department can perform anomaly detection to identify unusual expenses and income. For example, if there are unusually high expenses in a particular month, the analysis department can identify the cause and propose tax-saving plans. In this way, by presenting tax-saving plans tailored to the user's financial situation, the department can provide the user with the most suitable tax-saving method.

[0063] The generation unit can generate tax-saving effect simulations based on income and expenditure data. For example, the generation unit can simulate the effects of each tax-saving plan based on income and expenditure data. For instance, the generation unit can simulate the net amount received when making a hometown tax donation. It can also simulate the net amount received when applying medical expense deductions. Furthermore, the generation unit can simulate the effects of combining multiple tax-saving plans based on income and expenditure data. For example, it can simulate the net amount received when combining medical expense deductions and hometown tax donations. This allows users to check the effects of tax-saving plans in advance by generating tax-saving effect simulations.

[0064] The application unit can present the necessary procedures for a tax-saving plan and generate application forms. For example, the application unit can present the necessary procedures for a tax-saving plan. For example, the application unit can present the necessary documents and procedures for applying for a medical expense deduction. The application unit can also present the necessary documents and procedures for applying for a dependent deduction. Furthermore, the application unit can generate application forms based on the tax-saving plan. For example, the application unit can generate an application form for a medical expense deduction. The application unit can also generate an application form for a dependent deduction. This allows users to easily perform tax-saving procedures by presenting the necessary procedures for a tax-saving plan and generating application forms.

[0065] The application unit can generate application forms for tax returns and year-end tax adjustments. For example, the application unit can generate tax return application forms. For example, the application unit can provide instructions on how to fill out and submit tax return forms. The application unit can also generate application forms for year-end tax adjustments. For example, the application unit can provide instructions on how to fill out and submit year-end tax adjustment documents. This allows users to easily complete tax procedures by generating application forms for tax returns and year-end tax adjustments.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis method of the financial data based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple analysis result and omit detailed explanations. For example, the analysis unit can highlight only the key points of the financial data and generate a concise report. Conversely, if the user is relaxed, the analysis unit can provide a detailed analysis result and explain each item of the financial data in detail. For example, the analysis unit can generate a report that includes detailed explanations for each item of the financial data. Furthermore, if the user is in a hurry, the analysis unit can provide an analysis result that highlights only the important points. For example, the analysis unit can highlight only the important points of the financial data and generate a concise report. This allows the analysis method of financial data to be adjusted according to the user's emotions, thereby providing the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The analysis unit can select the optimal analysis method by referring to the user's past income and expense history when analyzing income and expense data. For example, the analysis unit can extract specific spending patterns from the user's past income and expense history and select an analysis method based on those patterns. For example, the analysis unit can extract specific spending patterns based on past income and expense data and select the optimal analysis method based on those patterns. The analysis unit can also select an analysis method that takes into account seasonal spending trends based on the user's past income and expense history. For example, the analysis unit can select the optimal analysis method by taking into account seasonal spending trends based on past income and expense data. Furthermore, the analysis unit can refer to the user's past income and expense history and select an analysis method based on specific events (e.g., bonus payment period). For example, the analysis unit can select an analysis method specifically for bonus payment periods based on past income and expense data. In this way, the optimal analysis method can be selected by referring to the user's past income and expense history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis department can input past revenue and expenditure data into a generating AI and have the AI ​​select the optimal analysis method.

[0068] The analysis unit can filter income and expenditure data based on the user's lifestyle and spending patterns. For example, the analysis unit can filter relevant spending items based on the user's lifestyle (e.g., family structure, type of housing). For example, the analysis unit can filter spending items for the entire family based on family structure. The analysis unit can also filter out abnormal spending items based on the user's spending patterns (e.g., monthly spending trends). For example, the analysis unit can analyze monthly spending trends and filter out abnormal spending items. Furthermore, the analysis unit can prioritize the analysis of specific spending items (e.g., education expenses, medical expenses) considering the user's lifestyle and spending patterns. For example, the analysis unit can prioritize the analysis of education expenses and medical expenses and present a tax-saving plan. This allows for more accurate analysis results by filtering based on the user's lifestyle and spending patterns. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the user's lifestyle and spending patterns into a generating AI and have the generating AI perform the filtering.

[0069] The analysis unit can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is stressed, the analysis unit will display important analysis results first, delaying detailed explanations. For instance, it might highlight key points in the financial data and generate a concise report. Conversely, if the user is relaxed, the analysis unit can display detailed analysis results sequentially to facilitate overall understanding. For instance, it might generate a report with detailed explanations for each item in the financial data. Furthermore, if the user is in a hurry, the analysis unit can highlight only the most important analysis results. For instance, it might highlight only key points in the financial data and generate a concise report. This allows for prioritizing information important to the user by determining the priority of analysis results according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The analysis department can prioritize the analysis of highly relevant data by considering the user's geographical location when analyzing income and expenditure data. For example, the analysis department can prioritize the analysis of region-specific expenditure items (e.g., local taxes, public utility charges) based on the user's place of residence. For example, the analysis department can prioritize the analysis of local taxes and public utility charges based on the user's place of residence. The analysis department can also analyze the usage of nearby stores and services based on the user's geographical location. For example, the analysis department can analyze the usage of nearby stores and services based on the user's geographical location and present a tax-saving plan. Furthermore, the analysis department can compare and analyze regional expenditure trends by considering the user's geographical location. For example, the analysis department can compare and analyze regional expenditure trends based on the user's geographical location and present a tax-saving plan. This allows for the priority analysis of region-specific expenditure items by considering the user's geographical location. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into the generating AI and have the AI ​​perform priority analysis of highly relevant data.

[0071] The analysis department can analyze users' social media activity and obtain relevant data when analyzing income and expenditure data. For example, the analysis department can obtain expenditure data related to specific events (e.g., travel, shopping) from users' social media activity. For example, the analysis department can analyze users' social media activity and obtain expenditure data related to travel and shopping. The analysis department can also analyze the usage of specific brands or stores based on users' social media activity. For example, the analysis department can analyze the usage of specific brands or stores based on users' social media activity and propose tax-saving plans. Furthermore, the analysis department can refer to users' social media activity to analyze spending trends over a specific period. For example, the analysis department can refer to users' social media activity to analyze spending trends over a specific period and propose tax-saving plans. In this way, relevant expenditure data can be obtained by analyzing users' social media activity. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data on users' social media activity into a generating AI and have the generating AI perform the acquisition of relevant data.

[0072] The generation unit can estimate the user's emotions and adjust the presentation of the tax-saving plan based on the estimated emotions. For example, if the user is stressed, the generation unit will present a simple and easy-to-understand tax-saving plan. For instance, it might highlight only the key points of the income and expense data and present a concise tax-saving plan. Conversely, if the user is relaxed, the generation unit can present a tax-saving plan with detailed explanations. For instance, it might present a tax-saving plan with detailed explanations for each item of the income and expense data. Furthermore, if the user is in a hurry, the generation unit can present a tax-saving plan that highlights only the important points. For instance, it might highlight only the important points of the income and expense data and present a concise tax-saving plan. By adjusting the presentation of the tax-saving plan according to the user's emotions, a plan that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The generation unit can adjust the level of detail in a tax-saving plan based on the importance of the income and expense data when generating the plan. For example, the generation unit can generate a detailed tax-saving plan based on the importance of the income and expense data. For example, the generation unit can generate a tax-saving plan that includes a detailed explanation based on the importance of the income and expense data. The generation unit can also generate a simplified tax-saving plan based on the importance of the income and expense data. For example, the generation unit can generate a tax-saving plan that includes a concise explanation based on the importance of the income and expense data. Furthermore, the generation unit can generate a tax-saving plan that focuses on specific items, taking into account the importance of the income and expense data. For example, the generation unit can generate a tax-saving plan that focuses on specific items based on the importance of the income and expense data. This allows the generation unit to provide the user with the optimal tax-saving plan by adjusting the level of detail based on the importance of the income and expense data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the income and expense data into the generation AI and have the generation AI perform the adjustment of the level of detail in the plan.

[0074] The generation unit can apply different generation algorithms depending on the category of income and expenditure data when generating tax-saving plans. For example, the generation unit generates the optimal tax-saving plan depending on the category of income and expenditure data (e.g., medical expenses, education expenses). For example, the generation unit generates a tax-saving plan for medical expense deductions based on income and expenditure data related to medical expenses. The generation unit can also generate a tax-saving plan for education expense deductions based on income and expenditure data related to education expenses. Furthermore, the generation unit can apply different generation algorithms based on the category of income and expenditure data to present the optimal tax-saving plan. For example, the generation unit applies an algorithm to generate a tax-saving plan for medical expense deductions based on income and expenditure data related to medical expenses. The generation unit can also apply an algorithm to generate a tax-saving plan for education expense deductions based on income and expenditure data related to education expenses. This allows the optimal tax-saving plan to be provided by applying different generation algorithms depending on the category of income and expenditure data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input categories of income and expenditure data into the generation AI and have the generation AI execute different generation algorithms.

[0075] The generation unit can estimate the user's emotions and adjust the length of the tax-saving plan based on the estimated emotions. For example, if the user is stressed, the generation unit will present a short, to-the-point tax-saving plan. For instance, it might highlight only the key points of the income and expense data and present a concise tax-saving plan. Conversely, if the user is relaxed, the generation unit can present a longer tax-saving plan with detailed explanations. For instance, it might present a longer tax-saving plan with detailed explanations for each item of the income and expense data. Furthermore, if the user is in a hurry, the generation unit can present a short tax-saving plan that highlights only the important points. For instance, it might highlight only the important points of the income and expense data and present a concise tax-saving plan. By adjusting the length of the tax-saving plan according to the user's emotions, a plan that is easy for the user to understand can be provided. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The generation unit can determine the priority of tax-saving plans based on the submission timing of income and expense data when generating tax-saving plans. For example, the generation unit can prioritize presenting tax-saving plans that need to be submitted as soon as possible based on the submission timing of income and expense data. For example, the generation unit can prioritize presenting tax-saving plans with approaching submission deadlines based on the submission timing of income and expense data. The generation unit can also postpone tax-saving plans with distant submission deadlines, taking into account the submission timing of income and expense data. For example, the generation unit can postpone tax-saving plans with distant submission deadlines based on the submission timing of income and expense data. By determining the priority of plans based on the submission timing of income and expense data, the generation unit can provide tax-saving plans at the optimal time for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the submission timing of income and expense data into the generation AI and have the generation AI perform the determination of plan priorities.

[0077] The generation unit can adjust the order of tax-saving plans based on the relevance of income and expense data when generating tax-saving plans. For example, the generation unit can present the most relevant tax-saving plan first based on the relevance of income and expense data. The generation unit can also postpone less relevant tax-saving plans, taking into account the relevance of income and expense data. For example, the generation unit postpones less relevant tax-saving plans based on the relevance of income and expense data. Furthermore, the generation unit can prioritize presenting highly relevant tax-saving plans based on the relevance of income and expense data. For example, the generation unit prioritizes presenting highly relevant tax-saving plans based on the relevance of income and expense data. This allows the system to provide the user with the optimal tax-saving plan by adjusting the order of plans based on the relevance of income and expense data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of income and expense data into a generation AI and have the generation AI perform the adjustment of the plan order.

[0078] The application system can estimate the user's emotions and adjust the application generation method based on the estimated emotions. For example, if the user is stressed, the system can generate a simple and easy-to-understand application. For instance, it can highlight only the essential points of the income and expenditure data and generate a concise application. If the user is relaxed, the system can also generate an application with detailed explanations. For instance, it can generate an application with detailed explanations for each item of the income and expenditure data. If the user is in a hurry, the system can also generate an application that highlights only the important points. For instance, it can highlight only the important points of the income and expenditure data and generate a concise application. By adjusting the application generation method according to the user's emotions, the system can provide an application that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The application unit can adjust the content of an application form based on the level of detail of the income and expenditure data when generating the application form. For example, the application unit can generate a detailed application form depending on the level of detail of the income and expenditure data. For example, the application unit can generate an application form that includes a detailed explanation based on the level of detail of the income and expenditure data. The application unit can also generate a simplified application form based on the level of detail of the income and expenditure data. For example, the application unit can generate an application form that includes a concise explanation based on the level of detail of the income and expenditure data. Furthermore, the application unit can generate an application form that focuses on specific items, taking into account the level of detail of the income and expenditure data. For example, the application unit can generate an application form that focuses on specific items based on the level of detail of the income and expenditure data. This allows the application unit to provide the user with the most suitable application form by adjusting the content of the application form based on the level of detail of the income and expenditure data. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can input the level of detail of the income and expenditure data into the generation AI and have the generation AI perform the adjustment of the application form content.

[0080] The application unit can apply different application generation algorithms depending on the category of income and expenditure data when generating an application form. For example, the application unit generates the optimal application form depending on the category of income and expenditure data (e.g., medical expenses, education expenses). For example, the application unit generates an application form for medical expense deductions based on income and expenditure data related to medical expenses. The application unit can also generate an application form for education expense deductions based on income and expenditure data related to education expenses. Furthermore, the application unit can apply different application generation algorithms based on the category of income and expenditure data to present the optimal application form. For example, the application unit applies an algorithm to generate an application form for medical expense deductions based on income and expenditure data related to medical expenses. The application unit can also apply an algorithm to generate an application form for education expense deductions based on income and expenditure data related to education expenses. This allows the application unit to provide the optimal application form by applying different application generation algorithms depending on the category of income and expenditure data. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application department can input the categories of income and expenditure data into the generation AI and have the generation AI execute different application form generation algorithms.

[0081] The application process can estimate the user's emotions and prioritize applications based on those emotions. For example, if the user is stressed, the application process might generate important applications first, delaying detailed explanations. For instance, it might highlight key points of the financial data and generate a concise application. Conversely, if the user is relaxed, the application process might generate detailed applications sequentially to facilitate overall understanding. For instance, it might generate an application that includes detailed explanations for each item of the financial data. Furthermore, if the user is in a hurry, the application process might highlight and generate only the most important applications. For instance, it might highlight only key points of the financial data and generate a concise application. This allows the application process to prioritize applications based on the user's emotions, ensuring that important applications are provided to the user first. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The application department can adjust the order of application forms based on the submission timing of the income and expenditure data when generating applications. For example, the application department can prioritize generating applications that need to be submitted as soon as possible based on the submission timing of the income and expenditure data. For example, the application department can prioritize generating applications with approaching submission deadlines based on the submission timing of the income and expenditure data. The application department can also postpone applications with later submission deadlines, taking into account the submission timing of the income and expenditure data. For example, the application department can postpone applications with later submission deadlines based on the submission timing of the income and expenditure data. By adjusting the order of application forms based on the submission timing of the income and expenditure data, applications can be provided to users at the optimal time. Some or all of the above processing in the application department may be performed using AI, for example, or not using AI. For example, the application department can input the submission timing of the income and expenditure data into the generation AI and have the generation AI perform the adjustment of the order of application forms.

[0083] The application unit can adjust the content of application forms based on the relevance of income and expenditure data when generating them. For example, the application unit can generate the most relevant application forms first based on the relevance of income and expenditure data. The application unit can also postpone less relevant application forms, taking into account the relevance of income and expenditure data. For example, the application unit can postpone less relevant application forms based on the relevance of income and expenditure data. Furthermore, the application unit can prioritize the generation of highly relevant application forms based on the relevance of income and expenditure data. For example, the application unit can prioritize the generation of highly relevant application forms based on the relevance of income and expenditure data. This allows the application unit to provide the user with the most suitable application form by adjusting the content of the application forms based on the relevance of income and expenditure data. Some or all of the above processing in the application unit may be performed using AI, for example, or not. For example, the application unit can input the relevance of income and expenditure data into a generation AI and have the generation AI perform the adjustment of the application form content.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analytics department can adjust the analysis of income and expenditure data based on the user's health status. For example, if a user provides the results of a health checkup, the analytics department can take those results into account to predict medical expenses and suggest an appropriate tax-saving plan. Furthermore, if a user has a specific illness, the department can predict the cost of treatment and suggest applying for medical expense deductions. It can also analyze the user's activities for maintaining health (e.g., gym memberships, purchases of health foods) and adjust the tax-saving plan accordingly. This allows for the provision of an optimal tax-saving plan tailored to the user's health condition.

[0086] The generation unit can adjust tax-saving plans based on the user's life events. For example, if a user gets married, the generation unit can present an optimal tax-saving plan that takes into account the changes in income and expenses associated with marriage. Similarly, if a user has children, the generation unit can adjust the tax-saving plan to account for expenses related to children (e.g., education costs, medical expenses). Furthermore, if a user purchases a home, the generation unit can present a tax-saving plan that applies the mortgage interest deduction. This allows the system to provide optimal tax-saving plans tailored to the user's life events.

[0087] The application system can adjust the content of the application form based on the user's occupation. For example, if the user is a freelancer, the system can generate an application form that takes into account freelance-specific expenses (e.g., office rent, communication costs). If the user is a salaried employee, the system can also generate an application form that applies the salary income deduction. Furthermore, if the user is self-employed, the system can generate an application form that takes into account expenses related to business income. This allows the system to provide the most suitable application form for each user's occupation.

[0088] The analytics department can adjust the analysis method of income and expenditure data based on the user's hobbies and preferences. For example, if a user enjoys traveling, the analytics department can propose a tax-saving plan that takes travel-related expenses into account. Similarly, if a user enjoys sports, the tax-saving plan can be adjusted to take sports-related expenses (e.g., gym membership fees, sports equipment purchases) into account. Furthermore, if a user enjoys music or movies, the department can analyze related expenses and propose the most suitable tax-saving plan. This allows for the provision of optimal tax-saving plans tailored to the user's hobbies and preferences.

[0089] The generation unit can adjust tax-saving plans based on the user's future goals. For example, if a user wants to save for future education expenses, the generation unit can present a tax-saving plan that applies education expense deductions. Similarly, if a user aims to purchase a home in the future, the generation unit can present a tax-saving plan that applies mortgage interest deductions. Furthermore, if a user is planning for future retirement, the generation unit can present a tax-saving plan that applies retirement allowance deductions. This allows the system to provide the optimal tax-saving plan tailored to the user's future goals.

[0090] The analytics department can estimate the user's emotions and adjust the analysis method of the financial data based on those emotions. For example, if the user is stressed, it can provide a simple analysis result and omit detailed explanations. For instance, the analytics department can highlight only the key points of the financial data and generate a concise report. Conversely, if the user is relaxed, the analytics department can provide a detailed analysis result and explain each item of the financial data in detail. For example, the analytics department can generate a report that includes detailed explanations for each item of the financial data. Furthermore, if the user is in a hurry, the analytics department can provide an analysis result that highlights only the important points. For example, the analytics department can highlight only the important points of the financial data and generate a concise report. This allows the analytics department to provide the user with the most optimal analysis result by adjusting the analysis method of the financial data according to the user's emotions.

[0091] The generation unit can estimate the user's emotions and adjust the presentation of the tax-saving plan based on those emotions. For example, if the user is stressed, the generation unit will present a simple and easy-to-understand tax-saving plan. For instance, it might highlight only the key points of the income and expense data and present a concise tax-saving plan. Conversely, if the user is relaxed, the generation unit can present a tax-saving plan with detailed explanations. For example, it might present a tax-saving plan with detailed explanations for each item of the income and expense data. Furthermore, if the user is in a hurry, the generation unit can present a tax-saving plan that highlights only the important points. For example, it might highlight only the important points of the income and expense data and present a concise tax-saving plan. By adjusting the presentation of the tax-saving plan according to the user's emotions, the system can provide a plan that is easy for the user to understand.

[0092] The application system can estimate the user's emotions and adjust the application generation method based on those emotions. For example, if the user is stressed, the system will generate a simple and easy-to-understand application. For instance, it might highlight only the essential points of the income and expense data, creating a concise application. Conversely, if the user is relaxed, the system can also generate an application with detailed explanations. For example, it might generate an application with detailed explanations for each item of the income and expense data. Furthermore, if the user is in a hurry, the system can generate an application that highlights only the important points. For instance, it might highlight only the important points of the income and expense data, creating a concise application. By adjusting the application generation method according to the user's emotions, the system can provide an application that is easy for the user to understand.

[0093] The analytics department can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is stressed, the analytics department will display the most important analysis results first, delaying detailed explanations. For instance, it might highlight key points in the financial data and generate a concise report. Conversely, if the user is relaxed, the analytics department can display detailed analysis results sequentially to facilitate overall understanding. For instance, it might generate a report with detailed explanations for each item in the financial data. Furthermore, if the user is in a hurry, the analytics department can highlight only the most important analysis results. For instance, it might highlight only key points in the financial data and generate a concise report. This allows the system to prioritize information that is important to the user by prioritizing analysis results according to their emotions.

[0094] The generation unit can estimate the user's emotions and adjust the length of the tax-saving plan based on those emotions. For example, if the user is stressed, the generation unit will present a short, to-the-point tax-saving plan. For instance, it might highlight only the essential points of the income and expense data and present a concise tax-saving plan. Conversely, if the user is relaxed, the generation unit can present a longer tax-saving plan with detailed explanations. For example, it might present a longer tax-saving plan with detailed explanations for each item of the income and expense data. Furthermore, if the user is in a hurry, the generation unit can present a short tax-saving plan that highlights only the important points. For instance, it might highlight only the important points of the income and expense data and present a concise tax-saving plan. By adjusting the length of the tax-saving plan according to the user's emotions, the system can provide a plan that is easy for the user to understand.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The analysis department analyzes the income and expenditure data. This data includes income information such as salary, bonuses, and investment returns, as well as expenditure information such as rent, utilities, and food expenses. The analysis department statistically analyzes the income and expenditure data to evaluate the balance between income and expenses. They also perform trend analysis to understand the fluctuation patterns of income and expenses, detect anomalies, and identify abnormal expenses and income. For example, the income and expenditure data can be treated as time-series data to detect abnormal fluctuations. Step 2: The generation unit generates tax-saving plans based on the income and expenditure data analyzed by the analysis unit. These tax-saving plans include dependent deductions, medical expense deductions, self-medication tax deductions, life insurance premium deductions, earthquake insurance premium deductions, specific expense deductions, mortgage interest deductions, and hometown tax donations. The generation unit proposes the optimal tax-saving plan based on the income and expenditure data and generates a tax-saving effect simulation to calculate the effect of each tax-saving plan. For example, it simulates the net amount received when making hometown tax donations or when applying medical expense deductions. Step 3: The application unit generates application forms based on the tax-saving plan generated by the generation unit. These application forms include tax return forms, year-end adjustment documents, medical expense deduction application forms, and dependent deduction application forms. The application unit presents the necessary procedures for the tax-saving plan and generates the application forms. It can also generate tax return and year-end adjustment application forms.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements described above, including the analysis unit, generation unit, and application unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes income and expenditure data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a tax-saving plan based on the analyzed income and expenditure data. The application unit is implemented by the control unit 46A of the smart device 14 and generates an application form based on the generated tax-saving plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the analysis unit, generation unit, and application unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the income and expenditure data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a tax saving plan based on the analyzed income and expenditure data. The application unit is implemented, for example, by the control unit 46A of the smart glasses 214 and generates an application form based on the generated tax saving plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the analysis unit, generation unit, and application unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the income and expenditure data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a tax saving plan based on the analyzed income and expenditure data. The application unit is implemented by the control unit 46A of the headset terminal 314 and generates an application form based on the generated tax saving plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the analysis unit, generation unit, and application unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the income and expenditure data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a tax saving plan based on the analyzed income and expenditure data. The application unit is implemented by, for example, the control unit 46A of the robot 414 and generates an application form based on the generated tax saving plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) The analysis department analyzes the income and expenditure data, A generation unit generates a tax-saving plan based on the income and expenditure data analyzed by the aforementioned analysis unit, An application unit that generates an application form based on the tax-saving plan generated by the generation unit, A system characterized by comprising the following features. (Note 2) The aforementioned analysis unit is We analyze income and expenditure data and propose tax-saving plans tailored to your financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate a tax-saving effect simulation based on income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned application department, We will present the necessary procedures for your tax-saving plan and generate the application form. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned application department, Generate tax return and year-end tax adjustment application forms. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of revenue and expenditure data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is When analyzing income and expenditure data, the system selects the optimal analysis method by referring to the user's past income and expenditure history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is When analyzing income and expenditure data, filtering is performed based on the user's lifestyle and spending patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When analyzing revenue and expenditure data, the system prioritizes analyzing data with high relevance based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing revenue and expenditure data, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and adjusts how the tax-saving plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a tax-saving plan, adjust the level of detail in the plan based on the importance of the income and expense data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating tax-saving plans, different generation algorithms are applied depending on the category of income and expense data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the length of the tax-saving plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a tax-saving plan, the plan's priority is determined based on the timing of income and expense data submission. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a tax-saving plan, adjust the order of the plan based on the relevance of income and expense data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned application department, The system estimates the user's emotions and adjusts the application generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned application department, When generating the application form, adjust the content of the application form based on the level of detail in the income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned application department, When generating application forms, different application generation algorithms are applied depending on the category of income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned application department, The system estimates the user's emotions and prioritizes applications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned application department, When generating application forms, the order of the application forms will be adjusted based on the submission timing of the income and expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned application department, When generating the application form, adjust the content of the application form based on the relevance of the income and expenditure data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the income and expenditure data, A generation unit generates a tax-saving plan based on the income and expenditure data analyzed by the aforementioned analysis unit, An application unit that generates an application form based on the tax-saving plan generated by the generation unit, A system characterized by comprising the following features.

2. The aforementioned analysis unit is We analyze income and expenditure data and propose tax-saving plans tailored to your financial situation. The system according to feature 1.

3. The generating unit is Generate a tax-saving effect simulation based on income and expenditure data. The system according to feature 1.

4. The aforementioned application department, We will present the necessary procedures for your tax-saving plan and generate the application form. The system according to feature 1.

5. The aforementioned application department, Generate tax return and year-end tax adjustment application forms. The system according to feature 1.

6. The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of revenue and expenditure data based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned analysis unit is When analyzing income and expenditure data, the system selects the optimal analysis method by referring to the user's past income and expenditure history. The system according to feature 1.

8. The aforementioned analysis unit is When analyzing income and expenditure data, filtering is performed based on the user's lifestyle and spending patterns. The system according to feature 1.

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