System

Abstract

An object of a system according to an embodiment is to simplify a declaration method of a final tax return and guidance of necessary documents and to reduce handling man-hours.SOLUTION: A system includes an input part, a selection part, a guide part, and a check part. The input part inputs declaration contents. The selection unit selects a reporting method based on the information input by the input unit. The guidance unit guides a necessary document or procedure according to the reporting method selected by the selection unit. The check part confirms the declaration contents based on the procedure guided by the guide part, and checks an error.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that instructions on how to file tax returns and the necessary documents are complicated, resulting in a large amount of work for taxpayers and tax office staff to deal with.

[0005] The system according to the embodiment aims to simplify the process of filing tax returns and the necessary documents, thereby reducing the amount of work required. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a selection unit, a guidance unit, and a check unit. The input unit inputs the declaration content. The selection unit selects a declaration method based on the information input by the input unit. The guidance unit provides guidance on the necessary documents or procedures according to the declaration method selected by the selection unit. The check unit checks the declaration content based on the procedures provided by the guidance unit and checks for errors. [Effects of the Invention]

[0007] The system according to the embodiment simplifies the process of filing tax returns and the necessary documents, thereby reducing the amount of work required. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The tax return support system according to an embodiment of the present invention streamlines the entire process, from inputting tax return information to error checking. The system provides an interface for taxpayers to input their tax return information, selects a filing method based on the input information, guides them through the necessary documents and procedures, and checks the tax return for errors. For example, the system allows taxpayers to input the necessary tax return information by selecting the type of income and deductions. The system then recommends e-Tax if an internet connection is available, and paper filing if an internet connection is not available. Furthermore, for electronic filing, the system provides detailed instructions on the electronic filing procedure and guides users on how to electronically submit the necessary documents. For paper filing, the system guides users on how to print and mail the necessary documents. Finally, the system provides a function for reviewing tax return information and checking for errors before submission. This allows taxpayers to select the filing method that best suits them and easily file their tax returns. It also reduces the workload of tax office staff, contributing to greater efficiency across society. This allows taxpayers to choose the filing method that best suits them and file their tax returns easily. It also reduces the workload of tax office staff, contributing to greater efficiency across society.

[0029] The tax return support system according to the embodiment includes an input unit, a selection unit, a guidance unit, and a check unit. The input unit provides an interface for a taxpayer to input tax return details. The taxpayer can input necessary tax return details by selecting, for example, the type of income or the type of deduction. The selection unit selects a tax return method based on the information input by the input unit. For example, the selection unit recommends e-Tax if an internet connection is available, and recommends paper filing if an internet connection is not available. The guidance unit provides guidance on necessary documents and procedures according to the tax return method selected by the selection unit. For example, in the case of electronic filing, the guidance unit provides detailed explanations of the electronic filing procedure and guidance on how to electronically submit the necessary documents. In the case of paper filing, the guidance unit provides guidance on how to print and mail the necessary documents. The check unit verifies the tax return details based on the procedures provided by the guidance unit and checks for errors. For example, the check unit checks for errors before submission to prevent mistakes in the tax return details. This allows the tax return support system according to the embodiment to streamline a series of procedures from inputting tax return details to error checking.

[0030] The selection unit includes an environment confirmation unit that confirms whether or not an internet environment is available. The environment confirmation unit confirms whether or not an internet environment is available. For example, the environment confirmation unit evaluates the internet connection speed and connection stability to determine whether or not an internet environment is available. This enables the selection unit to select a filing method that suits the internet environment. For example, if the internet connection is stable, e-Tax is recommended, and if the internet connection is unstable, paper filing is recommended. This enables the selection of a filing method that suits the internet environment.

[0031] The guidance unit includes an electronic filing guidance unit that provides detailed explanations of the electronic filing procedures. The electronic filing guidance unit provides detailed explanations of the electronic filing procedures. For example, the electronic filing guidance unit provides information on the software to be used and the necessary documents, and provides detailed explanations of the electronic filing procedures. This provides detailed guidance on the electronic filing procedures, thereby improving user convenience. For example, the electronic filing guidance unit provides step-by-step explanations of the electronic filing procedures, allowing the user to proceed with the procedures without confusion. The electronic filing guidance unit also provides information on how to electronically submit the necessary documents, allowing the user to file electronically smoothly. This provides detailed guidance on the electronic filing procedures, thereby improving user convenience.

[0032] The guidance unit includes a paper application guidance unit that provides detailed explanations of the paper application procedures. The paper application guidance unit provides detailed explanations of the paper application procedures. For example, the paper application guidance unit provides guidance on the procedures for printing and mailing the necessary documents. This provides detailed guidance on the paper application procedures, thereby improving user convenience. For example, the paper application guidance unit provides step-by-step explanations of the paper application procedures, allowing the user to proceed with the procedures without confusion. The paper application guidance unit also provides guidance on how to print and mail the necessary documents, allowing the user to smoothly complete the paper application. This provides detailed guidance on the paper application procedures, thereby improving user convenience.

[0033] The checking unit checks for errors in the declaration content. The checking unit checks for errors in the declaration content. For example, the checking unit checks for errors before submission to prevent mistakes in the declaration content. In this way, mistakes can be prevented by checking for errors in the declaration content in advance. For example, the checking unit detects deficiencies or errors in the declaration content and prompts the user to correct them. The checking unit also verifies the consistency of the declaration content and checks for errors. In this way, mistakes can be prevented by checking for errors in the declaration content in advance.

[0034] The input unit analyzes the user's past declaration history and suggests the optimal input method. The input unit analyzes the user's past declaration history and suggests the optimal input method. For example, the input unit automatically displays declaration details that the user has frequently entered in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest declaration details to be used in a specific time period based on the user's past declaration history. This makes it possible to make input work more efficient by suggesting the optimal input method based on the user's past declaration history.

[0035] The input unit customizes input fields based on the user's current income status and deduction items at the time of input. The input unit customizes input fields based on the user's current income status and deduction items at the time of input. For example, the input unit automatically displays necessary input fields according to the user's income status. The input unit can also preferentially display related input fields based on the user's deduction items. The input unit can also suggest an optimal input method based on the user's income status and deduction items. This makes it possible to streamline input work by providing input fields according to the user's income status and deduction items.

[0036] The input unit selects the optimal input means depending on the user's input method when inputting. The input unit selects the optimal input means depending on the user's input method when inputting. For example, if the user selects voice input, the input unit automatically converts the input content into text using voice recognition technology. Furthermore, if the user selects text input, the input unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the input unit can automatically analyze the input content using image recognition technology. This makes it possible to streamline input work by providing the optimal means depending on the user's input method.

[0037] The input unit, when inputting data, prioritizes displaying highly relevant input fields taking into account the user's geographical location information. The input unit, when inputting data, prioritizes displaying highly relevant input fields taking into account the user's geographical location information. For example, if the user lives in a specific region, the input unit prioritizes displaying input fields related to that region. The input unit can also automatically display region-specific deduction items based on the user's geographical location information. The input unit can also suggest an optimal input method taking into account the user's geographical location information. This makes it possible to streamline input work by providing input fields based on the user's geographical location information.

[0038] The input unit analyzes the user's social media activity during input and suggests related input content. The input unit analyzes the user's social media activity during input and suggests related input content. For example, the input unit suggests related input fields based on income information shared by the user on social media. The input unit can also analyze the user's social media activity and suggest related deduction items. The input unit can also suggest related input content based on the activity of the user's friends on social media. This makes it possible to make input work more efficient by suggesting input content based on the user's social media activity.

[0039] The input unit customizes the input method by reflecting the user's past feedback when inputting data. The input unit customizes the input method by reflecting the user's past feedback when inputting data. For example, the input unit suggests an optimal input method based on feedback provided by the user in the past. The input unit can also customize the input interface by reflecting the user's past feedback. The input unit can also adjust the priority of input content based on the user's past feedback. This makes it possible to improve the efficiency of input work by providing an input method based on the user's past feedback.

[0040] At the time of selection, the selection unit evaluates the stability of the user's Internet environment and suggests the optimal filing method. At the time of selection, the selection unit evaluates the stability of the user's Internet environment and suggests the optimal filing method. For example, if the user's Internet connection is unstable, the selection unit may preferentially suggest paper filing. Also, if the user's Internet connection is stable, the selection unit may preferentially suggest e-Tax. Also, the selection unit may evaluate the user's Internet environment and suggest the optimal filing method. This makes it possible to streamline the filing process by providing a filing method based on the user's Internet environment.

[0041] The selection unit customizes the options by referring to the user's past history of reporting methods when making a selection. The selection unit customizes the options by referring to the user's past history of reporting methods when making a selection. For example, the selection unit preferentially suggests reporting methods that the user has used in the past. The selection unit can also suggest the optimal reporting method based on the user's past reporting history. The selection unit can also customize the options by referring to the user's past history of reporting methods. This makes it possible to streamline reporting work by providing reporting methods based on the user's past reporting history.

[0042] The selection unit suggests a reporting method based on the user's current living situation at the time of selection. The selection unit suggests a reporting method based on the user's current living situation at the time of selection. For example, if the user is busy, the selection unit suggests a method that allows for quick reporting. The selection unit can also suggest a detailed reporting method if the user is relaxed. The selection unit can also suggest an optimal reporting method taking the user's living situation into consideration. This makes it possible to streamline reporting work by providing a reporting method based on the user's living situation.

[0043] The selection unit, at the time of selection, proposes the optimal reporting method taking into consideration the user's geographical location information. The selection unit, at the time of selection, proposes the optimal reporting method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the selection unit proposes a reporting method suitable for that area. The selection unit can also propose a reporting method specific to that area based on the user's geographical location information. The selection unit can also propose the optimal reporting method taking into consideration the user's geographical location information. This makes it possible to streamline reporting work by providing a reporting method based on the user's geographical location information.

[0044] At the time of selection, the selection unit analyzes the user's social media activity and suggests a relevant reporting method. At the time of selection, the selection unit analyzes the user's social media activity and suggests a relevant reporting method. For example, the selection unit suggests a relevant reporting method based on information shared by the user on social media. The selection unit can also analyze the content of the user's social media activity and suggest a relevant reporting method. The selection unit can also suggest a relevant reporting method based on the activity of the user's friends on social media. This makes it possible to streamline reporting by providing a reporting method based on the user's social media activity.

[0045] The selection unit customizes the reporting method by reflecting the user's past feedback when making a selection. The selection unit customizes the reporting method by reflecting the user's past feedback when making a selection. For example, the selection unit suggests an optimal reporting method based on feedback provided by the user in the past. The selection unit can also customize the reporting method by reflecting the user's past feedback. The selection unit can also adjust the priority of the reporting methods based on the user's past feedback. This makes it possible to streamline reporting work by providing a reporting method based on the user's past feedback.

[0046] The guidance unit customizes the guidance content by referring to the user's past declaration history when providing guidance. The guidance unit customizes the guidance content by referring to the user's past declaration history when providing guidance. For example, the guidance unit provides optimal guidance content based on the content previously declared by the user. The guidance unit can also provide related guidance content by referring to the user's past declaration history. The guidance unit can also customize the guidance content based on the user's past declaration history. In this way, by providing guidance content based on the user's past declaration history, it is possible to promote understanding of the guidance.

[0047] The guidance unit adjusts the guidance content based on the user's current living situation when providing guidance. The guidance unit adjusts the guidance content based on the user's current living situation when providing guidance. For example, if the user is busy, the guidance unit provides guidance content that can be quickly understood. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance content. Furthermore, the guidance unit can provide optimal guidance content taking the user's living situation into consideration. In this way, by providing guidance content based on the user's living situation, it is possible to promote understanding of the guidance.

[0048] The guidance unit improves the guidance content by reflecting user feedback when providing guidance. The guidance unit improves the guidance content by reflecting user feedback when providing guidance. For example, the guidance unit improves the guidance content based on feedback previously provided by the user. The guidance unit can also customize the guidance content by reflecting user feedback. The guidance unit can also adjust the priority of the guidance content based on user feedback. In this way, by providing guidance content based on user feedback, it is possible to promote understanding of the guidance.

[0049] The guidance unit provides optimal guidance content by taking into consideration the user's geographical location information when providing guidance. The guidance unit provides optimal guidance content by taking into consideration the user's geographical location information when providing guidance. For example, if the user lives in a specific area, the guidance unit may preferentially display guidance content related to that area. The guidance unit may also automatically display area-specific guidance content based on the user's geographical location information. The guidance unit may also suggest an optimal guidance method by taking into consideration the user's geographical location information. This may facilitate understanding of the guidance by providing guidance content based on the user's geographical location information.

[0050] The guidance unit analyzes the user's social media activity and provides related guidance content when providing guidance. The guidance unit analyzes the user's social media activity and provides related guidance content when providing guidance. For example, the guidance unit suggests related guidance content based on information shared by the user on social media. The guidance unit can also analyze the user's social media activity and provide related guidance content. The guidance unit can also suggest related guidance content by referring to the activity of the user's friends on social media. In this way, by providing guidance content based on the user's social media activity, it is possible to promote understanding of the guidance.

[0051] The guidance unit customizes the guidance content by reflecting the user's past feedback when providing guidance. The guidance unit customizes the guidance content by reflecting the user's past feedback when providing guidance. For example, the guidance unit proposes optimal guidance content based on feedback provided by the user in the past. The guidance unit can also customize the guidance interface by reflecting the user's past feedback. The guidance unit can also adjust the priority of the guidance content based on the user's past feedback. This makes it possible to promote understanding of the guidance by providing guidance content based on the user's past feedback.

[0052] The check unit improves the accuracy of the error check by referring to the user's past declaration history when checking. The check unit improves the accuracy of the error check by referring to the user's past declaration history when checking. For example, the check unit improves the accuracy of the error check based on the content of previous declarations made by the user. The check unit can also add related error check items by referring to the user's past declaration history. The check unit can also customize the error check method based on the user's past declaration history. This makes it possible to improve the accuracy of the error check by providing an error check based on the user's past declaration history.

[0053] The check unit customizes the error check items based on the user's current living situation when checking. The check unit customizes the error check items based on the user's current living situation when checking. For example, if the user is busy, the check unit may prioritize displaying important error check items. The check unit may also provide detailed error check items when the user is relaxed. The check unit may also suggest optimal error check items taking the user's living situation into consideration. This allows for improved accuracy of error checks by providing error check items based on the user's living situation.

[0054] The checking unit improves the error check method by reflecting user feedback during the check. The checking unit improves the error check method by reflecting user feedback during the check. For example, the checking unit improves the error check method based on feedback previously provided by the user. The checking unit can also customize the error check interface by reflecting user feedback. The checking unit can also adjust the priority of error checks based on user feedback. In this way, the accuracy of error checks can be improved by providing an error check method based on user feedback.

[0055] The check unit adjusts the error check items during a check by taking into account the user's geographical location information. The check unit adjusts the error check items during a check by taking into account the user's geographical location information. For example, if the user lives in a specific area, the check unit may preferentially display error check items related to that area. The check unit may also automatically display error check items specific to that area based on the user's geographical location information. The check unit may also suggest an optimal error check method by taking into account the user's geographical location information. This allows for improved accuracy of error checks by providing error check items based on the user's geographical location information.

[0056] The check unit analyzes the user's social media activities during the check and adds related error check items. The check unit analyzes the user's social media activities during the check and adds related error check items. For example, the check unit suggests related error check items based on information shared by the user on social media. The check unit can also analyze the user's social media activities and provide related error check items. The check unit can also suggest related error check items based on the activities of the user's friends on social media. In this way, the accuracy of error checks can be improved by providing error check items based on the user's social media activities.

[0057] The check unit customizes the error check method by reflecting the user's past feedback during the check. The check unit customizes the error check method by reflecting the user's past feedback during the check. For example, the check unit suggests an optimal error check method based on feedback provided by the user in the past. The check unit can also customize the error check interface by reflecting the user's past feedback. The check unit can also adjust the priority of error checks based on the user's past feedback. In this way, the accuracy of error checks can be improved by providing an error check method based on the user's past feedback.

[0058] The environment confirmation unit optimizes the confirmation method by referring to the user's past Internet usage history when confirming the environment. The environment confirmation unit optimizes the confirmation method by referring to the user's past Internet usage history when confirming the environment. For example, the environment confirmation unit suggests an optimal confirmation method based on the Internet environment the user has used in the past. The environment confirmation unit can also add related confirmation items by referring to the user's past Internet usage history. The environment confirmation unit can also customize the confirmation method based on the user's past Internet usage history. This makes it possible to streamline the confirmation process by providing a confirmation method based on the user's past Internet usage history.

[0059] The environment confirmation unit adjusts the confirmation method based on the user's current Internet connection status when checking the environment. The environment confirmation unit adjusts the confirmation method based on the user's current Internet connection status when checking the environment. For example, the environment confirmation unit provides a simple confirmation method when the user's Internet connection is unstable. The environment confirmation unit can also provide a detailed confirmation method when the user's Internet connection is stable. The environment confirmation unit can also propose an optimal confirmation method taking into account the user's Internet connection status. This makes it possible to streamline the confirmation process by providing a confirmation method based on the user's Internet connection status.

[0060] The environment confirmation unit optimizes the confirmation method by taking into account the user's geographical location information when confirming the environment. The environment confirmation unit optimizes the confirmation method by taking into account the user's geographical location information when confirming the environment. For example, if the user lives in a specific area, the environment confirmation unit preferentially displays confirmation methods related to that area. The environment confirmation unit can also automatically display confirmation methods specific to that area based on the user's geographical location information. The environment confirmation unit can also suggest the optimal confirmation method by taking into account the user's geographical location information. This makes it possible to streamline the confirmation work by providing confirmation methods based on the user's geographical location information.

[0061] The environment confirmation unit analyzes the user's social media activity and adds related check items when checking the environment. The environment confirmation unit analyzes the user's social media activity and adds related check items when checking the environment. For example, the environment confirmation unit suggests related check items based on information shared by the user on social media. The environment confirmation unit can also analyze the user's social media activity and provide related check items. The environment confirmation unit can also suggest related check items based on the activity of the user's friends on social media. This makes it possible to streamline the check work by providing check items based on the user's social media activity.

[0062] When providing electronic filing guidance, the electronic filing guidance unit optimizes the guidance content by referring to the user's past electronic filing history. When providing electronic filing guidance, the electronic filing guidance unit optimizes the guidance content by referring to the user's past electronic filing history. For example, the electronic filing guidance unit provides optimal guidance content based on the content of past filings made by the user. The electronic filing guidance unit can also provide related guidance content by referring to the user's past electronic filing history. The electronic filing guidance unit can also customize the guidance content based on the user's past electronic filing history. This makes it possible to provide guidance content based on the user's past electronic filing history, thereby making the guidance work more efficient.

[0063] The electronic filing guidance unit adjusts the guidance content based on the user's current internet connection status when providing electronic filing guidance. The electronic filing guidance unit adjusts the guidance content based on the user's current internet connection status when providing electronic filing guidance. For example, the electronic filing guidance unit provides simple guidance content when the user's internet connection is unstable. The electronic filing guidance unit can also provide detailed guidance content when the user's internet connection is stable. The electronic filing guidance unit can also suggest the optimal guidance method taking into account the user's internet connection status. This makes it possible to provide guidance content based on the user's internet connection status, thereby making the guidance work more efficient.

[0064] The electronic filing guidance unit optimizes the guidance content by taking into account the user's geographical location information when providing electronic filing guidance. The electronic filing guidance unit optimizes the guidance content by taking into account the user's geographical location information when providing electronic filing guidance. For example, if the user lives in a specific area, the electronic filing guidance unit prioritizes displaying guidance content related to that area. The electronic filing guidance unit can also automatically display guidance content specific to the area based on the user's geographical location information. The electronic filing guidance unit can also suggest the optimal guidance method by taking into account the user's geographical location information. This makes it possible to streamline guidance work by providing guidance content based on the user's geographical location information.

[0065] The electronic filing guidance unit analyzes the user's social media activity and adds related guidance items when providing electronic filing guidance. The electronic filing guidance unit analyzes the user's social media activity and adds related guidance items when providing electronic filing guidance. For example, the electronic filing guidance unit suggests related guidance items based on information shared by the user on social media. The electronic filing guidance unit can also analyze the user's social media activity and provide related guidance items. The electronic filing guidance unit can also suggest related guidance items based on the activity of the user's friends on social media. This makes it possible to provide guidance items based on the user's social media activity, thereby making the guidance process more efficient.

[0066] When providing paper application guidance, the paper application guidance unit optimizes the guidance content by referring to the user's past paper application history. When providing paper application guidance, the paper application guidance unit optimizes the guidance content by referring to the user's past paper application history. For example, the paper application guidance unit provides optimal guidance content based on the content the user has previously declared. The paper application guidance unit can also provide related guidance content by referring to the user's past paper application history. The paper application guidance unit can also customize the guidance content based on the user's past paper application history. This makes it possible to streamline guidance work by providing guidance content based on the user's past paper application history.

[0067] The paper application guide unit adjusts the guide content based on the user's current living situation when providing paper application guidance. The paper application guide unit adjusts the guide content based on the user's current living situation when providing paper application guidance. For example, if the user is busy, the paper application guide unit provides guide content that can be quickly understood. Furthermore, if the user is relaxed, the paper application guide unit can also provide detailed guide content. Furthermore, the paper application guide unit can also provide optimal guide content taking into account the user's living situation. This makes it possible to streamline guidance work by providing guide content based on the user's living situation.

[0068] The paper application guide unit optimizes the guide content by taking into account the user's geographical location information when providing paper application guidance. The paper application guide unit optimizes the guide content by taking into account the user's geographical location information when providing paper application guidance. For example, if the user lives in a specific area, the paper application guide unit prioritizes displaying guide content related to that area. The paper application guide unit can also automatically display area-specific guide content based on the user's geographical location information. The paper application guide unit can also suggest the optimal guide method by taking into account the user's geographical location information. This makes it possible to streamline guidance work by providing guide content based on the user's geographical location information.

[0069] The paper application guide unit analyzes the user's social media activity and adds related guidance items when providing paper application guidance. The paper application guide unit analyzes the user's social media activity and adds related guidance items when providing paper application guidance. For example, the paper application guide unit suggests related guidance items based on information shared by the user on social media. The paper application guide unit can also analyze the user's social media activity and provide related guidance items. The paper application guide unit can also suggest related guidance items based on the activity of the user's friends on social media. This makes it possible to streamline guidance work by providing guidance items based on the user's social media activity.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The input unit can also analyze the user's input speed in real time and adjust the response speed of the interface according to the input speed. For example, if the user is inputting quickly, the input unit can increase the response speed of the interface to minimize input delays. Also, if the user is inputting slowly, the input unit can adjust the response speed of the interface to make it easier for the user to check the input content. Furthermore, the input unit can dynamically change the display order of input fields based on the user's input speed to enable the user to input efficiently. This can improve the efficiency of input work by providing an interface that suits the user's input speed.

[0072] The selection unit can also analyze the user's past selection history of reporting methods and suggest the optimal reporting method. For example, the selection unit can preferentially suggest reporting methods that the user has frequently selected in the past. The selection unit can also predict and suggest a reporting method that the user will prefer based on the user's past selection history of reporting methods. Furthermore, the selection unit can also customize the reporting method options by referring to the user's past selection history of reporting methods. This can make reporting work more efficient by providing the optimal reporting method based on the user's past selection history of reporting methods.

[0073] The input unit can also analyze the user's past declaration history and suggest the optimal input method. For example, the input unit can automatically display declaration details that the user has frequently entered in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest declaration details to be used in a specific time period based on the user's past declaration history. This can make input work more efficient by suggesting the optimal input method based on the user's past declaration history.

[0074] When making a selection, the selection unit can evaluate the stability of the user's internet environment and suggest the optimal filing method. For example, if the user's internet connection is unstable, the selection unit can preferentially suggest paper filing. Also, if the user's internet connection is stable, the selection unit can preferentially suggest e-Tax. Furthermore, the selection unit can evaluate the user's internet environment and suggest the optimal filing method. This can make the filing process more efficient by providing a filing method based on the user's internet environment.

[0075] The guidance unit can also customize the guidance content by referring to the user's past declaration history when providing guidance. For example, the guidance unit can provide optimal guidance content based on the content previously declared by the user. The guidance unit can also provide relevant guidance content by referring to the user's past declaration history. Furthermore, the guidance unit can also customize the guidance content based on the user's past declaration history. This can promote understanding of the guidance by providing guidance content based on the user's past declaration history.

[0076] The check unit can also improve the accuracy of the error check by referring to the user's past declaration history during the check. For example, the check unit can improve the accuracy of the error check based on the content of the user's past declarations. The check unit can also add related error check items by referring to the user's past declaration history. Furthermore, the check unit can also customize the error check method based on the user's past declaration history. In this way, the accuracy of the error check can be improved by providing an error check based on the user's past declaration history.

[0077] The environment confirmation unit can also optimize the confirmation method by taking into account the user's geographical location information when confirming the environment. For example, if the user lives in a specific area, the environment confirmation unit can preferentially display confirmation methods related to that area. The environment confirmation unit can also automatically display confirmation methods specific to that area based on the user's geographical location information. Furthermore, the environment confirmation unit can also suggest the optimal confirmation method by taking into account the user's geographical location information. This can make the confirmation process more efficient by providing confirmation methods based on the user's geographical location information.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The input section provides an interface for taxpayers to input the tax return details. Taxpayers can input the necessary tax return details by selecting, for example, the type of income or the type of deduction. Step 2: The selection unit selects a filing method based on the information entered by the input unit. For example, the selection unit recommends e-Tax if an internet connection is available, and paper filing if an internet connection is not available. Step 3: The guidance section guides the user through the necessary documents and procedures depending on the filing method selected by the selection section. For example, in the case of electronic filing, the guidance section provides a detailed explanation of the electronic filing procedure and guides the user on how to submit the necessary documents electronically. In the case of paper filing, the guidance section guides the user on the procedure for printing and mailing the necessary documents. Step 4: The Checking Department checks the declaration contents based on the procedures provided by the Guidance Department and checks for errors. For example, the Checking Department checks for errors before submission to prevent mistakes in the declaration contents.

[0080] (Example 2) The tax return support system according to an embodiment of the present invention streamlines the entire process, from inputting tax return information to error checking. The system provides an interface for taxpayers to input their tax return information, selects a filing method based on the input information, guides them through the necessary documents and procedures, and checks the tax return for errors. For example, the system allows taxpayers to input the necessary tax return information by selecting the type of income and deductions. The system then recommends e-Tax if an internet connection is available, and paper filing if an internet connection is not available. Furthermore, for electronic filing, the system provides detailed instructions on the electronic filing procedure and guides users on how to electronically submit the necessary documents. For paper filing, the system guides users on how to print and mail the necessary documents. Finally, the system provides a function for reviewing tax return information and checking for errors before submission. This allows taxpayers to select the filing method that best suits them and easily file their tax returns. It also reduces the workload of tax office staff, contributing to greater efficiency across society. This allows taxpayers to choose the filing method that best suits them and file their tax returns easily. It also reduces the workload of tax office staff, contributing to greater efficiency across society.

[0081] The tax return support system according to the embodiment includes an input unit, a selection unit, a guidance unit, and a check unit. The input unit provides an interface for a taxpayer to input tax return details. The taxpayer can input necessary tax return details by selecting, for example, the type of income or the type of deduction. The selection unit selects a tax return method based on the information input by the input unit. For example, the selection unit recommends e-Tax if an internet connection is available, and recommends paper filing if an internet connection is not available. The guidance unit provides guidance on necessary documents and procedures according to the tax return method selected by the selection unit. For example, in the case of electronic filing, the guidance unit provides detailed explanations of the electronic filing procedure and guidance on how to electronically submit the necessary documents. In the case of paper filing, the guidance unit provides guidance on how to print and mail the necessary documents. The check unit verifies the tax return details based on the procedures provided by the guidance unit and checks for errors. For example, the check unit checks for errors before submission to prevent mistakes in the tax return details. This allows the tax return support system according to the embodiment to streamline a series of procedures from inputting tax return details to error checking.

[0082] The selection unit includes an environment confirmation unit that confirms whether or not an internet environment is available. The environment confirmation unit confirms whether or not an internet environment is available. For example, the environment confirmation unit evaluates the internet connection speed and connection stability to determine whether or not an internet environment is available. This enables the selection unit to select a filing method that suits the internet environment. For example, if the internet connection is stable, e-Tax is recommended, and if the internet connection is unstable, paper filing is recommended. This enables the selection of a filing method that suits the internet environment.

[0083] The guidance unit includes an electronic filing guidance unit that provides detailed explanations of the electronic filing procedures. The electronic filing guidance unit provides detailed explanations of the electronic filing procedures. For example, the electronic filing guidance unit provides information on the software to be used and the necessary documents, and provides detailed explanations of the electronic filing procedures. This provides detailed guidance on the electronic filing procedures, thereby improving user convenience. For example, the electronic filing guidance unit provides step-by-step explanations of the electronic filing procedures, allowing the user to proceed with the procedures without confusion. The electronic filing guidance unit also provides information on how to electronically submit the necessary documents, allowing the user to file electronically smoothly. This provides detailed guidance on the electronic filing procedures, thereby improving user convenience.

[0084] The guidance unit includes a paper application guidance unit that provides detailed explanations of the paper application procedures. The paper application guidance unit provides detailed explanations of the paper application procedures. For example, the paper application guidance unit provides guidance on the procedures for printing and mailing the necessary documents. This provides detailed guidance on the paper application procedures, thereby improving user convenience. For example, the paper application guidance unit provides step-by-step explanations of the paper application procedures, allowing the user to proceed with the procedures without confusion. The paper application guidance unit also provides guidance on how to print and mail the necessary documents, allowing the user to smoothly complete the paper application. This provides detailed guidance on the paper application procedures, thereby improving user convenience.

[0085] The checking unit checks for errors in the declaration content. The checking unit checks for errors in the declaration content. For example, the checking unit checks for errors before submission to prevent mistakes in the declaration content. In this way, mistakes can be prevented by checking for errors in the declaration content in advance. For example, the checking unit detects deficiencies or errors in the declaration content and prompts the user to correct them. The checking unit also verifies the consistency of the declaration content and checks for errors. In this way, mistakes can be prevented by checking for errors in the declaration content in advance.

[0086] The input unit estimates the user's emotion and adjusts the design of the input interface based on the estimated user emotion. The input unit estimates the user's emotion and adjusts the design of the input interface based on the estimated user emotion. For example, if the user is nervous, the input unit provides an interface with subdued colors to reduce visual stress. If the user is having fun, the input unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the input unit can provide a simple, highly visible interface to make input work easier. This reduces the stress of input work by providing an interface that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The input unit analyzes the user's past declaration history and suggests the optimal input method. The input unit analyzes the user's past declaration history and suggests the optimal input method. For example, the input unit automatically displays declaration details that the user has frequently entered in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest declaration details to be used in a specific time period based on the user's past declaration history. This makes it possible to make input work more efficient by suggesting the optimal input method based on the user's past declaration history.

[0088] The input unit customizes input fields based on the user's current income status and deduction items at the time of input. The input unit customizes input fields based on the user's current income status and deduction items at the time of input. For example, the input unit automatically displays necessary input fields according to the user's income status. The input unit can also preferentially display related input fields based on the user's deduction items. The input unit can also suggest an optimal input method based on the user's income status and deduction items. This makes it possible to streamline input work by providing input fields according to the user's income status and deduction items.

[0089] The input unit selects the optimal input means depending on the user's input method when inputting. The input unit selects the optimal input means depending on the user's input method when inputting. For example, if the user selects voice input, the input unit automatically converts the input content into text using voice recognition technology. Furthermore, if the user selects text input, the input unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the input unit can automatically analyze the input content using image recognition technology. This makes it possible to streamline input work by providing the optimal means depending on the user's input method.

[0090] The input unit estimates the user's emotions and prioritizes input content based on the estimated user emotions. The input unit estimates the user's emotions and prioritizes input content based on the estimated user emotions. For example, when the user is stressed, the input unit prioritizes displaying important input content to simplify the input procedure. Furthermore, when the user is relaxed, the input unit can provide detailed input content and suggest a customizable input method. Furthermore, when the user is in a hurry, the input unit can prioritize displaying the most important input content to enable quick input. This can improve the efficiency of input work by prioritizing input content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The input unit, when inputting data, prioritizes displaying highly relevant input fields taking into account the user's geographical location information. The input unit, when inputting data, prioritizes displaying highly relevant input fields taking into account the user's geographical location information. For example, if the user lives in a specific region, the input unit prioritizes displaying input fields related to that region. The input unit can also automatically display region-specific deduction items based on the user's geographical location information. The input unit can also suggest an optimal input method taking into account the user's geographical location information. This makes it possible to streamline input work by providing input fields based on the user's geographical location information.

[0092] The input unit analyzes the user's social media activity during input and suggests related input content. The input unit analyzes the user's social media activity during input and suggests related input content. For example, the input unit suggests related input fields based on income information shared by the user on social media. The input unit can also analyze the user's social media activity and suggest related deduction items. The input unit can also suggest related input content based on the activity of the user's friends on social media. This makes it possible to make input work more efficient by suggesting input content based on the user's social media activity.

[0093] The input unit customizes the input method by reflecting the user's past feedback when inputting data. The input unit customizes the input method by reflecting the user's past feedback when inputting data. For example, the input unit suggests an optimal input method based on feedback provided by the user in the past. The input unit can also customize the input interface by reflecting the user's past feedback. The input unit can also adjust the priority of input content based on the user's past feedback. This makes it possible to improve the efficiency of input work by providing an input method based on the user's past feedback.

[0094] The selection unit estimates the user's emotions and presents options for reporting methods based on the estimated user emotions. The selection unit estimates the user's emotions and presents options for reporting methods based on the estimated user emotions. For example, if the user is stressed, the selection unit may preferentially present a simple reporting method. Alternatively, if the user is relaxed, the selection unit may present a detailed reporting method and provide customizable options. Alternatively, if the user is in a hurry, the selection unit may preferentially present a method that allows for quick reporting. This provides options for reporting methods according to the user's emotions, thereby making the reporting process more efficient. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] At the time of selection, the selection unit evaluates the stability of the user's Internet environment and suggests the optimal filing method. At the time of selection, the selection unit evaluates the stability of the user's Internet environment and suggests the optimal filing method. For example, if the user's Internet connection is unstable, the selection unit may preferentially suggest paper filing. Also, if the user's Internet connection is stable, the selection unit may preferentially suggest e-Tax. Also, the selection unit may evaluate the user's Internet environment and suggest the optimal filing method. This makes it possible to streamline the filing process by providing a filing method based on the user's Internet environment.

[0096] The selection unit customizes the options by referring to the user's past history of reporting methods when making a selection. The selection unit customizes the options by referring to the user's past history of reporting methods when making a selection. For example, the selection unit preferentially suggests reporting methods that the user has used in the past. The selection unit can also suggest the optimal reporting method based on the user's past reporting history. The selection unit can also customize the options by referring to the user's past history of reporting methods. This makes it possible to streamline reporting work by providing reporting methods based on the user's past reporting history.

[0097] The selection unit suggests a reporting method based on the user's current living situation at the time of selection. The selection unit suggests a reporting method based on the user's current living situation at the time of selection. For example, if the user is busy, the selection unit suggests a method that allows for quick reporting. The selection unit can also suggest a detailed reporting method if the user is relaxed. The selection unit can also suggest an optimal reporting method taking the user's living situation into consideration. This makes it possible to streamline reporting work by providing a reporting method based on the user's living situation.

[0098] The selection unit estimates the user's emotions and prioritizes the reporting methods based on the estimated user emotions. The selection unit estimates the user's emotions and prioritizes the reporting methods based on the estimated user emotions. For example, if the user is stressed, the selection unit prioritizes simple reporting methods. If the user is relaxed, the selection unit can also present detailed reporting methods and provide customizable options. If the user is in a hurry, the selection unit can prioritize methods that allow for quick reporting. This allows for more efficient reporting by prioritizing reporting methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The selection unit, at the time of selection, proposes the optimal reporting method taking into consideration the user's geographical location information. The selection unit, at the time of selection, proposes the optimal reporting method taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the selection unit proposes a reporting method suitable for that area. The selection unit can also propose a reporting method specific to that area based on the user's geographical location information. The selection unit can also propose the optimal reporting method taking into consideration the user's geographical location information. This makes it possible to streamline reporting work by providing a reporting method based on the user's geographical location information.

[0100] At the time of selection, the selection unit analyzes the user's social media activity and suggests a relevant reporting method. At the time of selection, the selection unit analyzes the user's social media activity and suggests a relevant reporting method. For example, the selection unit suggests a relevant reporting method based on information shared by the user on social media. The selection unit can also analyze the content of the user's social media activity and suggest a relevant reporting method. The selection unit can also suggest a relevant reporting method based on the activity of the user's friends on social media. This makes it possible to streamline reporting by providing a reporting method based on the user's social media activity.

[0101] The selection unit customizes the reporting method by reflecting the user's past feedback when making a selection. The selection unit customizes the reporting method by reflecting the user's past feedback when making a selection. For example, the selection unit suggests an optimal reporting method based on feedback provided by the user in the past. The selection unit can also customize the reporting method by reflecting the user's past feedback. The selection unit can also adjust the priority of the reporting methods based on the user's past feedback. This makes it possible to streamline reporting work by providing a reporting method based on the user's past feedback.

[0102] The guidance unit estimates the user's emotions and adjusts the presentation method of the guidance content based on the estimated user emotions. The guidance unit estimates the user's emotions and adjusts the presentation method of the guidance content based on the estimated user emotions. For example, if the user is nervous, the guidance unit provides guidance content in a calm tone. If the user is enjoying themselves, the guidance unit can also provide guidance content in a bright tone. If the user is tired, the guidance unit can also provide guidance content that is simple and highly visible. This makes it possible to promote understanding of the guidance by providing guidance content that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The guidance unit customizes the guidance content by referring to the user's past declaration history when providing guidance. The guidance unit customizes the guidance content by referring to the user's past declaration history when providing guidance. For example, the guidance unit provides optimal guidance content based on the content previously declared by the user. The guidance unit can also provide related guidance content by referring to the user's past declaration history. The guidance unit can also customize the guidance content based on the user's past declaration history. In this way, by providing guidance content based on the user's past declaration history, it is possible to promote understanding of the guidance.

[0104] The guidance unit adjusts the guidance content based on the user's current living situation when providing guidance. The guidance unit adjusts the guidance content based on the user's current living situation when providing guidance. For example, if the user is busy, the guidance unit provides guidance content that can be quickly understood. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance content. Furthermore, the guidance unit can provide optimal guidance content taking the user's living situation into consideration. In this way, by providing guidance content based on the user's living situation, it is possible to promote understanding of the guidance.

[0105] The guidance unit improves the guidance content by reflecting user feedback when providing guidance. The guidance unit improves the guidance content by reflecting user feedback when providing guidance. For example, the guidance unit improves the guidance content based on feedback previously provided by the user. The guidance unit can also customize the guidance content by reflecting user feedback. The guidance unit can also adjust the priority of the guidance content based on user feedback. In this way, by providing guidance content based on user feedback, it is possible to promote understanding of the guidance.

[0106] The guidance unit estimates the user's emotions and determines the priority of guidance content based on the estimated user emotions. The guidance unit estimates the user's emotions and determines the priority of guidance content based on the estimated user emotions. For example, if the user is feeling stressed, the guidance unit prioritizes displaying important guidance content and simplifying the guidance procedure. Furthermore, if the user is relaxed, the guidance unit can provide detailed guidance content and suggest customizable guidance methods. Furthermore, if the user is in a hurry, the guidance unit can prioritize displaying the most important guidance content to enable quick guidance. This can promote understanding of the guidance by providing a priority of guidance content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The guidance unit provides optimal guidance content by taking into consideration the user's geographical location information when providing guidance. The guidance unit provides optimal guidance content by taking into consideration the user's geographical location information when providing guidance. For example, if the user lives in a specific area, the guidance unit may preferentially display guidance content related to that area. The guidance unit may also automatically display area-specific guidance content based on the user's geographical location information. The guidance unit may also suggest an optimal guidance method by taking into consideration the user's geographical location information. This may facilitate understanding of the guidance by providing guidance content based on the user's geographical location information.

[0108] The guidance unit analyzes the user's social media activity and provides related guidance content when providing guidance. The guidance unit analyzes the user's social media activity and provides related guidance content when providing guidance. For example, the guidance unit suggests related guidance content based on information shared by the user on social media. The guidance unit can also analyze the user's social media activity and provide related guidance content. The guidance unit can also suggest related guidance content by referring to the activity of the user's friends on social media. In this way, by providing guidance content based on the user's social media activity, it is possible to promote understanding of the guidance.

[0109] The guidance unit customizes the guidance content by reflecting the user's past feedback when providing guidance. The guidance unit customizes the guidance content by reflecting the user's past feedback when providing guidance. For example, the guidance unit proposes optimal guidance content based on feedback provided by the user in the past. The guidance unit can also customize the guidance interface by reflecting the user's past feedback. The guidance unit can also adjust the priority of the guidance content based on the user's past feedback. This makes it possible to promote understanding of the guidance by providing guidance content based on the user's past feedback.

[0110] The check unit estimates the user's emotion and adjusts the error check method based on the estimated user emotion. The check unit estimates the user's emotion and adjusts the error check method based on the estimated user emotion. For example, if the user is nervous, the check unit provides a simple, highly visible error check method. If the user is relaxed, the check unit can also provide a detailed error check method. If the user is in a hurry, the check unit can also provide an error check method that focuses on the main points. This provides an error check method that corresponds to the user's emotion, thereby improving the accuracy of error check. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The check unit improves the accuracy of the error check by referring to the user's past declaration history when checking. The check unit improves the accuracy of the error check by referring to the user's past declaration history when checking. For example, the check unit improves the accuracy of the error check based on the content of previous declarations made by the user. The check unit can also add related error check items by referring to the user's past declaration history. The check unit can also customize the error check method based on the user's past declaration history. This makes it possible to improve the accuracy of the error check by providing an error check based on the user's past declaration history.

[0112] The check unit customizes the error check items based on the user's current living situation when checking. The check unit customizes the error check items based on the user's current living situation when checking. For example, if the user is busy, the check unit may prioritize displaying important error check items. The check unit may also provide detailed error check items when the user is relaxed. The check unit may also suggest optimal error check items taking the user's living situation into consideration. This allows for improved accuracy of error checks by providing error check items based on the user's living situation.

[0113] The checking unit improves the error check method by reflecting user feedback during the check. The checking unit improves the error check method by reflecting user feedback during the check. For example, the checking unit improves the error check method based on feedback previously provided by the user. The checking unit can also customize the error check interface by reflecting user feedback. The checking unit can also adjust the priority of error checks based on user feedback. In this way, the accuracy of error checks can be improved by providing an error check method based on user feedback.

[0114] The check unit estimates the user's emotions and determines the priority of error checks based on the estimated user emotions. The check unit estimates the user's emotions and determines the priority of error checks based on the estimated user emotions. For example, if the user is feeling stressed, the check unit prioritizes displaying important error check items to simplify the check procedure. Furthermore, if the user is relaxed, the check unit can provide detailed error check items and suggest a customizable check method. Furthermore, if the user is in a hurry, the check unit can prioritize displaying the most important error check items to enable quick checks. This improves the accuracy of error checks by prioritizing error checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The check unit adjusts the error check items during a check by taking into account the user's geographical location information. The check unit adjusts the error check items during a check by taking into account the user's geographical location information. For example, if the user lives in a specific area, the check unit may preferentially display error check items related to that area. The check unit may also automatically display error check items specific to that area based on the user's geographical location information. The check unit may also suggest an optimal error check method by taking into account the user's geographical location information. This allows for improved accuracy of error checks by providing error check items based on the user's geographical location information.

[0116] The check unit analyzes the user's social media activities during the check and adds related error check items. The check unit analyzes the user's social media activities during the check and adds related error check items. For example, the check unit suggests related error check items based on information shared by the user on social media. The check unit can also analyze the user's social media activities and provide related error check items. The check unit can also suggest related error check items based on the activities of the user's friends on social media. In this way, the accuracy of error checks can be improved by providing error check items based on the user's social media activities.

[0117] The check unit customizes the error check method by reflecting the user's past feedback during the check. The check unit customizes the error check method by reflecting the user's past feedback during the check. For example, the check unit suggests an optimal error check method based on feedback provided by the user in the past. The check unit can also customize the error check interface by reflecting the user's past feedback. The check unit can also adjust the priority of error checks based on the user's past feedback. In this way, the accuracy of error checks can be improved by providing an error check method based on the user's past feedback.

[0118] The environment confirmation unit estimates the user's emotions and adjusts the method of checking the Internet environment based on the estimated user emotions. The environment confirmation unit estimates the user's emotions and adjusts the method of checking the Internet environment based on the estimated user emotions. For example, if the user is nervous, the environment confirmation unit provides a simple and highly visible method of checking. If the user is relaxed, the environment confirmation unit can also provide a detailed method of checking. If the user is in a hurry, the environment confirmation unit can also provide a method of checking that focuses on the main points. This allows for the efficiency of checking by providing a method of checking the Internet environment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The environment confirmation unit optimizes the confirmation method by referring to the user's past Internet usage history when confirming the environment. The environment confirmation unit optimizes the confirmation method by referring to the user's past Internet usage history when confirming the environment. For example, the environment confirmation unit suggests an optimal confirmation method based on the Internet environment the user has used in the past. The environment confirmation unit can also add related confirmation items by referring to the user's past Internet usage history. The environment confirmation unit can also customize the confirmation method based on the user's past Internet usage history. This makes it possible to streamline the confirmation process by providing a confirmation method based on the user's past Internet usage history.

[0120] The environment confirmation unit adjusts the confirmation method based on the user's current Internet connection status when checking the environment. The environment confirmation unit adjusts the confirmation method based on the user's current Internet connection status when checking the environment. For example, the environment confirmation unit provides a simple confirmation method when the user's Internet connection is unstable. The environment confirmation unit can also provide a detailed confirmation method when the user's Internet connection is stable. The environment confirmation unit can also propose an optimal confirmation method taking into account the user's Internet connection status. This makes it possible to streamline the confirmation process by providing a confirmation method based on the user's Internet connection status.

[0121] The environment confirmation unit estimates the user's emotions and adjusts the frequency of checking the Internet environment based on the estimated user emotions. The environment confirmation unit estimates the user's emotions and adjusts the frequency of checking the Internet environment based on the estimated user emotions. For example, if the user is feeling stressed, the environment confirmation unit reduces the checking frequency and provides a simple checking method. Furthermore, if the user is relaxed, the environment confirmation unit can provide a detailed checking method and increase the checking frequency. Furthermore, if the user is in a hurry, the environment confirmation unit can provide a checking method that focuses on the main points and adjust the checking frequency. This allows for the efficiency of checking work by providing a checking frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0122] The environment confirmation unit optimizes the confirmation method by taking into account the user's geographical location information when confirming the environment. The environment confirmation unit optimizes the confirmation method by taking into account the user's geographical location information when confirming the environment. For example, if the user lives in a specific area, the environment confirmation unit preferentially displays confirmation methods related to that area. The environment confirmation unit can also automatically display confirmation methods specific to that area based on the user's geographical location information. The environment confirmation unit can also suggest the optimal confirmation method by taking into account the user's geographical location information. This makes it possible to streamline the confirmation work by providing confirmation methods based on the user's geographical location information.

[0123] The environment confirmation unit analyzes the user's social media activity and adds related check items when checking the environment. The environment confirmation unit analyzes the user's social media activity and adds related check items when checking the environment. For example, the environment confirmation unit suggests related check items based on information shared by the user on social media. The environment confirmation unit can also analyze the user's social media activity and provide related check items. The environment confirmation unit can also suggest related check items based on the activity of the user's friends on social media. This makes it possible to streamline the check work by providing check items based on the user's social media activity.

[0124] The electronic filing guidance unit estimates the user's emotions and adjusts the electronic filing guidance method based on the estimated user emotions. The electronic filing guidance unit estimates the user's emotions and adjusts the electronic filing guidance method based on the estimated user emotions. For example, if the user is nervous, the electronic filing guidance unit can provide the electronic filing guidance in a calm tone. If the user is enjoying themselves, the electronic filing guidance unit can provide the electronic filing guidance in a cheerful tone. If the user is tired, the electronic filing guidance unit can provide the electronic filing guidance in a simple, highly visible manner. This makes it possible to provide an electronic filing guidance method that corresponds to the user's emotions, thereby improving the efficiency of the guidance work. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0125] When providing electronic filing guidance, the electronic filing guidance unit optimizes the guidance content by referring to the user's past electronic filing history. When providing electronic filing guidance, the electronic filing guidance unit optimizes the guidance content by referring to the user's past electronic filing history. For example, the electronic filing guidance unit provides optimal guidance content based on the content of past filings made by the user. The electronic filing guidance unit can also provide related guidance content by referring to the user's past electronic filing history. The electronic filing guidance unit can also customize the guidance content based on the user's past electronic filing history. This makes it possible to provide guidance content based on the user's past electronic filing history, thereby making the guidance work more efficient.

[0126] The electronic filing guidance unit adjusts the guidance content based on the user's current internet connection status when providing electronic filing guidance. The electronic filing guidance unit adjusts the guidance content based on the user's current internet connection status when providing electronic filing guidance. For example, the electronic filing guidance unit provides simple guidance content when the user's internet connection is unstable. The electronic filing guidance unit can also provide detailed guidance content when the user's internet connection is stable. The electronic filing guidance unit can also suggest the optimal guidance method taking into account the user's internet connection status. This makes it possible to provide guidance content based on the user's internet connection status, thereby making the guidance work more efficient.

[0127] The electronic filing guidance unit estimates the user's emotions and adjusts the frequency of electronic filing notifications based on the estimated user emotions. The electronic filing guidance unit estimates the user's emotions and adjusts the frequency of electronic filing notifications based on the estimated user emotions. For example, if the user is feeling stressed, the electronic filing guidance unit reduces the frequency of notifications and provides a simpler method of notification. If the user is relaxed, the electronic filing guidance unit can provide a more detailed method of notification and increase the frequency of notifications. If the user is in a hurry, the electronic filing guidance unit can provide a method of notification that focuses on the main points and adjust the frequency of notifications. This makes it possible to provide a frequency of notifications according to the user's emotions, thereby improving the efficiency of guidance work. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The electronic filing guidance unit optimizes the guidance content by taking into account the user's geographical location information when providing electronic filing guidance. The electronic filing guidance unit optimizes the guidance content by taking into account the user's geographical location information when providing electronic filing guidance. For example, if the user lives in a specific area, the electronic filing guidance unit prioritizes displaying guidance content related to that area. The electronic filing guidance unit can also automatically display guidance content specific to the area based on the user's geographical location information. The electronic filing guidance unit can also suggest the optimal guidance method by taking into account the user's geographical location information. This makes it possible to streamline guidance work by providing guidance content based on the user's geographical location information.

[0129] The electronic filing guidance unit analyzes the user's social media activity and adds related guidance items when providing electronic filing guidance. The electronic filing guidance unit analyzes the user's social media activity and adds related guidance items when providing electronic filing guidance. For example, the electronic filing guidance unit suggests related guidance items based on information shared by the user on social media. The electronic filing guidance unit can also analyze the user's social media activity and provide related guidance items. The electronic filing guidance unit can also suggest related guidance items based on the activity of the user's friends on social media. This makes it possible to provide guidance items based on the user's social media activity, thereby making the guidance process more efficient.

[0130] The paper application guidance unit estimates the user's emotions and adjusts the paper application guidance method based on the estimated user emotions. The paper application guidance unit estimates the user's emotions and adjusts the paper application guidance method based on the estimated user emotions. For example, if the user is nervous, the paper application guidance unit provides paper application guidance in a calm tone. Furthermore, if the user is enjoying themselves, the paper application guidance unit can provide paper application guidance in a bright tone. Furthermore, if the user is tired, the paper application guidance unit can provide paper application guidance that is simple and highly visible. This allows for the efficiency of guidance work by providing a paper application guidance method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] When providing paper application guidance, the paper application guidance unit optimizes the guidance content by referring to the user's past paper application history. When providing paper application guidance, the paper application guidance unit optimizes the guidance content by referring to the user's past paper application history. For example, the paper application guidance unit provides optimal guidance content based on the content the user has previously declared. The paper application guidance unit can also provide related guidance content by referring to the user's past paper application history. The paper application guidance unit can also customize the guidance content based on the user's past paper application history. This makes it possible to streamline guidance work by providing guidance content based on the user's past paper application history.

[0132] The paper application guide unit adjusts the guide content based on the user's current living situation when providing paper application guidance. The paper application guide unit adjusts the guide content based on the user's current living situation when providing paper application guidance. For example, if the user is busy, the paper application guide unit provides guide content that can be quickly understood. Furthermore, if the user is relaxed, the paper application guide unit can also provide detailed guide content. Furthermore, the paper application guide unit can also provide optimal guide content taking into account the user's living situation. This makes it possible to streamline guidance work by providing guide content based on the user's living situation.

[0133] The paper application guidance unit estimates the user's emotions and adjusts the frequency of paper application guidance based on the estimated user emotions. The paper application guidance unit estimates the user's emotions and adjusts the frequency of paper application guidance based on the estimated user emotions. For example, if the user is feeling stressed, the paper application guidance unit reduces the frequency of guidance and provides a simple guidance method. Also, if the user is relaxed, the paper application guidance unit can provide a detailed guidance method and increase the frequency of guidance. Also, if the user is in a hurry, the paper application guidance unit can provide a guidance method that focuses on the main points and adjust the frequency of guidance. This allows for the efficiency of guidance work by providing a frequency of guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0134] The paper application guide unit optimizes the guide content by taking into account the user's geographical location information when providing paper application guidance. The paper application guide unit optimizes the guide content by taking into account the user's geographical location information when providing paper application guidance. For example, if the user lives in a specific area, the paper application guide unit prioritizes displaying guide content related to that area. The paper application guide unit can also automatically display area-specific guide content based on the user's geographical location information. The paper application guide unit can also suggest the optimal guide method by taking into account the user's geographical location information. This makes it possible to streamline guidance work by providing guide content based on the user's geographical location information.

[0135] The paper application guide unit analyzes the user's social media activity and adds related guidance items when providing paper application guidance. The paper application guide unit analyzes the user's social media activity and adds related guidance items when providing paper application guidance. For example, the paper application guide unit suggests related guidance items based on information shared by the user on social media. The paper application guide unit can also analyze the user's social media activity and provide related guidance items. The paper application guide unit can also suggest related guidance items based on the activity of the user's friends on social media. This makes it possible to streamline guidance work by providing guidance items based on the user's social media activity. === Hard Collateral 1-1 === For example, each of the multiple elements including the input unit, selection unit, guidance unit, and check unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the input unit provides an interface for the taxpayer to input the tax return details using the reception device 38 of the smart device 14. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects a tax return method based on the input information. The guidance unit uses the output device 40 of the smart device 14 to guide the taxpayer through the necessary documents and procedures. The check unit verifies the tax return details and checks for errors using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === For example, each of the multiple elements including the input unit, selection unit, guidance unit, and check unit is realized in at least one of the smart glasses 214 and the data processing device 12. For example, the input unit provides an interface for the taxpayer to input the tax return details using the microphone 238 of the smart glasses 214. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects the tax return method based on the input information. The guidance unit uses the speaker 240 of the smart glasses 214 to guide the taxpayer through the necessary documents and procedures. The check unit verifies the tax return details and checks for errors using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === For example, each of the multiple elements including the input unit, selection unit, guidance unit, and check unit is realized in at least one of the headset terminal 314 and the data processing device 12. For example, the input unit provides an interface for the taxpayer to input the tax return details using the microphone 238 of the headset terminal 314. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects the tax return method based on the input information. The guidance unit provides guidance on the necessary documents and procedures using the speaker 240 of the headset terminal 314. The check unit verifies the tax return details and checks for errors using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === For example, each of the multiple elements including the input unit, selection unit, guidance unit, and check unit is realized by at least one of the robot 414 and the data processing device 12. For example, the input unit provides an interface for the taxpayer to input the tax return details using the microphone 238 of the robot 414. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and selects the tax return method based on the input information. The guidance unit uses the speaker 240 of the robot 414 to guide the taxpayer through the necessary documents and procedures. The check unit verifies the tax return details and checks for errors using the specific processing unit 290 of the data processing device 12.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The input unit can also analyze the user's input speed in real time and adjust the response speed of the interface according to the input speed. For example, if the user is inputting quickly, the input unit can increase the response speed of the interface to minimize input delays. Also, if the user is inputting slowly, the input unit can adjust the response speed of the interface to make it easier for the user to check the input content. Furthermore, the input unit can dynamically change the display order of input fields based on the user's input speed to enable the user to input efficiently. This can improve the efficiency of input work by providing an interface that suits the user's input speed.

[0138] The selection unit can also analyze the user's past selection history of reporting methods and suggest the optimal reporting method. For example, the selection unit can preferentially suggest reporting methods that the user has frequently selected in the past. The selection unit can also predict and suggest a reporting method that the user will prefer based on the user's past selection history of reporting methods. Furthermore, the selection unit can also customize the reporting method options by referring to the user's past selection history of reporting methods. This can make reporting work more efficient by providing the optimal reporting method based on the user's past selection history of reporting methods.

[0139] The guidance unit can also estimate the user's emotions and adjust the way the guidance content is presented based on the estimated user's emotions. For example, if the user is nervous, the guidance unit can provide guidance content in a calm tone. If the user is having fun, the guidance unit can provide guidance content in a bright tone. Furthermore, if the user is tired, the guidance unit can provide guidance content that is simple and highly visible. This can promote understanding of the guidance by providing guidance content that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0140] The check unit can also estimate the user's emotions and adjust the error check method based on the estimated user emotions. For example, if the user is nervous, the check unit can provide a simple, highly visible error check method. If the user is relaxed, the check unit can also provide a detailed error check method. Furthermore, if the user is in a hurry, the check unit can also provide an error check method that focuses on the main points. This allows for improving the accuracy of error checks by providing an error check method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0141] The environment confirmation unit can also estimate the user's emotions and adjust the method of checking the Internet environment based on the estimated user emotions. For example, if the user is nervous, the environment confirmation unit can provide a simple, highly visible method of checking. If the user is relaxed, the environment confirmation unit can also provide a detailed method of checking. Furthermore, if the user is in a hurry, the environment confirmation unit can also provide a method of checking that focuses on the main points. This can improve the efficiency of the checking process by providing a method of checking the Internet environment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0142] The input unit can also analyze the user's past declaration history and suggest the optimal input method. For example, the input unit can automatically display declaration details that the user has frequently entered in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest declaration details to be used in a specific time period based on the user's past declaration history. This can make input work more efficient by suggesting the optimal input method based on the user's past declaration history.

[0143] When making a selection, the selection unit can evaluate the stability of the user's internet environment and suggest the optimal filing method. For example, if the user's internet connection is unstable, the selection unit can preferentially suggest paper filing. Also, if the user's internet connection is stable, the selection unit can preferentially suggest e-Tax. Furthermore, the selection unit can evaluate the user's internet environment and suggest the optimal filing method. This can make the filing process more efficient by providing a filing method based on the user's internet environment.

[0144] The guidance unit can also customize the guidance content by referring to the user's past declaration history when providing guidance. For example, the guidance unit can provide optimal guidance content based on the content previously declared by the user. The guidance unit can also provide relevant guidance content by referring to the user's past declaration history. Furthermore, the guidance unit can also customize the guidance content based on the user's past declaration history. This can promote understanding of the guidance by providing guidance content based on the user's past declaration history.

[0145] The check unit can also improve the accuracy of the error check by referring to the user's past declaration history during the check. For example, the check unit can improve the accuracy of the error check based on the content of the user's past declarations. The check unit can also add related error check items by referring to the user's past declaration history. Furthermore, the check unit can also customize the error check method based on the user's past declaration history. In this way, the accuracy of the error check can be improved by providing an error check based on the user's past declaration history.

[0146] The environment confirmation unit can also optimize the confirmation method by taking into account the user's geographical location information when confirming the environment. For example, if the user lives in a specific area, the environment confirmation unit can preferentially display confirmation methods related to that area. The environment confirmation unit can also automatically display confirmation methods specific to that area based on the user's geographical location information. Furthermore, the environment confirmation unit can also suggest the optimal confirmation method by taking into account the user's geographical location information. This can make the confirmation process more efficient by providing confirmation methods based on the user's geographical location information.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The input section provides an interface for taxpayers to input the tax return details. Taxpayers can input the necessary tax return details by selecting, for example, the type of income or the type of deduction. Step 2: The selection unit selects a filing method based on the information entered by the input unit. For example, the selection unit recommends e-Tax if an internet connection is available, and paper filing if an internet connection is not available. Step 3: The guidance section guides the user through the necessary documents and procedures depending on the filing method selected by the selection section. For example, in the case of electronic filing, the guidance section provides a detailed explanation of the electronic filing procedure and guides the user on how to submit the necessary documents electronically. In the case of paper filing, the guidance section guides the user on the procedure for printing and mailing the necessary documents. Step 4: The Checking Department checks the declaration contents based on the procedures provided by the Guidance Department and checks for errors. For example, the Checking Department checks for errors before submission to prevent mistakes in the declaration contents.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] 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.

[0212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting declaration details; a selection unit that selects a reporting method based on the information input by the input unit; a guidance unit that provides guidance on necessary documents or procedures according to the reporting method selected by the selection unit; a check unit that checks the declaration contents based on the procedure guided by the guide unit and checks for errors. A system characterized by:

2. The selection unit Equipped with an environment check section that checks whether an internet connection is available 2. The system of claim 1.

3. The guide unit is Equipped with an electronic filing guide section that provides detailed explanations of the electronic filing procedure 2. The system of claim 1.

4. The guide unit is Equipped with a paper application guide section that provides detailed instructions on the paper application procedure 2. The system of claim 1.

5. The checking unit Check the declaration for errors 2. The system of claim 1.

6. The input unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyzes the user's past tax return history and suggests the optimal input method 2. The system of claim 1.

8. The input unit Customize input fields based on the user's current income status and deductions as they are entered 2. The system of claim 1.

9. The input unit When inputting, select the most appropriate input method depending on the user's input method 2. The system of claim 1.

10. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

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