system

Abstract

The system according to this embodiment aims to propose an optimal tax strategy based on the user's lifestyle and economic situation. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, a guide unit, an update unit, and a security unit. The collection unit collects information about the user's lifestyle and economic situation. The proposal unit proposes the optimal tax strategy to the user based on the information collected by the collection unit. The guide unit shows specific procedures based on the content proposed by the proposal unit. The update unit collects information on the tax system, which changes every year, and makes proposals based on the latest tax law. The security unit protects the user's information.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose an optimal tax strategy for each individual user in response to the annually changing tax system.

[0005] The system according to the embodiment aims to propose an optimal tax strategy based on the user's lifestyle and economic situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a proposal unit, a guide unit, an update unit, and a security unit. The collection unit collects information about the user's lifestyle and financial situation. The proposal unit proposes the optimal tax strategy to the user based on the information collected by the collection unit. The guide unit shows specific procedures based on the content proposed by the proposal unit. The update unit collects information on the tax system, which changes every year, and makes proposals based on the latest tax law. The security unit protects the user's information. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal tax strategy based on the user's lifestyle and economic situation. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 Sense system according to an embodiment of the present invention is a comprehensive service that provides AI-powered tax advice. The Tax Sense system provides tax strategies tailored to the user's lifestyle and financial situation. The Tax Sense system collects information on the tax system, which changes every year, and proposes the most suitable tax-saving measures to the user, thereby minimizing the user's tax burden. For example, the Tax Sense system collects information on the user's lifestyle and financial situation. For example, it collects information such as income, expenses, investments, and family structure. This information is input into the AI. Next, based on the collected information, the AI ​​proposes the most suitable tax strategy to the user. For example, it proposes tax-saving measures such as hometown tax donations, medical expense deductions, and loss offsetting. This allows the user to learn about the most suitable tax-saving measures for them. Furthermore, the AI ​​collects information on the tax system, which changes every year, and makes proposals based on the latest tax laws. For example, it proposes new deductions and preferential treatments due to tax law revisions. This allows the user to learn about tax-saving measures that are in line with the latest tax laws. This mechanism minimizes the user's tax burden. For example, by implementing the tax-saving measures proposed by the AI, the user can reduce their tax burden. Furthermore, because the AI ​​provides suggestions based on the latest tax laws, users can always know the best tax-saving strategies. In this way, the Tax Sense system is a service that utilizes AI to propose the optimal tax strategy to users, minimizing their tax burden. As a result, the Tax Sense system can minimize the tax burden on users.

[0029] The Tax Sense system according to this embodiment comprises a collection unit, a suggestion unit, a guide unit, an update unit, and a security unit. The collection unit collects information about the user's lifestyle and financial situation. For example, the collection unit collects information such as income, expenses, investments, and family structure. For example, the collection unit can collect the user's income information. For example, the collection unit can collect the user's expense information. For example, the collection unit can collect the user's investment information. The suggestion unit proposes the optimal tax strategy to the user based on the information collected by the collection unit. For example, the suggestion unit can propose hometown tax donations. For example, the suggestion unit can propose medical expense deductions. For example, the suggestion unit can propose offsetting profits and losses. The guide unit shows specific procedures based on the content proposed by the suggestion unit. For example, the guide unit can show the procedures for hometown tax donations. For example, the guide unit can show the procedures for medical expense deductions. For example, the guide unit can show the procedures for offsetting profits and losses. The update unit collects information on the tax system that changes every year and makes suggestions based on the latest tax system. The update unit can, for example, collect the latest tax information from government websites. The update unit can, for example, collect new deductions and preferential treatments resulting from tax reforms. The update unit can, for example, collect the latest tax rate information. The security unit protects user information. The security unit can, for example, perform data encryption. The security unit can, for example, perform access control. The security unit can, for example, perform backups. As a result, the Tax Sense system according to this embodiment can provide an optimal tax strategy based on the user's lifestyle and economic situation, thereby minimizing the tax burden.

[0030] The data collection unit collects information about the user's lifestyle and financial situation. Specifically, it collects information such as income, expenses, investments, and family structure. Income information includes pay stubs, bank account transaction history, and income from freelance work or side jobs. This information can be entered manually by the user, but it can also be automatically retrieved from banks and payroll systems via APIs. Expense information includes credit card statements, e-money usage history, and cash expenditure records. This data is automatically collected by linking with budgeting apps and expense management tools. Investment information includes portfolio information such as stocks, bonds, mutual funds, and real estate investments. This information can be obtained from securities companies and investment platforms via APIs. Family structure information includes the number and ages of dependents and the spouse's income, and this information is entered manually by the user. The data collection unit centrally manages this diverse information and stores it in a database. Furthermore, the data collection unit has a function to check the consistency of the collected data, detect missing or inaccurate information, and notify the user. This allows the data collection unit to accurately understand the user's financial situation and provide a foundation for subsequent proposal and guidance units to make optimal suggestions and procedures.

[0031] The Proposal Department proposes the most suitable tax strategy to users based on information collected by the Data Collection Department. Specifically, it proposes tax strategies such as hometown tax donations, medical expense deductions, and loss offsetting. For hometown tax donations, it calculates the optimal donation amount based on the user's income and family structure and presents options for recipient municipalities and return gifts. For medical expense deductions, it analyzes the user's medical expenses and identifies deductible expenses. Furthermore, it provides specific information on the documents and procedures required to apply for medical expense deductions. For loss offsetting, it analyzes the user's investment portfolio and proposes methods to reduce the tax burden by combining investments that are incurring losses with investments that are generating profits. The Proposal Department uses AI to analyze collected data and automatically generates the most suitable tax strategy for the user's economic situation and lifestyle. For example, the AI ​​predicts the user's future income and expenses based on past data and statistical information and proposes the optimal tax strategy based on that. The Proposal Department also has a function to collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Proposal Department can provide users with the most suitable tax strategy and minimize their tax burden.

[0032] The Guide Department provides specific procedures based on the proposals made by the Proposal Department. Specifically, it outlines procedures for hometown tax donations, medical expense deductions, and loss offsetting. For hometown tax donations, it provides detailed guidance on how to select a recipient municipality, how to pay donations, and how to obtain donation certificates. For medical expense deductions, it provides specific guidance on how to organize receipts for eligible medical expenses, how to fill out application forms, and how to submit them to the tax office. For loss offsetting, it provides detailed guidance on how to combine investments that have incurred losses with investments that have generated profits, how to fill out loss offsetting application forms, and how to submit them to the tax office. The Guide Department provides step-by-step guides and checklists to help users proceed smoothly through the procedures. The Guide Department also has a function to automatically generate and provide users with the necessary documents and information when they perform the procedures. Furthermore, the Guide Department has a chatbot function that provides real-time support for questions and problems that users may encounter when performing the procedures. This allows the Guide Department to smoothly guide users through the specific procedures required to implement the proposed tax strategy.

[0033] The Update Department collects information on the tax system, which changes annually, and makes recommendations based on the latest tax laws. Specifically, it collects the latest tax information from the government's official website, including new deductions and preferential treatments resulting from tax reforms, and the latest tax rate information. The Update Department automatically collects this information and stores it in a database. Furthermore, the Update Department has the function to update the recommendations and procedures provided by the Recommendations Department and Guide Department based on the latest collected tax information. For example, if a new deduction is introduced due to a tax reform, the Update Department collects that information so that the Recommendations Department can propose the new deduction to the user. Also, if the tax rate changes, the Update Department collects that information and updates the procedure guides provided by the Guide Department based on the latest tax rate. The Update Department also has the function to analyze the collected information using AI and predict the impact of tax reforms. As a result, the Update Department can always provide users with recommendations and procedures based on the latest tax information, minimizing their tax burden.

[0034] The Security Department protects user information. Specifically, it implements security measures such as data encryption, access control, and backups. For data encryption, strong encryption algorithms are used when storing user information to prevent unauthorized access and data leaks. For access control, the department strictly manages the permissions to access user information, ensuring that only users with the necessary permissions can access the information. For backups, data is backed up regularly to prepare for data loss or corruption. Furthermore, the Security Department develops incident response plans to respond quickly in the event of a security incident and conducts regular training. The Security Department constantly incorporates the latest security technologies and best practices to continuously strengthen the security of the entire system. In addition, the Security Department provides security education and awareness activities to users to help them raise their own security awareness. In this way, the Security Department can safely protect user information and improve the reliability and security of the entire system.

[0035] The data collection unit can collect information such as income, expenses, investments, and family structure. For example, the data collection unit can collect income information. For example, the data collection unit can collect expense information. For example, the data collection unit can collect investment information. This allows for the collection of more accurate tax strategies by collecting information about the user's detailed lifestyle and financial situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the collected information into AI, which can analyze the information to improve the accuracy of the collection.

[0036] The proposal unit can propose tax-saving measures such as hometown tax donations, medical expense deductions, and loss offsetting. For example, the proposal unit can propose hometown tax donations. For example, the proposal unit can propose medical expense deductions. For example, the proposal unit can propose loss offsetting. This allows the system to reduce the tax burden by proposing the most suitable tax-saving measures for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information provided by the collection unit into the AI, which can then propose the most suitable tax-saving measures.

[0037] The update department can collect the latest tax information from government websites and other sources. For example, the update department can collect the latest tax information from government websites. For example, the update department can collect new deductions and preferential treatments resulting from tax reforms. For example, the update department can collect the latest tax rate information. By collecting the latest tax information, it is possible to always propose the most up-to-date tax strategies. Some or all of the above-mentioned processes in the update department may be performed using AI, for example, or not using AI. For example, the update department can input information collected from government websites into AI, which can then analyze the latest tax information and reflect it in its proposals.

[0038] The guide unit can show users the steps they should take after receiving a suggestion. For example, the guide unit can show the procedure for making a hometown tax donation. For example, the guide unit can show the procedure for claiming medical expense deductions. For example, the guide unit can show the procedure for offsetting profits and losses. This makes it easier for users to take action by showing them the specific steps they need to take after receiving a suggestion. Some or all of the above processing in the guide unit may be performed using AI, for example, or not using AI. For example, the guide unit can input information provided by the suggestion unit into the AI, and the AI ​​can show the specific steps.

[0039] The security unit can securely protect user information. For example, the security unit can perform data encryption. For example, the security unit can perform access control. For example, the security unit can perform backups. This ensures that users can use the service with peace of mind by securely protecting their information. Some or all of the above processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input user information into AI, and the AI ​​can protect the information.

[0040] The data collection unit can analyze the user's past tax history and select the optimal information collection method. For example, the data collection unit can automatically extract and collect necessary information based on the tax information the user has previously filed. For example, the data collection unit can prioritize collecting items that frequently change from the user's past tax history. For example, the data collection unit can analyze the user's past tax history and focus on collecting information related to specific tax items. This enables efficient information collection by selecting the optimal information collection method based on past tax history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past tax history into AI, which can then select the optimal information collection method.

[0041] The data collection unit can filter data based on the user's current living situation and areas of interest during the collection process. For example, if a user has started a new job, the data collection unit will prioritize collecting tax information related to that job. For example, if a user has a family, the data collection unit will focus on collecting tax information related to the family structure. For example, if a user is interested in a particular investment, the data collection unit will collect tax information related to that investment. By filtering information based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's current living situation and areas of interest into an AI, which can then filter the information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of tax information related to that region. For example, if the user moves, the data collection unit will collect tax information related to the new region. For example, if the user lives overseas, the data collection unit will collect tax information for that country. This allows for the collection of more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant tax information based on information shared by the user on social media. For example, the data collection unit can collect tax information related to areas of interest from the user's social media activity. For example, the data collection unit can collect tax information related to events mentioned by the user on social media. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant information.

[0044] The proposal unit can adjust the level of detail in its proposals based on the importance of the tax strategies. For example, it might provide detailed explanations for important tax strategies, or concise proposals for less important ones. The proposal unit can also adjust the level of detail based on the user's level of interest. This allows for more effective proposals by adjusting the level of detail based on the importance of the tax strategies. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of tax strategies into the AI, which can then adjust the level of detail in the proposals.

[0045] The proposal unit can make optimal suggestions based on the user's past tax history. For example, the proposal unit can analyze the user's past tax history and propose the most suitable tax-saving measures. For example, the proposal unit can make new suggestions based on tax-saving measures the user has used in the past. For example, the proposal unit can propose the most effective tax-saving measures based on the user's past tax history. In this way, by making optimal suggestions based on the user's past tax history, more effective tax-saving measures can be proposed. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past tax history into AI, and the AI ​​can make optimal suggestions.

[0046] The proposal unit can propose the optimal tax strategy by considering the user's geographical location information. For example, if the user lives in a specific region, the proposal unit will propose a tax strategy relevant to that region. For example, if the user moves, the proposal unit will propose a tax strategy relevant to the new region. For example, if the user lives overseas, the proposal unit will propose a tax strategy for that country. This allows for the proposal of a more relevant tax strategy by considering the user's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's geographical location information into AI, which can then propose the optimal tax strategy.

[0047] The proposal unit can analyze the user's social media activity and propose relevant tax strategies when making a proposal. For example, the proposal unit can propose relevant tax strategies based on information shared by the user on social media. For example, the proposal unit can propose tax strategies related to areas of interest based on the user's social media activity. For example, the proposal unit can propose tax strategies related to events mentioned by the user on social media. This allows for the proposal of more relevant tax strategies by analyzing the user's social media activity. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's social media activity into AI, which can then propose relevant tax strategies.

[0048] The guiding unit can provide the optimal procedure by referring to the user's past behavior history during the guiding process. For example, the guiding unit can suggest the optimal procedure based on the user's past behavior history. For example, the guiding unit can suggest a new procedure based on the procedure the user has previously performed. For example, the guiding unit can suggest the most effective procedure from the user's past behavior history. In this way, by referring to the user's past behavior history, a more effective procedure can be provided. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's past behavior history into AI, and the AI ​​can provide the optimal procedure.

[0049] The guide unit can customize the procedures based on the user's current living situation during the guidance process. For example, if the user has started a new job, the guide unit will provide procedures related to that job. For example, if the user has a family, the guide unit will provide procedures related to the family structure. For example, if the user is interested in a particular investment, the guide unit will provide procedures related to that investment. By customizing the procedures based on the user's current living situation, more appropriate procedures can be provided. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's current living situation into the AI, which can then customize the procedures.

[0050] The guiding unit can provide the most appropriate steps while considering the user's geographical location. For example, if the user lives in a specific region, the guiding unit will provide steps relevant to that region. For example, if the user moves, the guiding unit will provide steps relevant to the new region. For example, if the user lives overseas, the guiding unit will provide steps for that country. This allows for the provision of more relevant steps by considering the user's geographical location. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's geographical location into AI, which can then provide the most appropriate steps.

[0051] The guiding unit can analyze the user's social media activity and provide relevant steps during the guiding process. For example, the guiding unit can provide relevant steps based on information the user has shared on social media. For example, the guiding unit can provide steps related to areas of interest from the user's social media activity. For example, the guiding unit can provide steps related to events the user has mentioned on social media. By analyzing the user's social media activity, it is possible to provide more relevant steps. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's social media activity into AI, and the AI ​​can provide relevant steps.

[0052] The update unit can optimize the latest tax information by referring to past tax information during the update process. For example, the update unit can optimize and provide the latest tax information based on past tax information. For example, the update unit can analyze trends in past tax reforms and predict and provide the latest tax information. For example, the update unit can refer to past tax information, compare it with the latest tax information, and provide it. In this way, by referring to past tax information, the latest tax information can be optimized and provided. Some or all of the above processing in the update unit may be performed using AI, for example, or without using AI. For example, the update unit can input past tax information into AI, and the AI ​​can optimize the latest tax information.

[0053] The update unit can provide optimal update information based on the user's past tax history at the time of update. For example, the update unit provides the latest tax information based on the user's past tax history. For example, the update unit provides new update information based on tax information previously used by the user. For example, the update unit provides the most effective update information from the user's past tax history. This makes it possible to provide more effective information by providing optimal update information based on the user's past tax history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's past tax history into AI, and the AI ​​can provide optimal update information.

[0054] The update unit can prioritize updating highly relevant information by considering the user's geographical location during the update process. For example, if the user lives in a specific region, the update unit will prioritize updating tax information related to that region. For example, if the user moves, the update unit will update tax information related to the new region. For example, if the user lives overseas, the update unit will update tax information for that country. This allows the system to provide more relevant information by considering the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location information into the AI, which can then prioritize updating highly relevant information.

[0055] The update unit can analyze the user's social media activity and update relevant information during the update process. For example, the update unit can update relevant tax information based on information shared by the user on social media. For example, the update unit can update tax information related to areas of interest based on the user's social media activity. For example, the update unit can update tax information related to events mentioned by the user on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media activity into AI, which can then update the relevant information.

[0056] The security department can provide optimal security measures by referring to the user's past security history. For example, the security department can provide optimal security measures based on the user's past security history. For example, the security department can provide new measures based on security measures the user has used in the past. For example, the security department can provide the most effective security measures from the user's past security history. In this way, more effective security measures can be provided by referring to the user's past security history. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's past security history into AI, and the AI ​​can provide optimal measures.

[0057] The security department can customize security measures based on the user's current living situation. For example, if a user starts a new job, the security department will provide security measures related to that job. For example, if a user has a family, the security department will provide security measures related to their family structure. For example, if a user is interested in a particular investment, the security department will provide security measures related to that investment. By customizing measures based on the user's current living situation, more appropriate security measures can be provided. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's current living situation into AI, and the AI ​​can customize the measures.

[0058] The security department can provide optimal security measures by considering the user's geographical location information. For example, if the user lives in a specific region, the security department will provide security measures relevant to that region. For example, if the user moves, the security department will provide security measures relevant to the new region. For example, if the user lives overseas, the security department will provide security measures for that country. By considering the user's geographical location information, the security department can provide more relevant security measures. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's geographical location information into AI, and the AI ​​can provide the optimal measures.

[0059] The security department can analyze users' social media activity and provide relevant security measures when implementing security measures. For example, the security department can provide relevant security measures based on information shared by users on social media. For example, the security department can provide security measures related to areas of interest based on users' social media activity. For example, the security department can provide security measures related to events mentioned by users on social media. By analyzing users' social media activity, the security department can provide more relevant security measures. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input users' social media activity into AI, and the AI ​​can provide relevant measures.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The Tax Sense system can analyze a user's past tax history and provide optimal tax advice. For example, it can suggest new tax-saving strategies based on the user's past tax-saving methods. It can also prioritize advice on frequently changing items based on past tax history. Furthermore, it can analyze past tax history and provide focused information on specific tax items. This allows for the provision of optimal advice based on past tax history, enabling the proposal of efficient tax strategies.

[0062] The Tax Sense system can provide highly relevant tax advice by taking into account the user's geographical location. For example, if a user lives in a specific region, it will prioritize providing tax information relevant to that region. Furthermore, if a user moves, it can provide tax information relevant to their new location. Additionally, if a user lives abroad, it can provide tax information relevant to that country. This allows for the provision of more relevant tax advice by considering the user's geographical location.

[0063] The Tax Sense system can analyze a user's social media activity and provide relevant tax advice. For example, it can provide relevant tax information based on information a user shares on social media. It can also provide tax information related to areas of interest based on the user's social media activity. Furthermore, it can provide tax information related to events that the user mentions on social media. This makes it possible to provide more relevant tax advice by analyzing a user's social media activity.

[0064] The Tax Sense system can customize tax advice based on the user's current circumstances and areas of interest. For example, if a user starts a new job, it can provide tax information related to that job. If the user has a family, it can provide tax information related to their family structure. Furthermore, if the user is interested in a particular investment, it can provide tax information related to that investment. This allows for more appropriate tax advice by tailoring it to the user's current circumstances and areas of interest.

[0065] The Tax Sense system can provide optimal tax advice by referencing the user's past activity history. For example, it can suggest the best advice based on the user's past activity history. It can also suggest new procedures based on the steps the user has taken in the past. Furthermore, it can suggest the most effective procedures based on the user's past activity history. This makes it possible to provide more effective tax advice by referring to the user's past activity history.

[0066] The Tax Sense system can provide highly relevant tax advice by taking into account the user's geographical location. For example, if a user lives in a specific region, it will prioritize providing tax information relevant to that region. Furthermore, if a user moves, it can provide tax information relevant to their new location. Additionally, if a user lives abroad, it can provide tax information relevant to that country. This allows for the provision of more relevant tax advice by considering the user's geographical location.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects information about the user's lifestyle and financial situation. Specifically, it collects information such as income, expenses, investments, and family structure. For example, it can collect income information, expense information, and investment information. Step 2: The proposal department proposes the most suitable tax strategy to the user based on the information collected by the data collection department. Specifically, they can propose options such as hometown tax donations, medical expense deductions, and loss offsetting. Step 3: The guide section outlines specific procedures based on the proposals submitted by the proposal section. Specifically, this can include procedures for hometown tax donations, medical expense deductions, and loss offsetting. Step 4: The update department collects information on the tax system, which changes every year, and makes proposals based on the latest tax laws. Specifically, they collect the latest tax information from the government's official website and gather information on new deductions and preferential treatments due to tax reforms, as well as the latest tax rates. Step 5: The security department protects user information. Specifically, this includes data encryption, access control, and backups.

[0069] (Example of form 2) The Tax Sense system according to an embodiment of the present invention is a comprehensive service that provides AI-powered tax advice. The Tax Sense system provides tax strategies tailored to the user's lifestyle and financial situation. The Tax Sense system collects information on the tax system, which changes every year, and proposes the most suitable tax-saving measures to the user, thereby minimizing the user's tax burden. For example, the Tax Sense system collects information on the user's lifestyle and financial situation. For example, it collects information such as income, expenses, investments, and family structure. This information is input into the AI. Next, based on the collected information, the AI ​​proposes the most suitable tax strategy to the user. For example, it proposes tax-saving measures such as hometown tax donations, medical expense deductions, and loss offsetting. This allows the user to learn about the most suitable tax-saving measures for them. Furthermore, the AI ​​collects information on the tax system, which changes every year, and makes proposals based on the latest tax laws. For example, it proposes new deductions and preferential treatments due to tax law revisions. This allows the user to learn about tax-saving measures that are in line with the latest tax laws. This mechanism minimizes the user's tax burden. For example, by implementing the tax-saving measures proposed by the AI, the user can reduce their tax burden. Furthermore, because the AI ​​provides suggestions based on the latest tax laws, users can always know the best tax-saving strategies. In this way, the Tax Sense system is a service that utilizes AI to propose the optimal tax strategy to users, minimizing their tax burden. As a result, the Tax Sense system can minimize the tax burden on users.

[0070] The Tax Sense system according to this embodiment comprises a collection unit, a suggestion unit, a guide unit, an update unit, and a security unit. The collection unit collects information about the user's lifestyle and financial situation. For example, the collection unit collects information such as income, expenses, investments, and family structure. For example, the collection unit can collect the user's income information. For example, the collection unit can collect the user's expense information. For example, the collection unit can collect the user's investment information. The suggestion unit proposes the optimal tax strategy to the user based on the information collected by the collection unit. For example, the suggestion unit can propose hometown tax donations. For example, the suggestion unit can propose medical expense deductions. For example, the suggestion unit can propose offsetting profits and losses. The guide unit shows specific procedures based on the content proposed by the suggestion unit. For example, the guide unit can show the procedures for hometown tax donations. For example, the guide unit can show the procedures for medical expense deductions. For example, the guide unit can show the procedures for offsetting profits and losses. The update unit collects information on the tax system that changes every year and makes suggestions based on the latest tax system. The update unit can, for example, collect the latest tax information from government websites. The update unit can, for example, collect new deductions and preferential treatments resulting from tax reforms. The update unit can, for example, collect the latest tax rate information. The security unit protects user information. The security unit can, for example, perform data encryption. The security unit can, for example, perform access control. The security unit can, for example, perform backups. As a result, the Tax Sense system according to this embodiment can provide an optimal tax strategy based on the user's lifestyle and economic situation, thereby minimizing the tax burden.

[0071] The data collection unit collects information about the user's lifestyle and financial situation. Specifically, it collects information such as income, expenses, investments, and family structure. Income information includes pay stubs, bank account transaction history, and income from freelance work or side jobs. This information can be entered manually by the user, but it can also be automatically retrieved from banks and payroll systems via APIs. Expense information includes credit card statements, e-money usage history, and cash expenditure records. This data is automatically collected by linking with budgeting apps and expense management tools. Investment information includes portfolio information such as stocks, bonds, mutual funds, and real estate investments. This information can be obtained from securities companies and investment platforms via APIs. Family structure information includes the number and ages of dependents and the spouse's income, and this information is entered manually by the user. The data collection unit centrally manages this diverse information and stores it in a database. Furthermore, the data collection unit has a function to check the consistency of the collected data, detect missing or inaccurate information, and notify the user. This allows the data collection unit to accurately understand the user's financial situation and provide a foundation for subsequent proposal and guidance units to make optimal suggestions and procedures.

[0072] The Proposal Department proposes the most suitable tax strategy to users based on information collected by the Data Collection Department. Specifically, it proposes tax strategies such as hometown tax donations, medical expense deductions, and loss offsetting. For hometown tax donations, it calculates the optimal donation amount based on the user's income and family structure and presents options for recipient municipalities and return gifts. For medical expense deductions, it analyzes the user's medical expenses and identifies deductible expenses. Furthermore, it provides specific information on the documents and procedures required to apply for medical expense deductions. For loss offsetting, it analyzes the user's investment portfolio and proposes methods to reduce the tax burden by combining investments that are incurring losses with investments that are generating profits. The Proposal Department uses AI to analyze collected data and automatically generates the most suitable tax strategy for the user's economic situation and lifestyle. For example, the AI ​​predicts the user's future income and expenses based on past data and statistical information and proposes the optimal tax strategy based on that. The Proposal Department also has a function to collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Proposal Department can provide users with the most suitable tax strategy and minimize their tax burden.

[0073] The Guide Department provides specific procedures based on the proposals made by the Proposal Department. Specifically, it outlines procedures for hometown tax donations, medical expense deductions, and loss offsetting. For hometown tax donations, it provides detailed guidance on how to select a recipient municipality, how to pay donations, and how to obtain donation certificates. For medical expense deductions, it provides specific guidance on how to organize receipts for eligible medical expenses, how to fill out application forms, and how to submit them to the tax office. For loss offsetting, it provides detailed guidance on how to combine investments that have incurred losses with investments that have generated profits, how to fill out loss offsetting application forms, and how to submit them to the tax office. The Guide Department provides step-by-step guides and checklists to help users proceed smoothly through the procedures. The Guide Department also has a function to automatically generate and provide users with the necessary documents and information when they perform the procedures. Furthermore, the Guide Department has a chatbot function that provides real-time support for questions and problems that users may encounter when performing the procedures. This allows the Guide Department to smoothly guide users through the specific procedures required to implement the proposed tax strategy.

[0074] The Update Department collects information on the tax system, which changes annually, and makes recommendations based on the latest tax laws. Specifically, it collects the latest tax information from the government's official website, including new deductions and preferential treatments resulting from tax reforms, and the latest tax rate information. The Update Department automatically collects this information and stores it in a database. Furthermore, the Update Department has the function to update the recommendations and procedures provided by the Recommendations Department and Guide Department based on the latest collected tax information. For example, if a new deduction is introduced due to a tax reform, the Update Department collects that information so that the Recommendations Department can propose the new deduction to the user. Also, if the tax rate changes, the Update Department collects that information and updates the procedure guides provided by the Guide Department based on the latest tax rate. The Update Department also has the function to analyze the collected information using AI and predict the impact of tax reforms. As a result, the Update Department can always provide users with recommendations and procedures based on the latest tax information, minimizing their tax burden.

[0075] The Security Department protects user information. Specifically, it implements security measures such as data encryption, access control, and backups. For data encryption, strong encryption algorithms are used when storing user information to prevent unauthorized access and data leaks. For access control, the department strictly manages the permissions to access user information, ensuring that only users with the necessary permissions can access the information. For backups, data is backed up regularly to prepare for data loss or corruption. Furthermore, the Security Department develops incident response plans to respond quickly in the event of a security incident and conducts regular training. The Security Department constantly incorporates the latest security technologies and best practices to continuously strengthen the security of the entire system. In addition, the Security Department provides security education and awareness activities to users to help them raise their own security awareness. In this way, the Security Department can safely protect user information and improve the reliability and security of the entire system.

[0076] The data collection unit can collect information such as income, expenses, investments, and family structure. For example, the data collection unit can collect income information. For example, the data collection unit can collect expense information. For example, the data collection unit can collect investment information. This allows for the collection of more accurate tax strategies by collecting information about the user's detailed lifestyle and financial situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the collected information into AI, which can analyze the information to improve the accuracy of the collection.

[0077] The proposal unit can propose tax-saving measures such as hometown tax donations, medical expense deductions, and loss offsetting. For example, the proposal unit can propose hometown tax donations. For example, the proposal unit can propose medical expense deductions. For example, the proposal unit can propose loss offsetting. This allows the system to reduce the tax burden by proposing the most suitable tax-saving measures for the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input information provided by the collection unit into the AI, which can then propose the most suitable tax-saving measures.

[0078] The update department can collect the latest tax information from government websites and other sources. For example, the update department can collect the latest tax information from government websites. For example, the update department can collect new deductions and preferential treatments resulting from tax reforms. For example, the update department can collect the latest tax rate information. By collecting the latest tax information, it is possible to always propose the most up-to-date tax strategies. Some or all of the above-mentioned processes in the update department may be performed using AI, for example, or not using AI. For example, the update department can input information collected from government websites into AI, which can then analyze the latest tax information and reflect it in its proposals.

[0079] The guide unit can show users the steps they should take after receiving a suggestion. For example, the guide unit can show the procedure for making a hometown tax donation. For example, the guide unit can show the procedure for claiming medical expense deductions. For example, the guide unit can show the procedure for offsetting profits and losses. This makes it easier for users to take action by showing them the specific steps they need to take after receiving a suggestion. Some or all of the above processing in the guide unit may be performed using AI, for example, or not using AI. For example, the guide unit can input information provided by the suggestion unit into the AI, and the AI ​​can show the specific steps.

[0080] The security unit can securely protect user information. For example, the security unit can perform data encryption. For example, the security unit can perform access control. For example, the security unit can perform backups. This ensures that users can use the service with peace of mind by securely protecting their information. Some or all of the above processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input user information into AI, and the AI ​​can protect the information.

[0081] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting basic information such as income and expenses, and postpone collecting detailed information. For example, if the user is relaxed, the data collection unit will actively collect detailed information such as investments and family structure. For example, if the user is in a hurry, the data collection unit will quickly collect important information such as income and expenses, and supplement it with detailed information later. This allows for more appropriate information collection by adjusting the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of information.

[0082] The data collection unit can analyze the user's past tax history and select the optimal information collection method. For example, the data collection unit can automatically extract and collect necessary information based on the tax information the user has previously filed. For example, the data collection unit can prioritize collecting items that frequently change from the user's past tax history. For example, the data collection unit can analyze the user's past tax history and focus on collecting information related to specific tax items. This enables efficient information collection by selecting the optimal information collection method based on past tax history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past tax history into AI, which can then select the optimal information collection method.

[0083] The data collection unit can filter data based on the user's current living situation and areas of interest during the collection process. For example, if a user has started a new job, the data collection unit will prioritize collecting tax information related to that job. For example, if a user has a family, the data collection unit will focus on collecting tax information related to the family structure. For example, if a user is interested in a particular investment, the data collection unit will collect tax information related to that investment. By filtering information based on the user's current living situation and areas of interest, more relevant information can be collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's current living situation and areas of interest into an AI, which can then filter the information.

[0084] The data collection unit can estimate the user's emotions and adjust the level of detail of the information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect only basic information and postpone detailed information. For example, if the user is relaxed, the data collection unit will actively collect detailed information as well. For example, if the user is in a hurry, the data collection unit will quickly collect important information and supplement it with detailed information later. This allows for more appropriate information collection by adjusting the level of detail of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the level of detail of the information.

[0085] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of tax information related to that region. For example, if the user moves, the data collection unit will collect tax information related to the new region. For example, if the user lives overseas, the data collection unit will collect tax information for that country. This allows for the collection of more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant information.

[0086] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant tax information based on information shared by the user on social media. For example, the data collection unit can collect tax information related to areas of interest from the user's social media activity. For example, the data collection unit can collect tax information related to events mentioned by the user on social media. This allows for the collection of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant information.

[0087] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit will provide suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit will provide concise and quick suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the way suggestions are presented.

[0088] The proposal unit can adjust the level of detail in its proposals based on the importance of the tax strategies. For example, it might provide detailed explanations for important tax strategies, or concise proposals for less important ones. The proposal unit can also adjust the level of detail based on the user's level of interest. This allows for more effective proposals by adjusting the level of detail based on the importance of the tax strategies. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of tax strategies into the AI, which can then adjust the level of detail in the proposals.

[0089] The proposal unit can make optimal suggestions based on the user's past tax history. For example, the proposal unit can analyze the user's past tax history and propose the most suitable tax-saving measures. For example, the proposal unit can make new suggestions based on tax-saving measures the user has used in the past. For example, the proposal unit can propose the most effective tax-saving measures based on the user's past tax history. In this way, by making optimal suggestions based on the user's past tax history, more effective tax-saving measures can be proposed. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past tax history into AI, and the AI ​​can make optimal suggestions.

[0090] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. For example, if the user is relaxed, the suggestion unit will prioritize detailed suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize suggestions that can be acted upon quickly. By adjusting the priority of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of suggestions.

[0091] The proposal unit can propose the optimal tax strategy by considering the user's geographical location information. For example, if the user lives in a specific region, the proposal unit will propose a tax strategy relevant to that region. For example, if the user moves, the proposal unit will propose a tax strategy relevant to the new region. For example, if the user lives overseas, the proposal unit will propose a tax strategy for that country. This allows for the proposal of a more relevant tax strategy by considering the user's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's geographical location information into AI, which can then propose the optimal tax strategy.

[0092] The proposal unit can analyze the user's social media activity and propose relevant tax strategies when making a proposal. For example, the proposal unit can propose relevant tax strategies based on information shared by the user on social media. For example, the proposal unit can propose tax strategies related to areas of interest based on the user's social media activity. For example, the proposal unit can propose tax strategies related to events mentioned by the user on social media. This allows for the proposal of more relevant tax strategies by analyzing the user's social media activity. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the user's social media activity into AI, which can then propose relevant tax strategies.

[0093] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated emotions. For example, if the user is stressed, the guide unit provides a simple and easy-to-understand guide. For example, if the user is relaxed, the guide unit provides a guide that includes detailed explanations. For example, if the user is in a hurry, the guide unit provides a concise and quick guide. By adjusting the way the guide is presented according to the user's emotions, a more appropriate guide becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI, for example, or not using AI. For example, the guide unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way the guide is presented.

[0094] The guiding unit can provide the optimal procedure by referring to the user's past behavior history during the guiding process. For example, the guiding unit can suggest the optimal procedure based on the user's past behavior history. For example, the guiding unit can suggest a new procedure based on the procedure the user has previously performed. For example, the guiding unit can suggest the most effective procedure from the user's past behavior history. In this way, by referring to the user's past behavior history, a more effective procedure can be provided. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's past behavior history into AI, and the AI ​​can provide the optimal procedure.

[0095] The guide unit can customize the procedures based on the user's current living situation during the guidance process. For example, if the user has started a new job, the guide unit will provide procedures related to that job. For example, if the user has a family, the guide unit will provide procedures related to the family structure. For example, if the user is interested in a particular investment, the guide unit will provide procedures related to that investment. By customizing the procedures based on the user's current living situation, more appropriate procedures can be provided. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's current living situation into the AI, which can then customize the procedures.

[0096] The guide unit can estimate the user's emotions and determine the priority of the guide based on the estimated emotions. For example, if the user is stressed, the guide unit will prioritize providing important steps. For example, if the user is relaxed, the guide unit will prioritize providing detailed steps. For example, if the user is in a hurry, the guide unit will prioritize providing steps that can be performed quickly. By adjusting the priority of the guide according to the user's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not using AI. For example, the guide unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the guide.

[0097] The guiding unit can provide the most appropriate steps while considering the user's geographical location. For example, if the user lives in a specific region, the guiding unit will provide steps relevant to that region. For example, if the user moves, the guiding unit will provide steps relevant to the new region. For example, if the user lives overseas, the guiding unit will provide steps for that country. This allows for the provision of more relevant steps by considering the user's geographical location. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's geographical location into AI, which can then provide the most appropriate steps.

[0098] The guiding unit can analyze the user's social media activity and provide relevant steps during the guiding process. For example, the guiding unit can provide relevant steps based on information the user has shared on social media. For example, the guiding unit can provide steps related to areas of interest from the user's social media activity. For example, the guiding unit can provide steps related to events the user has mentioned on social media. By analyzing the user's social media activity, it is possible to provide more relevant steps. Some or all of the above processing in the guiding unit may be performed using AI, for example, or without AI. For example, the guiding unit can input the user's social media activity into AI, and the AI ​​can provide relevant steps.

[0099] The update unit can estimate the user's emotions and prioritize update information based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize providing important updates. For example, if the user is relaxed, the update unit will prioritize providing detailed updates. For example, if the user is in a hurry, the update unit will prioritize providing updates that can be performed quickly. By adjusting the priority of update information according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of update information.

[0100] The update unit can optimize the latest tax information by referring to past tax information during the update process. For example, the update unit can optimize and provide the latest tax information based on past tax information. For example, the update unit can analyze trends in past tax reforms and predict and provide the latest tax information. For example, the update unit can refer to past tax information, compare it with the latest tax information, and provide it. In this way, by referring to past tax information, the latest tax information can be optimized and provided. Some or all of the above processing in the update unit may be performed using AI, for example, or without using AI. For example, the update unit can input past tax information into AI, and the AI ​​can optimize the latest tax information.

[0101] The update unit can provide optimal update information based on the user's past tax history at the time of update. For example, the update unit provides the latest tax information based on the user's past tax history. For example, the update unit provides new update information based on tax information previously used by the user. For example, the update unit provides the most effective update information from the user's past tax history. This makes it possible to provide more effective information by providing optimal update information based on the user's past tax history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's past tax history into AI, and the AI ​​can provide optimal update information.

[0102] The update unit can estimate the user's emotions and adjust the level of detail in the update information based on the estimated emotions. For example, if the user is stressed, the update unit will provide only basic update information. For example, if the user is relaxed, the update unit will provide detailed update information. For example, if the user is in a hurry, the update unit will quickly provide important update information. This allows for more appropriate information to be provided by adjusting the level of detail in the update information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the level of detail in the update information.

[0103] The update unit can prioritize updating highly relevant information by considering the user's geographical location during the update process. For example, if the user lives in a specific region, the update unit will prioritize updating tax information related to that region. For example, if the user moves, the update unit will update tax information related to the new region. For example, if the user lives overseas, the update unit will update tax information for that country. This allows the system to provide more relevant information by considering the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location information into the AI, which can then prioritize updating highly relevant information.

[0104] The update unit can analyze the user's social media activity and update relevant information during the update process. For example, the update unit can update relevant tax information based on information shared by the user on social media. For example, the update unit can update tax information related to areas of interest based on the user's social media activity. For example, the update unit can update tax information related to events mentioned by the user on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media activity into AI, which can then update the relevant information.

[0105] The security unit can estimate the user's emotions and prioritize security measures based on those emotions. For example, if the user is stressed, the security unit will prioritize providing critical security measures. If the user is relaxed, the security unit will prioritize providing detailed security measures. If the user is in a hurry, the security unit will prioritize providing security measures that can be implemented quickly. By adjusting the priority of security measures according to the user's emotions, more appropriate security measures can be implemented. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not. For example, the security unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of security measures.

[0106] The security department can provide optimal security measures by referring to the user's past security history. For example, the security department can provide optimal security measures based on the user's past security history. For example, the security department can provide new measures based on security measures the user has used in the past. For example, the security department can provide the most effective security measures from the user's past security history. In this way, more effective security measures can be provided by referring to the user's past security history. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's past security history into AI, and the AI ​​can provide optimal measures.

[0107] The security department can customize security measures based on the user's current living situation. For example, if a user starts a new job, the security department will provide security measures related to that job. For example, if a user has a family, the security department will provide security measures related to their family structure. For example, if a user is interested in a particular investment, the security department will provide security measures related to that investment. By customizing measures based on the user's current living situation, more appropriate security measures can be provided. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's current living situation into AI, and the AI ​​can customize the measures.

[0108] The security unit can estimate the user's emotions and adjust the level of detail of security measures based on the estimated emotions. For example, if the user is stressed, the security unit will provide only basic security measures. For example, if the user is relaxed, the security unit will provide detailed security measures. For example, if the user is in a hurry, the security unit will quickly provide critical security measures. This allows for more appropriate security measures by adjusting the level of detail of security measures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not using AI. For example, the security unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the level of detail of security measures.

[0109] The security department can provide optimal security measures by considering the user's geographical location information. For example, if the user lives in a specific region, the security department will provide security measures relevant to that region. For example, if the user moves, the security department will provide security measures relevant to the new region. For example, if the user lives overseas, the security department will provide security measures for that country. By considering the user's geographical location information, the security department can provide more relevant security measures. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's geographical location information into AI, and the AI ​​can provide the optimal measures.

[0110] The security department can analyze users' social media activity and provide relevant security measures when implementing security measures. For example, the security department can provide relevant security measures based on information shared by users on social media. For example, the security department can provide security measures related to areas of interest based on users' social media activity. For example, the security department can provide security measures related to events mentioned by users on social media. By analyzing users' social media activity, the security department can provide more relevant security measures. Some or all of the above processes in the security department may be performed using AI, for example, or not using AI. For example, the security department can input users' social media activity into AI, and the AI ​​can provide relevant measures.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The Tax Sense system can estimate a user's emotions and adjust the timing of tax advice based on those emotions. For example, if a user is stressed, advice will be withheld, while advice will be provided when the user is relaxed. Similarly, if a user is in a hurry, concise and quick advice will be provided, while more detailed advice will be offered when the user has ample time. This allows for advice to be delivered at the optimal time according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. For example, user emotion data can be input into the generative AI, which can then estimate the emotion and adjust the timing of advice.

[0113] The Tax Sense system can analyze a user's past tax history and provide optimal tax advice. For example, it can suggest new tax-saving strategies based on the user's past tax-saving methods. It can also prioritize advice on frequently changing items based on past tax history. Furthermore, it can analyze past tax history and provide focused information on specific tax items. This allows for the provision of optimal advice based on past tax history, enabling the proposal of efficient tax strategies.

[0114] The Tax Sense system can provide highly relevant tax advice by taking into account the user's geographical location. For example, if a user lives in a specific region, it will prioritize providing tax information relevant to that region. Furthermore, if a user moves, it can provide tax information relevant to their new location. Additionally, if a user lives abroad, it can provide tax information relevant to that country. This allows for the provision of more relevant tax advice by considering the user's geographical location.

[0115] The Tax Sense system can analyze a user's social media activity and provide relevant tax advice. For example, it can provide relevant tax information based on information a user shares on social media. It can also provide tax information related to areas of interest based on the user's social media activity. Furthermore, it can provide tax information related to events that the user mentions on social media. This makes it possible to provide more relevant tax advice by analyzing a user's social media activity.

[0116] The Tax Sense system can estimate a user's emotions and adjust the way tax advice is presented based on those emotions. For example, if a user is stressed, it can provide simple and easy-to-understand advice. If a user is relaxed, it can provide advice with detailed explanations. Furthermore, if a user is in a hurry, it can provide concise and quick advice. This allows for more appropriate tax advice to be provided by adjusting the presentation of advice according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. For example, user emotion data can be input into the generative AI, which can estimate the emotion and adjust the presentation of the advice accordingly.

[0117] The Tax Sense system can customize tax advice based on the user's current circumstances and areas of interest. For example, if a user starts a new job, it can provide tax information related to that job. If the user has a family, it can provide tax information related to their family structure. Furthermore, if the user is interested in a particular investment, it can provide tax information related to that investment. This allows for more appropriate tax advice by tailoring it to the user's current circumstances and areas of interest.

[0118] The Tax Sense system can estimate a user's emotions and prioritize tax advice based on those emotions. For example, if a user is stressed, it can prioritize important advice. If a user is relaxed, it can prioritize detailed advice. Furthermore, if a user is in a hurry, it can prioritize advice that can be acted upon quickly. This allows for more appropriate tax advice by adjusting the priority of advice according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. For example, user emotion data can be input into a generative AI, which can estimate emotions and determine the priority of advice.

[0119] The Tax Sense system can provide optimal tax advice by referencing the user's past activity history. For example, it can suggest the best advice based on the user's past activity history. It can also suggest new procedures based on the steps the user has taken in the past. Furthermore, it can suggest the most effective procedures based on the user's past activity history. This makes it possible to provide more effective tax advice by referring to the user's past activity history.

[0120] The Tax Sense system can estimate a user's emotions and adjust the level of detail in tax advice based on that estimation. For example, if a user is stressed, it will provide only basic information, delaying detailed information. Conversely, if a user is relaxed, it can proactively provide detailed information. Furthermore, if a user is in a hurry, it can quickly provide important information, supplementing it with detailed information later. This allows for more appropriate tax advice by adjusting the level of detail according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. For example, user emotion data can be input into the generative AI, which can estimate the emotion and adjust the level of detail in the advice.

[0121] The Tax Sense system can provide highly relevant tax advice by taking into account the user's geographical location. For example, if a user lives in a specific region, it will prioritize providing tax information relevant to that region. Furthermore, if a user moves, it can provide tax information relevant to their new location. Additionally, if a user lives abroad, it can provide tax information relevant to that country. This allows for the provision of more relevant tax advice by considering the user's geographical location.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects information about the user's lifestyle and financial situation. Specifically, it collects information such as income, expenses, investments, and family structure. For example, it can collect income information, expense information, and investment information. Step 2: The proposal department proposes the most suitable tax strategy to the user based on the information collected by the data collection department. Specifically, they can propose options such as hometown tax donations, medical expense deductions, and loss offsetting. Step 3: The guide section outlines specific procedures based on the proposals submitted by the proposal section. Specifically, this can include procedures for hometown tax donations, medical expense deductions, and loss offsetting. Step 4: The update department collects information on the tax system, which changes every year, and makes proposals based on the latest tax laws. Specifically, they collect the latest tax information from the government's official website and gather information on new deductions and preferential treatments due to tax reforms, as well as the latest tax rates. Step 5: The security department protects user information. Specifically, this includes data encryption, access control, and backups.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, proposal unit, guide unit, update unit, and security unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information about the user's lifestyle and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal tax strategy based on the collected information. The guide unit is implemented by the control unit 46A of the smart device 14 and shows specific procedures based on the proposed content. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the latest tax information and updates the proposal. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's information. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, proposal unit, guide unit, update unit, and security unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information about the user's lifestyle and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal tax strategy based on the collected information. The guide unit is implemented by the control unit 46A of the smart glasses 214 and shows specific procedures based on the proposed content. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the latest tax information and updates the proposal. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's information. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, proposal unit, guide unit, update unit, and security unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information about the user's lifestyle and economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal tax strategy based on the collected information. The guide unit is implemented by the control unit 46A of the headset terminal 314 and shows specific procedures based on the proposed content. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the latest tax information and updates the proposal. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's information. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, proposal unit, guide unit, update unit, and security unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information about the user's lifestyle and economic situation. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal tax strategy based on the collected information. The guide unit is implemented by, for example, the control unit 46A of the robot 414 and shows specific procedures based on the proposed content. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects the latest tax information and updates the proposal. The security unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and protects the user's information. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection unit that collects information about the user's lifestyle and economic situation, Based on the information collected by the aforementioned collection unit, a proposal unit proposes the most suitable tax strategy to the user. A guide section that shows specific procedures based on the content proposed by the aforementioned proposal section, The update department collects information on the tax system, which changes every year, and makes proposals based on the latest tax laws. It includes a security unit that protects user information. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as income, expenses, investments, and family structure. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose tax-saving strategies such as hometown tax donations, medical expense deductions, and loss offsetting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update unit is, Gather the latest tax information from government websites and other sources. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide portion is This outlines the steps a user should take after receiving a suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned security unit is Securely protect user information The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past tax history and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the level of detail of the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the tax strategy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, we will provide the most suitable suggestions based on the user's past tax history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we will suggest the optimal tax strategy taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant tax strategies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide portion is The system estimates the user's emotions and adjusts the way the guide is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide portion is During the guidance process, the system provides the optimal steps by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide portion is During the guidance process, the steps are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide portion is It estimates the user's emotions and determines the priority of the guide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide portion is When providing guidance, the optimal steps are provided, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide portion is During the guidance process, we analyze the user's social media activity and provide relevant steps. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is, It estimates user sentiment and prioritizes update information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is, During updates, the system optimizes the latest tax information by referencing past tax data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is, When updating, the system provides the most relevant update information based on the user's past tax history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is, It estimates the user's sentiment and adjusts the level of detail in the update information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is, During updates, the system prioritizes updating highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is, When updating, we analyze users' social media activity and update relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned security unit is It estimates user sentiment and prioritizes security measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned security unit is When implementing security measures, we refer to the user's past security history to provide the most appropriate solutions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned security unit is When implementing security measures, customize the measures based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned security unit is It estimates user sentiment and adjusts the level of detail of security measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned security unit is When implementing security measures, we provide optimal solutions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned security unit is During security measures, we analyze users' social media activity and provide relevant countermeasures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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. A collection unit that collects information about the user's lifestyle and economic situation, Based on the information collected by the aforementioned collection unit, a proposal unit proposes the most suitable tax strategy to the user. A guide section that shows specific procedures based on the content proposed by the aforementioned proposal section, The update department collects information on the tax system, which changes every year, and makes proposals based on the latest tax laws. It includes a security unit that protects user information. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as income, expenses, investments, and family structure. The system according to feature 1.

3. The aforementioned proposal section is, We propose tax-saving strategies such as hometown tax donations, medical expense deductions, and loss offsetting. The system according to feature 1.

4. The aforementioned update unit is Gather the latest tax information from government websites and other sources. The system according to feature 1.

5. The aforementioned guide section is This outlines the steps a user should take after receiving a suggestion. The system according to feature 1.

6. The aforementioned security unit is Securely protect user information The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past tax history and select the most suitable information gathering method. The system according to feature 1.

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