system

Abstract

The system according to this embodiment aims to propose the most suitable tax-saving measures to the user. [Solution] The system according to the embodiment comprises a collection unit, a reception unit, an analysis unit, a proposal unit, and a reminder unit. The collection unit collects tax information. The reception unit inputs user information. The analysis unit analyzes the information collected by the collection unit and the reception unit. The proposal unit proposes tax-saving measures based on the analysis results obtained by the analysis unit. The reminder unit sends reminders based on the tax-saving measures proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently collect tax system information that changes every year and propose an optimal tax-saving strategy to users.

[0005] [[ID=??]] The system according to the embodiment aims to propose an optimal tax-saving strategy to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a reception unit, an analysis unit, a proposal unit, and a reminder unit. The collection unit collects tax information. The reception unit inputs user information. The analysis unit analyzes the information collected by the collection unit and the reception unit. The proposal unit proposes tax-saving measures based on the analysis results obtained by the analysis unit. The reminder unit sends reminders based on the tax-saving measures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose the most suitable tax-saving measures to the user. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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 personalized tax advisor system according to an embodiment of the present invention is a system that provides a tax strategy tailored to the user's lifestyle and economic situation. This system utilizes an AI agent to collect 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 personalized tax advisor system first uses an AI agent to collect information on the tax system, which changes every year. In this process, it collects the tax reform outline and changes in related laws announced by the Ministry of Finance to grasp the latest tax information. For example, it collects information on changes to medical expense deductions and gift tax. Next, the personalized tax advisor system inputs information on the user's lifestyle and economic situation. Specifically, it inputs information such as income, expenses, investments, and family structure. This allows for a detailed understanding of the user's economic situation. For example, it considers the income deduction system available when the user's annual medical expenses exceed 100,000 yen, and changes to the gift tax exemption limit. Next, the personalized tax advisor system analyzes the collected tax information and the user's economic situation. Based on the analysis results, it proposes the most suitable tax-saving measures to the user. For example, it proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. Furthermore, the personalized tax advisor system also determines whether a tax return is required, accounts for expenses, and calculates estimated tax payments. In addition, the personalized tax advisor system collects tax law change information in real time and sends reminders to users to file tax returns or take tax-saving measures. This allows users to implement tax-saving strategies that are in line with the latest tax laws. This system enables users to implement individually optimized tax-saving measures without spending time and effort. For example, it comprehensively covers changes in medical expense deductions and gift tax exemptions, minimizing the user's tax burden. Moreover, the personalized tax advisor system automatically organizes documents and tracks expenses, allowing users to implement tax-saving measures without effort. This further minimizes the user's tax burden.

[0029] The personalized tax advisor system according to this embodiment comprises a collection unit, a reception unit, an analysis unit, a proposal unit, and a reminder unit. The collection unit collects tax information. For example, the collection unit collects changes to the tax reform outline and related laws announced by the Ministry of Finance. The collection unit can also collect changes to medical expense deductions and gift tax. The collection unit can automatically collect the latest tax information using AI. The reception unit inputs user information. For example, the reception unit inputs information such as income, expenses, investments, and family structure. The reception unit can input information to understand the user's financial situation in detail. The reception unit can automatically input user information using AI. The analysis unit analyzes the information collected by the collection unit and the reception unit. For example, the analysis unit analyzes the collected tax information and the user's financial situation. The analysis unit can automatically analyze the collected information using AI. The proposal unit proposes tax-saving measures based on the analysis results obtained by the analysis unit. The proposal unit proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. The proposal unit can automatically propose the most suitable tax-saving measures using AI. The reminder unit sends reminders based on the tax-saving measures proposed by the proposal unit. The reminder unit can collect information on tax law changes in real time and send reminders to users to file tax returns or take tax-saving procedures. The reminder unit can automatically send reminders using AI. As a result, the personalized tax advisor system according to this embodiment can minimize the user's tax burden.

[0030] The data collection unit collects tax information. For example, it collects changes to tax outlines and related laws published by the Ministry of Finance. Specifically, the data collection unit uses crawlers to obtain the latest tax information from the Ministry of Finance's official website and the websites of related government agencies. This allows for real-time collection of information on revisions to tax outlines and the implementation of new laws. The data collection unit can also collect information on changes to medical expense deductions and gift tax. For example, it collects important tax change information for users, such as changes to the scope and amount of medical expenses eligible for medical expense deductions, and changes to the gift tax exemption limit. The data collection unit can use AI to automatically collect the latest tax information. The AI ​​uses natural language processing technology to analyze the collected information and extract important changes. For example, the AI ​​extracts keywords from tax outline documents and identifies changes. The AI ​​also organizes the collected information into a database, making it accessible to other departments. This allows the data collection unit to efficiently and accurately collect the latest tax information and improve the overall system performance. Furthermore, the data collection unit can regularly update the collected information, ensuring that users always have the most up-to-date information. This allows users to take appropriate tax measures based on the latest tax information.

[0031] The reception desk inputs user information. For example, it inputs information such as income, expenses, investments, and family structure. Specifically, users input detailed information such as their sources of income, annual income, various expense items, types and amounts of investments, and family structure (including whether they have a spouse or dependents) through a dedicated input form. The reception desk can input information to gain a detailed understanding of the user's financial situation. For example, it collects tax-important information such as the user's occupation and workplace, sources of income other than salary (such as real estate income or side job income), annual medical and education expenses, and whether they have a mortgage. The reception desk can use AI to automatically input user information. The AI ​​supplements the input based on information previously entered by the user and data obtained from other systems. For example, the AI ​​analyzes the user's past tax return data and transaction history provided by financial institutions to automatically input income and expense items. The AI ​​also checks the consistency of the information entered by the user and prompts for corrections if there are errors or deficiencies. This allows the reception desk to efficiently and accurately collect user information and improve the overall system performance. Furthermore, the reception department will securely manage the collected information and implement measures to protect privacy. This allows users to confidently enter their financial situation and receive appropriate tax advice.

[0032] The Analysis Department analyzes information collected by the Collection Department and the Reception Department. For example, the Analysis Department analyzes collected tax information against the user's financial situation. Specifically, the Analysis Department compares the latest tax information provided by the Collection Department with the user's financial situation provided by the Reception Department to identify applicable tax changes and tax-saving opportunities for the user. The Analysis Department can use AI to automatically analyze the collected information. The AI ​​uses machine learning algorithms to analyze the collected data and propose the most suitable tax measures for each user. For example, the AI ​​analyzes the user's income and expenditure patterns to identify tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. The AI ​​also predicts future tax risks based on historical data and statistical information and takes appropriate measures. This allows the Analysis Department to quickly and accurately analyze collected data and provide users with optimal tax advice. Furthermore, the Analysis Department stores the analysis results in a database, making them accessible to other departments. This allows the Proposal Department and Reminder Department to provide users with appropriate advice and reminders based on the analysis results. This allows the analysis unit to improve the overall system performance and minimize the tax burden on users.

[0033] The Proposal Department proposes tax-saving measures based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. Specifically, the Proposal Department proposes the most suitable tax-saving measures for the user based on the analysis results provided by the Analysis Department. For example, if a user has high annual medical expenses, the Proposal Department proposes a method to reduce the income tax burden by utilizing medical expense deductions. It also proposes a method to receive donation deductions by utilizing hometown tax donations. Furthermore, if a user has investment losses, the Proposal Department proposes a method to offset them against other income using loss offsetting. The Proposal Department can automatically propose the most suitable tax-saving measures using AI. The AI ​​analyzes the user's economic situation and tax information to identify the most effective tax-saving measures. For example, the AI ​​analyzes the user's income and expenditure patterns and proposes the most suitable tax-saving measures. The AI ​​also predicts future tax risks based on past data and statistical information and takes appropriate measures. This allows the Proposal Department to provide users with optimal tax advice and minimize their tax burden. Furthermore, the proposal department stores the proposals in a database, making them accessible to other departments. This allows the reminder department to provide users with appropriate reminders based on the proposals. As a result, the proposal department can improve the overall system performance and minimize the tax burden on users.

[0034] The Reminders Department sends reminders based on tax-saving measures proposed by the Proposal Department. For example, the Reminders Department collects tax law change information in real time and sends reminders to users to file tax returns or take tax-saving procedures. Specifically, the Reminders Department sends reminders to users at the appropriate time based on tax-saving measures provided by the Proposal Department. For example, when the tax return deadline is approaching, it sends a reminder to the user to file their tax return. Also, when the deadline for making a donation to the Furusato Nozei (hometown tax) program is approaching, it sends a reminder to the user to make a donation. The Reminders Department can automatically send reminders using AI. The AI ​​analyzes the user's schedule and past behavior patterns and sends reminders at the optimal time. For example, the AI ​​analyzes when and how often the user has filed tax returns in the past and sends reminders at the optimal time. The AI ​​also analyzes the user's behavior patterns and optimizes the content and method of sending reminders. This allows the reminder unit to send reminders to users at the appropriate time, facilitating smooth tax procedures. Furthermore, the reminder unit stores the reminder transmission history in a database, making it accessible to other departments. This enables the proposal and analysis departments to provide users with appropriate advice and suggestions based on the reminder transmission history. As a result, the reminder unit can improve the overall system performance and minimize the tax burden on users.

[0035] The analysis unit can analyze collected tax information and the user's financial situation. For example, the analysis unit can analyze collected tax information and the user's financial situation. The analysis unit can use AI to automatically analyze the collected information. This allows it to propose optimal tax-saving strategies by analyzing the collected tax information and the user's financial situation.

[0036] The proposal department can suggest tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. For example, the proposal department can suggest tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. Using AI, the proposal department can automatically suggest the most suitable tax-saving measures. This allows the system to minimize the user's tax burden by suggesting tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting.

[0037] The reminder function can instantly collect information on tax law changes and send reminders to users to file their tax returns or take tax-saving measures. For example, the reminder function can collect information on tax law changes in real time and send reminders to users to file their tax returns or take tax-saving measures. The reminder function can use AI to automatically send reminders. This allows for the implementation of tax-saving measures that are in line with the latest tax laws by collecting information on tax law changes in real time and sending reminders to users to file their tax returns or take tax-saving measures.

[0038] The proposal unit can determine whether a tax return is required, account for expenses, and calculate estimated tax payments. For example, the proposal unit can determine whether a tax return is required, account for expenses, and calculate estimated tax payments. Using AI, the proposal unit can automatically determine whether a tax return is required, account for expenses, and calculate estimated tax payments. This streamlines the user's tax procedures by automatically determining whether a tax return is required, accounting for expenses, and calculating estimated tax payments.

[0039] The reminder function can send reminders to users to implement tax-saving measures based on the latest tax laws. For example, the reminder function can send reminders to users to implement tax-saving measures based on the latest tax laws. The reminder function can also use AI to automatically send reminders. This allows for minimizing the tax burden by sending reminders to users to implement tax-saving measures that comply with the latest tax laws.

[0040] The data collection unit can improve the accuracy of its collection by referring to past tax law change history. For example, the data collection unit can analyze past tax law change history and prioritize the collection of items that change frequently. The data collection unit can also identify and collect changes from past tax law change history that are likely to affect users. Based on past tax law change history, the data collection unit can also predict future changes and determine what to collect. This allows for improved data collection accuracy by referring to past tax law change history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0041] The data collection unit can prioritize the collection of tax information specific to particular industries and regions. For example, the data collection unit can prioritize the collection of tax change information related to a particular industry. The data collection unit can also prioritize the collection of tax change information in a particular region. The data collection unit can also utilize specialized data sources to collect tax information specific to particular industries and regions. This allows the data collection unit to provide users with highly relevant information by prioritizing the collection of tax information specific to particular industries and regions. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI.

[0042] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location. For example, the data collection unit can prioritize the collection of information on tax changes related to the user's location. The data collection unit can also prioritize the collection of information on specific tax incentives in the user's location. The data collection unit can also analyze the impact of tax changes in the user's location and collect highly relevant information. This allows the system to provide users with highly relevant information by prioritizing the collection of relevant information while considering the user's geographical location. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0043] The collection unit can analyze a user's social media activity and collect relevant tax information. For example, the collection unit can identify and collect tax information of interest from a user's social media activity. The collection unit can also analyze a user's social media activity and collect relevant tax change information. The collection unit can also determine which tax information to collect based on a user's social media activity. This allows the collection unit to collect relevant tax information by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI.

[0044] The reception desk can improve input accuracy by referring to past input history. For example, the reception desk can automatically display input suggestions based on information previously entered by the user. The reception desk can also analyze the user's past input history and make suggestions to reduce input errors. The reception desk can also automatically complete input fields based on the user's past input history. This allows for improved input accuracy by referring to past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without using AI.

[0045] The reception desk can prioritize displaying input fields specific to particular industries and occupations. For example, the reception desk can prioritize displaying input fields related to a particular industry. The reception desk can also prioritize displaying input fields related to a particular occupation. The reception desk can also utilize specialized data sources to display input fields specific to particular industries or occupations. This allows the reception desk to provide users with highly relevant information by prioritizing the display of input fields specific to particular industries or occupations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0046] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location. For example, the reception unit can prioritize inputting information related to the user's location. The reception unit can also prioritize inputting information related to specific tax incentives in the user's location. The reception unit can also analyze the impact of tax changes in the user's location and input highly relevant information. This allows the reception unit to provide users with highly relevant information by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0047] The reception desk can analyze the user's social media activity and input relevant information. For example, the reception desk can identify and input information of interest from the user's social media activity. The reception desk can also analyze the user's social media activity and input relevant information. The reception desk can also determine the priority of the information to input based on the user's social media activity. This allows relevant information to be input by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0048] The analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit improves the analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results and make adjustments to improve analysis accuracy. The analysis unit can also optimize the parameters of the analysis algorithm by referring to past analysis results. In this way, by referring to past analysis results, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0049] The analysis unit can apply analytical methods specific to particular industries and occupations. For example, the analysis unit applies analytical methods based on data related to a particular industry. The analysis unit can also apply analytical methods based on data related to a particular occupation. The analysis unit can also utilize specialized data sources for applying analytical methods specific to particular industries and occupations. This allows for improved accuracy of the analysis by applying analytical methods specific to particular industries and occupations. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0050] The analysis unit can improve the accuracy of the analysis based on the user's geographical location information. For example, the analysis unit improves the accuracy of the analysis based on data related to the user's location. The analysis unit can also perform the analysis considering specific tax incentives in the user's location. The analysis unit can also analyze the impact of tax changes in the user's location and reflect it in the analysis. In this way, the accuracy of the analysis can be improved by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0051] The analysis unit can analyze users' social media activity and reflect relevant information in the analysis. For example, the analysis unit can identify information of interest from users' social media activity and reflect it in the analysis. The analysis unit can also analyze users' social media activity and reflect relevant information in the analysis. The analysis unit can also determine the priority of information to reflect in the analysis based on users' social media activity. This allows relevant information to be reflected in the analysis by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0052] The proposal department can improve the accuracy of proposals by referring to past proposal history. For example, the proposal department can improve proposal content based on past proposal history. The proposal department can also analyze past proposal history and make adjustments to improve proposal accuracy. The proposal department can also optimize the parameters of proposal content by referring to past proposal history. In this way, the accuracy of proposals can be improved by referring to past proposal history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without using AI.

[0053] The proposal department can apply proposal methodologies specific to particular industries and occupations. For example, the proposal department can apply proposal methodologies based on data related to a particular industry. The proposal department can also apply proposal methodologies based on data related to a particular occupation. The proposal department can also utilize specialized data sources for applying proposal methodologies specific to particular industries and occupations. This allows for improved accuracy of proposals by applying proposal methodologies specific to particular industries and occupations. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0054] The suggestion unit can prioritize highly relevant suggestions based on the user's geographical location. For example, the suggestion unit can prioritize suggestions related to the user's location. The suggestion unit can also prioritize suggestions related to specific tax incentives in the user's location. The suggestion unit can also analyze the impact of tax changes in the user's location and make highly relevant suggestions. This allows for highly relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0055] The suggestion department can analyze users' social media activity and make relevant suggestions. For example, the suggestion department can identify and make suggestions of interest based on users' social media activity. The suggestion department can also analyze users' social media activity and make relevant suggestions. The suggestion department can also determine the priority of suggestions based on users' social media activity. This allows the suggestion department to make relevant suggestions by analyzing users' social media activity. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI.

[0056] The reminder unit can improve the accuracy of sending reminders by referring to past reminder history. For example, the reminder unit can improve the content of the reminder based on past reminder history. The reminder unit can also analyze past reminder history and make adjustments to improve the accuracy of the reminder. The reminder unit can also optimize the parameters of the content of the reminder by referring to past reminder history. This allows the accuracy of the reminder to be improved by referring to past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0057] The reminder unit can prioritize sending reminder content specific to particular industries and occupations. For example, the reminder unit can prioritize sending reminder content related to a particular industry. The reminder unit can also prioritize sending reminder content related to a particular occupation. The reminder unit can also utilize specialized data sources to send reminder content specific to particular industries and occupations. This allows the system to provide users with highly relevant information by prioritizing the sending of reminder content specific to particular industries and occupations. Some or all of the above processing in the reminder unit may be performed using AI, for example, or not using AI.

[0058] The reminder unit can prioritize sending highly relevant reminders based on the user's geographical location. For example, the reminder unit can prioritize sending reminders related to the user's location. The reminder unit can also prioritize sending reminders related to specific tax incentives in the user's location. The reminder unit can also analyze the impact of tax changes in the user's location and send highly relevant reminders. This allows the system to send highly relevant reminders by considering the user's geographical location. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI.

[0059] The reminder unit can analyze a user's social media activity and send relevant reminders. For example, the reminder unit can identify and send reminders of interest based on the user's social media activity. The reminder unit can also analyze a user's social media activity and send relevant reminders. The reminder unit can also determine the priority of reminders based on the user's social media activity. This allows the system to send relevant reminders by analyzing the user's social media activity. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI.

[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 data collection unit can prioritize the collection of highly relevant tax information based on the user's geographical location. For example, it can prioritize the collection of tax change information related to the user's location. It can also prioritize the collection of specific tax incentives in the user's location. It can also analyze the impact of tax changes in the user's location and collect highly relevant information. By prioritizing the collection of highly relevant information while considering the user's geographical location, the system can provide users with highly relevant information.

[0062] The reception desk can analyze users' social media activity and input relevant information. For example, it can identify and input information of interest based on the user's social media activity. It can also input relevant tax law change information based on the user's social media activity. It can also prioritize the information to be input based on the user's social media activity. This allows relevant information to be input by analyzing the user's social media activity.

[0063] The proposal department can improve the accuracy of proposals by referring to past proposal history. For example, they can improve proposal content based on past proposal history. They can also analyze past proposal history and make adjustments to improve proposal accuracy. They can also optimize proposal parameters by referring to past proposal history. In this way, the accuracy of proposals can be improved by referring to past proposal history.

[0064] The analysis unit can apply analytical methods specific to particular industries and occupations. For example, it can apply analytical methods based on data related to a particular industry. It can also apply analytical methods based on data related to a particular occupation. It can also utilize specialized data sources for applying analytical methods specific to particular industries or occupations. This allows for improved accuracy of the analysis by applying analytical methods specific to particular industries or occupations.

[0065] The reminder function can improve the accuracy of its messages by referring to past reminder history. For example, it can improve the content of messages based on past reminder history. It can also analyze past reminder history and make adjustments to improve sending accuracy. It can also optimize the parameters of the message content by referring to past reminder history. In this way, the accuracy of messages can be improved by referring to past reminder history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects tax information. For example, the collection unit collects tax reform outlines and changes to related laws published by the Ministry of Finance. The collection unit can also collect changes to medical expense deductions and gift tax, etc. The collection unit can use AI to automatically collect the latest tax information. Step 2: The reception desk enters user information. The reception desk enters information such as income, expenses, investments, and family structure. The reception desk can enter information to understand the user's financial situation in detail. The reception desk can use AI to automatically enter user information. Step 3: The analysis unit analyzes the information collected by the collection unit and the reception unit. For example, the analysis unit analyzes the collected tax information and the user's financial situation. The analysis unit can use AI to automatically analyze the collected information. Step 4: The proposal unit proposes tax-saving measures based on the analysis results obtained by the analysis unit. The proposal unit proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. The proposal unit can use AI to automatically propose the optimal tax-saving measures. Step 5: The Reminder Unit sends reminders based on the tax-saving measures proposed by the Proposal Unit. For example, the Reminder Unit collects tax law change information in real time and sends reminders to users to file their tax returns or take tax-saving actions. The Reminder Unit can use AI to automatically send reminders.

[0068] (Example of form 2) The personalized tax advisor system according to an embodiment of the present invention is a system that provides a tax strategy tailored to the user's lifestyle and economic situation. This system utilizes an AI agent to collect 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 personalized tax advisor system first uses an AI agent to collect information on the tax system, which changes every year. In this process, it collects the tax reform outline and changes in related laws announced by the Ministry of Finance to grasp the latest tax information. For example, it collects information on changes to medical expense deductions and gift tax. Next, the personalized tax advisor system inputs information on the user's lifestyle and economic situation. Specifically, it inputs information such as income, expenses, investments, and family structure. This allows for a detailed understanding of the user's economic situation. For example, it considers the income deduction system available when the user's annual medical expenses exceed 100,000 yen, and changes to the gift tax exemption limit. Next, the personalized tax advisor system analyzes the collected tax information and the user's economic situation. Based on the analysis results, it proposes the most suitable tax-saving measures to the user. For example, it proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. Furthermore, the personalized tax advisor system also determines whether a tax return is required, accounts for expenses, and calculates estimated tax payments. In addition, the personalized tax advisor system collects tax law change information in real time and sends reminders to users to file tax returns or take tax-saving measures. This allows users to implement tax-saving strategies that are in line with the latest tax laws. This system enables users to implement individually optimized tax-saving measures without spending time and effort. For example, it comprehensively covers changes in medical expense deductions and gift tax exemptions, minimizing the user's tax burden. Moreover, the personalized tax advisor system automatically organizes documents and tracks expenses, allowing users to implement tax-saving measures without effort. This further minimizes the user's tax burden.

[0069] The personalized tax advisor system according to this embodiment comprises a collection unit, a reception unit, an analysis unit, a proposal unit, and a reminder unit. The collection unit collects tax information. For example, the collection unit collects changes to the tax reform outline and related laws announced by the Ministry of Finance. The collection unit can also collect changes to medical expense deductions and gift tax. The collection unit can automatically collect the latest tax information using AI. The reception unit inputs user information. For example, the reception unit inputs information such as income, expenses, investments, and family structure. The reception unit can input information to understand the user's financial situation in detail. The reception unit can automatically input user information using AI. The analysis unit analyzes the information collected by the collection unit and the reception unit. For example, the analysis unit analyzes the collected tax information and the user's financial situation. The analysis unit can automatically analyze the collected information using AI. The proposal unit proposes tax-saving measures based on the analysis results obtained by the analysis unit. The proposal unit proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. The proposal unit can automatically propose the most suitable tax-saving measures using AI. The reminder unit sends reminders based on the tax-saving measures proposed by the proposal unit. The reminder unit can collect information on tax law changes in real time and send reminders to users to file tax returns or take tax-saving procedures. The reminder unit can automatically send reminders using AI. As a result, the personalized tax advisor system according to this embodiment can minimize the user's tax burden.

[0070] The data collection unit collects tax information. For example, it collects changes to tax outlines and related laws published by the Ministry of Finance. Specifically, the data collection unit uses crawlers to obtain the latest tax information from the Ministry of Finance's official website and the websites of related government agencies. This allows for real-time collection of information on revisions to tax outlines and the implementation of new laws. The data collection unit can also collect information on changes to medical expense deductions and gift tax. For example, it collects important tax change information for users, such as changes to the scope and amount of medical expenses eligible for medical expense deductions, and changes to the gift tax exemption limit. The data collection unit can use AI to automatically collect the latest tax information. The AI ​​uses natural language processing technology to analyze the collected information and extract important changes. For example, the AI ​​extracts keywords from tax outline documents and identifies changes. The AI ​​also organizes the collected information into a database, making it accessible to other departments. This allows the data collection unit to efficiently and accurately collect the latest tax information and improve the overall system performance. Furthermore, the data collection unit can regularly update the collected information, ensuring that users always have the most up-to-date information. This allows users to take appropriate tax measures based on the latest tax information.

[0071] The reception desk inputs user information. For example, it inputs information such as income, expenses, investments, and family structure. Specifically, users input detailed information such as their sources of income, annual income, various expense items, types and amounts of investments, and family structure (including whether they have a spouse or dependents) through a dedicated input form. The reception desk can input information to gain a detailed understanding of the user's financial situation. For example, it collects tax-important information such as the user's occupation and workplace, sources of income other than salary (such as real estate income or side job income), annual medical and education expenses, and whether they have a mortgage. The reception desk can use AI to automatically input user information. The AI ​​supplements the input based on information previously entered by the user and data obtained from other systems. For example, the AI ​​analyzes the user's past tax return data and transaction history provided by financial institutions to automatically input income and expense items. The AI ​​also checks the consistency of the information entered by the user and prompts for corrections if there are errors or deficiencies. This allows the reception desk to efficiently and accurately collect user information and improve the overall system performance. Furthermore, the reception department will securely manage the collected information and implement measures to protect privacy. This allows users to confidently enter their financial situation and receive appropriate tax advice.

[0072] The Analysis Department analyzes information collected by the Collection Department and the Reception Department. For example, the Analysis Department analyzes collected tax information against the user's financial situation. Specifically, the Analysis Department compares the latest tax information provided by the Collection Department with the user's financial situation provided by the Reception Department to identify applicable tax changes and tax-saving opportunities for the user. The Analysis Department can use AI to automatically analyze the collected information. The AI ​​uses machine learning algorithms to analyze the collected data and propose the most suitable tax measures for each user. For example, the AI ​​analyzes the user's income and expenditure patterns to identify tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. The AI ​​also predicts future tax risks based on historical data and statistical information and takes appropriate measures. This allows the Analysis Department to quickly and accurately analyze collected data and provide users with optimal tax advice. Furthermore, the Analysis Department stores the analysis results in a database, making them accessible to other departments. This allows the Proposal Department and Reminder Department to provide users with appropriate advice and reminders based on the analysis results. This allows the analysis unit to improve the overall system performance and minimize the tax burden on users.

[0073] The Proposal Department proposes tax-saving measures based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. Specifically, the Proposal Department proposes the most suitable tax-saving measures for the user based on the analysis results provided by the Analysis Department. For example, if a user has high annual medical expenses, the Proposal Department proposes a method to reduce the income tax burden by utilizing medical expense deductions. It also proposes a method to receive donation deductions by utilizing hometown tax donations. Furthermore, if a user has investment losses, the Proposal Department proposes a method to offset them against other income using loss offsetting. The Proposal Department can automatically propose the most suitable tax-saving measures using AI. The AI ​​analyzes the user's economic situation and tax information to identify the most effective tax-saving measures. For example, the AI ​​analyzes the user's income and expenditure patterns and proposes the most suitable tax-saving measures. The AI ​​also predicts future tax risks based on past data and statistical information and takes appropriate measures. This allows the Proposal Department to provide users with optimal tax advice and minimize their tax burden. Furthermore, the proposal department stores the proposals in a database, making them accessible to other departments. This allows the reminder department to provide users with appropriate reminders based on the proposals. As a result, the proposal department can improve the overall system performance and minimize the tax burden on users.

[0074] The Reminders Department sends reminders based on tax-saving measures proposed by the Proposal Department. For example, the Reminders Department collects tax law change information in real time and sends reminders to users to file tax returns or take tax-saving procedures. Specifically, the Reminders Department sends reminders to users at the appropriate time based on tax-saving measures provided by the Proposal Department. For example, when the tax return deadline is approaching, it sends a reminder to the user to file their tax return. Also, when the deadline for making a donation to the Furusato Nozei (hometown tax) program is approaching, it sends a reminder to the user to make a donation. The Reminders Department can automatically send reminders using AI. The AI ​​analyzes the user's schedule and past behavior patterns and sends reminders at the optimal time. For example, the AI ​​analyzes when and how often the user has filed tax returns in the past and sends reminders at the optimal time. The AI ​​also analyzes the user's behavior patterns and optimizes the content and method of sending reminders. This allows the reminder unit to send reminders to users at the appropriate time, facilitating smooth tax procedures. Furthermore, the reminder unit stores the reminder transmission history in a database, making it accessible to other departments. This enables the proposal and analysis departments to provide users with appropriate advice and suggestions based on the reminder transmission history. As a result, the reminder unit can improve the overall system performance and minimize the tax burden on users.

[0075] The analysis unit can analyze collected tax information and the user's financial situation. For example, the analysis unit can analyze collected tax information and the user's financial situation. The analysis unit can use AI to automatically analyze the collected information. This allows it to propose optimal tax-saving strategies by analyzing the collected tax information and the user's financial situation.

[0076] The proposal department can suggest tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. For example, the proposal department can suggest tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. Using AI, the proposal department can automatically suggest the most suitable tax-saving measures. This allows the system to minimize the user's tax burden by suggesting tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting.

[0077] The reminder function can instantly collect information on tax law changes and send reminders to users to file their tax returns or take tax-saving measures. For example, the reminder function can collect information on tax law changes in real time and send reminders to users to file their tax returns or take tax-saving measures. The reminder function can use AI to automatically send reminders. This allows for the implementation of tax-saving measures that are in line with the latest tax laws by collecting information on tax law changes in real time and sending reminders to users to file their tax returns or take tax-saving measures.

[0078] The proposal unit can determine whether a tax return is required, account for expenses, and calculate estimated tax payments. For example, the proposal unit can determine whether a tax return is required, account for expenses, and calculate estimated tax payments. Using AI, the proposal unit can automatically determine whether a tax return is required, account for expenses, and calculate estimated tax payments. This streamlines the user's tax procedures by automatically determining whether a tax return is required, accounting for expenses, and calculating estimated tax payments.

[0079] The reminder function can send reminders to users to implement tax-saving measures based on the latest tax laws. For example, the reminder function can send reminders to users to implement tax-saving measures based on the latest tax laws. The reminder function can also use AI to automatically send reminders. This allows for minimizing the tax burden by sending reminders to users to implement tax-saving measures that comply with the latest tax laws.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of tax information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of collection and collect only important changes. If the user is relaxed, the data collection unit can also collect detailed tax information more frequently. If the user is in a hurry, the data collection unit can prioritize collecting the most important tax change information. This reduces the user's burden by adjusting the timing of tax information collection 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.

[0081] The data collection unit can improve the accuracy of its collection by referring to past tax law change history. For example, the data collection unit can analyze past tax law change history and prioritize the collection of items that change frequently. The data collection unit can also identify and collect changes from past tax law change history that are likely to affect users. Based on past tax law change history, the data collection unit can also predict future changes and determine what to collect. This allows for improved data collection accuracy by referring to past tax law change history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0082] The data collection unit can prioritize the collection of tax information specific to particular industries and regions. For example, the data collection unit can prioritize the collection of tax change information related to a particular industry. The data collection unit can also prioritize the collection of tax change information in a particular region. The data collection unit can also utilize specialized data sources to collect tax information specific to particular industries and regions. This allows the data collection unit to provide users with highly relevant information by prioritizing the collection of tax information specific to particular industries and regions. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI.

[0083] The data collection unit can estimate the user's emotions and determine the priority of tax information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important tax change information. If the user is relaxed, the data collection unit may also prioritize collecting detailed tax information. If the user is in a hurry, the data collection unit may also prioritize collecting the most important tax change information. This reduces the user's burden by prioritizing the tax information to collect 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.

[0084] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location. For example, the data collection unit can prioritize the collection of information on tax changes related to the user's location. The data collection unit can also prioritize the collection of information on specific tax incentives in the user's location. The data collection unit can also analyze the impact of tax changes in the user's location and collect highly relevant information. This allows the system to provide users with highly relevant information by prioritizing the collection of relevant information while considering the user's geographical location. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI.

[0085] The collection unit can analyze a user's social media activity and collect relevant tax information. For example, the collection unit can identify and collect tax information of interest from a user's social media activity. The collection unit can also analyze a user's social media activity and collect relevant tax change information. The collection unit can also determine which tax information to collect based on a user's social media activity. This allows the collection unit to collect relevant tax information by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI.

[0086] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may delay the input timing. If the user is relaxed, the reception desk may speed up the input timing. If the user is in a hurry, the reception desk may prompt for quick input. This reduces the user's burden by adjusting the timing of information input 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 reception desk may be performed using AI, for example, or without AI.

[0087] The reception desk can improve input accuracy by referring to past input history. For example, the reception desk can automatically display input suggestions based on information previously entered by the user. The reception desk can also analyze the user's past input history and make suggestions to reduce input errors. The reception desk can also automatically complete input fields based on the user's past input history. This allows for improved input accuracy by referring to past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without using AI.

[0088] The reception desk can prioritize displaying input fields specific to particular industries and occupations. For example, the reception desk can prioritize displaying input fields related to a particular industry. The reception desk can also prioritize displaying input fields related to a particular occupation. The reception desk can also utilize specialized data sources to display input fields specific to particular industries or occupations. This allows the reception desk to provide users with highly relevant information by prioritizing the display of input fields specific to particular industries or occupations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0089] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize inputting only important information. If the user is relaxed, the reception desk may also prioritize inputting detailed information. If the user is in a hurry, the reception desk may also prioritize inputting the most important information. This reduces the user's burden by prioritizing the information to be entered 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 reception desk may be performed using AI, for example, or not using AI.

[0090] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location. For example, the reception unit can prioritize inputting information related to the user's location. The reception unit can also prioritize inputting information related to specific tax incentives in the user's location. The reception unit can also analyze the impact of tax changes in the user's location and input highly relevant information. This allows the reception unit to provide users with highly relevant information by prioritizing the input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0091] The reception desk can analyze the user's social media activity and input relevant information. For example, the reception desk can identify and input information of interest from the user's social media activity. The reception desk can also analyze the user's social media activity and input relevant information. The reception desk can also determine the priority of the information to input based on the user's social media activity. This allows relevant information to be input by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method. If the user is relaxed, the analysis unit can also apply a detailed analysis method. If the user is in a hurry, the analysis unit can also apply a rapid analysis method. This reduces the user's burden by adjusting the analysis method 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 analysis unit may be performed using AI, for example, or without AI.

[0093] The analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit improves the analysis algorithm based on past analysis results. The analysis unit can also analyze past analysis results and make adjustments to improve analysis accuracy. The analysis unit can also optimize the parameters of the analysis algorithm by referring to past analysis results. In this way, by referring to past analysis results, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0094] The analysis unit can apply analytical methods specific to particular industries and occupations. For example, the analysis unit applies analytical methods based on data related to a particular industry. The analysis unit can also apply analytical methods based on data related to a particular occupation. The analysis unit can also utilize specialized data sources for applying analytical methods specific to particular industries and occupations. This allows for improved accuracy of the analysis by applying analytical methods specific to particular industries and occupations. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the burden on the user can be reduced by adjusting the display method of the analysis results 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 analysis unit may be performed using AI, for example, or without using AI.

[0096] The analysis unit can improve the accuracy of the analysis based on the user's geographical location information. For example, the analysis unit improves the accuracy of the analysis based on data related to the user's location. The analysis unit can also perform the analysis considering specific tax incentives in the user's location. The analysis unit can also analyze the impact of tax changes in the user's location and reflect it in the analysis. In this way, the accuracy of the analysis can be improved by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0097] The analysis unit can analyze users' social media activity and reflect relevant information in the analysis. For example, the analysis unit can identify information of interest from users' social media activity and reflect it in the analysis. The analysis unit can also analyze users' social media activity and reflect relevant information in the analysis. The analysis unit can also determine the priority of information to reflect in the analysis based on users' social media activity. This allows relevant information to be reflected in the analysis by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0098] 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 offer simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit may offer suggestions that include more detailed information. If the user is in a hurry, the suggestion unit may offer quick and concise suggestions. This reduces the user's burden by adjusting the way suggestions are presented according to their emotions. 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 processing described above in the suggestion unit may be performed using AI or not using AI.

[0099] The proposal department can improve the accuracy of proposals by referring to past proposal history. For example, the proposal department can improve proposal content based on past proposal history. The proposal department can also analyze past proposal history and make adjustments to improve proposal accuracy. The proposal department can also optimize the parameters of proposal content by referring to past proposal history. In this way, the accuracy of proposals can be improved by referring to past proposal history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without using AI.

[0100] The proposal department can apply proposal methodologies specific to particular industries and occupations. For example, the proposal department can apply proposal methodologies based on data related to a particular industry. The proposal department can also apply proposal methodologies based on data related to a particular occupation. The proposal department can also utilize specialized data sources for applying proposal methodologies specific to particular industries and occupations. This allows for improved accuracy of proposals by applying proposal methodologies specific to particular industries and occupations. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0101] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize only important suggestions. If the user is relaxed, the suggestion unit may also prioritize detailed suggestions. If the user is in a hurry, the suggestion unit may also prioritize the most important suggestions. This reduces the user's burden by prioritizing suggestions according to their emotions. 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 processing described above in the suggestion unit may be performed using AI or not using AI.

[0102] The suggestion unit can prioritize highly relevant suggestions based on the user's geographical location. For example, the suggestion unit can prioritize suggestions related to the user's location. The suggestion unit can also prioritize suggestions related to specific tax incentives in the user's location. The suggestion unit can also analyze the impact of tax changes in the user's location and make highly relevant suggestions. This allows for highly relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI.

[0103] The suggestion department can analyze users' social media activity and make relevant suggestions. For example, the suggestion department can identify and make suggestions of interest based on users' social media activity. The suggestion department can also analyze users' social media activity and make relevant suggestions. The suggestion department can also determine the priority of suggestions based on users' social media activity. This allows the suggestion department to make relevant suggestions by analyzing users' social media activity. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI.

[0104] The reminder unit can estimate the user's emotions and adjust the timing of reminder sending based on the estimated emotions. For example, if the user is stressed, the reminder unit can reduce the frequency of sending reminders. If the user is relaxed, the reminder unit can also increase the frequency of sending reminders. If the user is in a hurry, the reminder unit can prioritize sending important reminders. This reduces the user's burden by adjusting the timing of reminder sending 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 reminder unit may be performed using AI, for example, or without AI.

[0105] The reminder unit can improve the accuracy of sending reminders by referring to past reminder history. For example, the reminder unit can improve the content of the reminder based on past reminder history. The reminder unit can also analyze past reminder history and make adjustments to improve the accuracy of the reminder. The reminder unit can also optimize the parameters of the content of the reminder by referring to past reminder history. This allows the accuracy of the reminder to be improved by referring to past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without using AI.

[0106] The reminder unit can prioritize sending reminder content specific to particular industries and occupations. For example, the reminder unit can prioritize sending reminder content related to a particular industry. The reminder unit can also prioritize sending reminder content related to a particular occupation. The reminder unit can also utilize specialized data sources to send reminder content specific to particular industries and occupations. This allows the system to provide users with highly relevant information by prioritizing the sending of reminder content specific to particular industries and occupations. Some or all of the above processing in the reminder unit may be performed using AI, for example, or not using AI.

[0107] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit will prioritize sending only important reminders. If the user is relaxed, the reminder unit may also prioritize sending detailed reminders. If the user is in a hurry, the reminder unit may also prioritize sending the most important reminders. This reduces the user's burden by prioritizing reminders according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 reminder unit may be performed using AI, for example, or not using AI.

[0108] The reminder unit can prioritize sending highly relevant reminders based on the user's geographical location. For example, the reminder unit can prioritize sending reminders related to the user's location. The reminder unit can also prioritize sending reminders related to specific tax incentives in the user's location. The reminder unit can also analyze the impact of tax changes in the user's location and send highly relevant reminders. This allows the system to send highly relevant reminders by considering the user's geographical location. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI.

[0109] The reminder unit can analyze a user's social media activity and send relevant reminders. For example, the reminder unit can identify and send reminders of interest based on the user's social media activity. The reminder unit can also analyze a user's social media activity and send relevant reminders. The reminder unit can also determine the priority of reminders based on the user's social media activity. This allows the system to send relevant reminders by analyzing the user's social media activity. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function can propose simple and easy-to-understand tax-saving strategies. If the user is relaxed, the suggestion function can propose tax-saving strategies that include detailed information. If the user is in a hurry, the suggestion function can propose quick and concise tax-saving strategies. In this way, the burden on the user can be reduced by adjusting the content of the suggestions according to the user's emotions.

[0112] The data collection unit can prioritize the collection of highly relevant tax information based on the user's geographical location. For example, it can prioritize the collection of tax change information related to the user's location. It can also prioritize the collection of specific tax incentives in the user's location. It can also analyze the impact of tax changes in the user's location and collect highly relevant information. By prioritizing the collection of highly relevant information while considering the user's geographical location, the system can provide users with highly relevant information.

[0113] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple and easy-to-read display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the burden on the user can be reduced by adjusting the display method of the analysis results according to the user's emotions.

[0114] The reception desk can analyze users' social media activity and input relevant information. For example, it can identify and input information of interest based on the user's social media activity. It can also input relevant tax law change information based on the user's social media activity. It can also prioritize the information to be input based on the user's social media activity. This allows relevant information to be input by analyzing the user's social media activity.

[0115] The reminder function can estimate the user's emotions and adjust the timing of reminder sending based on those emotions. For example, if the user is stressed, the frequency of reminders can be reduced. If the user is relaxed, the frequency of reminders can be increased. If the user is in a hurry, important reminders can be prioritized. This reduces the user's burden by adjusting the timing of reminders according to their emotions.

[0116] The proposal department can improve the accuracy of proposals by referring to past proposal history. For example, they can improve proposal content based on past proposal history. They can also analyze past proposal history and make adjustments to improve proposal accuracy. They can also optimize proposal parameters by referring to past proposal history. In this way, the accuracy of proposals can be improved by referring to past proposal history.

[0117] The data collection unit can estimate the user's emotions and determine the priority of tax information to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting only important tax change information. If the user is relaxed, it can prioritize collecting detailed tax information. If the user is in a hurry, it can prioritize collecting the most important tax change information. This reduces the user's burden by prioritizing the tax information to collect according to their emotions.

[0118] The analysis unit can apply analytical methods specific to particular industries and occupations. For example, it can apply analytical methods based on data related to a particular industry. It can also apply analytical methods based on data related to a particular occupation. It can also utilize specialized data sources for applying analytical methods specific to particular industries or occupations. This allows for improved accuracy of the analysis by applying analytical methods specific to particular industries or occupations.

[0119] The reception desk can estimate the user's emotions and adjust the timing of information input based on those estimates. For example, if the user is stressed, the input timing can be delayed. If the user is relaxed, the input timing can be sped up. If the user is in a hurry, the system can prompt them to input quickly. By adjusting the timing of information input according to the user's emotions, the system can reduce the burden on the user.

[0120] The reminder function can improve the accuracy of its messages by referring to past reminder history. For example, it can improve the content of messages based on past reminder history. It can also analyze past reminder history and make adjustments to improve sending accuracy. It can also optimize the parameters of the message content by referring to past reminder history. In this way, the accuracy of messages can be improved by referring to past reminder history.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit collects tax information. For example, the collection unit collects tax reform outlines and changes to related laws published by the Ministry of Finance. The collection unit can also collect changes to medical expense deductions and gift tax, etc. The collection unit can use AI to automatically collect the latest tax information. Step 2: The reception desk enters user information. The reception desk enters information such as income, expenses, investments, and family structure. The reception desk can enter information to understand the user's financial situation in detail. The reception desk can use AI to automatically enter user information. Step 3: The analysis unit analyzes the information collected by the collection unit and the reception unit. For example, the analysis unit analyzes the collected tax information and the user's financial situation. The analysis unit can use AI to automatically analyze the collected information. Step 4: The proposal unit proposes tax-saving measures based on the analysis results obtained by the analysis unit. The proposal unit proposes tax-saving measures such as medical expense deductions, hometown tax donations, and loss offsetting. The proposal unit can use AI to automatically propose the optimal tax-saving measures. Step 5: The Reminder Unit sends reminders based on the tax-saving measures proposed by the Proposal Unit. For example, the Reminder Unit collects tax law change information in real time and sends reminders to users to file their tax returns or take tax-saving actions. The Reminder Unit can use AI to automatically send reminders.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, and reminder 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 changes to the tax reform outline and related laws announced by the Ministry of Finance. The reception unit is implemented by the control unit 46A of the smart device 14 and inputs information such as the user's income, expenses, investments, and family structure. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected tax information and the user's economic situation. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal tax-saving measures. The reminder unit is implemented by the control unit 46A of the smart device 14 and collects tax reform change information in real time and sends reminders to the user to file tax returns or take tax-saving procedures. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, and reminder unit, is implemented, for example, 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 changes to the tax reform outline and related laws announced by the Ministry of Finance. The reception unit is implemented, for example, by the control unit 46A of the smart glasses 214 and inputs information such as the user's income, expenses, investments, and family structure. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected tax information and the user's economic situation. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal tax-saving measures. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collects tax reform change information in real time and sends reminders to the user to file tax returns or take tax-saving procedures. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, and reminder unit, is implemented by, for example, 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 changes to the tax system outline and related laws announced by the Ministry of Finance. The reception unit is implemented by, for example, the control unit 46A of the headset terminal 314 and inputs information such as the user's income, expenses, investments, and family structure. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected tax information and the user's economic situation. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal tax-saving measures. The reminder unit is implemented by, for example, the control unit 46A of the headset terminal 314 and collects tax system change information in real time and sends reminders to the user to file tax returns or take tax-saving procedures. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, reception unit, analysis unit, proposal unit, and reminder 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 changes to the tax reform outline and related laws announced by the Ministry of Finance. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and inputs information such as the user's income, expenses, investments, and family structure. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected tax information and the user's economic situation. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal tax-saving measures. The reminder unit is implemented by, for example, the control unit 46A of the robot 414 and collects tax reform change information in real time and sends reminders to the user to file tax returns or take tax-saving procedures. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection department that collects tax information, The reception area where user information is entered, An analysis unit that analyzes the information collected by the collection unit and the reception unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes tax-saving measures. The system includes a reminder unit that sends reminders based on the tax-saving measures proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We analyze the collected tax information and the user's economic situation. 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 medical expense deductions, hometown tax donations, and loss offsetting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The reminder unit is, We instantly collect information on tax law changes and send reminders to users to file their tax returns or take tax-saving measures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, This involves determining whether or not a tax return is required, accounting for expenses, and calculating the estimated tax amount. The system described in Appendix 1, characterized by the features described herein. (Note 6) The reminder unit is, Send a reminder to users to implement tax-saving strategies based on the latest tax laws. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of tax information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We improve the accuracy of data collection by referring to past tax law change history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Prioritize the collection of tax information specific to particular industries and regions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of tax information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Prioritize collecting relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze users' social media activity and collect relevant tax information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is Improve input accuracy by referring to past input history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is Prioritize displaying input fields specific to particular industries and occupations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is Prioritize inputting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is Analyze users' social media activity and input relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Optimize the analysis algorithm by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, Apply analytical methods specific to particular industries and occupations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, Improve the accuracy of analysis based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, Analyze users' social media activity and incorporate relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) 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 26) The aforementioned proposal section is, Improve the accuracy of proposals by referring to past proposal history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Apply proposal methodologies specific to particular industries and occupations. The system described in Appendix 1, characterized by the features described herein. (Note 28) 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 29) The aforementioned proposal section is, We prioritize relevant suggestions based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Analyze users' social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reminder unit is, It estimates the user's emotions and adjusts the timing of sending reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reminder unit is, Referencing past reminder history improves the accuracy of sending reminders. The system described in Appendix 1, characterized by the features described herein. (Note 33) The reminder unit is, Prioritize sending reminders tailored to specific industries and professions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The reminder unit is, Prioritize sending relevant reminders based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The reminder unit is, Analyze users' social media activity and send relevant reminders. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 department that collects tax information, The reception area where user information is entered, An analysis unit that analyzes the information collected by the collection unit and the reception unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes tax-saving measures. The system includes a reminder unit that sends reminders based on the tax-saving measures proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, We analyze the collected tax information and the user's economic situation. The system according to feature 1.

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

4. The reminder unit is, We instantly collect information on tax law changes and send reminders to users to file their tax returns or take tax-saving measures. The system according to feature 1.

5. The aforementioned proposal section is, This involves determining whether or not a tax return is required, accounting for expenses, and calculating the estimated tax amount. The system according to feature 1.

6. The reminder unit is, Send a reminder to users to implement tax-saving strategies based on the latest tax laws. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of tax information collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is We improve the accuracy of data collection by referring to past tax law change history. The system according to feature 1.

9. The aforementioned collection unit is Prioritize the collection of tax information specific to particular industries and regions. The system according to feature 1.

10. The aforementioned collection unit is It estimates user sentiment and determines the priority of tax information to collect based on the estimated user sentiment. The system according to feature 1.

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